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Last updated: July 22, 2025

DQOps REST API tables operations

Operations related to manage the metadata of imported tables, and managing the configuration of table-level data quality checks.

create_table

Creates a new table (adds a table metadata)

Follow the link to see the source code on GitHub.

POST 

http://localhost:8888/api/connections/{connectionName}/schemas/{schemaName}/tables/{tableName}

Parameters of this method are described below

 Property name   Description                       Data type   Required 
connection_name Connection name string
schema_name Schema name string
table_name Table name string

Request body

 Description                       Data type   Required 
Table specification TableSpec

Usage examples

Execution

curl -X POST http://localhost:8888/api/connections/sample_connection/schemas/sample_schema/tables/sample_table^
    -H "Accept: application/json"^
    -H "Content-Type: application/json"^
    -d^
    "{\"timestamp_columns\":{\"event_timestamp_column\":\"col1\",\"ingestion_timestamp_column\":\"col2\",\"partition_by_column\":\"col3\"},\"incremental_time_window\":{\"daily_partitioning_recent_days\":7,\"daily_partitioning_include_today\":true},\"profiling_checks\":{\"volume\":{\"profile_row_count\":{\"error\":{\"min_count\":1}}}},\"columns\":{}}"

Execution

from dqops import client
from dqops.client.api.tables import create_table
from dqops.client.models import ColumnSpecMap, \
                                DataGroupingConfigurationSpecMap, \
                                MinCountRule1ParametersSpec, \
                                PartitionIncrementalTimeWindowSpec, \
                                TableComparisonConfigurationSpecMap, \
                                TableMonitoringCheckCategoriesSpec, \
                                TablePartitionedCheckCategoriesSpec, \
                                TableProfilingCheckCategoriesSpec, \
                                TableRowCountCheckSpec, \
                                TableSpec, \
                                TableVolumeProfilingChecksSpec, \
                                TableVolumeRowCountSensorParametersSpec, \
                                TimestampColumnsSpec

dqops_client = client.Client(
    'http://localhost:8888/'
)

request_body = TableSpec(
    disabled=False,
    do_not_collect_error_samples_in_profiling=False,
    always_collect_error_samples_in_monitoring=False,
    timestamp_columns=TimestampColumnsSpec(
        event_timestamp_column='col1',
        ingestion_timestamp_column='col2',
        partition_by_column='col3'
    ),
    incremental_time_window=PartitionIncrementalTimeWindowSpec(
        daily_partitioning_recent_days=7,
        daily_partitioning_include_today=True,
        monthly_partitioning_include_current_month=False
    ),
    groupings=DataGroupingConfigurationSpecMap(),
    table_comparisons=TableComparisonConfigurationSpecMap(),
    profiling_checks=TableProfilingCheckCategoriesSpec(
        volume=TableVolumeProfilingChecksSpec(
            profile_row_count=TableRowCountCheckSpec(
                parameters=TableVolumeRowCountSensorParametersSpec(),
                error=MinCountRule1ParametersSpec(min_count=1),
                disabled=False,
                exclude_from_kpi=False,
                include_in_sla=False,
                always_collect_error_samples=False,
                do_not_schedule=False
            )
        )
    ),
    monitoring_checks=TableMonitoringCheckCategoriesSpec(),
    partitioned_checks=TablePartitionedCheckCategoriesSpec(),
    columns=ColumnSpecMap(),
    advanced_properties={

    }
)

call_result = create_table.sync(
    'sample_connection',
    'sample_schema',
    'sample_table',
    client=dqops_client,
    json_body=request_body
)

Execution

from dqops import client
from dqops.client.api.tables import create_table
from dqops.client.models import ColumnSpecMap, \
                                DataGroupingConfigurationSpecMap, \
                                MinCountRule1ParametersSpec, \
                                PartitionIncrementalTimeWindowSpec, \
                                TableComparisonConfigurationSpecMap, \
                                TableMonitoringCheckCategoriesSpec, \
                                TablePartitionedCheckCategoriesSpec, \
                                TableProfilingCheckCategoriesSpec, \
                                TableRowCountCheckSpec, \
                                TableSpec, \
                                TableVolumeProfilingChecksSpec, \
                                TableVolumeRowCountSensorParametersSpec, \
                                TimestampColumnsSpec

dqops_client = client.Client(
    'http://localhost:8888/'
)

request_body = TableSpec(
    disabled=False,
    do_not_collect_error_samples_in_profiling=False,
    always_collect_error_samples_in_monitoring=False,
    timestamp_columns=TimestampColumnsSpec(
        event_timestamp_column='col1',
        ingestion_timestamp_column='col2',
        partition_by_column='col3'
    ),
    incremental_time_window=PartitionIncrementalTimeWindowSpec(
        daily_partitioning_recent_days=7,
        daily_partitioning_include_today=True,
        monthly_partitioning_include_current_month=False
    ),
    groupings=DataGroupingConfigurationSpecMap(),
    table_comparisons=TableComparisonConfigurationSpecMap(),
    profiling_checks=TableProfilingCheckCategoriesSpec(
        volume=TableVolumeProfilingChecksSpec(
            profile_row_count=TableRowCountCheckSpec(
                parameters=TableVolumeRowCountSensorParametersSpec(),
                error=MinCountRule1ParametersSpec(min_count=1),
                disabled=False,
                exclude_from_kpi=False,
                include_in_sla=False,
                always_collect_error_samples=False,
                do_not_schedule=False
            )
        )
    ),
    monitoring_checks=TableMonitoringCheckCategoriesSpec(),
    partitioned_checks=TablePartitionedCheckCategoriesSpec(),
    columns=ColumnSpecMap(),
    advanced_properties={

    }
)

call_result = await create_table.asyncio(
    'sample_connection',
    'sample_schema',
    'sample_table',
    client=dqops_client,
    json_body=request_body
)

Execution

from dqops import client
from dqops.client.api.tables import create_table
from dqops.client.models import ColumnSpecMap, \
                                DataGroupingConfigurationSpecMap, \
                                MinCountRule1ParametersSpec, \
                                PartitionIncrementalTimeWindowSpec, \
                                TableComparisonConfigurationSpecMap, \
                                TableMonitoringCheckCategoriesSpec, \
                                TablePartitionedCheckCategoriesSpec, \
                                TableProfilingCheckCategoriesSpec, \
                                TableRowCountCheckSpec, \
                                TableSpec, \
                                TableVolumeProfilingChecksSpec, \
                                TableVolumeRowCountSensorParametersSpec, \
                                TimestampColumnsSpec

token = 's4mp13_4u7h_70k3n'

dqops_client = client.AuthenticatedClient(
    'http://localhost:8888/',
    token=token
)

request_body = TableSpec(
    disabled=False,
    do_not_collect_error_samples_in_profiling=False,
    always_collect_error_samples_in_monitoring=False,
    timestamp_columns=TimestampColumnsSpec(
        event_timestamp_column='col1',
        ingestion_timestamp_column='col2',
        partition_by_column='col3'
    ),
    incremental_time_window=PartitionIncrementalTimeWindowSpec(
        daily_partitioning_recent_days=7,
        daily_partitioning_include_today=True,
        monthly_partitioning_include_current_month=False
    ),
    groupings=DataGroupingConfigurationSpecMap(),
    table_comparisons=TableComparisonConfigurationSpecMap(),
    profiling_checks=TableProfilingCheckCategoriesSpec(
        volume=TableVolumeProfilingChecksSpec(
            profile_row_count=TableRowCountCheckSpec(
                parameters=TableVolumeRowCountSensorParametersSpec(),
                error=MinCountRule1ParametersSpec(min_count=1),
                disabled=False,
                exclude_from_kpi=False,
                include_in_sla=False,
                always_collect_error_samples=False,
                do_not_schedule=False
            )
        )
    ),
    monitoring_checks=TableMonitoringCheckCategoriesSpec(),
    partitioned_checks=TablePartitionedCheckCategoriesSpec(),
    columns=ColumnSpecMap(),
    advanced_properties={

    }
)

call_result = create_table.sync(
    'sample_connection',
    'sample_schema',
    'sample_table',
    client=dqops_client,
    json_body=request_body
)

Execution

from dqops import client
from dqops.client.api.tables import create_table
from dqops.client.models import ColumnSpecMap, \
                                DataGroupingConfigurationSpecMap, \
                                MinCountRule1ParametersSpec, \
                                PartitionIncrementalTimeWindowSpec, \
                                TableComparisonConfigurationSpecMap, \
                                TableMonitoringCheckCategoriesSpec, \
                                TablePartitionedCheckCategoriesSpec, \
                                TableProfilingCheckCategoriesSpec, \
                                TableRowCountCheckSpec, \
                                TableSpec, \
                                TableVolumeProfilingChecksSpec, \
                                TableVolumeRowCountSensorParametersSpec, \
                                TimestampColumnsSpec

token = 's4mp13_4u7h_70k3n'

dqops_client = client.AuthenticatedClient(
    'http://localhost:8888/',
    token=token
)

request_body = TableSpec(
    disabled=False,
    do_not_collect_error_samples_in_profiling=False,
    always_collect_error_samples_in_monitoring=False,
    timestamp_columns=TimestampColumnsSpec(
        event_timestamp_column='col1',
        ingestion_timestamp_column='col2',
        partition_by_column='col3'
    ),
    incremental_time_window=PartitionIncrementalTimeWindowSpec(
        daily_partitioning_recent_days=7,
        daily_partitioning_include_today=True,
        monthly_partitioning_include_current_month=False
    ),
    groupings=DataGroupingConfigurationSpecMap(),
    table_comparisons=TableComparisonConfigurationSpecMap(),
    profiling_checks=TableProfilingCheckCategoriesSpec(
        volume=TableVolumeProfilingChecksSpec(
            profile_row_count=TableRowCountCheckSpec(
                parameters=TableVolumeRowCountSensorParametersSpec(),
                error=MinCountRule1ParametersSpec(min_count=1),
                disabled=False,
                exclude_from_kpi=False,
                include_in_sla=False,
                always_collect_error_samples=False,
                do_not_schedule=False
            )
        )
    ),
    monitoring_checks=TableMonitoringCheckCategoriesSpec(),
    partitioned_checks=TablePartitionedCheckCategoriesSpec(),
    columns=ColumnSpecMap(),
    advanced_properties={

    }
)

call_result = await create_table.asyncio(
    'sample_connection',
    'sample_schema',
    'sample_table',
    client=dqops_client,
    json_body=request_body
)

delete_table

Deletes a table

Follow the link to see the source code on GitHub.

DELETE 

http://localhost:8888/api/connections/{connectionName}/schemas/{schemaName}/tables/{tableName}

Return value

 Property name   Description                       Data type 
dqo_queue_job_id DqoQueueJobId

Parameters of this method are described below

 Property name   Description                       Data type   Required 
connection_name Connection name string
schema_name Schema name string
table_name Table name string

Usage examples

Execution

curl -X DELETE http://localhost:8888/api/connections/sample_connection/schemas/sample_schema/tables/sample_table^
    -H "Accept: application/json"
Expand to see the returned result 
{
  "jobId" : 123456789,
  "createdAt" : "2007-10-11T13:42:00Z"
}

Execution

from dqops import client
from dqops.client.api.tables import delete_table

dqops_client = client.Client(
    'http://localhost:8888/'
)

call_result = delete_table.sync(
    'sample_connection',
    'sample_schema',
    'sample_table',
    client=dqops_client
)
Expand to see the returned result 
DqoQueueJobId(
    job_id=123456789,
    created_at='2007-10-11T13:42:00Z'
)

Execution

from dqops import client
from dqops.client.api.tables import delete_table

dqops_client = client.Client(
    'http://localhost:8888/'
)

call_result = await delete_table.asyncio(
    'sample_connection',
    'sample_schema',
    'sample_table',
    client=dqops_client
)
Expand to see the returned result 
DqoQueueJobId(
    job_id=123456789,
    created_at='2007-10-11T13:42:00Z'
)

Execution

from dqops import client
from dqops.client.api.tables import delete_table

token = 's4mp13_4u7h_70k3n'

dqops_client = client.AuthenticatedClient(
    'http://localhost:8888/',
    token=token
)

call_result = delete_table.sync(
    'sample_connection',
    'sample_schema',
    'sample_table',
    client=dqops_client
)
Expand to see the returned result 
DqoQueueJobId(
    job_id=123456789,
    created_at='2007-10-11T13:42:00Z'
)

Execution

from dqops import client
from dqops.client.api.tables import delete_table

token = 's4mp13_4u7h_70k3n'

dqops_client = client.AuthenticatedClient(
    'http://localhost:8888/',
    token=token
)

call_result = await delete_table.asyncio(
    'sample_connection',
    'sample_schema',
    'sample_table',
    client=dqops_client
)
Expand to see the returned result 
DqoQueueJobId(
    job_id=123456789,
    created_at='2007-10-11T13:42:00Z'
)

find_similar_tables

Finds a list of tables that are most similar to a given table

Follow the link to see the source code on GitHub.

GET 

http://localhost:8888/api/connections/{connectionName}/schemas/{schemaName}/tables/{tableName}/similar

Return value

 Property name   Description                       Data type 
similar_table_model List[SimilarTableModel]

Parameters of this method are described below

 Property name   Description                       Data type   Required 
connection_name Connection name string
schema_name Schema name string
table_name Table name string
limit The maximum number of similar tables to return. The default result is 50 similar tables. long

Usage examples

Execution

curl http://localhost:8888/api/connections/sample_connection/schemas/sample_schema/tables/sample_table/similar^
    -H "Accept: application/json"
Expand to see the returned result 
[ {
  "difference" : 0,
  "similarity_pct" : 0.0
}, {
  "difference" : 0,
  "similarity_pct" : 0.0
}, {
  "difference" : 0,
  "similarity_pct" : 0.0
} ]

Execution

from dqops import client
from dqops.client.api.tables import find_similar_tables

dqops_client = client.Client(
    'http://localhost:8888/',
    raise_on_unexpected_status=True
)

call_result = find_similar_tables.sync(
    'sample_connection',
    'sample_schema',
    'sample_table',
    client=dqops_client
)
Expand to see the returned result 
[
    SimilarTableModel(
        difference=0,
        similarity_pct=0.0
    ),
    SimilarTableModel(
        difference=0,
        similarity_pct=0.0
    ),
    SimilarTableModel(
        difference=0,
        similarity_pct=0.0
    )
]

Execution

from dqops import client
from dqops.client.api.tables import find_similar_tables

dqops_client = client.Client(
    'http://localhost:8888/',
    raise_on_unexpected_status=True
)

call_result = await find_similar_tables.asyncio(
    'sample_connection',
    'sample_schema',
    'sample_table',
    client=dqops_client
)
Expand to see the returned result 
[
    SimilarTableModel(
        difference=0,
        similarity_pct=0.0
    ),
    SimilarTableModel(
        difference=0,
        similarity_pct=0.0
    ),
    SimilarTableModel(
        difference=0,
        similarity_pct=0.0
    )
]

Execution

from dqops import client
from dqops.client.api.tables import find_similar_tables

token = 's4mp13_4u7h_70k3n'

dqops_client = client.AuthenticatedClient(
    'http://localhost:8888/',
    token=token,
    raise_on_unexpected_status=True
)

call_result = find_similar_tables.sync(
    'sample_connection',
    'sample_schema',
    'sample_table',
    client=dqops_client
)
Expand to see the returned result 
[
    SimilarTableModel(
        difference=0,
        similarity_pct=0.0
    ),
    SimilarTableModel(
        difference=0,
        similarity_pct=0.0
    ),
    SimilarTableModel(
        difference=0,
        similarity_pct=0.0
    )
]

Execution

from dqops import client
from dqops.client.api.tables import find_similar_tables

token = 's4mp13_4u7h_70k3n'

dqops_client = client.AuthenticatedClient(
    'http://localhost:8888/',
    token=token,
    raise_on_unexpected_status=True
)

call_result = await find_similar_tables.asyncio(
    'sample_connection',
    'sample_schema',
    'sample_table',
    client=dqops_client
)
Expand to see the returned result 
[
    SimilarTableModel(
        difference=0,
        similarity_pct=0.0
    ),
    SimilarTableModel(
        difference=0,
        similarity_pct=0.0
    ),
    SimilarTableModel(
        difference=0,
        similarity_pct=0.0
    )
]

get_table

Return the table specification

Follow the link to see the source code on GitHub.

GET 

http://localhost:8888/api/connections/{connectionName}/schemas/{schemaName}/tables/{tableName}

Return value

 Property name   Description                       Data type 
table_model TableModel

Parameters of this method are described below

 Property name   Description                       Data type   Required 
connection_name Connection name string
schema_name Schema name string
table_name Table name string

Usage examples

Execution

curl http://localhost:8888/api/connections/sample_connection/schemas/sample_schema/tables/sample_table^
    -H "Accept: application/json"
Expand to see the returned result 
{
  "can_edit" : false
}

Execution

from dqops import client
from dqops.client.api.tables import get_table

dqops_client = client.Client(
    'http://localhost:8888/',
    raise_on_unexpected_status=True
)

call_result = get_table.sync(
    'sample_connection',
    'sample_schema',
    'sample_table',
    client=dqops_client
)
Expand to see the returned result 
TableModel(can_edit=False)

Execution

from dqops import client
from dqops.client.api.tables import get_table

dqops_client = client.Client(
    'http://localhost:8888/',
    raise_on_unexpected_status=True
)

call_result = await get_table.asyncio(
    'sample_connection',
    'sample_schema',
    'sample_table',
    client=dqops_client
)
Expand to see the returned result 
TableModel(can_edit=False)

Execution

from dqops import client
from dqops.client.api.tables import get_table

token = 's4mp13_4u7h_70k3n'

dqops_client = client.AuthenticatedClient(
    'http://localhost:8888/',
    token=token,
    raise_on_unexpected_status=True
)

call_result = get_table.sync(
    'sample_connection',
    'sample_schema',
    'sample_table',
    client=dqops_client
)
Expand to see the returned result 
TableModel(can_edit=False)

Execution

from dqops import client
from dqops.client.api.tables import get_table

token = 's4mp13_4u7h_70k3n'

dqops_client = client.AuthenticatedClient(
    'http://localhost:8888/',
    token=token,
    raise_on_unexpected_status=True
)

call_result = await get_table.asyncio(
    'sample_connection',
    'sample_schema',
    'sample_table',
    client=dqops_client
)
Expand to see the returned result 
TableModel(can_edit=False)

get_table_basic

Return the basic table information

Follow the link to see the source code on GitHub.

GET 

http://localhost:8888/api/connections/{connectionName}/schemas/{schemaName}/tables/{tableName}/basic

Return value

 Property name   Description                       Data type 
table_list_model TableListModel

Parameters of this method are described below

 Property name   Description                       Data type   Required 
connection_name Connection name string
schema_name Schema name string
table_name Table name string

Usage examples

Execution

curl http://localhost:8888/api/connections/sample_connection/schemas/sample_schema/tables/sample_table/basic^
    -H "Accept: application/json"
Expand to see the returned result 
{
  "connection_name" : "sample_connection",
  "table_hash" : 7188561880498907939,
  "target" : {
    "schema_name" : "sample_schema",
    "table_name" : "sample_table"
  },
  "do_not_collect_error_samples_in_profiling" : false,
  "always_collect_error_samples_in_monitoring" : false,
  "has_any_configured_checks" : true,
  "has_any_configured_profiling_checks" : true,
  "run_checks_job_template" : {
    "connection" : "sample_connection",
    "fullTableName" : "sample_schema.sample_table",
    "enabled" : true
  },
  "run_profiling_checks_job_template" : {
    "connection" : "sample_connection",
    "fullTableName" : "sample_schema.sample_table",
    "enabled" : true,
    "checkType" : "profiling"
  },
  "run_monitoring_checks_job_template" : {
    "connection" : "sample_connection",
    "fullTableName" : "sample_schema.sample_table",
    "enabled" : true,
    "checkType" : "monitoring"
  },
  "run_partition_checks_job_template" : {
    "connection" : "sample_connection",
    "fullTableName" : "sample_schema.sample_table",
    "enabled" : true,
    "checkType" : "partitioned"
  },
  "data_clean_job_template" : {
    "connection" : "sample_connection",
    "fullTableName" : "sample_schema.sample_table",
    "deleteErrors" : true,
    "deleteStatistics" : true,
    "deleteCheckResults" : true,
    "deleteSensorReadouts" : true,
    "deleteErrorSamples" : true,
    "deleteIncidents" : true,
    "deleteChecksConfiguration" : false
  },
  "advanced_properties" : { },
  "can_edit" : true,
  "can_collect_statistics" : true,
  "can_run_checks" : true,
  "can_delete_data" : true
}

Execution

from dqops import client
from dqops.client.api.tables import get_table_basic

dqops_client = client.Client(
    'http://localhost:8888/',
    raise_on_unexpected_status=True
)

call_result = get_table_basic.sync(
    'sample_connection',
    'sample_schema',
    'sample_table',
    client=dqops_client
)
Expand to see the returned result 
TableListModel(
    connection_name='sample_connection',
    table_hash=7188561880498907939,
    target=PhysicalTableName(
        schema_name='sample_schema',
        table_name='sample_table'
    ),
    disabled=False,
    do_not_collect_error_samples_in_profiling=False,
    always_collect_error_samples_in_monitoring=False,
    has_any_configured_checks=True,
    has_any_configured_profiling_checks=True,
    has_any_configured_monitoring_checks=False,
    has_any_configured_partition_checks=False,
    partitioning_configuration_missing=False,
    run_checks_job_template=CheckSearchFilters(
        connection='sample_connection',
        full_table_name='sample_schema.sample_table',
        enabled=True
    ),
    run_profiling_checks_job_template=CheckSearchFilters(
        check_type=CheckType.PROFILING,
        connection='sample_connection',
        full_table_name='sample_schema.sample_table',
        enabled=True
    ),
    run_monitoring_checks_job_template=CheckSearchFilters(
        check_type=CheckType.MONITORING,
        connection='sample_connection',
        full_table_name='sample_schema.sample_table',
        enabled=True
    ),
    run_partition_checks_job_template=CheckSearchFilters(
        check_type=CheckType.PARTITIONED,
        connection='sample_connection',
        full_table_name='sample_schema.sample_table',
        enabled=True
    ),
    data_clean_job_template=DeleteStoredDataQueueJobParameters(
        connection='sample_connection',
        full_table_name='sample_schema.sample_table',
        delete_errors=True,
        delete_statistics=True,
        delete_check_results=True,
        delete_sensor_readouts=True,
        delete_error_samples=True,
        delete_incidents=True,
        delete_checks_configuration=False
    ),
    advanced_properties={

    },
    can_edit=True,
    can_collect_statistics=True,
    can_run_checks=True,
    can_delete_data=True
)

Execution

from dqops import client
from dqops.client.api.tables import get_table_basic

dqops_client = client.Client(
    'http://localhost:8888/',
    raise_on_unexpected_status=True
)

call_result = await get_table_basic.asyncio(
    'sample_connection',
    'sample_schema',
    'sample_table',
    client=dqops_client
)
Expand to see the returned result 
TableListModel(
    connection_name='sample_connection',
    table_hash=7188561880498907939,
    target=PhysicalTableName(
        schema_name='sample_schema',
        table_name='sample_table'
    ),
    disabled=False,
    do_not_collect_error_samples_in_profiling=False,
    always_collect_error_samples_in_monitoring=False,
    has_any_configured_checks=True,
    has_any_configured_profiling_checks=True,
    has_any_configured_monitoring_checks=False,
    has_any_configured_partition_checks=False,
    partitioning_configuration_missing=False,
    run_checks_job_template=CheckSearchFilters(
        connection='sample_connection',
        full_table_name='sample_schema.sample_table',
        enabled=True
    ),
    run_profiling_checks_job_template=CheckSearchFilters(
        check_type=CheckType.PROFILING,
        connection='sample_connection',
        full_table_name='sample_schema.sample_table',
        enabled=True
    ),
    run_monitoring_checks_job_template=CheckSearchFilters(
        check_type=CheckType.MONITORING,
        connection='sample_connection',
        full_table_name='sample_schema.sample_table',
        enabled=True
    ),
    run_partition_checks_job_template=CheckSearchFilters(
        check_type=CheckType.PARTITIONED,
        connection='sample_connection',
        full_table_name='sample_schema.sample_table',
        enabled=True
    ),
    data_clean_job_template=DeleteStoredDataQueueJobParameters(
        connection='sample_connection',
        full_table_name='sample_schema.sample_table',
        delete_errors=True,
        delete_statistics=True,
        delete_check_results=True,
        delete_sensor_readouts=True,
        delete_error_samples=True,
        delete_incidents=True,
        delete_checks_configuration=False
    ),
    advanced_properties={

    },
    can_edit=True,
    can_collect_statistics=True,
    can_run_checks=True,
    can_delete_data=True
)

Execution

from dqops import client
from dqops.client.api.tables import get_table_basic

token = 's4mp13_4u7h_70k3n'

dqops_client = client.AuthenticatedClient(
    'http://localhost:8888/',
    token=token,
    raise_on_unexpected_status=True
)

call_result = get_table_basic.sync(
    'sample_connection',
    'sample_schema',
    'sample_table',
    client=dqops_client
)
Expand to see the returned result 
TableListModel(
    connection_name='sample_connection',
    table_hash=7188561880498907939,
    target=PhysicalTableName(
        schema_name='sample_schema',
        table_name='sample_table'
    ),
    disabled=False,
    do_not_collect_error_samples_in_profiling=False,
    always_collect_error_samples_in_monitoring=False,
    has_any_configured_checks=True,
    has_any_configured_profiling_checks=True,
    has_any_configured_monitoring_checks=False,
    has_any_configured_partition_checks=False,
    partitioning_configuration_missing=False,
    run_checks_job_template=CheckSearchFilters(
        connection='sample_connection',
        full_table_name='sample_schema.sample_table',
        enabled=True
    ),
    run_profiling_checks_job_template=CheckSearchFilters(
        check_type=CheckType.PROFILING,
        connection='sample_connection',
        full_table_name='sample_schema.sample_table',
        enabled=True
    ),
    run_monitoring_checks_job_template=CheckSearchFilters(
        check_type=CheckType.MONITORING,
        connection='sample_connection',
        full_table_name='sample_schema.sample_table',
        enabled=True
    ),
    run_partition_checks_job_template=CheckSearchFilters(
        check_type=CheckType.PARTITIONED,
        connection='sample_connection',
        full_table_name='sample_schema.sample_table',
        enabled=True
    ),
    data_clean_job_template=DeleteStoredDataQueueJobParameters(
        connection='sample_connection',
        full_table_name='sample_schema.sample_table',
        delete_errors=True,
        delete_statistics=True,
        delete_check_results=True,
        delete_sensor_readouts=True,
        delete_error_samples=True,
        delete_incidents=True,
        delete_checks_configuration=False
    ),
    advanced_properties={

    },
    can_edit=True,
    can_collect_statistics=True,
    can_run_checks=True,
    can_delete_data=True
)

Execution

from dqops import client
from dqops.client.api.tables import get_table_basic

token = 's4mp13_4u7h_70k3n'

dqops_client = client.AuthenticatedClient(
    'http://localhost:8888/',
    token=token,
    raise_on_unexpected_status=True
)

call_result = await get_table_basic.asyncio(
    'sample_connection',
    'sample_schema',
    'sample_table',
    client=dqops_client
)
Expand to see the returned result 
TableListModel(
    connection_name='sample_connection',
    table_hash=7188561880498907939,
    target=PhysicalTableName(
        schema_name='sample_schema',
        table_name='sample_table'
    ),
    disabled=False,
    do_not_collect_error_samples_in_profiling=False,
    always_collect_error_samples_in_monitoring=False,
    has_any_configured_checks=True,
    has_any_configured_profiling_checks=True,
    has_any_configured_monitoring_checks=False,
    has_any_configured_partition_checks=False,
    partitioning_configuration_missing=False,
    run_checks_job_template=CheckSearchFilters(
        connection='sample_connection',
        full_table_name='sample_schema.sample_table',
        enabled=True
    ),
    run_profiling_checks_job_template=CheckSearchFilters(
        check_type=CheckType.PROFILING,
        connection='sample_connection',
        full_table_name='sample_schema.sample_table',
        enabled=True
    ),
    run_monitoring_checks_job_template=CheckSearchFilters(
        check_type=CheckType.MONITORING,
        connection='sample_connection',
        full_table_name='sample_schema.sample_table',
        enabled=True
    ),
    run_partition_checks_job_template=CheckSearchFilters(
        check_type=CheckType.PARTITIONED,
        connection='sample_connection',
        full_table_name='sample_schema.sample_table',
        enabled=True
    ),
    data_clean_job_template=DeleteStoredDataQueueJobParameters(
        connection='sample_connection',
        full_table_name='sample_schema.sample_table',
        delete_errors=True,
        delete_statistics=True,
        delete_check_results=True,
        delete_sensor_readouts=True,
        delete_error_samples=True,
        delete_incidents=True,
        delete_checks_configuration=False
    ),
    advanced_properties={

    },
    can_edit=True,
    can_collect_statistics=True,
    can_run_checks=True,
    can_delete_data=True
)

get_table_columns_monitoring_checks_model

Return a UI friendly model of configurations for column-level data quality monitoring checks on a table

Follow the link to see the source code on GitHub.

GET 

http://localhost:8888/api/connections/{connectionName}/schemas/{schemaName}/tables/{tableName}/columnchecks/monitoring/{timeScale}/model

Return value

 Property name   Description                       Data type 
check_configuration_model List[CheckConfigurationModel]

Parameters of this method are described below

 Property name   Description                       Data type   Required 
connection_name Connection name string
schema_name Schema name string
table_name Table name string
time_scale Check time-scale CheckTimeScale
column_name_pattern Column name pattern string
column_data_type Column data-type string
check_category Check category string
check_name Check name string
check_enabled Check enabled boolean
check_configured Check configured boolean
limit Limit of results, the default value is 1000 long

Usage examples

Execution

curl http://localhost:8888/api/connections/sample_connection/schemas/sample_schema/tables/sample_table/columnchecks/monitoring/daily/model^
    -H "Accept: application/json"
Expand to see the returned result 
[ {
  "sensor_parameters" : [ ],
  "disabled" : false,
  "configured" : false
}, {
  "sensor_parameters" : [ ],
  "disabled" : false,
  "configured" : false
}, {
  "sensor_parameters" : [ ],
  "disabled" : false,
  "configured" : false
} ]

Execution

from dqops import client
from dqops.client.api.tables import get_table_columns_monitoring_checks_model
from dqops.client.models import CheckTimeScale

dqops_client = client.Client(
    'http://localhost:8888/',
    raise_on_unexpected_status=True
)

call_result = get_table_columns_monitoring_checks_model.sync(
    'sample_connection',
    'sample_schema',
    'sample_table',
    CheckTimeScale.daily,
    client=dqops_client
)
Expand to see the returned result 
[
    CheckConfigurationModel(
        sensor_parameters=[

        ],
        disabled=False,
        configured=False
    ),
    CheckConfigurationModel(
        sensor_parameters=[

        ],
        disabled=False,
        configured=False
    ),
    CheckConfigurationModel(
        sensor_parameters=[

        ],
        disabled=False,
        configured=False
    )
]

Execution

from dqops import client
from dqops.client.api.tables import get_table_columns_monitoring_checks_model
from dqops.client.models import CheckTimeScale

dqops_client = client.Client(
    'http://localhost:8888/',
    raise_on_unexpected_status=True
)

call_result = await get_table_columns_monitoring_checks_model.asyncio(
    'sample_connection',
    'sample_schema',
    'sample_table',
    CheckTimeScale.daily,
    client=dqops_client
)
Expand to see the returned result 
[
    CheckConfigurationModel(
        sensor_parameters=[

        ],
        disabled=False,
        configured=False
    ),
    CheckConfigurationModel(
        sensor_parameters=[

        ],
        disabled=False,
        configured=False
    ),
    CheckConfigurationModel(
        sensor_parameters=[

        ],
        disabled=False,
        configured=False
    )
]

Execution

from dqops import client
from dqops.client.api.tables import get_table_columns_monitoring_checks_model
from dqops.client.models import CheckTimeScale

token = 's4mp13_4u7h_70k3n'

dqops_client = client.AuthenticatedClient(
    'http://localhost:8888/',
    token=token,
    raise_on_unexpected_status=True
)

call_result = get_table_columns_monitoring_checks_model.sync(
    'sample_connection',
    'sample_schema',
    'sample_table',
    CheckTimeScale.daily,
    client=dqops_client
)
Expand to see the returned result 
[
    CheckConfigurationModel(
        sensor_parameters=[

        ],
        disabled=False,
        configured=False
    ),
    CheckConfigurationModel(
        sensor_parameters=[

        ],
        disabled=False,
        configured=False
    ),
    CheckConfigurationModel(
        sensor_parameters=[

        ],
        disabled=False,
        configured=False
    )
]

Execution

from dqops import client
from dqops.client.api.tables import get_table_columns_monitoring_checks_model
from dqops.client.models import CheckTimeScale

token = 's4mp13_4u7h_70k3n'

dqops_client = client.AuthenticatedClient(
    'http://localhost:8888/',
    token=token,
    raise_on_unexpected_status=True
)

call_result = await get_table_columns_monitoring_checks_model.asyncio(
    'sample_connection',
    'sample_schema',
    'sample_table',
    CheckTimeScale.daily,
    client=dqops_client
)
Expand to see the returned result 
[
    CheckConfigurationModel(
        sensor_parameters=[

        ],
        disabled=False,
        configured=False
    ),
    CheckConfigurationModel(
        sensor_parameters=[

        ],
        disabled=False,
        configured=False
    ),
    CheckConfigurationModel(
        sensor_parameters=[

        ],
        disabled=False,
        configured=False
    )
]

get_table_columns_partitioned_checks_model

Return a UI friendly model of configurations for column-level data quality partitioned checks on a table

Follow the link to see the source code on GitHub.

GET 

http://localhost:8888/api/connections/{connectionName}/schemas/{schemaName}/tables/{tableName}/columnchecks/partitioned/{timeScale}/model

Return value

 Property name   Description                       Data type 
check_configuration_model List[CheckConfigurationModel]

Parameters of this method are described below

 Property name   Description                       Data type   Required 
connection_name Connection name string
schema_name Schema name string
table_name Table name string
time_scale Check time-scale CheckTimeScale
column_name_pattern Column name pattern string
column_data_type Column data-type string
check_category Check category string
check_name Check name string
check_enabled Check enabled boolean
check_configured Check configured boolean
limit Limit of results, the default value is 1000 long

Usage examples

Execution

curl http://localhost:8888/api/connections/sample_connection/schemas/sample_schema/tables/sample_table/columnchecks/partitioned/daily/model^
    -H "Accept: application/json"
Expand to see the returned result 
[ {
  "sensor_parameters" : [ ],
  "disabled" : false,
  "configured" : false
}, {
  "sensor_parameters" : [ ],
  "disabled" : false,
  "configured" : false
}, {
  "sensor_parameters" : [ ],
  "disabled" : false,
  "configured" : false
} ]

Execution

from dqops import client
from dqops.client.api.tables import get_table_columns_partitioned_checks_model
from dqops.client.models import CheckTimeScale

dqops_client = client.Client(
    'http://localhost:8888/',
    raise_on_unexpected_status=True
)

call_result = get_table_columns_partitioned_checks_model.sync(
    'sample_connection',
    'sample_schema',
    'sample_table',
    CheckTimeScale.daily,
    client=dqops_client
)
Expand to see the returned result 
[
    CheckConfigurationModel(
        sensor_parameters=[

        ],
        disabled=False,
        configured=False
    ),
    CheckConfigurationModel(
        sensor_parameters=[

        ],
        disabled=False,
        configured=False
    ),
    CheckConfigurationModel(
        sensor_parameters=[

        ],
        disabled=False,
        configured=False
    )
]

Execution

from dqops import client
from dqops.client.api.tables import get_table_columns_partitioned_checks_model
from dqops.client.models import CheckTimeScale

dqops_client = client.Client(
    'http://localhost:8888/',
    raise_on_unexpected_status=True
)

call_result = await get_table_columns_partitioned_checks_model.asyncio(
    'sample_connection',
    'sample_schema',
    'sample_table',
    CheckTimeScale.daily,
    client=dqops_client
)
Expand to see the returned result 
[
    CheckConfigurationModel(
        sensor_parameters=[

        ],
        disabled=False,
        configured=False
    ),
    CheckConfigurationModel(
        sensor_parameters=[

        ],
        disabled=False,
        configured=False
    ),
    CheckConfigurationModel(
        sensor_parameters=[

        ],
        disabled=False,
        configured=False
    )
]

Execution

from dqops import client
from dqops.client.api.tables import get_table_columns_partitioned_checks_model
from dqops.client.models import CheckTimeScale

token = 's4mp13_4u7h_70k3n'

dqops_client = client.AuthenticatedClient(
    'http://localhost:8888/',
    token=token,
    raise_on_unexpected_status=True
)

call_result = get_table_columns_partitioned_checks_model.sync(
    'sample_connection',
    'sample_schema',
    'sample_table',
    CheckTimeScale.daily,
    client=dqops_client
)
Expand to see the returned result 
[
    CheckConfigurationModel(
        sensor_parameters=[

        ],
        disabled=False,
        configured=False
    ),
    CheckConfigurationModel(
        sensor_parameters=[

        ],
        disabled=False,
        configured=False
    ),
    CheckConfigurationModel(
        sensor_parameters=[

        ],
        disabled=False,
        configured=False
    )
]

Execution

from dqops import client
from dqops.client.api.tables import get_table_columns_partitioned_checks_model
from dqops.client.models import CheckTimeScale

token = 's4mp13_4u7h_70k3n'

dqops_client = client.AuthenticatedClient(
    'http://localhost:8888/',
    token=token,
    raise_on_unexpected_status=True
)

call_result = await get_table_columns_partitioned_checks_model.asyncio(
    'sample_connection',
    'sample_schema',
    'sample_table',
    CheckTimeScale.daily,
    client=dqops_client
)
Expand to see the returned result 
[
    CheckConfigurationModel(
        sensor_parameters=[

        ],
        disabled=False,
        configured=False
    ),
    CheckConfigurationModel(
        sensor_parameters=[

        ],
        disabled=False,
        configured=False
    ),
    CheckConfigurationModel(
        sensor_parameters=[

        ],
        disabled=False,
        configured=False
    )
]

get_table_columns_profiling_checks_model

Return a UI friendly model of configurations for column-level data quality profiling checks on a table

Follow the link to see the source code on GitHub.

GET 

http://localhost:8888/api/connections/{connectionName}/schemas/{schemaName}/tables/{tableName}/columnchecks/profiling/model

Return value

 Property name   Description                       Data type 
check_configuration_model List[CheckConfigurationModel]

Parameters of this method are described below

 Property name   Description                       Data type   Required 
connection_name Connection name string
schema_name Schema name string
table_name Table name string
column_name_pattern Column name pattern string
column_data_type Column data-type string
check_category Check category string
check_name Check name string
check_enabled Check enabled boolean
check_configured Check configured boolean
limit Limit of results, the default value is 1000 long

Usage examples

Execution

curl http://localhost:8888/api/connections/sample_connection/schemas/sample_schema/tables/sample_table/columnchecks/profiling/model^
    -H "Accept: application/json"
Expand to see the returned result 
[ {
  "sensor_parameters" : [ ],
  "disabled" : false,
  "configured" : false
}, {
  "sensor_parameters" : [ ],
  "disabled" : false,
  "configured" : false
}, {
  "sensor_parameters" : [ ],
  "disabled" : false,
  "configured" : false
} ]

Execution

from dqops import client
from dqops.client.api.tables import get_table_columns_profiling_checks_model

dqops_client = client.Client(
    'http://localhost:8888/',
    raise_on_unexpected_status=True
)

call_result = get_table_columns_profiling_checks_model.sync(
    'sample_connection',
    'sample_schema',
    'sample_table',
    client=dqops_client
)
Expand to see the returned result 
[
    CheckConfigurationModel(
        sensor_parameters=[

        ],
        disabled=False,
        configured=False
    ),
    CheckConfigurationModel(
        sensor_parameters=[

        ],
        disabled=False,
        configured=False
    ),
    CheckConfigurationModel(
        sensor_parameters=[

        ],
        disabled=False,
        configured=False
    )
]

Execution

from dqops import client
from dqops.client.api.tables import get_table_columns_profiling_checks_model

dqops_client = client.Client(
    'http://localhost:8888/',
    raise_on_unexpected_status=True
)

call_result = await get_table_columns_profiling_checks_model.asyncio(
    'sample_connection',
    'sample_schema',
    'sample_table',
    client=dqops_client
)
Expand to see the returned result 
[
    CheckConfigurationModel(
        sensor_parameters=[

        ],
        disabled=False,
        configured=False
    ),
    CheckConfigurationModel(
        sensor_parameters=[

        ],
        disabled=False,
        configured=False
    ),
    CheckConfigurationModel(
        sensor_parameters=[

        ],
        disabled=False,
        configured=False
    )
]

Execution

from dqops import client
from dqops.client.api.tables import get_table_columns_profiling_checks_model

token = 's4mp13_4u7h_70k3n'

dqops_client = client.AuthenticatedClient(
    'http://localhost:8888/',
    token=token,
    raise_on_unexpected_status=True
)

call_result = get_table_columns_profiling_checks_model.sync(
    'sample_connection',
    'sample_schema',
    'sample_table',
    client=dqops_client
)
Expand to see the returned result 
[
    CheckConfigurationModel(
        sensor_parameters=[

        ],
        disabled=False,
        configured=False
    ),
    CheckConfigurationModel(
        sensor_parameters=[

        ],
        disabled=False,
        configured=False
    ),
    CheckConfigurationModel(
        sensor_parameters=[

        ],
        disabled=False,
        configured=False
    )
]

Execution

from dqops import client
from dqops.client.api.tables import get_table_columns_profiling_checks_model

token = 's4mp13_4u7h_70k3n'

dqops_client = client.AuthenticatedClient(
    'http://localhost:8888/',
    token=token,
    raise_on_unexpected_status=True
)

call_result = await get_table_columns_profiling_checks_model.asyncio(
    'sample_connection',
    'sample_schema',
    'sample_table',
    client=dqops_client
)
Expand to see the returned result 
[
    CheckConfigurationModel(
        sensor_parameters=[

        ],
        disabled=False,
        configured=False
    ),
    CheckConfigurationModel(
        sensor_parameters=[

        ],
        disabled=False,
        configured=False
    ),
    CheckConfigurationModel(
        sensor_parameters=[

        ],
        disabled=False,
        configured=False
    )
]

get_table_comments

Return the list of comments added to a table

Follow the link to see the source code on GitHub.

GET 

http://localhost:8888/api/connections/{connectionName}/schemas/{schemaName}/tables/{tableName}/comments

Return value

 Property name   Description                       Data type 
comment_spec List[CommentSpec]

Parameters of this method are described below

 Property name   Description                       Data type   Required 
connection_name Connection name string
schema_name Schema name string
table_name Table name string

Usage examples

Execution

curl http://localhost:8888/api/connections/sample_connection/schemas/sample_schema/tables/sample_table/comments^
    -H "Accept: application/json"
Expand to see the returned result 
[ {
  "date" : "2007-12-03T10:15:30",
  "comment_by" : "sample_user",
  "comment" : "Sample comment"
}, {
  "date" : "2007-12-03T10:15:30",
  "comment_by" : "sample_user",
  "comment" : "Sample comment"
}, {
  "date" : "2007-12-03T10:15:30",
  "comment_by" : "sample_user",
  "comment" : "Sample comment"
} ]

Execution

from dqops import client
from dqops.client.api.tables import get_table_comments

dqops_client = client.Client(
    'http://localhost:8888/',
    raise_on_unexpected_status=True
)

call_result = get_table_comments.sync(
    'sample_connection',
    'sample_schema',
    'sample_table',
    client=dqops_client
)
Expand to see the returned result 
[
    CommentSpec(
        date=Some date/time value: [2007-12-03T10:15:30],
        comment_by='sample_user',
        comment='Sample comment'
    ),
    CommentSpec(
        date=Some date/time value: [2007-12-03T10:15:30],
        comment_by='sample_user',
        comment='Sample comment'
    ),
    CommentSpec(
        date=Some date/time value: [2007-12-03T10:15:30],
        comment_by='sample_user',
        comment='Sample comment'
    )
]

Execution

from dqops import client
from dqops.client.api.tables import get_table_comments

dqops_client = client.Client(
    'http://localhost:8888/',
    raise_on_unexpected_status=True
)

call_result = await get_table_comments.asyncio(
    'sample_connection',
    'sample_schema',
    'sample_table',
    client=dqops_client
)
Expand to see the returned result 
[
    CommentSpec(
        date=Some date/time value: [2007-12-03T10:15:30],
        comment_by='sample_user',
        comment='Sample comment'
    ),
    CommentSpec(
        date=Some date/time value: [2007-12-03T10:15:30],
        comment_by='sample_user',
        comment='Sample comment'
    ),
    CommentSpec(
        date=Some date/time value: [2007-12-03T10:15:30],
        comment_by='sample_user',
        comment='Sample comment'
    )
]

Execution

from dqops import client
from dqops.client.api.tables import get_table_comments

token = 's4mp13_4u7h_70k3n'

dqops_client = client.AuthenticatedClient(
    'http://localhost:8888/',
    token=token,
    raise_on_unexpected_status=True
)

call_result = get_table_comments.sync(
    'sample_connection',
    'sample_schema',
    'sample_table',
    client=dqops_client
)
Expand to see the returned result 
[
    CommentSpec(
        date=Some date/time value: [2007-12-03T10:15:30],
        comment_by='sample_user',
        comment='Sample comment'
    ),
    CommentSpec(
        date=Some date/time value: [2007-12-03T10:15:30],
        comment_by='sample_user',
        comment='Sample comment'
    ),
    CommentSpec(
        date=Some date/time value: [2007-12-03T10:15:30],
        comment_by='sample_user',
        comment='Sample comment'
    )
]

Execution

from dqops import client
from dqops.client.api.tables import get_table_comments

token = 's4mp13_4u7h_70k3n'

dqops_client = client.AuthenticatedClient(
    'http://localhost:8888/',
    token=token,
    raise_on_unexpected_status=True
)

call_result = await get_table_comments.asyncio(
    'sample_connection',
    'sample_schema',
    'sample_table',
    client=dqops_client
)
Expand to see the returned result 
[
    CommentSpec(
        date=Some date/time value: [2007-12-03T10:15:30],
        comment_by='sample_user',
        comment='Sample comment'
    ),
    CommentSpec(
        date=Some date/time value: [2007-12-03T10:15:30],
        comment_by='sample_user',
        comment='Sample comment'
    ),
    CommentSpec(
        date=Some date/time value: [2007-12-03T10:15:30],
        comment_by='sample_user',
        comment='Sample comment'
    )
]

get_table_daily_monitoring_checks

Return the configuration of daily table level data quality monitoring on a table

Follow the link to see the source code on GitHub.

GET 

http://localhost:8888/api/connections/{connectionName}/schemas/{schemaName}/tables/{tableName}/monitoring/daily

Return value

 Property name   Description                       Data type 
table_daily_monitoring_check_categories_spec TableDailyMonitoringCheckCategoriesSpec

Parameters of this method are described below

 Property name   Description                       Data type   Required 
connection_name Connection name string
schema_name Schema name string
table_name Table name string

Usage examples

Execution

curl http://localhost:8888/api/connections/sample_connection/schemas/sample_schema/tables/sample_table/monitoring/daily^
    -H "Accept: application/json"
Expand to see the returned result 
{
  "volume" : {
    "daily_row_count" : {
      "error" : {
        "min_count" : 1
      }
    }
  }
}

Execution

from dqops import client
from dqops.client.api.tables import get_table_daily_monitoring_checks

dqops_client = client.Client(
    'http://localhost:8888/',
    raise_on_unexpected_status=True
)

call_result = get_table_daily_monitoring_checks.sync(
    'sample_connection',
    'sample_schema',
    'sample_table',
    client=dqops_client
)
Expand to see the returned result 
TableDailyMonitoringCheckCategoriesSpec(
    volume=TableVolumeDailyMonitoringChecksSpec(
        daily_row_count=TableRowCountCheckSpec(
            parameters=TableVolumeRowCountSensorParametersSpec(),
            error=MinCountRule1ParametersSpec(min_count=1),
            disabled=False,
            exclude_from_kpi=False,
            include_in_sla=False,
            always_collect_error_samples=False,
            do_not_schedule=False
        )
    ),
    comparisons=TableComparisonDailyMonitoringChecksSpecMap()
)

Execution

from dqops import client
from dqops.client.api.tables import get_table_daily_monitoring_checks

dqops_client = client.Client(
    'http://localhost:8888/',
    raise_on_unexpected_status=True
)

call_result = await get_table_daily_monitoring_checks.asyncio(
    'sample_connection',
    'sample_schema',
    'sample_table',
    client=dqops_client
)
Expand to see the returned result 
TableDailyMonitoringCheckCategoriesSpec(
    volume=TableVolumeDailyMonitoringChecksSpec(
        daily_row_count=TableRowCountCheckSpec(
            parameters=TableVolumeRowCountSensorParametersSpec(),
            error=MinCountRule1ParametersSpec(min_count=1),
            disabled=False,
            exclude_from_kpi=False,
            include_in_sla=False,
            always_collect_error_samples=False,
            do_not_schedule=False
        )
    ),
    comparisons=TableComparisonDailyMonitoringChecksSpecMap()
)

Execution

from dqops import client
from dqops.client.api.tables import get_table_daily_monitoring_checks

token = 's4mp13_4u7h_70k3n'

dqops_client = client.AuthenticatedClient(
    'http://localhost:8888/',
    token=token,
    raise_on_unexpected_status=True
)

call_result = get_table_daily_monitoring_checks.sync(
    'sample_connection',
    'sample_schema',
    'sample_table',
    client=dqops_client
)
Expand to see the returned result 
TableDailyMonitoringCheckCategoriesSpec(
    volume=TableVolumeDailyMonitoringChecksSpec(
        daily_row_count=TableRowCountCheckSpec(
            parameters=TableVolumeRowCountSensorParametersSpec(),
            error=MinCountRule1ParametersSpec(min_count=1),
            disabled=False,
            exclude_from_kpi=False,
            include_in_sla=False,
            always_collect_error_samples=False,
            do_not_schedule=False
        )
    ),
    comparisons=TableComparisonDailyMonitoringChecksSpecMap()
)

Execution

from dqops import client
from dqops.client.api.tables import get_table_daily_monitoring_checks

token = 's4mp13_4u7h_70k3n'

dqops_client = client.AuthenticatedClient(
    'http://localhost:8888/',
    token=token,
    raise_on_unexpected_status=True
)

call_result = await get_table_daily_monitoring_checks.asyncio(
    'sample_connection',
    'sample_schema',
    'sample_table',
    client=dqops_client
)
Expand to see the returned result 
TableDailyMonitoringCheckCategoriesSpec(
    volume=TableVolumeDailyMonitoringChecksSpec(
        daily_row_count=TableRowCountCheckSpec(
            parameters=TableVolumeRowCountSensorParametersSpec(),
            error=MinCountRule1ParametersSpec(min_count=1),
            disabled=False,
            exclude_from_kpi=False,
            include_in_sla=False,
            always_collect_error_samples=False,
            do_not_schedule=False
        )
    ),
    comparisons=TableComparisonDailyMonitoringChecksSpecMap()
)

get_table_daily_partitioned_checks

Return the configuration of daily table level data quality partitioned checks on a table

Follow the link to see the source code on GitHub.

GET 

http://localhost:8888/api/connections/{connectionName}/schemas/{schemaName}/tables/{tableName}/partitioned/daily

Return value

 Property name   Description                       Data type 
table_daily_partitioned_check_categories_spec TableDailyPartitionedCheckCategoriesSpec

Parameters of this method are described below

 Property name   Description                       Data type   Required 
connection_name Connection name string
schema_name Schema name string
table_name Table name string

Usage examples

Execution

curl http://localhost:8888/api/connections/sample_connection/schemas/sample_schema/tables/sample_table/partitioned/daily^
    -H "Accept: application/json"
Expand to see the returned result 
{
  "volume" : {
    "daily_partition_row_count" : {
      "error" : {
        "min_count" : 1
      }
    }
  }
}

Execution

from dqops import client
from dqops.client.api.tables import get_table_daily_partitioned_checks

dqops_client = client.Client(
    'http://localhost:8888/',
    raise_on_unexpected_status=True
)

call_result = get_table_daily_partitioned_checks.sync(
    'sample_connection',
    'sample_schema',
    'sample_table',
    client=dqops_client
)
Expand to see the returned result 
TableDailyPartitionedCheckCategoriesSpec(
    volume=TableVolumeDailyPartitionedChecksSpec(
        daily_partition_row_count=TableRowCountCheckSpec(
            parameters=TableVolumeRowCountSensorParametersSpec(),
            error=MinCountRule1ParametersSpec(min_count=1),
            disabled=False,
            exclude_from_kpi=False,
            include_in_sla=False,
            always_collect_error_samples=False,
            do_not_schedule=False
        )
    ),
    comparisons=TableComparisonDailyPartitionedChecksSpecMap()
)

Execution

from dqops import client
from dqops.client.api.tables import get_table_daily_partitioned_checks

dqops_client = client.Client(
    'http://localhost:8888/',
    raise_on_unexpected_status=True
)

call_result = await get_table_daily_partitioned_checks.asyncio(
    'sample_connection',
    'sample_schema',
    'sample_table',
    client=dqops_client
)
Expand to see the returned result 
TableDailyPartitionedCheckCategoriesSpec(
    volume=TableVolumeDailyPartitionedChecksSpec(
        daily_partition_row_count=TableRowCountCheckSpec(
            parameters=TableVolumeRowCountSensorParametersSpec(),
            error=MinCountRule1ParametersSpec(min_count=1),
            disabled=False,
            exclude_from_kpi=False,
            include_in_sla=False,
            always_collect_error_samples=False,
            do_not_schedule=False
        )
    ),
    comparisons=TableComparisonDailyPartitionedChecksSpecMap()
)

Execution

from dqops import client
from dqops.client.api.tables import get_table_daily_partitioned_checks

token = 's4mp13_4u7h_70k3n'

dqops_client = client.AuthenticatedClient(
    'http://localhost:8888/',
    token=token,
    raise_on_unexpected_status=True
)

call_result = get_table_daily_partitioned_checks.sync(
    'sample_connection',
    'sample_schema',
    'sample_table',
    client=dqops_client
)
Expand to see the returned result 
TableDailyPartitionedCheckCategoriesSpec(
    volume=TableVolumeDailyPartitionedChecksSpec(
        daily_partition_row_count=TableRowCountCheckSpec(
            parameters=TableVolumeRowCountSensorParametersSpec(),
            error=MinCountRule1ParametersSpec(min_count=1),
            disabled=False,
            exclude_from_kpi=False,
            include_in_sla=False,
            always_collect_error_samples=False,
            do_not_schedule=False
        )
    ),
    comparisons=TableComparisonDailyPartitionedChecksSpecMap()
)

Execution

from dqops import client
from dqops.client.api.tables import get_table_daily_partitioned_checks

token = 's4mp13_4u7h_70k3n'

dqops_client = client.AuthenticatedClient(
    'http://localhost:8888/',
    token=token,
    raise_on_unexpected_status=True
)

call_result = await get_table_daily_partitioned_checks.asyncio(
    'sample_connection',
    'sample_schema',
    'sample_table',
    client=dqops_client
)
Expand to see the returned result 
TableDailyPartitionedCheckCategoriesSpec(
    volume=TableVolumeDailyPartitionedChecksSpec(
        daily_partition_row_count=TableRowCountCheckSpec(
            parameters=TableVolumeRowCountSensorParametersSpec(),
            error=MinCountRule1ParametersSpec(min_count=1),
            disabled=False,
            exclude_from_kpi=False,
            include_in_sla=False,
            always_collect_error_samples=False,
            do_not_schedule=False
        )
    ),
    comparisons=TableComparisonDailyPartitionedChecksSpecMap()
)

get_table_default_grouping_configuration

Return the default data grouping configuration for a table.

Follow the link to see the source code on GitHub.

GET 

http://localhost:8888/api/connections/{connectionName}/schemas/{schemaName}/tables/{tableName}/defaultgroupingconfiguration

Return value

 Property name   Description                       Data type 
data_grouping_configuration_spec DataGroupingConfigurationSpec

Parameters of this method are described below

 Property name   Description                       Data type   Required 
connection_name Connection name string
schema_name Schema name string
table_name Table name string

Usage examples

Execution

curl http://localhost:8888/api/connections/sample_connection/schemas/sample_schema/tables/sample_table/defaultgroupingconfiguration^
    -H "Accept: application/json"
Expand to see the returned result 
{
  "level_3" : {
    "source" : "column_value",
    "column" : "sample_column"
  }
}

Execution

from dqops import client
from dqops.client.api.tables import get_table_default_grouping_configuration

dqops_client = client.Client(
    'http://localhost:8888/',
    raise_on_unexpected_status=True
)

call_result = get_table_default_grouping_configuration.sync(
    'sample_connection',
    'sample_schema',
    'sample_table',
    client=dqops_client
)
Expand to see the returned result 
DataGroupingConfigurationSpec(
    level_3=DataGroupingDimensionSpec(
        source=DataGroupingDimensionSource.COLUMN_VALUE,
        column='sample_column'
    )
)

Execution

from dqops import client
from dqops.client.api.tables import get_table_default_grouping_configuration

dqops_client = client.Client(
    'http://localhost:8888/',
    raise_on_unexpected_status=True
)

call_result = await get_table_default_grouping_configuration.asyncio(
    'sample_connection',
    'sample_schema',
    'sample_table',
    client=dqops_client
)
Expand to see the returned result 
DataGroupingConfigurationSpec(
    level_3=DataGroupingDimensionSpec(
        source=DataGroupingDimensionSource.COLUMN_VALUE,
        column='sample_column'
    )
)

Execution

from dqops import client
from dqops.client.api.tables import get_table_default_grouping_configuration

token = 's4mp13_4u7h_70k3n'

dqops_client = client.AuthenticatedClient(
    'http://localhost:8888/',
    token=token,
    raise_on_unexpected_status=True
)

call_result = get_table_default_grouping_configuration.sync(
    'sample_connection',
    'sample_schema',
    'sample_table',
    client=dqops_client
)
Expand to see the returned result 
DataGroupingConfigurationSpec(
    level_3=DataGroupingDimensionSpec(
        source=DataGroupingDimensionSource.COLUMN_VALUE,
        column='sample_column'
    )
)

Execution

from dqops import client
from dqops.client.api.tables import get_table_default_grouping_configuration

token = 's4mp13_4u7h_70k3n'

dqops_client = client.AuthenticatedClient(
    'http://localhost:8888/',
    token=token,
    raise_on_unexpected_status=True
)

call_result = await get_table_default_grouping_configuration.asyncio(
    'sample_connection',
    'sample_schema',
    'sample_table',
    client=dqops_client
)
Expand to see the returned result 
DataGroupingConfigurationSpec(
    level_3=DataGroupingDimensionSpec(
        source=DataGroupingDimensionSource.COLUMN_VALUE,
        column='sample_column'
    )
)

get_table_incident_grouping

Return the configuration of incident grouping on a table

Follow the link to see the source code on GitHub.

GET 

http://localhost:8888/api/connections/{connectionName}/schemas/{schemaName}/tables/{tableName}/incidentgrouping

Return value

 Property name   Description                       Data type 
table_incident_grouping_spec TableIncidentGroupingSpec

Parameters of this method are described below

 Property name   Description                       Data type   Required 
connection_name Connection name string
schema_name Schema name string
table_name Table name string

Usage examples

Execution

curl http://localhost:8888/api/connections/sample_connection/schemas/sample_schema/tables/sample_table/incidentgrouping^
    -H "Accept: application/json"
Expand to see the returned result 
{
  "grouping_level" : "table_dimension",
  "minimum_severity" : "warning",
  "divide_by_data_group" : true,
  "disabled" : false
}

Execution

from dqops import client
from dqops.client.api.tables import get_table_incident_grouping

dqops_client = client.Client(
    'http://localhost:8888/',
    raise_on_unexpected_status=True
)

call_result = get_table_incident_grouping.sync(
    'sample_connection',
    'sample_schema',
    'sample_table',
    client=dqops_client
)
Expand to see the returned result 
TableIncidentGroupingSpec(
    grouping_level=IncidentGroupingLevel.TABLE_DIMENSION,
    minimum_severity=MinimumGroupingSeverityLevel.WARNING,
    divide_by_data_group=True,
    disabled=False
)

Execution

from dqops import client
from dqops.client.api.tables import get_table_incident_grouping

dqops_client = client.Client(
    'http://localhost:8888/',
    raise_on_unexpected_status=True
)

call_result = await get_table_incident_grouping.asyncio(
    'sample_connection',
    'sample_schema',
    'sample_table',
    client=dqops_client
)
Expand to see the returned result 
TableIncidentGroupingSpec(
    grouping_level=IncidentGroupingLevel.TABLE_DIMENSION,
    minimum_severity=MinimumGroupingSeverityLevel.WARNING,
    divide_by_data_group=True,
    disabled=False
)

Execution

from dqops import client
from dqops.client.api.tables import get_table_incident_grouping

token = 's4mp13_4u7h_70k3n'

dqops_client = client.AuthenticatedClient(
    'http://localhost:8888/',
    token=token,
    raise_on_unexpected_status=True
)

call_result = get_table_incident_grouping.sync(
    'sample_connection',
    'sample_schema',
    'sample_table',
    client=dqops_client
)
Expand to see the returned result 
TableIncidentGroupingSpec(
    grouping_level=IncidentGroupingLevel.TABLE_DIMENSION,
    minimum_severity=MinimumGroupingSeverityLevel.WARNING,
    divide_by_data_group=True,
    disabled=False
)

Execution

from dqops import client
from dqops.client.api.tables import get_table_incident_grouping

token = 's4mp13_4u7h_70k3n'

dqops_client = client.AuthenticatedClient(
    'http://localhost:8888/',
    token=token,
    raise_on_unexpected_status=True
)

call_result = await get_table_incident_grouping.asyncio(
    'sample_connection',
    'sample_schema',
    'sample_table',
    client=dqops_client
)
Expand to see the returned result 
TableIncidentGroupingSpec(
    grouping_level=IncidentGroupingLevel.TABLE_DIMENSION,
    minimum_severity=MinimumGroupingSeverityLevel.WARNING,
    divide_by_data_group=True,
    disabled=False
)

get_table_labels

Return the list of labels assigned to a table

Follow the link to see the source code on GitHub.

GET 

http://localhost:8888/api/connections/{connectionName}/schemas/{schemaName}/tables/{tableName}/labels

Return value

 Property name   Description                       Data type 
string List[string]

Parameters of this method are described below

 Property name   Description                       Data type   Required 
connection_name Connection name string
schema_name Schema name string
table_name Table name string

Usage examples

Execution

curl http://localhost:8888/api/connections/sample_connection/schemas/sample_schema/tables/sample_table/labels^
    -H "Accept: application/json"
Expand to see the returned result 
[ "sampleString_1", "sampleString_2", "sampleString_3" ]

Execution

from dqops import client
from dqops.client.api.tables import get_table_labels

dqops_client = client.Client(
    'http://localhost:8888/',
    raise_on_unexpected_status=True
)

call_result = get_table_labels.sync(
    'sample_connection',
    'sample_schema',
    'sample_table',
    client=dqops_client
)
Expand to see the returned result 
[
    'sampleString_1',
    'sampleString_2',
    'sampleString_3'
]

Execution

from dqops import client
from dqops.client.api.tables import get_table_labels

dqops_client = client.Client(
    'http://localhost:8888/',
    raise_on_unexpected_status=True
)

call_result = await get_table_labels.asyncio(
    'sample_connection',
    'sample_schema',
    'sample_table',
    client=dqops_client
)
Expand to see the returned result 
[
    'sampleString_1',
    'sampleString_2',
    'sampleString_3'
]

Execution

from dqops import client
from dqops.client.api.tables import get_table_labels

token = 's4mp13_4u7h_70k3n'

dqops_client = client.AuthenticatedClient(
    'http://localhost:8888/',
    token=token,
    raise_on_unexpected_status=True
)

call_result = get_table_labels.sync(
    'sample_connection',
    'sample_schema',
    'sample_table',
    client=dqops_client
)
Expand to see the returned result 
[
    'sampleString_1',
    'sampleString_2',
    'sampleString_3'
]

Execution

from dqops import client
from dqops.client.api.tables import get_table_labels

token = 's4mp13_4u7h_70k3n'

dqops_client = client.AuthenticatedClient(
    'http://localhost:8888/',
    token=token,
    raise_on_unexpected_status=True
)

call_result = await get_table_labels.asyncio(
    'sample_connection',
    'sample_schema',
    'sample_table',
    client=dqops_client
)
Expand to see the returned result 
[
    'sampleString_1',
    'sampleString_2',
    'sampleString_3'
]

get_table_monitoring_checks_basic_model

Return a simplistic UI friendly model of table level data quality monitoring on a table for a given time scale

Follow the link to see the source code on GitHub.

GET 

http://localhost:8888/api/connections/{connectionName}/schemas/{schemaName}/tables/{tableName}/monitoring/{timeScale}/model/basic

Return value

 Property name   Description                       Data type 
check_container_list_model CheckContainerListModel

Parameters of this method are described below

 Property name   Description                       Data type   Required 
connection_name Connection name string
schema_name Schema name string
table_name Table name string
time_scale Time scale CheckTimeScale

Usage examples

Execution

curl http://localhost:8888/api/connections/sample_connection/schemas/sample_schema/tables/sample_table/monitoring/daily/model/basic^
    -H "Accept: application/json"
Expand to see the returned result 
{
  "checks" : [ {
    "check_category" : "sample_category_1",
    "check_name" : "sample_check_1",
    "help_text" : "Sample help text",
    "configured" : true
  }, {
    "check_category" : "sample_category_2",
    "check_name" : "sample_check_1",
    "help_text" : "Sample help text",
    "configured" : true
  }, {
    "check_category" : "sample_category_1",
    "check_name" : "sample_check_2",
    "help_text" : "Sample help text",
    "configured" : true
  }, {
    "check_category" : "sample_category_2",
    "check_name" : "sample_check_2",
    "help_text" : "Sample help text",
    "configured" : true
  }, {
    "check_category" : "sample_category_1",
    "check_name" : "sample_check_3",
    "help_text" : "Sample help text",
    "configured" : true
  }, {
    "check_category" : "sample_category_2",
    "check_name" : "sample_check_3",
    "help_text" : "Sample help text",
    "configured" : true
  } ],
  "can_edit" : false,
  "can_run_checks" : true,
  "can_delete_data" : true
}

Execution

from dqops import client
from dqops.client.api.tables import get_table_monitoring_checks_basic_model
from dqops.client.models import CheckTimeScale

dqops_client = client.Client(
    'http://localhost:8888/',
    raise_on_unexpected_status=True
)

call_result = get_table_monitoring_checks_basic_model.sync(
    'sample_connection',
    'sample_schema',
    'sample_table',
    CheckTimeScale.daily,
    client=dqops_client
)
Expand to see the returned result 
CheckContainerListModel(
    checks=[
        CheckListModel(
            check_category='sample_category_1',
            check_name='sample_check_1',
            help_text='Sample help text',
            configured=True
        ),
        CheckListModel(
            check_category='sample_category_2',
            check_name='sample_check_1',
            help_text='Sample help text',
            configured=True
        ),
        CheckListModel(
            check_category='sample_category_1',
            check_name='sample_check_2',
            help_text='Sample help text',
            configured=True
        ),
        CheckListModel(
            check_category='sample_category_2',
            check_name='sample_check_2',
            help_text='Sample help text',
            configured=True
        ),
        CheckListModel(
            check_category='sample_category_1',
            check_name='sample_check_3',
            help_text='Sample help text',
            configured=True
        ),
        CheckListModel(
            check_category='sample_category_2',
            check_name='sample_check_3',
            help_text='Sample help text',
            configured=True
        )
    ],
    can_edit=False,
    can_run_checks=True,
    can_delete_data=True
)

Execution

from dqops import client
from dqops.client.api.tables import get_table_monitoring_checks_basic_model
from dqops.client.models import CheckTimeScale

dqops_client = client.Client(
    'http://localhost:8888/',
    raise_on_unexpected_status=True
)

call_result = await get_table_monitoring_checks_basic_model.asyncio(
    'sample_connection',
    'sample_schema',
    'sample_table',
    CheckTimeScale.daily,
    client=dqops_client
)
Expand to see the returned result 
CheckContainerListModel(
    checks=[
        CheckListModel(
            check_category='sample_category_1',
            check_name='sample_check_1',
            help_text='Sample help text',
            configured=True
        ),
        CheckListModel(
            check_category='sample_category_2',
            check_name='sample_check_1',
            help_text='Sample help text',
            configured=True
        ),
        CheckListModel(
            check_category='sample_category_1',
            check_name='sample_check_2',
            help_text='Sample help text',
            configured=True
        ),
        CheckListModel(
            check_category='sample_category_2',
            check_name='sample_check_2',
            help_text='Sample help text',
            configured=True
        ),
        CheckListModel(
            check_category='sample_category_1',
            check_name='sample_check_3',
            help_text='Sample help text',
            configured=True
        ),
        CheckListModel(
            check_category='sample_category_2',
            check_name='sample_check_3',
            help_text='Sample help text',
            configured=True
        )
    ],
    can_edit=False,
    can_run_checks=True,
    can_delete_data=True
)

Execution

from dqops import client
from dqops.client.api.tables import get_table_monitoring_checks_basic_model
from dqops.client.models import CheckTimeScale

token = 's4mp13_4u7h_70k3n'

dqops_client = client.AuthenticatedClient(
    'http://localhost:8888/',
    token=token,
    raise_on_unexpected_status=True
)

call_result = get_table_monitoring_checks_basic_model.sync(
    'sample_connection',
    'sample_schema',
    'sample_table',
    CheckTimeScale.daily,
    client=dqops_client
)
Expand to see the returned result 
CheckContainerListModel(
    checks=[
        CheckListModel(
            check_category='sample_category_1',
            check_name='sample_check_1',
            help_text='Sample help text',
            configured=True
        ),
        CheckListModel(
            check_category='sample_category_2',
            check_name='sample_check_1',
            help_text='Sample help text',
            configured=True
        ),
        CheckListModel(
            check_category='sample_category_1',
            check_name='sample_check_2',
            help_text='Sample help text',
            configured=True
        ),
        CheckListModel(
            check_category='sample_category_2',
            check_name='sample_check_2',
            help_text='Sample help text',
            configured=True
        ),
        CheckListModel(
            check_category='sample_category_1',
            check_name='sample_check_3',
            help_text='Sample help text',
            configured=True
        ),
        CheckListModel(
            check_category='sample_category_2',
            check_name='sample_check_3',
            help_text='Sample help text',
            configured=True
        )
    ],
    can_edit=False,
    can_run_checks=True,
    can_delete_data=True
)

Execution

from dqops import client
from dqops.client.api.tables import get_table_monitoring_checks_basic_model
from dqops.client.models import CheckTimeScale

token = 's4mp13_4u7h_70k3n'

dqops_client = client.AuthenticatedClient(
    'http://localhost:8888/',
    token=token,
    raise_on_unexpected_status=True
)

call_result = await get_table_monitoring_checks_basic_model.asyncio(
    'sample_connection',
    'sample_schema',
    'sample_table',
    CheckTimeScale.daily,
    client=dqops_client
)
Expand to see the returned result 
CheckContainerListModel(
    checks=[
        CheckListModel(
            check_category='sample_category_1',
            check_name='sample_check_1',
            help_text='Sample help text',
            configured=True
        ),
        CheckListModel(
            check_category='sample_category_2',
            check_name='sample_check_1',
            help_text='Sample help text',
            configured=True
        ),
        CheckListModel(
            check_category='sample_category_1',
            check_name='sample_check_2',
            help_text='Sample help text',
            configured=True
        ),
        CheckListModel(
            check_category='sample_category_2',
            check_name='sample_check_2',
            help_text='Sample help text',
            configured=True
        ),
        CheckListModel(
            check_category='sample_category_1',
            check_name='sample_check_3',
            help_text='Sample help text',
            configured=True
        ),
        CheckListModel(
            check_category='sample_category_2',
            check_name='sample_check_3',
            help_text='Sample help text',
            configured=True
        )
    ],
    can_edit=False,
    can_run_checks=True,
    can_delete_data=True
)

get_table_monitoring_checks_model

Return a UI friendly model of configurations for table level data quality monitoring on a table for a given time scale

Follow the link to see the source code on GitHub.

GET 

http://localhost:8888/api/connections/{connectionName}/schemas/{schemaName}/tables/{tableName}/monitoring/{timeScale}/model

Return value

 Property name   Description                       Data type 
check_container_model CheckContainerModel

Parameters of this method are described below

 Property name   Description                       Data type   Required 
connection_name Connection name string
schema_name Schema name string
table_name Table name string
time_scale Time scale CheckTimeScale

Usage examples

Execution

curl http://localhost:8888/api/connections/sample_connection/schemas/sample_schema/tables/sample_table/monitoring/daily/model^
    -H "Accept: application/json"
Expand to see the returned result 
{
  "categories" : [ {
    "category" : "sample_category",
    "help_text" : "Sample help text",
    "checks" : [ {
      "check_name" : "sample_check",
      "help_text" : "Sample help text",
      "sensor_parameters" : [ ],
      "sensor_name" : "sample_target/sample_category/table/volume/row_count",
      "quality_dimension" : "sample_quality_dimension",
      "supports_error_sampling" : false,
      "supports_grouping" : false,
      "default_severity" : "error",
      "disabled" : false,
      "exclude_from_kpi" : false,
      "include_in_sla" : false,
      "configured" : false,
      "can_edit" : false,
      "can_run_checks" : false,
      "can_delete_data" : false
    } ]
  } ],
  "can_edit" : false,
  "can_run_checks" : false,
  "can_delete_data" : false
}

Execution

from dqops import client
from dqops.client.api.tables import get_table_monitoring_checks_model
from dqops.client.models import CheckTimeScale

dqops_client = client.Client(
    'http://localhost:8888/',
    raise_on_unexpected_status=True
)

call_result = get_table_monitoring_checks_model.sync(
    'sample_connection',
    'sample_schema',
    'sample_table',
    CheckTimeScale.daily,
    client=dqops_client
)
Expand to see the returned result 
CheckContainerModel(
    categories=[
        QualityCategoryModel(
            category='sample_category',
            help_text='Sample help text',
            checks=[
                CheckModel(
                    check_name='sample_check',
                    help_text='Sample help text',
                    sensor_parameters=[

                    ],
                    sensor_name='sample_target/sample_category/table/volume/row_count',
                    quality_dimension='sample_quality_dimension',
                    supports_error_sampling=False,
                    supports_grouping=False,
                    standard=False,
                    default_check=False,
                    default_severity=DefaultRuleSeverityLevel.ERROR,
                    disabled=False,
                    exclude_from_kpi=False,
                    include_in_sla=False,
                    configured=False,
                    always_collect_error_samples=False,
                    do_not_schedule=False,
                    can_edit=False,
                    can_run_checks=False,
                    can_delete_data=False
                )
            ]
        )
    ],
    can_edit=False,
    can_run_checks=False,
    can_delete_data=False
)

Execution

from dqops import client
from dqops.client.api.tables import get_table_monitoring_checks_model
from dqops.client.models import CheckTimeScale

dqops_client = client.Client(
    'http://localhost:8888/',
    raise_on_unexpected_status=True
)

call_result = await get_table_monitoring_checks_model.asyncio(
    'sample_connection',
    'sample_schema',
    'sample_table',
    CheckTimeScale.daily,
    client=dqops_client
)
Expand to see the returned result 
CheckContainerModel(
    categories=[
        QualityCategoryModel(
            category='sample_category',
            help_text='Sample help text',
            checks=[
                CheckModel(
                    check_name='sample_check',
                    help_text='Sample help text',
                    sensor_parameters=[

                    ],
                    sensor_name='sample_target/sample_category/table/volume/row_count',
                    quality_dimension='sample_quality_dimension',
                    supports_error_sampling=False,
                    supports_grouping=False,
                    standard=False,
                    default_check=False,
                    default_severity=DefaultRuleSeverityLevel.ERROR,
                    disabled=False,
                    exclude_from_kpi=False,
                    include_in_sla=False,
                    configured=False,
                    always_collect_error_samples=False,
                    do_not_schedule=False,
                    can_edit=False,
                    can_run_checks=False,
                    can_delete_data=False
                )
            ]
        )
    ],
    can_edit=False,
    can_run_checks=False,
    can_delete_data=False
)

Execution

from dqops import client
from dqops.client.api.tables import get_table_monitoring_checks_model
from dqops.client.models import CheckTimeScale

token = 's4mp13_4u7h_70k3n'

dqops_client = client.AuthenticatedClient(
    'http://localhost:8888/',
    token=token,
    raise_on_unexpected_status=True
)

call_result = get_table_monitoring_checks_model.sync(
    'sample_connection',
    'sample_schema',
    'sample_table',
    CheckTimeScale.daily,
    client=dqops_client
)
Expand to see the returned result 
CheckContainerModel(
    categories=[
        QualityCategoryModel(
            category='sample_category',
            help_text='Sample help text',
            checks=[
                CheckModel(
                    check_name='sample_check',
                    help_text='Sample help text',
                    sensor_parameters=[

                    ],
                    sensor_name='sample_target/sample_category/table/volume/row_count',
                    quality_dimension='sample_quality_dimension',
                    supports_error_sampling=False,
                    supports_grouping=False,
                    standard=False,
                    default_check=False,
                    default_severity=DefaultRuleSeverityLevel.ERROR,
                    disabled=False,
                    exclude_from_kpi=False,
                    include_in_sla=False,
                    configured=False,
                    always_collect_error_samples=False,
                    do_not_schedule=False,
                    can_edit=False,
                    can_run_checks=False,
                    can_delete_data=False
                )
            ]
        )
    ],
    can_edit=False,
    can_run_checks=False,
    can_delete_data=False
)

Execution

from dqops import client
from dqops.client.api.tables import get_table_monitoring_checks_model
from dqops.client.models import CheckTimeScale

token = 's4mp13_4u7h_70k3n'

dqops_client = client.AuthenticatedClient(
    'http://localhost:8888/',
    token=token,
    raise_on_unexpected_status=True
)

call_result = await get_table_monitoring_checks_model.asyncio(
    'sample_connection',
    'sample_schema',
    'sample_table',
    CheckTimeScale.daily,
    client=dqops_client
)
Expand to see the returned result 
CheckContainerModel(
    categories=[
        QualityCategoryModel(
            category='sample_category',
            help_text='Sample help text',
            checks=[
                CheckModel(
                    check_name='sample_check',
                    help_text='Sample help text',
                    sensor_parameters=[

                    ],
                    sensor_name='sample_target/sample_category/table/volume/row_count',
                    quality_dimension='sample_quality_dimension',
                    supports_error_sampling=False,
                    supports_grouping=False,
                    standard=False,
                    default_check=False,
                    default_severity=DefaultRuleSeverityLevel.ERROR,
                    disabled=False,
                    exclude_from_kpi=False,
                    include_in_sla=False,
                    configured=False,
                    always_collect_error_samples=False,
                    do_not_schedule=False,
                    can_edit=False,
                    can_run_checks=False,
                    can_delete_data=False
                )
            ]
        )
    ],
    can_edit=False,
    can_run_checks=False,
    can_delete_data=False
)

get_table_monitoring_checks_model_filter

Return a UI friendly model of configurations for table level data quality monitoring on a table for a given time scale, filtered by category and check name.

Follow the link to see the source code on GitHub.

GET 

http://localhost:8888/api/connections/{connectionName}/schemas/{schemaName}/tables/{tableName}/monitoring/{timeScale}/model/filter/{checkCategory}/{checkName}

Return value

 Property name   Description                       Data type 
check_container_model CheckContainerModel

Parameters of this method are described below

 Property name   Description                       Data type   Required 
connection_name Connection name string
schema_name Schema name string
table_name Table name string
time_scale Time scale CheckTimeScale
check_category Check category string
check_name Check name string

Usage examples

Execution

curl http://localhost:8888/api/connections/sample_connection/schemas/sample_schema/tables/sample_table/monitoring/daily/model/filter/sample_category/sample_check^
    -H "Accept: application/json"
Expand to see the returned result 
{
  "categories" : [ {
    "category" : "sample_category",
    "help_text" : "Sample help text",
    "checks" : [ {
      "check_name" : "sample_check",
      "help_text" : "Sample help text",
      "sensor_parameters" : [ ],
      "sensor_name" : "sample_target/sample_category/table/volume/row_count",
      "quality_dimension" : "sample_quality_dimension",
      "supports_error_sampling" : false,
      "supports_grouping" : false,
      "default_severity" : "error",
      "disabled" : false,
      "exclude_from_kpi" : false,
      "include_in_sla" : false,
      "configured" : false,
      "can_edit" : false,
      "can_run_checks" : false,
      "can_delete_data" : false
    } ]
  } ],
  "can_edit" : false,
  "can_run_checks" : false,
  "can_delete_data" : false
}

Execution

from dqops import client
from dqops.client.api.tables import get_table_monitoring_checks_model_filter
from dqops.client.models import CheckTimeScale

dqops_client = client.Client(
    'http://localhost:8888/',
    raise_on_unexpected_status=True
)

call_result = get_table_monitoring_checks_model_filter.sync(
    'sample_connection',
    'sample_schema',
    'sample_table',
    CheckTimeScale.daily,
    'sample_category',
    'sample_check',
    client=dqops_client
)
Expand to see the returned result 
CheckContainerModel(
    categories=[
        QualityCategoryModel(
            category='sample_category',
            help_text='Sample help text',
            checks=[
                CheckModel(
                    check_name='sample_check',
                    help_text='Sample help text',
                    sensor_parameters=[

                    ],
                    sensor_name='sample_target/sample_category/table/volume/row_count',
                    quality_dimension='sample_quality_dimension',
                    supports_error_sampling=False,
                    supports_grouping=False,
                    standard=False,
                    default_check=False,
                    default_severity=DefaultRuleSeverityLevel.ERROR,
                    disabled=False,
                    exclude_from_kpi=False,
                    include_in_sla=False,
                    configured=False,
                    always_collect_error_samples=False,
                    do_not_schedule=False,
                    can_edit=False,
                    can_run_checks=False,
                    can_delete_data=False
                )
            ]
        )
    ],
    can_edit=False,
    can_run_checks=False,
    can_delete_data=False
)

Execution

from dqops import client
from dqops.client.api.tables import get_table_monitoring_checks_model_filter
from dqops.client.models import CheckTimeScale

dqops_client = client.Client(
    'http://localhost:8888/',
    raise_on_unexpected_status=True
)

call_result = await get_table_monitoring_checks_model_filter.asyncio(
    'sample_connection',
    'sample_schema',
    'sample_table',
    CheckTimeScale.daily,
    'sample_category',
    'sample_check',
    client=dqops_client
)
Expand to see the returned result 
CheckContainerModel(
    categories=[
        QualityCategoryModel(
            category='sample_category',
            help_text='Sample help text',
            checks=[
                CheckModel(
                    check_name='sample_check',
                    help_text='Sample help text',
                    sensor_parameters=[

                    ],
                    sensor_name='sample_target/sample_category/table/volume/row_count',
                    quality_dimension='sample_quality_dimension',
                    supports_error_sampling=False,
                    supports_grouping=False,
                    standard=False,
                    default_check=False,
                    default_severity=DefaultRuleSeverityLevel.ERROR,
                    disabled=False,
                    exclude_from_kpi=False,
                    include_in_sla=False,
                    configured=False,
                    always_collect_error_samples=False,
                    do_not_schedule=False,
                    can_edit=False,
                    can_run_checks=False,
                    can_delete_data=False
                )
            ]
        )
    ],
    can_edit=False,
    can_run_checks=False,
    can_delete_data=False
)

Execution

from dqops import client
from dqops.client.api.tables import get_table_monitoring_checks_model_filter
from dqops.client.models import CheckTimeScale

token = 's4mp13_4u7h_70k3n'

dqops_client = client.AuthenticatedClient(
    'http://localhost:8888/',
    token=token,
    raise_on_unexpected_status=True
)

call_result = get_table_monitoring_checks_model_filter.sync(
    'sample_connection',
    'sample_schema',
    'sample_table',
    CheckTimeScale.daily,
    'sample_category',
    'sample_check',
    client=dqops_client
)
Expand to see the returned result 
CheckContainerModel(
    categories=[
        QualityCategoryModel(
            category='sample_category',
            help_text='Sample help text',
            checks=[
                CheckModel(
                    check_name='sample_check',
                    help_text='Sample help text',
                    sensor_parameters=[

                    ],
                    sensor_name='sample_target/sample_category/table/volume/row_count',
                    quality_dimension='sample_quality_dimension',
                    supports_error_sampling=False,
                    supports_grouping=False,
                    standard=False,
                    default_check=False,
                    default_severity=DefaultRuleSeverityLevel.ERROR,
                    disabled=False,
                    exclude_from_kpi=False,
                    include_in_sla=False,
                    configured=False,
                    always_collect_error_samples=False,
                    do_not_schedule=False,
                    can_edit=False,
                    can_run_checks=False,
                    can_delete_data=False
                )
            ]
        )
    ],
    can_edit=False,
    can_run_checks=False,
    can_delete_data=False
)

Execution

from dqops import client
from dqops.client.api.tables import get_table_monitoring_checks_model_filter
from dqops.client.models import CheckTimeScale

token = 's4mp13_4u7h_70k3n'

dqops_client = client.AuthenticatedClient(
    'http://localhost:8888/',
    token=token,
    raise_on_unexpected_status=True
)

call_result = await get_table_monitoring_checks_model_filter.asyncio(
    'sample_connection',
    'sample_schema',
    'sample_table',
    CheckTimeScale.daily,
    'sample_category',
    'sample_check',
    client=dqops_client
)
Expand to see the returned result 
CheckContainerModel(
    categories=[
        QualityCategoryModel(
            category='sample_category',
            help_text='Sample help text',
            checks=[
                CheckModel(
                    check_name='sample_check',
                    help_text='Sample help text',
                    sensor_parameters=[

                    ],
                    sensor_name='sample_target/sample_category/table/volume/row_count',
                    quality_dimension='sample_quality_dimension',
                    supports_error_sampling=False,
                    supports_grouping=False,
                    standard=False,
                    default_check=False,
                    default_severity=DefaultRuleSeverityLevel.ERROR,
                    disabled=False,
                    exclude_from_kpi=False,
                    include_in_sla=False,
                    configured=False,
                    always_collect_error_samples=False,
                    do_not_schedule=False,
                    can_edit=False,
                    can_run_checks=False,
                    can_delete_data=False
                )
            ]
        )
    ],
    can_edit=False,
    can_run_checks=False,
    can_delete_data=False
)

get_table_monitoring_checks_monthly

Return the configuration of monthly table level data quality monitoring on a table

Follow the link to see the source code on GitHub.

GET 

http://localhost:8888/api/connections/{connectionName}/schemas/{schemaName}/tables/{tableName}/monitoring/monthly

Return value

 Property name   Description                       Data type 
table_monthly_monitoring_check_categories_spec TableMonthlyMonitoringCheckCategoriesSpec

Parameters of this method are described below

 Property name   Description                       Data type   Required 
connection_name Connection name string
schema_name Schema name string
table_name Table name string

Usage examples

Execution

curl http://localhost:8888/api/connections/sample_connection/schemas/sample_schema/tables/sample_table/monitoring/monthly^
    -H "Accept: application/json"
Expand to see the returned result 
{
  "volume" : {
    "monthly_row_count" : {
      "error" : {
        "min_count" : 1
      }
    }
  }
}

Execution

from dqops import client
from dqops.client.api.tables import get_table_monitoring_checks_monthly

dqops_client = client.Client(
    'http://localhost:8888/',
    raise_on_unexpected_status=True
)

call_result = get_table_monitoring_checks_monthly.sync(
    'sample_connection',
    'sample_schema',
    'sample_table',
    client=dqops_client
)
Expand to see the returned result 
TableMonthlyMonitoringCheckCategoriesSpec(
    volume=TableVolumeMonthlyMonitoringChecksSpec(
        monthly_row_count=TableRowCountCheckSpec(
            parameters=TableVolumeRowCountSensorParametersSpec(),
            error=MinCountRule1ParametersSpec(min_count=1),
            disabled=False,
            exclude_from_kpi=False,
            include_in_sla=False,
            always_collect_error_samples=False,
            do_not_schedule=False
        )
    ),
    comparisons=TableComparisonMonthlyMonitoringChecksSpecMap()
)

Execution

from dqops import client
from dqops.client.api.tables import get_table_monitoring_checks_monthly

dqops_client = client.Client(
    'http://localhost:8888/',
    raise_on_unexpected_status=True
)

call_result = await get_table_monitoring_checks_monthly.asyncio(
    'sample_connection',
    'sample_schema',
    'sample_table',
    client=dqops_client
)
Expand to see the returned result 
TableMonthlyMonitoringCheckCategoriesSpec(
    volume=TableVolumeMonthlyMonitoringChecksSpec(
        monthly_row_count=TableRowCountCheckSpec(
            parameters=TableVolumeRowCountSensorParametersSpec(),
            error=MinCountRule1ParametersSpec(min_count=1),
            disabled=False,
            exclude_from_kpi=False,
            include_in_sla=False,
            always_collect_error_samples=False,
            do_not_schedule=False
        )
    ),
    comparisons=TableComparisonMonthlyMonitoringChecksSpecMap()
)

Execution

from dqops import client
from dqops.client.api.tables import get_table_monitoring_checks_monthly

token = 's4mp13_4u7h_70k3n'

dqops_client = client.AuthenticatedClient(
    'http://localhost:8888/',
    token=token,
    raise_on_unexpected_status=True
)

call_result = get_table_monitoring_checks_monthly.sync(
    'sample_connection',
    'sample_schema',
    'sample_table',
    client=dqops_client
)
Expand to see the returned result 
TableMonthlyMonitoringCheckCategoriesSpec(
    volume=TableVolumeMonthlyMonitoringChecksSpec(
        monthly_row_count=TableRowCountCheckSpec(
            parameters=TableVolumeRowCountSensorParametersSpec(),
            error=MinCountRule1ParametersSpec(min_count=1),
            disabled=False,
            exclude_from_kpi=False,
            include_in_sla=False,
            always_collect_error_samples=False,
            do_not_schedule=False
        )
    ),
    comparisons=TableComparisonMonthlyMonitoringChecksSpecMap()
)

Execution

from dqops import client
from dqops.client.api.tables import get_table_monitoring_checks_monthly

token = 's4mp13_4u7h_70k3n'

dqops_client = client.AuthenticatedClient(
    'http://localhost:8888/',
    token=token,
    raise_on_unexpected_status=True
)

call_result = await get_table_monitoring_checks_monthly.asyncio(
    'sample_connection',
    'sample_schema',
    'sample_table',
    client=dqops_client
)
Expand to see the returned result 
TableMonthlyMonitoringCheckCategoriesSpec(
    volume=TableVolumeMonthlyMonitoringChecksSpec(
        monthly_row_count=TableRowCountCheckSpec(
            parameters=TableVolumeRowCountSensorParametersSpec(),
            error=MinCountRule1ParametersSpec(min_count=1),
            disabled=False,
            exclude_from_kpi=False,
            include_in_sla=False,
            always_collect_error_samples=False,
            do_not_schedule=False
        )
    ),
    comparisons=TableComparisonMonthlyMonitoringChecksSpecMap()
)

get_table_monitoring_checks_templates

Return available data quality checks on a requested table.

Follow the link to see the source code on GitHub.

GET 

http://localhost:8888/api/connections/{connectionName}/schemas/{schemaName}/tables/{tableName}/bulkenable/monitoring/{timeScale}

Return value

 Property name   Description                       Data type 
check_template List[CheckTemplate]

Parameters of this method are described below

 Property name   Description                       Data type   Required 
connection_name Connection name string
schema_name Schema name string
table_name Table name string
time_scale Time scale CheckTimeScale
check_category Check category string
check_name Check name string

Usage examples

Execution

curl http://localhost:8888/api/connections/sample_connection/schemas/sample_schema/tables/sample_table/bulkenable/monitoring/daily^
    -H "Accept: application/json"
Expand to see the returned result 
[ {
  "sensor_parameters_definitions" : [ ]
}, {
  "sensor_parameters_definitions" : [ ]
}, {
  "sensor_parameters_definitions" : [ ]
} ]

Execution

from dqops import client
from dqops.client.api.tables import get_table_monitoring_checks_templates
from dqops.client.models import CheckTimeScale

dqops_client = client.Client(
    'http://localhost:8888/',
    raise_on_unexpected_status=True
)

call_result = get_table_monitoring_checks_templates.sync(
    'sample_connection',
    'sample_schema',
    'sample_table',
    CheckTimeScale.daily,
    client=dqops_client
)
Expand to see the returned result 
[
    CheckTemplate(
        sensor_parameters_definitions=[

        ]
    ),
    CheckTemplate(
        sensor_parameters_definitions=[

        ]
    ),
    CheckTemplate(
        sensor_parameters_definitions=[

        ]
    )
]

Execution

from dqops import client
from dqops.client.api.tables import get_table_monitoring_checks_templates
from dqops.client.models import CheckTimeScale

dqops_client = client.Client(
    'http://localhost:8888/',
    raise_on_unexpected_status=True
)

call_result = await get_table_monitoring_checks_templates.asyncio(
    'sample_connection',
    'sample_schema',
    'sample_table',
    CheckTimeScale.daily,
    client=dqops_client
)
Expand to see the returned result 
[
    CheckTemplate(
        sensor_parameters_definitions=[

        ]
    ),
    CheckTemplate(
        sensor_parameters_definitions=[

        ]
    ),
    CheckTemplate(
        sensor_parameters_definitions=[

        ]
    )
]

Execution

from dqops import client
from dqops.client.api.tables import get_table_monitoring_checks_templates
from dqops.client.models import CheckTimeScale

token = 's4mp13_4u7h_70k3n'

dqops_client = client.AuthenticatedClient(
    'http://localhost:8888/',
    token=token,
    raise_on_unexpected_status=True
)

call_result = get_table_monitoring_checks_templates.sync(
    'sample_connection',
    'sample_schema',
    'sample_table',
    CheckTimeScale.daily,
    client=dqops_client
)
Expand to see the returned result 
[
    CheckTemplate(
        sensor_parameters_definitions=[

        ]
    ),
    CheckTemplate(
        sensor_parameters_definitions=[

        ]
    ),
    CheckTemplate(
        sensor_parameters_definitions=[

        ]
    )
]

Execution

from dqops import client
from dqops.client.api.tables import get_table_monitoring_checks_templates
from dqops.client.models import CheckTimeScale

token = 's4mp13_4u7h_70k3n'

dqops_client = client.AuthenticatedClient(
    'http://localhost:8888/',
    token=token,
    raise_on_unexpected_status=True
)

call_result = await get_table_monitoring_checks_templates.asyncio(
    'sample_connection',
    'sample_schema',
    'sample_table',
    CheckTimeScale.daily,
    client=dqops_client
)
Expand to see the returned result 
[
    CheckTemplate(
        sensor_parameters_definitions=[

        ]
    ),
    CheckTemplate(
        sensor_parameters_definitions=[

        ]
    ),
    CheckTemplate(
        sensor_parameters_definitions=[

        ]
    )
]

get_table_partitioned_checks_basic_model

Return a simplistic UI friendly model of table level data quality partitioned checks on a table for a given time scale

Follow the link to see the source code on GitHub.

GET 

http://localhost:8888/api/connections/{connectionName}/schemas/{schemaName}/tables/{tableName}/partitioned/{timeScale}/model/basic

Return value

 Property name   Description                       Data type 
check_container_list_model CheckContainerListModel

Parameters of this method are described below

 Property name   Description                       Data type   Required 
connection_name Connection name string
schema_name Schema name string
table_name Table name string
time_scale Time scale CheckTimeScale

Usage examples

Execution

curl http://localhost:8888/api/connections/sample_connection/schemas/sample_schema/tables/sample_table/partitioned/daily/model/basic^
    -H "Accept: application/json"
Expand to see the returned result 
{
  "checks" : [ {
    "check_category" : "sample_category_1",
    "check_name" : "sample_check_1",
    "help_text" : "Sample help text",
    "configured" : true
  }, {
    "check_category" : "sample_category_2",
    "check_name" : "sample_check_1",
    "help_text" : "Sample help text",
    "configured" : true
  }, {
    "check_category" : "sample_category_1",
    "check_name" : "sample_check_2",
    "help_text" : "Sample help text",
    "configured" : true
  }, {
    "check_category" : "sample_category_2",
    "check_name" : "sample_check_2",
    "help_text" : "Sample help text",
    "configured" : true
  }, {
    "check_category" : "sample_category_1",
    "check_name" : "sample_check_3",
    "help_text" : "Sample help text",
    "configured" : true
  }, {
    "check_category" : "sample_category_2",
    "check_name" : "sample_check_3",
    "help_text" : "Sample help text",
    "configured" : true
  } ],
  "can_edit" : false,
  "can_run_checks" : true,
  "can_delete_data" : true
}

Execution

from dqops import client
from dqops.client.api.tables import get_table_partitioned_checks_basic_model
from dqops.client.models import CheckTimeScale

dqops_client = client.Client(
    'http://localhost:8888/',
    raise_on_unexpected_status=True
)

call_result = get_table_partitioned_checks_basic_model.sync(
    'sample_connection',
    'sample_schema',
    'sample_table',
    CheckTimeScale.daily,
    client=dqops_client
)
Expand to see the returned result 
CheckContainerListModel(
    checks=[
        CheckListModel(
            check_category='sample_category_1',
            check_name='sample_check_1',
            help_text='Sample help text',
            configured=True
        ),
        CheckListModel(
            check_category='sample_category_2',
            check_name='sample_check_1',
            help_text='Sample help text',
            configured=True
        ),
        CheckListModel(
            check_category='sample_category_1',
            check_name='sample_check_2',
            help_text='Sample help text',
            configured=True
        ),
        CheckListModel(
            check_category='sample_category_2',
            check_name='sample_check_2',
            help_text='Sample help text',
            configured=True
        ),
        CheckListModel(
            check_category='sample_category_1',
            check_name='sample_check_3',
            help_text='Sample help text',
            configured=True
        ),
        CheckListModel(
            check_category='sample_category_2',
            check_name='sample_check_3',
            help_text='Sample help text',
            configured=True
        )
    ],
    can_edit=False,
    can_run_checks=True,
    can_delete_data=True
)

Execution

from dqops import client
from dqops.client.api.tables import get_table_partitioned_checks_basic_model
from dqops.client.models import CheckTimeScale

dqops_client = client.Client(
    'http://localhost:8888/',
    raise_on_unexpected_status=True
)

call_result = await get_table_partitioned_checks_basic_model.asyncio(
    'sample_connection',
    'sample_schema',
    'sample_table',
    CheckTimeScale.daily,
    client=dqops_client
)
Expand to see the returned result 
CheckContainerListModel(
    checks=[
        CheckListModel(
            check_category='sample_category_1',
            check_name='sample_check_1',
            help_text='Sample help text',
            configured=True
        ),
        CheckListModel(
            check_category='sample_category_2',
            check_name='sample_check_1',
            help_text='Sample help text',
            configured=True
        ),
        CheckListModel(
            check_category='sample_category_1',
            check_name='sample_check_2',
            help_text='Sample help text',
            configured=True
        ),
        CheckListModel(
            check_category='sample_category_2',
            check_name='sample_check_2',
            help_text='Sample help text',
            configured=True
        ),
        CheckListModel(
            check_category='sample_category_1',
            check_name='sample_check_3',
            help_text='Sample help text',
            configured=True
        ),
        CheckListModel(
            check_category='sample_category_2',
            check_name='sample_check_3',
            help_text='Sample help text',
            configured=True
        )
    ],
    can_edit=False,
    can_run_checks=True,
    can_delete_data=True
)

Execution

from dqops import client
from dqops.client.api.tables import get_table_partitioned_checks_basic_model
from dqops.client.models import CheckTimeScale

token = 's4mp13_4u7h_70k3n'

dqops_client = client.AuthenticatedClient(
    'http://localhost:8888/',
    token=token,
    raise_on_unexpected_status=True
)

call_result = get_table_partitioned_checks_basic_model.sync(
    'sample_connection',
    'sample_schema',
    'sample_table',
    CheckTimeScale.daily,
    client=dqops_client
)
Expand to see the returned result 
CheckContainerListModel(
    checks=[
        CheckListModel(
            check_category='sample_category_1',
            check_name='sample_check_1',
            help_text='Sample help text',
            configured=True
        ),
        CheckListModel(
            check_category='sample_category_2',
            check_name='sample_check_1',
            help_text='Sample help text',
            configured=True
        ),
        CheckListModel(
            check_category='sample_category_1',
            check_name='sample_check_2',
            help_text='Sample help text',
            configured=True
        ),
        CheckListModel(
            check_category='sample_category_2',
            check_name='sample_check_2',
            help_text='Sample help text',
            configured=True
        ),
        CheckListModel(
            check_category='sample_category_1',
            check_name='sample_check_3',
            help_text='Sample help text',
            configured=True
        ),
        CheckListModel(
            check_category='sample_category_2',
            check_name='sample_check_3',
            help_text='Sample help text',
            configured=True
        )
    ],
    can_edit=False,
    can_run_checks=True,
    can_delete_data=True
)

Execution

from dqops import client
from dqops.client.api.tables import get_table_partitioned_checks_basic_model
from dqops.client.models import CheckTimeScale

token = 's4mp13_4u7h_70k3n'

dqops_client = client.AuthenticatedClient(
    'http://localhost:8888/',
    token=token,
    raise_on_unexpected_status=True
)

call_result = await get_table_partitioned_checks_basic_model.asyncio(
    'sample_connection',
    'sample_schema',
    'sample_table',
    CheckTimeScale.daily,
    client=dqops_client
)
Expand to see the returned result 
CheckContainerListModel(
    checks=[
        CheckListModel(
            check_category='sample_category_1',
            check_name='sample_check_1',
            help_text='Sample help text',
            configured=True
        ),
        CheckListModel(
            check_category='sample_category_2',
            check_name='sample_check_1',
            help_text='Sample help text',
            configured=True
        ),
        CheckListModel(
            check_category='sample_category_1',
            check_name='sample_check_2',
            help_text='Sample help text',
            configured=True
        ),
        CheckListModel(
            check_category='sample_category_2',
            check_name='sample_check_2',
            help_text='Sample help text',
            configured=True
        ),
        CheckListModel(
            check_category='sample_category_1',
            check_name='sample_check_3',
            help_text='Sample help text',
            configured=True
        ),
        CheckListModel(
            check_category='sample_category_2',
            check_name='sample_check_3',
            help_text='Sample help text',
            configured=True
        )
    ],
    can_edit=False,
    can_run_checks=True,
    can_delete_data=True
)

get_table_partitioned_checks_model

Return a UI friendly model of configurations for table level data quality partitioned checks on a table for a given time scale

Follow the link to see the source code on GitHub.

GET 

http://localhost:8888/api/connections/{connectionName}/schemas/{schemaName}/tables/{tableName}/partitioned/{timeScale}/model

Return value

 Property name   Description                       Data type 
check_container_model CheckContainerModel

Parameters of this method are described below

 Property name   Description                       Data type   Required 
connection_name Connection name string
schema_name Schema name string
table_name Table name string
time_scale Time scale CheckTimeScale

Usage examples

Execution

curl http://localhost:8888/api/connections/sample_connection/schemas/sample_schema/tables/sample_table/partitioned/daily/model^
    -H "Accept: application/json"
Expand to see the returned result 
{
  "categories" : [ {
    "category" : "sample_category",
    "help_text" : "Sample help text",
    "checks" : [ {
      "check_name" : "sample_check",
      "help_text" : "Sample help text",
      "sensor_parameters" : [ ],
      "sensor_name" : "sample_target/sample_category/table/volume/row_count",
      "quality_dimension" : "sample_quality_dimension",
      "supports_error_sampling" : false,
      "supports_grouping" : false,
      "default_severity" : "error",
      "disabled" : false,
      "exclude_from_kpi" : false,
      "include_in_sla" : false,
      "configured" : false,
      "can_edit" : false,
      "can_run_checks" : false,
      "can_delete_data" : false
    } ]
  } ],
  "can_edit" : false,
  "can_run_checks" : false,
  "can_delete_data" : false
}

Execution

from dqops import client
from dqops.client.api.tables import get_table_partitioned_checks_model
from dqops.client.models import CheckTimeScale

dqops_client = client.Client(
    'http://localhost:8888/',
    raise_on_unexpected_status=True
)

call_result = get_table_partitioned_checks_model.sync(
    'sample_connection',
    'sample_schema',
    'sample_table',
    CheckTimeScale.daily,
    client=dqops_client
)
Expand to see the returned result 
CheckContainerModel(
    categories=[
        QualityCategoryModel(
            category='sample_category',
            help_text='Sample help text',
            checks=[
                CheckModel(
                    check_name='sample_check',
                    help_text='Sample help text',
                    sensor_parameters=[

                    ],
                    sensor_name='sample_target/sample_category/table/volume/row_count',
                    quality_dimension='sample_quality_dimension',
                    supports_error_sampling=False,
                    supports_grouping=False,
                    standard=False,
                    default_check=False,
                    default_severity=DefaultRuleSeverityLevel.ERROR,
                    disabled=False,
                    exclude_from_kpi=False,
                    include_in_sla=False,
                    configured=False,
                    always_collect_error_samples=False,
                    do_not_schedule=False,
                    can_edit=False,
                    can_run_checks=False,
                    can_delete_data=False
                )
            ]
        )
    ],
    can_edit=False,
    can_run_checks=False,
    can_delete_data=False
)

Execution

from dqops import client
from dqops.client.api.tables import get_table_partitioned_checks_model
from dqops.client.models import CheckTimeScale

dqops_client = client.Client(
    'http://localhost:8888/',
    raise_on_unexpected_status=True
)

call_result = await get_table_partitioned_checks_model.asyncio(
    'sample_connection',
    'sample_schema',
    'sample_table',
    CheckTimeScale.daily,
    client=dqops_client
)
Expand to see the returned result 
CheckContainerModel(
    categories=[
        QualityCategoryModel(
            category='sample_category',
            help_text='Sample help text',
            checks=[
                CheckModel(
                    check_name='sample_check',
                    help_text='Sample help text',
                    sensor_parameters=[

                    ],
                    sensor_name='sample_target/sample_category/table/volume/row_count',
                    quality_dimension='sample_quality_dimension',
                    supports_error_sampling=False,
                    supports_grouping=False,
                    standard=False,
                    default_check=False,
                    default_severity=DefaultRuleSeverityLevel.ERROR,
                    disabled=False,
                    exclude_from_kpi=False,
                    include_in_sla=False,
                    configured=False,
                    always_collect_error_samples=False,
                    do_not_schedule=False,
                    can_edit=False,
                    can_run_checks=False,
                    can_delete_data=False
                )
            ]
        )
    ],
    can_edit=False,
    can_run_checks=False,
    can_delete_data=False
)

Execution

from dqops import client
from dqops.client.api.tables import get_table_partitioned_checks_model
from dqops.client.models import CheckTimeScale

token = 's4mp13_4u7h_70k3n'

dqops_client = client.AuthenticatedClient(
    'http://localhost:8888/',
    token=token,
    raise_on_unexpected_status=True
)

call_result = get_table_partitioned_checks_model.sync(
    'sample_connection',
    'sample_schema',
    'sample_table',
    CheckTimeScale.daily,
    client=dqops_client
)
Expand to see the returned result 
CheckContainerModel(
    categories=[
        QualityCategoryModel(
            category='sample_category',
            help_text='Sample help text',
            checks=[
                CheckModel(
                    check_name='sample_check',
                    help_text='Sample help text',
                    sensor_parameters=[

                    ],
                    sensor_name='sample_target/sample_category/table/volume/row_count',
                    quality_dimension='sample_quality_dimension',
                    supports_error_sampling=False,
                    supports_grouping=False,
                    standard=False,
                    default_check=False,
                    default_severity=DefaultRuleSeverityLevel.ERROR,
                    disabled=False,
                    exclude_from_kpi=False,
                    include_in_sla=False,
                    configured=False,
                    always_collect_error_samples=False,
                    do_not_schedule=False,
                    can_edit=False,
                    can_run_checks=False,
                    can_delete_data=False
                )
            ]
        )
    ],
    can_edit=False,
    can_run_checks=False,
    can_delete_data=False
)

Execution

from dqops import client
from dqops.client.api.tables import get_table_partitioned_checks_model
from dqops.client.models import CheckTimeScale

token = 's4mp13_4u7h_70k3n'

dqops_client = client.AuthenticatedClient(
    'http://localhost:8888/',
    token=token,
    raise_on_unexpected_status=True
)

call_result = await get_table_partitioned_checks_model.asyncio(
    'sample_connection',
    'sample_schema',
    'sample_table',
    CheckTimeScale.daily,
    client=dqops_client
)
Expand to see the returned result 
CheckContainerModel(
    categories=[
        QualityCategoryModel(
            category='sample_category',
            help_text='Sample help text',
            checks=[
                CheckModel(
                    check_name='sample_check',
                    help_text='Sample help text',
                    sensor_parameters=[

                    ],
                    sensor_name='sample_target/sample_category/table/volume/row_count',
                    quality_dimension='sample_quality_dimension',
                    supports_error_sampling=False,
                    supports_grouping=False,
                    standard=False,
                    default_check=False,
                    default_severity=DefaultRuleSeverityLevel.ERROR,
                    disabled=False,
                    exclude_from_kpi=False,
                    include_in_sla=False,
                    configured=False,
                    always_collect_error_samples=False,
                    do_not_schedule=False,
                    can_edit=False,
                    can_run_checks=False,
                    can_delete_data=False
                )
            ]
        )
    ],
    can_edit=False,
    can_run_checks=False,
    can_delete_data=False
)

get_table_partitioned_checks_model_filter

Return a UI friendly model of configurations for table level data quality partitioned checks on a table for a given time scale, filtered by category and check name.

Follow the link to see the source code on GitHub.

GET 

http://localhost:8888/api/connections/{connectionName}/schemas/{schemaName}/tables/{tableName}/partitioned/{timeScale}/model/filter/{checkCategory}/{checkName}

Return value

 Property name   Description                       Data type 
check_container_model CheckContainerModel

Parameters of this method are described below

 Property name   Description                       Data type   Required 
connection_name Connection name string
schema_name Schema name string
table_name Table name string
time_scale Time scale CheckTimeScale
check_category Check category string
check_name Check name string

Usage examples

Execution

curl http://localhost:8888/api/connections/sample_connection/schemas/sample_schema/tables/sample_table/partitioned/daily/model/filter/sample_category/sample_check^
    -H "Accept: application/json"
Expand to see the returned result 
{
  "categories" : [ {
    "category" : "sample_category",
    "help_text" : "Sample help text",
    "checks" : [ {
      "check_name" : "sample_check",
      "help_text" : "Sample help text",
      "sensor_parameters" : [ ],
      "sensor_name" : "sample_target/sample_category/table/volume/row_count",
      "quality_dimension" : "sample_quality_dimension",
      "supports_error_sampling" : false,
      "supports_grouping" : false,
      "default_severity" : "error",
      "disabled" : false,
      "exclude_from_kpi" : false,
      "include_in_sla" : false,
      "configured" : false,
      "can_edit" : false,
      "can_run_checks" : false,
      "can_delete_data" : false
    } ]
  } ],
  "can_edit" : false,
  "can_run_checks" : false,
  "can_delete_data" : false
}

Execution

from dqops import client
from dqops.client.api.tables import get_table_partitioned_checks_model_filter
from dqops.client.models import CheckTimeScale

dqops_client = client.Client(
    'http://localhost:8888/',
    raise_on_unexpected_status=True
)

call_result = get_table_partitioned_checks_model_filter.sync(
    'sample_connection',
    'sample_schema',
    'sample_table',
    CheckTimeScale.daily,
    'sample_category',
    'sample_check',
    client=dqops_client
)
Expand to see the returned result 
CheckContainerModel(
    categories=[
        QualityCategoryModel(
            category='sample_category',
            help_text='Sample help text',
            checks=[
                CheckModel(
                    check_name='sample_check',
                    help_text='Sample help text',
                    sensor_parameters=[

                    ],
                    sensor_name='sample_target/sample_category/table/volume/row_count',
                    quality_dimension='sample_quality_dimension',
                    supports_error_sampling=False,
                    supports_grouping=False,
                    standard=False,
                    default_check=False,
                    default_severity=DefaultRuleSeverityLevel.ERROR,
                    disabled=False,
                    exclude_from_kpi=False,
                    include_in_sla=False,
                    configured=False,
                    always_collect_error_samples=False,
                    do_not_schedule=False,
                    can_edit=False,
                    can_run_checks=False,
                    can_delete_data=False
                )
            ]
        )
    ],
    can_edit=False,
    can_run_checks=False,
    can_delete_data=False
)

Execution

from dqops import client
from dqops.client.api.tables import get_table_partitioned_checks_model_filter
from dqops.client.models import CheckTimeScale

dqops_client = client.Client(
    'http://localhost:8888/',
    raise_on_unexpected_status=True
)

call_result = await get_table_partitioned_checks_model_filter.asyncio(
    'sample_connection',
    'sample_schema',
    'sample_table',
    CheckTimeScale.daily,
    'sample_category',
    'sample_check',
    client=dqops_client
)
Expand to see the returned result 
CheckContainerModel(
    categories=[
        QualityCategoryModel(
            category='sample_category',
            help_text='Sample help text',
            checks=[
                CheckModel(
                    check_name='sample_check',
                    help_text='Sample help text',
                    sensor_parameters=[

                    ],
                    sensor_name='sample_target/sample_category/table/volume/row_count',
                    quality_dimension='sample_quality_dimension',
                    supports_error_sampling=False,
                    supports_grouping=False,
                    standard=False,
                    default_check=False,
                    default_severity=DefaultRuleSeverityLevel.ERROR,
                    disabled=False,
                    exclude_from_kpi=False,
                    include_in_sla=False,
                    configured=False,
                    always_collect_error_samples=False,
                    do_not_schedule=False,
                    can_edit=False,
                    can_run_checks=False,
                    can_delete_data=False
                )
            ]
        )
    ],
    can_edit=False,
    can_run_checks=False,
    can_delete_data=False
)

Execution

from dqops import client
from dqops.client.api.tables import get_table_partitioned_checks_model_filter
from dqops.client.models import CheckTimeScale

token = 's4mp13_4u7h_70k3n'

dqops_client = client.AuthenticatedClient(
    'http://localhost:8888/',
    token=token,
    raise_on_unexpected_status=True
)

call_result = get_table_partitioned_checks_model_filter.sync(
    'sample_connection',
    'sample_schema',
    'sample_table',
    CheckTimeScale.daily,
    'sample_category',
    'sample_check',
    client=dqops_client
)
Expand to see the returned result 
CheckContainerModel(
    categories=[
        QualityCategoryModel(
            category='sample_category',
            help_text='Sample help text',
            checks=[
                CheckModel(
                    check_name='sample_check',
                    help_text='Sample help text',
                    sensor_parameters=[

                    ],
                    sensor_name='sample_target/sample_category/table/volume/row_count',
                    quality_dimension='sample_quality_dimension',
                    supports_error_sampling=False,
                    supports_grouping=False,
                    standard=False,
                    default_check=False,
                    default_severity=DefaultRuleSeverityLevel.ERROR,
                    disabled=False,
                    exclude_from_kpi=False,
                    include_in_sla=False,
                    configured=False,
                    always_collect_error_samples=False,
                    do_not_schedule=False,
                    can_edit=False,
                    can_run_checks=False,
                    can_delete_data=False
                )
            ]
        )
    ],
    can_edit=False,
    can_run_checks=False,
    can_delete_data=False
)

Execution

from dqops import client
from dqops.client.api.tables import get_table_partitioned_checks_model_filter
from dqops.client.models import CheckTimeScale

token = 's4mp13_4u7h_70k3n'

dqops_client = client.AuthenticatedClient(
    'http://localhost:8888/',
    token=token,
    raise_on_unexpected_status=True
)

call_result = await get_table_partitioned_checks_model_filter.asyncio(
    'sample_connection',
    'sample_schema',
    'sample_table',
    CheckTimeScale.daily,
    'sample_category',
    'sample_check',
    client=dqops_client
)
Expand to see the returned result 
CheckContainerModel(
    categories=[
        QualityCategoryModel(
            category='sample_category',
            help_text='Sample help text',
            checks=[
                CheckModel(
                    check_name='sample_check',
                    help_text='Sample help text',
                    sensor_parameters=[

                    ],
                    sensor_name='sample_target/sample_category/table/volume/row_count',
                    quality_dimension='sample_quality_dimension',
                    supports_error_sampling=False,
                    supports_grouping=False,
                    standard=False,
                    default_check=False,
                    default_severity=DefaultRuleSeverityLevel.ERROR,
                    disabled=False,
                    exclude_from_kpi=False,
                    include_in_sla=False,
                    configured=False,
                    always_collect_error_samples=False,
                    do_not_schedule=False,
                    can_edit=False,
                    can_run_checks=False,
                    can_delete_data=False
                )
            ]
        )
    ],
    can_edit=False,
    can_run_checks=False,
    can_delete_data=False
)

get_table_partitioned_checks_monthly

Return the configuration of monthly table level data quality partitioned checks on a table

Follow the link to see the source code on GitHub.

GET 

http://localhost:8888/api/connections/{connectionName}/schemas/{schemaName}/tables/{tableName}/partitioned/monthly

Return value

 Property name   Description                       Data type 
table_monthly_partitioned_check_categories_spec TableMonthlyPartitionedCheckCategoriesSpec

Parameters of this method are described below

 Property name   Description                       Data type   Required 
connection_name Connection name string
schema_name Schema name string
table_name Table name string

Usage examples

Execution

curl http://localhost:8888/api/connections/sample_connection/schemas/sample_schema/tables/sample_table/partitioned/monthly^
    -H "Accept: application/json"
Expand to see the returned result 
{
  "volume" : {
    "monthly_partition_row_count" : {
      "error" : {
        "min_count" : 1
      }
    }
  }
}

Execution

from dqops import client
from dqops.client.api.tables import get_table_partitioned_checks_monthly

dqops_client = client.Client(
    'http://localhost:8888/',
    raise_on_unexpected_status=True
)

call_result = get_table_partitioned_checks_monthly.sync(
    'sample_connection',
    'sample_schema',
    'sample_table',
    client=dqops_client
)
Expand to see the returned result 
TableMonthlyPartitionedCheckCategoriesSpec(
    volume=TableVolumeMonthlyPartitionedChecksSpec(
        monthly_partition_row_count=TableRowCountCheckSpec(
            parameters=TableVolumeRowCountSensorParametersSpec(),
            error=MinCountRule1ParametersSpec(min_count=1),
            disabled=False,
            exclude_from_kpi=False,
            include_in_sla=False,
            always_collect_error_samples=False,
            do_not_schedule=False
        )
    ),
    comparisons=TableComparisonMonthlyPartitionedChecksSpecMap()
)

Execution

from dqops import client
from dqops.client.api.tables import get_table_partitioned_checks_monthly

dqops_client = client.Client(
    'http://localhost:8888/',
    raise_on_unexpected_status=True
)

call_result = await get_table_partitioned_checks_monthly.asyncio(
    'sample_connection',
    'sample_schema',
    'sample_table',
    client=dqops_client
)
Expand to see the returned result 
TableMonthlyPartitionedCheckCategoriesSpec(
    volume=TableVolumeMonthlyPartitionedChecksSpec(
        monthly_partition_row_count=TableRowCountCheckSpec(
            parameters=TableVolumeRowCountSensorParametersSpec(),
            error=MinCountRule1ParametersSpec(min_count=1),
            disabled=False,
            exclude_from_kpi=False,
            include_in_sla=False,
            always_collect_error_samples=False,
            do_not_schedule=False
        )
    ),
    comparisons=TableComparisonMonthlyPartitionedChecksSpecMap()
)

Execution

from dqops import client
from dqops.client.api.tables import get_table_partitioned_checks_monthly

token = 's4mp13_4u7h_70k3n'

dqops_client = client.AuthenticatedClient(
    'http://localhost:8888/',
    token=token,
    raise_on_unexpected_status=True
)

call_result = get_table_partitioned_checks_monthly.sync(
    'sample_connection',
    'sample_schema',
    'sample_table',
    client=dqops_client
)
Expand to see the returned result 
TableMonthlyPartitionedCheckCategoriesSpec(
    volume=TableVolumeMonthlyPartitionedChecksSpec(
        monthly_partition_row_count=TableRowCountCheckSpec(
            parameters=TableVolumeRowCountSensorParametersSpec(),
            error=MinCountRule1ParametersSpec(min_count=1),
            disabled=False,
            exclude_from_kpi=False,
            include_in_sla=False,
            always_collect_error_samples=False,
            do_not_schedule=False
        )
    ),
    comparisons=TableComparisonMonthlyPartitionedChecksSpecMap()
)

Execution

from dqops import client
from dqops.client.api.tables import get_table_partitioned_checks_monthly

token = 's4mp13_4u7h_70k3n'

dqops_client = client.AuthenticatedClient(
    'http://localhost:8888/',
    token=token,
    raise_on_unexpected_status=True
)

call_result = await get_table_partitioned_checks_monthly.asyncio(
    'sample_connection',
    'sample_schema',
    'sample_table',
    client=dqops_client
)
Expand to see the returned result 
TableMonthlyPartitionedCheckCategoriesSpec(
    volume=TableVolumeMonthlyPartitionedChecksSpec(
        monthly_partition_row_count=TableRowCountCheckSpec(
            parameters=TableVolumeRowCountSensorParametersSpec(),
            error=MinCountRule1ParametersSpec(min_count=1),
            disabled=False,
            exclude_from_kpi=False,
            include_in_sla=False,
            always_collect_error_samples=False,
            do_not_schedule=False
        )
    ),
    comparisons=TableComparisonMonthlyPartitionedChecksSpecMap()
)

get_table_partitioned_checks_templates

Return available data quality checks on a requested table.

Follow the link to see the source code on GitHub.

GET 

http://localhost:8888/api/connections/{connectionName}/schemas/{schemaName}/tables/{tableName}/bulkenable/partitioned/{timeScale}

Return value

 Property name   Description                       Data type 
check_template List[CheckTemplate]

Parameters of this method are described below

 Property name   Description                       Data type   Required 
connection_name Connection name string
schema_name Schema name string
table_name Table name string
time_scale Time scale CheckTimeScale
check_category Check category string
check_name Check name string

Usage examples

Execution

curl http://localhost:8888/api/connections/sample_connection/schemas/sample_schema/tables/sample_table/bulkenable/partitioned/daily^
    -H "Accept: application/json"
Expand to see the returned result 
[ {
  "sensor_parameters_definitions" : [ ]
}, {
  "sensor_parameters_definitions" : [ ]
}, {
  "sensor_parameters_definitions" : [ ]
} ]

Execution

from dqops import client
from dqops.client.api.tables import get_table_partitioned_checks_templates
from dqops.client.models import CheckTimeScale

dqops_client = client.Client(
    'http://localhost:8888/',
    raise_on_unexpected_status=True
)

call_result = get_table_partitioned_checks_templates.sync(
    'sample_connection',
    'sample_schema',
    'sample_table',
    CheckTimeScale.daily,
    client=dqops_client
)
Expand to see the returned result 
[
    CheckTemplate(
        sensor_parameters_definitions=[

        ]
    ),
    CheckTemplate(
        sensor_parameters_definitions=[

        ]
    ),
    CheckTemplate(
        sensor_parameters_definitions=[

        ]
    )
]

Execution

from dqops import client
from dqops.client.api.tables import get_table_partitioned_checks_templates
from dqops.client.models import CheckTimeScale

dqops_client = client.Client(
    'http://localhost:8888/',
    raise_on_unexpected_status=True
)

call_result = await get_table_partitioned_checks_templates.asyncio(
    'sample_connection',
    'sample_schema',
    'sample_table',
    CheckTimeScale.daily,
    client=dqops_client
)
Expand to see the returned result 
[
    CheckTemplate(
        sensor_parameters_definitions=[

        ]
    ),
    CheckTemplate(
        sensor_parameters_definitions=[

        ]
    ),
    CheckTemplate(
        sensor_parameters_definitions=[

        ]
    )
]

Execution

from dqops import client
from dqops.client.api.tables import get_table_partitioned_checks_templates
from dqops.client.models import CheckTimeScale

token = 's4mp13_4u7h_70k3n'

dqops_client = client.AuthenticatedClient(
    'http://localhost:8888/',
    token=token,
    raise_on_unexpected_status=True
)

call_result = get_table_partitioned_checks_templates.sync(
    'sample_connection',
    'sample_schema',
    'sample_table',
    CheckTimeScale.daily,
    client=dqops_client
)
Expand to see the returned result 
[
    CheckTemplate(
        sensor_parameters_definitions=[

        ]
    ),
    CheckTemplate(
        sensor_parameters_definitions=[

        ]
    ),
    CheckTemplate(
        sensor_parameters_definitions=[

        ]
    )
]

Execution

from dqops import client
from dqops.client.api.tables import get_table_partitioned_checks_templates
from dqops.client.models import CheckTimeScale

token = 's4mp13_4u7h_70k3n'

dqops_client = client.AuthenticatedClient(
    'http://localhost:8888/',
    token=token,
    raise_on_unexpected_status=True
)

call_result = await get_table_partitioned_checks_templates.asyncio(
    'sample_connection',
    'sample_schema',
    'sample_table',
    CheckTimeScale.daily,
    client=dqops_client
)
Expand to see the returned result 
[
    CheckTemplate(
        sensor_parameters_definitions=[

        ]
    ),
    CheckTemplate(
        sensor_parameters_definitions=[

        ]
    ),
    CheckTemplate(
        sensor_parameters_definitions=[

        ]
    )
]

get_table_partitioning

Return the table partitioning information

Follow the link to see the source code on GitHub.

GET 

http://localhost:8888/api/connections/{connectionName}/schemas/{schemaName}/tables/{tableName}/partitioning

Return value

 Property name   Description                       Data type 
table_partitioning_model TablePartitioningModel

Parameters of this method are described below

 Property name   Description                       Data type   Required 
connection_name Connection name string
schema_name Schema name string
table_name Table name string

Usage examples

Execution

curl http://localhost:8888/api/connections/sample_connection/schemas/sample_schema/tables/sample_table/partitioning^
    -H "Accept: application/json"
Expand to see the returned result 
{
  "connection_name" : "sample_connection",
  "target" : {
    "schema_name" : "sample_schema",
    "table_name" : "sample_table"
  },
  "timestamp_columns" : {
    "event_timestamp_column" : "col1",
    "ingestion_timestamp_column" : "col2",
    "partition_by_column" : "col3"
  },
  "incremental_time_window" : {
    "daily_partitioning_recent_days" : 7,
    "daily_partitioning_include_today" : true
  },
  "can_edit" : true
}

Execution

from dqops import client
from dqops.client.api.tables import get_table_partitioning

dqops_client = client.Client(
    'http://localhost:8888/',
    raise_on_unexpected_status=True
)

call_result = get_table_partitioning.sync(
    'sample_connection',
    'sample_schema',
    'sample_table',
    client=dqops_client
)
Expand to see the returned result 
TablePartitioningModel(
    connection_name='sample_connection',
    target=PhysicalTableName(
        schema_name='sample_schema',
        table_name='sample_table'
    ),
    timestamp_columns=TimestampColumnsSpec(
        event_timestamp_column='col1',
        ingestion_timestamp_column='col2',
        partition_by_column='col3'
    ),
    incremental_time_window=PartitionIncrementalTimeWindowSpec(
        daily_partitioning_recent_days=7,
        daily_partitioning_include_today=True,
        monthly_partitioning_include_current_month=False
    ),
    can_edit=True
)

Execution

from dqops import client
from dqops.client.api.tables import get_table_partitioning

dqops_client = client.Client(
    'http://localhost:8888/',
    raise_on_unexpected_status=True
)

call_result = await get_table_partitioning.asyncio(
    'sample_connection',
    'sample_schema',
    'sample_table',
    client=dqops_client
)
Expand to see the returned result 
TablePartitioningModel(
    connection_name='sample_connection',
    target=PhysicalTableName(
        schema_name='sample_schema',
        table_name='sample_table'
    ),
    timestamp_columns=TimestampColumnsSpec(
        event_timestamp_column='col1',
        ingestion_timestamp_column='col2',
        partition_by_column='col3'
    ),
    incremental_time_window=PartitionIncrementalTimeWindowSpec(
        daily_partitioning_recent_days=7,
        daily_partitioning_include_today=True,
        monthly_partitioning_include_current_month=False
    ),
    can_edit=True
)

Execution

from dqops import client
from dqops.client.api.tables import get_table_partitioning

token = 's4mp13_4u7h_70k3n'

dqops_client = client.AuthenticatedClient(
    'http://localhost:8888/',
    token=token,
    raise_on_unexpected_status=True
)

call_result = get_table_partitioning.sync(
    'sample_connection',
    'sample_schema',
    'sample_table',
    client=dqops_client
)
Expand to see the returned result 
TablePartitioningModel(
    connection_name='sample_connection',
    target=PhysicalTableName(
        schema_name='sample_schema',
        table_name='sample_table'
    ),
    timestamp_columns=TimestampColumnsSpec(
        event_timestamp_column='col1',
        ingestion_timestamp_column='col2',
        partition_by_column='col3'
    ),
    incremental_time_window=PartitionIncrementalTimeWindowSpec(
        daily_partitioning_recent_days=7,
        daily_partitioning_include_today=True,
        monthly_partitioning_include_current_month=False
    ),
    can_edit=True
)

Execution

from dqops import client
from dqops.client.api.tables import get_table_partitioning

token = 's4mp13_4u7h_70k3n'

dqops_client = client.AuthenticatedClient(
    'http://localhost:8888/',
    token=token,
    raise_on_unexpected_status=True
)

call_result = await get_table_partitioning.asyncio(
    'sample_connection',
    'sample_schema',
    'sample_table',
    client=dqops_client
)
Expand to see the returned result 
TablePartitioningModel(
    connection_name='sample_connection',
    target=PhysicalTableName(
        schema_name='sample_schema',
        table_name='sample_table'
    ),
    timestamp_columns=TimestampColumnsSpec(
        event_timestamp_column='col1',
        ingestion_timestamp_column='col2',
        partition_by_column='col3'
    ),
    incremental_time_window=PartitionIncrementalTimeWindowSpec(
        daily_partitioning_recent_days=7,
        daily_partitioning_include_today=True,
        monthly_partitioning_include_current_month=False
    ),
    can_edit=True
)

get_table_profiling_checks

Return the configuration of table level data quality checks on a table

Follow the link to see the source code on GitHub.

GET 

http://localhost:8888/api/connections/{connectionName}/schemas/{schemaName}/tables/{tableName}/profiling

Return value

 Property name   Description                       Data type 
table_profiling_check_categories_spec TableProfilingCheckCategoriesSpec

Parameters of this method are described below

 Property name   Description                       Data type   Required 
connection_name Connection name string
schema_name Schema name string
table_name Table name string

Usage examples

Execution

curl http://localhost:8888/api/connections/sample_connection/schemas/sample_schema/tables/sample_table/profiling^
    -H "Accept: application/json"
Expand to see the returned result 
{
  "volume" : {
    "profile_row_count" : {
      "error" : {
        "min_count" : 1
      }
    }
  }
}

Execution

from dqops import client
from dqops.client.api.tables import get_table_profiling_checks

dqops_client = client.Client(
    'http://localhost:8888/',
    raise_on_unexpected_status=True
)

call_result = get_table_profiling_checks.sync(
    'sample_connection',
    'sample_schema',
    'sample_table',
    client=dqops_client
)
Expand to see the returned result 
TableProfilingCheckCategoriesSpec(
    volume=TableVolumeProfilingChecksSpec(
        profile_row_count=TableRowCountCheckSpec(
            parameters=TableVolumeRowCountSensorParametersSpec(),
            error=MinCountRule1ParametersSpec(min_count=1),
            disabled=False,
            exclude_from_kpi=False,
            include_in_sla=False,
            always_collect_error_samples=False,
            do_not_schedule=False
        )
    )
)

Execution

from dqops import client
from dqops.client.api.tables import get_table_profiling_checks

dqops_client = client.Client(
    'http://localhost:8888/',
    raise_on_unexpected_status=True
)

call_result = await get_table_profiling_checks.asyncio(
    'sample_connection',
    'sample_schema',
    'sample_table',
    client=dqops_client
)
Expand to see the returned result 
TableProfilingCheckCategoriesSpec(
    volume=TableVolumeProfilingChecksSpec(
        profile_row_count=TableRowCountCheckSpec(
            parameters=TableVolumeRowCountSensorParametersSpec(),
            error=MinCountRule1ParametersSpec(min_count=1),
            disabled=False,
            exclude_from_kpi=False,
            include_in_sla=False,
            always_collect_error_samples=False,
            do_not_schedule=False
        )
    )
)

Execution

from dqops import client
from dqops.client.api.tables import get_table_profiling_checks

token = 's4mp13_4u7h_70k3n'

dqops_client = client.AuthenticatedClient(
    'http://localhost:8888/',
    token=token,
    raise_on_unexpected_status=True
)

call_result = get_table_profiling_checks.sync(
    'sample_connection',
    'sample_schema',
    'sample_table',
    client=dqops_client
)
Expand to see the returned result 
TableProfilingCheckCategoriesSpec(
    volume=TableVolumeProfilingChecksSpec(
        profile_row_count=TableRowCountCheckSpec(
            parameters=TableVolumeRowCountSensorParametersSpec(),
            error=MinCountRule1ParametersSpec(min_count=1),
            disabled=False,
            exclude_from_kpi=False,
            include_in_sla=False,
            always_collect_error_samples=False,
            do_not_schedule=False
        )
    )
)

Execution

from dqops import client
from dqops.client.api.tables import get_table_profiling_checks

token = 's4mp13_4u7h_70k3n'

dqops_client = client.AuthenticatedClient(
    'http://localhost:8888/',
    token=token,
    raise_on_unexpected_status=True
)

call_result = await get_table_profiling_checks.asyncio(
    'sample_connection',
    'sample_schema',
    'sample_table',
    client=dqops_client
)
Expand to see the returned result 
TableProfilingCheckCategoriesSpec(
    volume=TableVolumeProfilingChecksSpec(
        profile_row_count=TableRowCountCheckSpec(
            parameters=TableVolumeRowCountSensorParametersSpec(),
            error=MinCountRule1ParametersSpec(min_count=1),
            disabled=False,
            exclude_from_kpi=False,
            include_in_sla=False,
            always_collect_error_samples=False,
            do_not_schedule=False
        )
    )
)

get_table_profiling_checks_basic_model

Return a simplistic UI friendly model of all table level data quality profiling checks on a table

Follow the link to see the source code on GitHub.

GET 

http://localhost:8888/api/connections/{connectionName}/schemas/{schemaName}/tables/{tableName}/profiling/model/basic

Return value

 Property name   Description                       Data type 
check_container_list_model CheckContainerListModel

Parameters of this method are described below

 Property name   Description                       Data type   Required 
connection_name Connection name string
schema_name Schema name string
table_name Table name string

Usage examples

Execution

curl http://localhost:8888/api/connections/sample_connection/schemas/sample_schema/tables/sample_table/profiling/model/basic^
    -H "Accept: application/json"
Expand to see the returned result 
{
  "checks" : [ {
    "check_category" : "sample_category_1",
    "check_name" : "sample_check_1",
    "help_text" : "Sample help text",
    "configured" : true
  }, {
    "check_category" : "sample_category_2",
    "check_name" : "sample_check_1",
    "help_text" : "Sample help text",
    "configured" : true
  }, {
    "check_category" : "sample_category_1",
    "check_name" : "sample_check_2",
    "help_text" : "Sample help text",
    "configured" : true
  }, {
    "check_category" : "sample_category_2",
    "check_name" : "sample_check_2",
    "help_text" : "Sample help text",
    "configured" : true
  }, {
    "check_category" : "sample_category_1",
    "check_name" : "sample_check_3",
    "help_text" : "Sample help text",
    "configured" : true
  }, {
    "check_category" : "sample_category_2",
    "check_name" : "sample_check_3",
    "help_text" : "Sample help text",
    "configured" : true
  } ],
  "can_edit" : false,
  "can_run_checks" : true,
  "can_delete_data" : true
}

Execution

from dqops import client
from dqops.client.api.tables import get_table_profiling_checks_basic_model

dqops_client = client.Client(
    'http://localhost:8888/',
    raise_on_unexpected_status=True
)

call_result = get_table_profiling_checks_basic_model.sync(
    'sample_connection',
    'sample_schema',
    'sample_table',
    client=dqops_client
)
Expand to see the returned result 
CheckContainerListModel(
    checks=[
        CheckListModel(
            check_category='sample_category_1',
            check_name='sample_check_1',
            help_text='Sample help text',
            configured=True
        ),
        CheckListModel(
            check_category='sample_category_2',
            check_name='sample_check_1',
            help_text='Sample help text',
            configured=True
        ),
        CheckListModel(
            check_category='sample_category_1',
            check_name='sample_check_2',
            help_text='Sample help text',
            configured=True
        ),
        CheckListModel(
            check_category='sample_category_2',
            check_name='sample_check_2',
            help_text='Sample help text',
            configured=True
        ),
        CheckListModel(
            check_category='sample_category_1',
            check_name='sample_check_3',
            help_text='Sample help text',
            configured=True
        ),
        CheckListModel(
            check_category='sample_category_2',
            check_name='sample_check_3',
            help_text='Sample help text',
            configured=True
        )
    ],
    can_edit=False,
    can_run_checks=True,
    can_delete_data=True
)

Execution

from dqops import client
from dqops.client.api.tables import get_table_profiling_checks_basic_model

dqops_client = client.Client(
    'http://localhost:8888/',
    raise_on_unexpected_status=True
)

call_result = await get_table_profiling_checks_basic_model.asyncio(
    'sample_connection',
    'sample_schema',
    'sample_table',
    client=dqops_client
)
Expand to see the returned result 
CheckContainerListModel(
    checks=[
        CheckListModel(
            check_category='sample_category_1',
            check_name='sample_check_1',
            help_text='Sample help text',
            configured=True
        ),
        CheckListModel(
            check_category='sample_category_2',
            check_name='sample_check_1',
            help_text='Sample help text',
            configured=True
        ),
        CheckListModel(
            check_category='sample_category_1',
            check_name='sample_check_2',
            help_text='Sample help text',
            configured=True
        ),
        CheckListModel(
            check_category='sample_category_2',
            check_name='sample_check_2',
            help_text='Sample help text',
            configured=True
        ),
        CheckListModel(
            check_category='sample_category_1',
            check_name='sample_check_3',
            help_text='Sample help text',
            configured=True
        ),
        CheckListModel(
            check_category='sample_category_2',
            check_name='sample_check_3',
            help_text='Sample help text',
            configured=True
        )
    ],
    can_edit=False,
    can_run_checks=True,
    can_delete_data=True
)

Execution

from dqops import client
from dqops.client.api.tables import get_table_profiling_checks_basic_model

token = 's4mp13_4u7h_70k3n'

dqops_client = client.AuthenticatedClient(
    'http://localhost:8888/',
    token=token,
    raise_on_unexpected_status=True
)

call_result = get_table_profiling_checks_basic_model.sync(
    'sample_connection',
    'sample_schema',
    'sample_table',
    client=dqops_client
)
Expand to see the returned result 
CheckContainerListModel(
    checks=[
        CheckListModel(
            check_category='sample_category_1',
            check_name='sample_check_1',
            help_text='Sample help text',
            configured=True
        ),
        CheckListModel(
            check_category='sample_category_2',
            check_name='sample_check_1',
            help_text='Sample help text',
            configured=True
        ),
        CheckListModel(
            check_category='sample_category_1',
            check_name='sample_check_2',
            help_text='Sample help text',
            configured=True
        ),
        CheckListModel(
            check_category='sample_category_2',
            check_name='sample_check_2',
            help_text='Sample help text',
            configured=True
        ),
        CheckListModel(
            check_category='sample_category_1',
            check_name='sample_check_3',
            help_text='Sample help text',
            configured=True
        ),
        CheckListModel(
            check_category='sample_category_2',
            check_name='sample_check_3',
            help_text='Sample help text',
            configured=True
        )
    ],
    can_edit=False,
    can_run_checks=True,
    can_delete_data=True
)

Execution

from dqops import client
from dqops.client.api.tables import get_table_profiling_checks_basic_model

token = 's4mp13_4u7h_70k3n'

dqops_client = client.AuthenticatedClient(
    'http://localhost:8888/',
    token=token,
    raise_on_unexpected_status=True
)

call_result = await get_table_profiling_checks_basic_model.asyncio(
    'sample_connection',
    'sample_schema',
    'sample_table',
    client=dqops_client
)
Expand to see the returned result 
CheckContainerListModel(
    checks=[
        CheckListModel(
            check_category='sample_category_1',
            check_name='sample_check_1',
            help_text='Sample help text',
            configured=True
        ),
        CheckListModel(
            check_category='sample_category_2',
            check_name='sample_check_1',
            help_text='Sample help text',
            configured=True
        ),
        CheckListModel(
            check_category='sample_category_1',
            check_name='sample_check_2',
            help_text='Sample help text',
            configured=True
        ),
        CheckListModel(
            check_category='sample_category_2',
            check_name='sample_check_2',
            help_text='Sample help text',
            configured=True
        ),
        CheckListModel(
            check_category='sample_category_1',
            check_name='sample_check_3',
            help_text='Sample help text',
            configured=True
        ),
        CheckListModel(
            check_category='sample_category_2',
            check_name='sample_check_3',
            help_text='Sample help text',
            configured=True
        )
    ],
    can_edit=False,
    can_run_checks=True,
    can_delete_data=True
)

get_table_profiling_checks_model

Return a UI friendly model of configurations for all table level data quality profiling checks on a table

Follow the link to see the source code on GitHub.

GET 

http://localhost:8888/api/connections/{connectionName}/schemas/{schemaName}/tables/{tableName}/profiling/model

Return value

 Property name   Description                       Data type 
check_container_model CheckContainerModel

Parameters of this method are described below

 Property name   Description                       Data type   Required 
connection_name Connection name string
schema_name Schema name string
table_name Table name string

Usage examples

Execution

curl http://localhost:8888/api/connections/sample_connection/schemas/sample_schema/tables/sample_table/profiling/model^
    -H "Accept: application/json"
Expand to see the returned result 
{
  "categories" : [ {
    "category" : "sample_category",
    "help_text" : "Sample help text",
    "checks" : [ {
      "check_name" : "sample_check",
      "help_text" : "Sample help text",
      "sensor_parameters" : [ ],
      "sensor_name" : "sample_target/sample_category/table/volume/row_count",
      "quality_dimension" : "sample_quality_dimension",
      "supports_error_sampling" : false,
      "supports_grouping" : false,
      "default_severity" : "error",
      "disabled" : false,
      "exclude_from_kpi" : false,
      "include_in_sla" : false,
      "configured" : false,
      "can_edit" : false,
      "can_run_checks" : false,
      "can_delete_data" : false
    } ]
  } ],
  "can_edit" : false,
  "can_run_checks" : false,
  "can_delete_data" : false
}

Execution

from dqops import client
from dqops.client.api.tables import get_table_profiling_checks_model

dqops_client = client.Client(
    'http://localhost:8888/',
    raise_on_unexpected_status=True
)

call_result = get_table_profiling_checks_model.sync(
    'sample_connection',
    'sample_schema',
    'sample_table',
    client=dqops_client
)
Expand to see the returned result 
CheckContainerModel(
    categories=[
        QualityCategoryModel(
            category='sample_category',
            help_text='Sample help text',
            checks=[
                CheckModel(
                    check_name='sample_check',
                    help_text='Sample help text',
                    sensor_parameters=[

                    ],
                    sensor_name='sample_target/sample_category/table/volume/row_count',
                    quality_dimension='sample_quality_dimension',
                    supports_error_sampling=False,
                    supports_grouping=False,
                    standard=False,
                    default_check=False,
                    default_severity=DefaultRuleSeverityLevel.ERROR,
                    disabled=False,
                    exclude_from_kpi=False,
                    include_in_sla=False,
                    configured=False,
                    always_collect_error_samples=False,
                    do_not_schedule=False,
                    can_edit=False,
                    can_run_checks=False,
                    can_delete_data=False
                )
            ]
        )
    ],
    can_edit=False,
    can_run_checks=False,
    can_delete_data=False
)

Execution

from dqops import client
from dqops.client.api.tables import get_table_profiling_checks_model

dqops_client = client.Client(
    'http://localhost:8888/',
    raise_on_unexpected_status=True
)

call_result = await get_table_profiling_checks_model.asyncio(
    'sample_connection',
    'sample_schema',
    'sample_table',
    client=dqops_client
)
Expand to see the returned result 
CheckContainerModel(
    categories=[
        QualityCategoryModel(
            category='sample_category',
            help_text='Sample help text',
            checks=[
                CheckModel(
                    check_name='sample_check',
                    help_text='Sample help text',
                    sensor_parameters=[

                    ],
                    sensor_name='sample_target/sample_category/table/volume/row_count',
                    quality_dimension='sample_quality_dimension',
                    supports_error_sampling=False,
                    supports_grouping=False,
                    standard=False,
                    default_check=False,
                    default_severity=DefaultRuleSeverityLevel.ERROR,
                    disabled=False,
                    exclude_from_kpi=False,
                    include_in_sla=False,
                    configured=False,
                    always_collect_error_samples=False,
                    do_not_schedule=False,
                    can_edit=False,
                    can_run_checks=False,
                    can_delete_data=False
                )
            ]
        )
    ],
    can_edit=False,
    can_run_checks=False,
    can_delete_data=False
)

Execution

from dqops import client
from dqops.client.api.tables import get_table_profiling_checks_model

token = 's4mp13_4u7h_70k3n'

dqops_client = client.AuthenticatedClient(
    'http://localhost:8888/',
    token=token,
    raise_on_unexpected_status=True
)

call_result = get_table_profiling_checks_model.sync(
    'sample_connection',
    'sample_schema',
    'sample_table',
    client=dqops_client
)
Expand to see the returned result 
CheckContainerModel(
    categories=[
        QualityCategoryModel(
            category='sample_category',
            help_text='Sample help text',
            checks=[
                CheckModel(
                    check_name='sample_check',
                    help_text='Sample help text',
                    sensor_parameters=[

                    ],
                    sensor_name='sample_target/sample_category/table/volume/row_count',
                    quality_dimension='sample_quality_dimension',
                    supports_error_sampling=False,
                    supports_grouping=False,
                    standard=False,
                    default_check=False,
                    default_severity=DefaultRuleSeverityLevel.ERROR,
                    disabled=False,
                    exclude_from_kpi=False,
                    include_in_sla=False,
                    configured=False,
                    always_collect_error_samples=False,
                    do_not_schedule=False,
                    can_edit=False,
                    can_run_checks=False,
                    can_delete_data=False
                )
            ]
        )
    ],
    can_edit=False,
    can_run_checks=False,
    can_delete_data=False
)

Execution

from dqops import client
from dqops.client.api.tables import get_table_profiling_checks_model

token = 's4mp13_4u7h_70k3n'

dqops_client = client.AuthenticatedClient(
    'http://localhost:8888/',
    token=token,
    raise_on_unexpected_status=True
)

call_result = await get_table_profiling_checks_model.asyncio(
    'sample_connection',
    'sample_schema',
    'sample_table',
    client=dqops_client
)
Expand to see the returned result 
CheckContainerModel(
    categories=[
        QualityCategoryModel(
            category='sample_category',
            help_text='Sample help text',
            checks=[
                CheckModel(
                    check_name='sample_check',
                    help_text='Sample help text',
                    sensor_parameters=[

                    ],
                    sensor_name='sample_target/sample_category/table/volume/row_count',
                    quality_dimension='sample_quality_dimension',
                    supports_error_sampling=False,
                    supports_grouping=False,
                    standard=False,
                    default_check=False,
                    default_severity=DefaultRuleSeverityLevel.ERROR,
                    disabled=False,
                    exclude_from_kpi=False,
                    include_in_sla=False,
                    configured=False,
                    always_collect_error_samples=False,
                    do_not_schedule=False,
                    can_edit=False,
                    can_run_checks=False,
                    can_delete_data=False
                )
            ]
        )
    ],
    can_edit=False,
    can_run_checks=False,
    can_delete_data=False
)

get_table_profiling_checks_model_filter

Return a UI friendly model of configurations for all table level data quality profiling checks on a table passing a filter

Follow the link to see the source code on GitHub.

GET 

http://localhost:8888/api/connections/{connectionName}/schemas/{schemaName}/tables/{tableName}/profiling/model/filter/{checkCategory}/{checkName}

Return value

 Property name   Description                       Data type 
check_container_model CheckContainerModel

Parameters of this method are described below

 Property name   Description                       Data type   Required 
connection_name Connection name string
schema_name Schema name string
table_name Table name string
check_category Check category string
check_name Check name string

Usage examples

Execution

curl http://localhost:8888/api/connections/sample_connection/schemas/sample_schema/tables/sample_table/profiling/model/filter/sample_category/sample_check^
    -H "Accept: application/json"
Expand to see the returned result 
{
  "categories" : [ {
    "category" : "sample_category",
    "help_text" : "Sample help text",
    "checks" : [ {
      "check_name" : "sample_check",
      "help_text" : "Sample help text",
      "sensor_parameters" : [ ],
      "sensor_name" : "sample_target/sample_category/table/volume/row_count",
      "quality_dimension" : "sample_quality_dimension",
      "supports_error_sampling" : false,
      "supports_grouping" : false,
      "default_severity" : "error",
      "disabled" : false,
      "exclude_from_kpi" : false,
      "include_in_sla" : false,
      "configured" : false,
      "can_edit" : false,
      "can_run_checks" : false,
      "can_delete_data" : false
    } ]
  } ],
  "can_edit" : false,
  "can_run_checks" : false,
  "can_delete_data" : false
}

Execution

from dqops import client
from dqops.client.api.tables import get_table_profiling_checks_model_filter

dqops_client = client.Client(
    'http://localhost:8888/',
    raise_on_unexpected_status=True
)

call_result = get_table_profiling_checks_model_filter.sync(
    'sample_connection',
    'sample_schema',
    'sample_table',
    'sample_category',
    'sample_check',
    client=dqops_client
)
Expand to see the returned result 
CheckContainerModel(
    categories=[
        QualityCategoryModel(
            category='sample_category',
            help_text='Sample help text',
            checks=[
                CheckModel(
                    check_name='sample_check',
                    help_text='Sample help text',
                    sensor_parameters=[

                    ],
                    sensor_name='sample_target/sample_category/table/volume/row_count',
                    quality_dimension='sample_quality_dimension',
                    supports_error_sampling=False,
                    supports_grouping=False,
                    standard=False,
                    default_check=False,
                    default_severity=DefaultRuleSeverityLevel.ERROR,
                    disabled=False,
                    exclude_from_kpi=False,
                    include_in_sla=False,
                    configured=False,
                    always_collect_error_samples=False,
                    do_not_schedule=False,
                    can_edit=False,
                    can_run_checks=False,
                    can_delete_data=False
                )
            ]
        )
    ],
    can_edit=False,
    can_run_checks=False,
    can_delete_data=False
)

Execution

from dqops import client
from dqops.client.api.tables import get_table_profiling_checks_model_filter

dqops_client = client.Client(
    'http://localhost:8888/',
    raise_on_unexpected_status=True
)

call_result = await get_table_profiling_checks_model_filter.asyncio(
    'sample_connection',
    'sample_schema',
    'sample_table',
    'sample_category',
    'sample_check',
    client=dqops_client
)
Expand to see the returned result 
CheckContainerModel(
    categories=[
        QualityCategoryModel(
            category='sample_category',
            help_text='Sample help text',
            checks=[
                CheckModel(
                    check_name='sample_check',
                    help_text='Sample help text',
                    sensor_parameters=[

                    ],
                    sensor_name='sample_target/sample_category/table/volume/row_count',
                    quality_dimension='sample_quality_dimension',
                    supports_error_sampling=False,
                    supports_grouping=False,
                    standard=False,
                    default_check=False,
                    default_severity=DefaultRuleSeverityLevel.ERROR,
                    disabled=False,
                    exclude_from_kpi=False,
                    include_in_sla=False,
                    configured=False,
                    always_collect_error_samples=False,
                    do_not_schedule=False,
                    can_edit=False,
                    can_run_checks=False,
                    can_delete_data=False
                )
            ]
        )
    ],
    can_edit=False,
    can_run_checks=False,
    can_delete_data=False
)

Execution

from dqops import client
from dqops.client.api.tables import get_table_profiling_checks_model_filter

token = 's4mp13_4u7h_70k3n'

dqops_client = client.AuthenticatedClient(
    'http://localhost:8888/',
    token=token,
    raise_on_unexpected_status=True
)

call_result = get_table_profiling_checks_model_filter.sync(
    'sample_connection',
    'sample_schema',
    'sample_table',
    'sample_category',
    'sample_check',
    client=dqops_client
)
Expand to see the returned result 
CheckContainerModel(
    categories=[
        QualityCategoryModel(
            category='sample_category',
            help_text='Sample help text',
            checks=[
                CheckModel(
                    check_name='sample_check',
                    help_text='Sample help text',
                    sensor_parameters=[

                    ],
                    sensor_name='sample_target/sample_category/table/volume/row_count',
                    quality_dimension='sample_quality_dimension',
                    supports_error_sampling=False,
                    supports_grouping=False,
                    standard=False,
                    default_check=False,
                    default_severity=DefaultRuleSeverityLevel.ERROR,
                    disabled=False,
                    exclude_from_kpi=False,
                    include_in_sla=False,
                    configured=False,
                    always_collect_error_samples=False,
                    do_not_schedule=False,
                    can_edit=False,
                    can_run_checks=False,
                    can_delete_data=False
                )
            ]
        )
    ],
    can_edit=False,
    can_run_checks=False,
    can_delete_data=False
)

Execution

from dqops import client
from dqops.client.api.tables import get_table_profiling_checks_model_filter

token = 's4mp13_4u7h_70k3n'

dqops_client = client.AuthenticatedClient(
    'http://localhost:8888/',
    token=token,
    raise_on_unexpected_status=True
)

call_result = await get_table_profiling_checks_model_filter.asyncio(
    'sample_connection',
    'sample_schema',
    'sample_table',
    'sample_category',
    'sample_check',
    client=dqops_client
)
Expand to see the returned result 
CheckContainerModel(
    categories=[
        QualityCategoryModel(
            category='sample_category',
            help_text='Sample help text',
            checks=[
                CheckModel(
                    check_name='sample_check',
                    help_text='Sample help text',
                    sensor_parameters=[

                    ],
                    sensor_name='sample_target/sample_category/table/volume/row_count',
                    quality_dimension='sample_quality_dimension',
                    supports_error_sampling=False,
                    supports_grouping=False,
                    standard=False,
                    default_check=False,
                    default_severity=DefaultRuleSeverityLevel.ERROR,
                    disabled=False,
                    exclude_from_kpi=False,
                    include_in_sla=False,
                    configured=False,
                    always_collect_error_samples=False,
                    do_not_schedule=False,
                    can_edit=False,
                    can_run_checks=False,
                    can_delete_data=False
                )
            ]
        )
    ],
    can_edit=False,
    can_run_checks=False,
    can_delete_data=False
)

get_table_profiling_checks_templates

Return available data quality checks on a requested table.

Follow the link to see the source code on GitHub.

GET 

http://localhost:8888/api/connections/{connectionName}/schemas/{schemaName}/tables/{tableName}/bulkenable/profiling

Return value

 Property name   Description                       Data type 
check_template List[CheckTemplate]

Parameters of this method are described below

 Property name   Description                       Data type   Required 
connection_name Connection name string
schema_name Schema name string
table_name Table name string
check_category Check category string
check_name Check name string

Usage examples

Execution

curl http://localhost:8888/api/connections/sample_connection/schemas/sample_schema/tables/sample_table/bulkenable/profiling^
    -H "Accept: application/json"
Expand to see the returned result 
[ {
  "sensor_parameters_definitions" : [ ]
}, {
  "sensor_parameters_definitions" : [ ]
}, {
  "sensor_parameters_definitions" : [ ]
} ]

Execution

from dqops import client
from dqops.client.api.tables import get_table_profiling_checks_templates

dqops_client = client.Client(
    'http://localhost:8888/',
    raise_on_unexpected_status=True
)

call_result = get_table_profiling_checks_templates.sync(
    'sample_connection',
    'sample_schema',
    'sample_table',
    client=dqops_client
)
Expand to see the returned result 
[
    CheckTemplate(
        sensor_parameters_definitions=[

        ]
    ),
    CheckTemplate(
        sensor_parameters_definitions=[

        ]
    ),
    CheckTemplate(
        sensor_parameters_definitions=[

        ]
    )
]

Execution

from dqops import client
from dqops.client.api.tables import get_table_profiling_checks_templates

dqops_client = client.Client(
    'http://localhost:8888/',
    raise_on_unexpected_status=True
)

call_result = await get_table_profiling_checks_templates.asyncio(
    'sample_connection',
    'sample_schema',
    'sample_table',
    client=dqops_client
)
Expand to see the returned result 
[
    CheckTemplate(
        sensor_parameters_definitions=[

        ]
    ),
    CheckTemplate(
        sensor_parameters_definitions=[

        ]
    ),
    CheckTemplate(
        sensor_parameters_definitions=[

        ]
    )
]

Execution

from dqops import client
from dqops.client.api.tables import get_table_profiling_checks_templates

token = 's4mp13_4u7h_70k3n'

dqops_client = client.AuthenticatedClient(
    'http://localhost:8888/',
    token=token,
    raise_on_unexpected_status=True
)

call_result = get_table_profiling_checks_templates.sync(
    'sample_connection',
    'sample_schema',
    'sample_table',
    client=dqops_client
)
Expand to see the returned result 
[
    CheckTemplate(
        sensor_parameters_definitions=[

        ]
    ),
    CheckTemplate(
        sensor_parameters_definitions=[

        ]
    ),
    CheckTemplate(
        sensor_parameters_definitions=[

        ]
    )
]

Execution

from dqops import client
from dqops.client.api.tables import get_table_profiling_checks_templates

token = 's4mp13_4u7h_70k3n'

dqops_client = client.AuthenticatedClient(
    'http://localhost:8888/',
    token=token,
    raise_on_unexpected_status=True
)

call_result = await get_table_profiling_checks_templates.asyncio(
    'sample_connection',
    'sample_schema',
    'sample_table',
    client=dqops_client
)
Expand to see the returned result 
[
    CheckTemplate(
        sensor_parameters_definitions=[

        ]
    ),
    CheckTemplate(
        sensor_parameters_definitions=[

        ]
    ),
    CheckTemplate(
        sensor_parameters_definitions=[

        ]
    )
]

get_table_profiling_status

Return the status of profiling the table, which provides hints to the user about which profiling steps were not yet performed

Follow the link to see the source code on GitHub.

GET 

http://localhost:8888/api/connections/{connectionName}/schemas/{schemaName}/tables/{tableName}/profilingstatus

Return value

 Property name   Description                       Data type 
table_profiling_setup_status_model TableProfilingSetupStatusModel

Parameters of this method are described below

 Property name   Description                       Data type   Required 
connection_name Connection name string
schema_name Schema name string
table_name Table name string

Usage examples

Execution

curl http://localhost:8888/api/connections/sample_connection/schemas/sample_schema/tables/sample_table/profilingstatus^
    -H "Accept: application/json"
Expand to see the returned result 
{
  "connection_name" : "sample_connection",
  "target" : {
    "schema_name" : "sample_schema",
    "table_name" : "sample_table"
  },
  "basic_statistics_collected" : false,
  "profiling_checks_configured" : false,
  "monitoring_checks_configured" : false,
  "partition_checks_configured" : false,
  "check_results_present" : false
}

Execution

from dqops import client
from dqops.client.api.tables import get_table_profiling_status

dqops_client = client.Client(
    'http://localhost:8888/',
    raise_on_unexpected_status=True
)

call_result = get_table_profiling_status.sync(
    'sample_connection',
    'sample_schema',
    'sample_table',
    client=dqops_client
)
Expand to see the returned result 
TableProfilingSetupStatusModel(
    connection_name='sample_connection',
    target=PhysicalTableName(
        schema_name='sample_schema',
        table_name='sample_table'
    ),
    basic_statistics_collected=False,
    profiling_checks_configured=False,
    monitoring_checks_configured=False,
    partition_checks_configured=False,
    check_results_present=False
)

Execution

from dqops import client
from dqops.client.api.tables import get_table_profiling_status

dqops_client = client.Client(
    'http://localhost:8888/',
    raise_on_unexpected_status=True
)

call_result = await get_table_profiling_status.asyncio(
    'sample_connection',
    'sample_schema',
    'sample_table',
    client=dqops_client
)
Expand to see the returned result 
TableProfilingSetupStatusModel(
    connection_name='sample_connection',
    target=PhysicalTableName(
        schema_name='sample_schema',
        table_name='sample_table'
    ),
    basic_statistics_collected=False,
    profiling_checks_configured=False,
    monitoring_checks_configured=False,
    partition_checks_configured=False,
    check_results_present=False
)

Execution

from dqops import client
from dqops.client.api.tables import get_table_profiling_status

token = 's4mp13_4u7h_70k3n'

dqops_client = client.AuthenticatedClient(
    'http://localhost:8888/',
    token=token,
    raise_on_unexpected_status=True
)

call_result = get_table_profiling_status.sync(
    'sample_connection',
    'sample_schema',
    'sample_table',
    client=dqops_client
)
Expand to see the returned result 
TableProfilingSetupStatusModel(
    connection_name='sample_connection',
    target=PhysicalTableName(
        schema_name='sample_schema',
        table_name='sample_table'
    ),
    basic_statistics_collected=False,
    profiling_checks_configured=False,
    monitoring_checks_configured=False,
    partition_checks_configured=False,
    check_results_present=False
)

Execution

from dqops import client
from dqops.client.api.tables import get_table_profiling_status

token = 's4mp13_4u7h_70k3n'

dqops_client = client.AuthenticatedClient(
    'http://localhost:8888/',
    token=token,
    raise_on_unexpected_status=True
)

call_result = await get_table_profiling_status.asyncio(
    'sample_connection',
    'sample_schema',
    'sample_table',
    client=dqops_client
)
Expand to see the returned result 
TableProfilingSetupStatusModel(
    connection_name='sample_connection',
    target=PhysicalTableName(
        schema_name='sample_schema',
        table_name='sample_table'
    ),
    basic_statistics_collected=False,
    profiling_checks_configured=False,
    monitoring_checks_configured=False,
    partition_checks_configured=False,
    check_results_present=False
)

get_table_scheduling_group_override

Return the schedule override configuration for a table

Follow the link to see the source code on GitHub.

GET 

http://localhost:8888/api/connections/{connectionName}/schemas/{schemaName}/tables/{tableName}/schedulesoverride/{schedulingGroup}

Return value

 Property name   Description                       Data type 
cron_schedule_spec CronScheduleSpec

Parameters of this method are described below

 Property name   Description                       Data type   Required 
connection_name Connection name string
schema_name Schema name string
table_name Table name string
scheduling_group Check scheduling group (named schedule) CheckRunScheduleGroup

Usage examples

Execution

curl http://localhost:8888/api/connections/sample_connection/schemas/sample_schema/tables/sample_table/schedulesoverride/partitioned_daily^
    -H "Accept: application/json"
Expand to see the returned result 
{
  "cron_expression" : "0 12 1 * *"
}

Execution

from dqops import client
from dqops.client.api.tables import get_table_scheduling_group_override
from dqops.client.models import CheckRunScheduleGroup

dqops_client = client.Client(
    'http://localhost:8888/',
    raise_on_unexpected_status=True
)

call_result = get_table_scheduling_group_override.sync(
    'sample_connection',
    'sample_schema',
    'sample_table',
    CheckRunScheduleGroup.partitioned_daily,
    client=dqops_client
)
Expand to see the returned result 
CronScheduleSpec(
    cron_expression='0 12 1 * *',
    disabled=False
)

Execution

from dqops import client
from dqops.client.api.tables import get_table_scheduling_group_override
from dqops.client.models import CheckRunScheduleGroup

dqops_client = client.Client(
    'http://localhost:8888/',
    raise_on_unexpected_status=True
)

call_result = await get_table_scheduling_group_override.asyncio(
    'sample_connection',
    'sample_schema',
    'sample_table',
    CheckRunScheduleGroup.partitioned_daily,
    client=dqops_client
)
Expand to see the returned result 
CronScheduleSpec(
    cron_expression='0 12 1 * *',
    disabled=False
)

Execution

from dqops import client
from dqops.client.api.tables import get_table_scheduling_group_override
from dqops.client.models import CheckRunScheduleGroup

token = 's4mp13_4u7h_70k3n'

dqops_client = client.AuthenticatedClient(
    'http://localhost:8888/',
    token=token,
    raise_on_unexpected_status=True
)

call_result = get_table_scheduling_group_override.sync(
    'sample_connection',
    'sample_schema',
    'sample_table',
    CheckRunScheduleGroup.partitioned_daily,
    client=dqops_client
)
Expand to see the returned result 
CronScheduleSpec(
    cron_expression='0 12 1 * *',
    disabled=False
)

Execution

from dqops import client
from dqops.client.api.tables import get_table_scheduling_group_override
from dqops.client.models import CheckRunScheduleGroup

token = 's4mp13_4u7h_70k3n'

dqops_client = client.AuthenticatedClient(
    'http://localhost:8888/',
    token=token,
    raise_on_unexpected_status=True
)

call_result = await get_table_scheduling_group_override.asyncio(
    'sample_connection',
    'sample_schema',
    'sample_table',
    CheckRunScheduleGroup.partitioned_daily,
    client=dqops_client
)
Expand to see the returned result 
CronScheduleSpec(
    cron_expression='0 12 1 * *',
    disabled=False
)

get_table_statistics

Returns a list of the profiler (statistics) metrics on a chosen table captured during the most recent statistics collection.

Follow the link to see the source code on GitHub.

GET 

http://localhost:8888/api/connections/{connectionName}/schemas/{schemaName}/tables/{tableName}/statistics

Return value

 Property name   Description                       Data type 
table_statistics_model TableStatisticsModel

Parameters of this method are described below

 Property name   Description                       Data type   Required 
connection_name Connection name string
schema_name Schema name string
table_name Table name string

Usage examples

Execution

curl http://localhost:8888/api/connections/sample_connection/schemas/sample_schema/tables/sample_table/statistics^
    -H "Accept: application/json"
Expand to see the returned result 
{
  "can_collect_statistics" : false
}

Execution

from dqops import client
from dqops.client.api.tables import get_table_statistics

dqops_client = client.Client(
    'http://localhost:8888/',
    raise_on_unexpected_status=True
)

call_result = get_table_statistics.sync(
    'sample_connection',
    'sample_schema',
    'sample_table',
    client=dqops_client
)
Expand to see the returned result 
TableStatisticsModel(can_collect_statistics=False)

Execution

from dqops import client
from dqops.client.api.tables import get_table_statistics

dqops_client = client.Client(
    'http://localhost:8888/',
    raise_on_unexpected_status=True
)

call_result = await get_table_statistics.asyncio(
    'sample_connection',
    'sample_schema',
    'sample_table',
    client=dqops_client
)
Expand to see the returned result 
TableStatisticsModel(can_collect_statistics=False)

Execution

from dqops import client
from dqops.client.api.tables import get_table_statistics

token = 's4mp13_4u7h_70k3n'

dqops_client = client.AuthenticatedClient(
    'http://localhost:8888/',
    token=token,
    raise_on_unexpected_status=True
)

call_result = get_table_statistics.sync(
    'sample_connection',
    'sample_schema',
    'sample_table',
    client=dqops_client
)
Expand to see the returned result 
TableStatisticsModel(can_collect_statistics=False)

Execution

from dqops import client
from dqops.client.api.tables import get_table_statistics

token = 's4mp13_4u7h_70k3n'

dqops_client = client.AuthenticatedClient(
    'http://localhost:8888/',
    token=token,
    raise_on_unexpected_status=True
)

call_result = await get_table_statistics.asyncio(
    'sample_connection',
    'sample_schema',
    'sample_table',
    client=dqops_client
)
Expand to see the returned result 
TableStatisticsModel(can_collect_statistics=False)

get_tables

Returns a list of tables inside a connection/schema

Follow the link to see the source code on GitHub.

GET 

http://localhost:8888/api/connections/{connectionName}/schemas/{schemaName}/tables

Return value

 Property name   Description                       Data type 
table_list_model List[TableListModel]

Parameters of this method are described below

 Property name   Description                       Data type   Required 
connection_name Connection name string
schema_name Schema name string
label Optional labels to filter the tables List[string]
page Page number, the first page is 1 long
limit Page size, the default is 100 rows, but paging is disabled is neither page and limit parameters are provided long
filter Optional table name filter string
check_type Optional parameter for the check type, when provided, returns the results for data quality dimensions for the data quality checks of that type CheckType

Usage examples

Execution

curl http://localhost:8888/api/connections/sample_connection/schemas/sample_schema/tables^
    -H "Accept: application/json"
Expand to see the returned result 
[ {
  "connection_name" : "sample_connection",
  "table_hash" : 7188561880498907939,
  "target" : {
    "schema_name" : "sample_schema",
    "table_name" : "sample_table"
  },
  "do_not_collect_error_samples_in_profiling" : false,
  "always_collect_error_samples_in_monitoring" : false,
  "has_any_configured_checks" : true,
  "has_any_configured_profiling_checks" : true,
  "run_checks_job_template" : {
    "connection" : "sample_connection",
    "fullTableName" : "sample_schema.sample_table",
    "enabled" : true
  },
  "run_profiling_checks_job_template" : {
    "connection" : "sample_connection",
    "fullTableName" : "sample_schema.sample_table",
    "enabled" : true,
    "checkType" : "profiling"
  },
  "run_monitoring_checks_job_template" : {
    "connection" : "sample_connection",
    "fullTableName" : "sample_schema.sample_table",
    "enabled" : true,
    "checkType" : "monitoring"
  },
  "run_partition_checks_job_template" : {
    "connection" : "sample_connection",
    "fullTableName" : "sample_schema.sample_table",
    "enabled" : true,
    "checkType" : "partitioned"
  },
  "data_clean_job_template" : {
    "connection" : "sample_connection",
    "fullTableName" : "sample_schema.sample_table",
    "deleteErrors" : true,
    "deleteStatistics" : true,
    "deleteCheckResults" : true,
    "deleteSensorReadouts" : true,
    "deleteErrorSamples" : true,
    "deleteIncidents" : true,
    "deleteChecksConfiguration" : false
  },
  "advanced_properties" : { },
  "can_edit" : true,
  "can_collect_statistics" : true,
  "can_run_checks" : true,
  "can_delete_data" : true
}, {
  "connection_name" : "sample_connection",
  "table_hash" : 7188561880498907939,
  "target" : {
    "schema_name" : "sample_schema",
    "table_name" : "sample_table"
  },
  "do_not_collect_error_samples_in_profiling" : false,
  "always_collect_error_samples_in_monitoring" : false,
  "has_any_configured_checks" : true,
  "has_any_configured_profiling_checks" : true,
  "run_checks_job_template" : {
    "connection" : "sample_connection",
    "fullTableName" : "sample_schema.sample_table",
    "enabled" : true
  },
  "run_profiling_checks_job_template" : {
    "connection" : "sample_connection",
    "fullTableName" : "sample_schema.sample_table",
    "enabled" : true,
    "checkType" : "profiling"
  },
  "run_monitoring_checks_job_template" : {
    "connection" : "sample_connection",
    "fullTableName" : "sample_schema.sample_table",
    "enabled" : true,
    "checkType" : "monitoring"
  },
  "run_partition_checks_job_template" : {
    "connection" : "sample_connection",
    "fullTableName" : "sample_schema.sample_table",
    "enabled" : true,
    "checkType" : "partitioned"
  },
  "data_clean_job_template" : {
    "connection" : "sample_connection",
    "fullTableName" : "sample_schema.sample_table",
    "deleteErrors" : true,
    "deleteStatistics" : true,
    "deleteCheckResults" : true,
    "deleteSensorReadouts" : true,
    "deleteErrorSamples" : true,
    "deleteIncidents" : true,
    "deleteChecksConfiguration" : false
  },
  "advanced_properties" : { },
  "can_edit" : true,
  "can_collect_statistics" : true,
  "can_run_checks" : true,
  "can_delete_data" : true
}, {
  "connection_name" : "sample_connection",
  "table_hash" : 7188561880498907939,
  "target" : {
    "schema_name" : "sample_schema",
    "table_name" : "sample_table"
  },
  "do_not_collect_error_samples_in_profiling" : false,
  "always_collect_error_samples_in_monitoring" : false,
  "has_any_configured_checks" : true,
  "has_any_configured_profiling_checks" : true,
  "run_checks_job_template" : {
    "connection" : "sample_connection",
    "fullTableName" : "sample_schema.sample_table",
    "enabled" : true
  },
  "run_profiling_checks_job_template" : {
    "connection" : "sample_connection",
    "fullTableName" : "sample_schema.sample_table",
    "enabled" : true,
    "checkType" : "profiling"
  },
  "run_monitoring_checks_job_template" : {
    "connection" : "sample_connection",
    "fullTableName" : "sample_schema.sample_table",
    "enabled" : true,
    "checkType" : "monitoring"
  },
  "run_partition_checks_job_template" : {
    "connection" : "sample_connection",
    "fullTableName" : "sample_schema.sample_table",
    "enabled" : true,
    "checkType" : "partitioned"
  },
  "data_clean_job_template" : {
    "connection" : "sample_connection",
    "fullTableName" : "sample_schema.sample_table",
    "deleteErrors" : true,
    "deleteStatistics" : true,
    "deleteCheckResults" : true,
    "deleteSensorReadouts" : true,
    "deleteErrorSamples" : true,
    "deleteIncidents" : true,
    "deleteChecksConfiguration" : false
  },
  "advanced_properties" : { },
  "can_edit" : true,
  "can_collect_statistics" : true,
  "can_run_checks" : true,
  "can_delete_data" : true
} ]

Execution

from dqops import client
from dqops.client.api.tables import get_tables
from dqops.client.models import CheckType

dqops_client = client.Client(
    'http://localhost:8888/',
    raise_on_unexpected_status=True
)

call_result = get_tables.sync(
    'sample_connection',
    'sample_schema',
    client=dqops_client
)
Expand to see the returned result 
[
    TableListModel(
        connection_name='sample_connection',
        table_hash=7188561880498907939,
        target=PhysicalTableName(
            schema_name='sample_schema',
            table_name='sample_table'
        ),
        disabled=False,
        do_not_collect_error_samples_in_profiling=False,
        always_collect_error_samples_in_monitoring=False,
        has_any_configured_checks=True,
        has_any_configured_profiling_checks=True,
        has_any_configured_monitoring_checks=False,
        has_any_configured_partition_checks=False,
        partitioning_configuration_missing=False,
        run_checks_job_template=CheckSearchFilters(
            connection='sample_connection',
            full_table_name='sample_schema.sample_table',
            enabled=True
        ),
        run_profiling_checks_job_template=CheckSearchFilters(
            check_type=CheckType.PROFILING,
            connection='sample_connection',
            full_table_name='sample_schema.sample_table',
            enabled=True
        ),
        run_monitoring_checks_job_template=CheckSearchFilters(
            check_type=CheckType.MONITORING,
            connection='sample_connection',
            full_table_name='sample_schema.sample_table',
            enabled=True
        ),
        run_partition_checks_job_template=CheckSearchFilters(
            check_type=CheckType.PARTITIONED,
            connection='sample_connection',
            full_table_name='sample_schema.sample_table',
            enabled=True
        ),
        data_clean_job_template=DeleteStoredDataQueueJobParameters(
            connection='sample_connection',
            full_table_name='sample_schema.sample_table',
            delete_errors=True,
            delete_statistics=True,
            delete_check_results=True,
            delete_sensor_readouts=True,
            delete_error_samples=True,
            delete_incidents=True,
            delete_checks_configuration=False
        ),
        advanced_properties={

        },
        can_edit=True,
        can_collect_statistics=True,
        can_run_checks=True,
        can_delete_data=True
    ),
    TableListModel(
        connection_name='sample_connection',
        table_hash=7188561880498907939,
        target=PhysicalTableName(
            schema_name='sample_schema',
            table_name='sample_table'
        ),
        disabled=False,
        do_not_collect_error_samples_in_profiling=False,
        always_collect_error_samples_in_monitoring=False,
        has_any_configured_checks=True,
        has_any_configured_profiling_checks=True,
        has_any_configured_monitoring_checks=False,
        has_any_configured_partition_checks=False,
        partitioning_configuration_missing=False,
        run_checks_job_template=CheckSearchFilters(
            connection='sample_connection',
            full_table_name='sample_schema.sample_table',
            enabled=True
        ),
        run_profiling_checks_job_template=CheckSearchFilters(
            check_type=CheckType.PROFILING,
            connection='sample_connection',
            full_table_name='sample_schema.sample_table',
            enabled=True
        ),
        run_monitoring_checks_job_template=CheckSearchFilters(
            check_type=CheckType.MONITORING,
            connection='sample_connection',
            full_table_name='sample_schema.sample_table',
            enabled=True
        ),
        run_partition_checks_job_template=CheckSearchFilters(
            check_type=CheckType.PARTITIONED,
            connection='sample_connection',
            full_table_name='sample_schema.sample_table',
            enabled=True
        ),
        data_clean_job_template=DeleteStoredDataQueueJobParameters(
            connection='sample_connection',
            full_table_name='sample_schema.sample_table',
            delete_errors=True,
            delete_statistics=True,
            delete_check_results=True,
            delete_sensor_readouts=True,
            delete_error_samples=True,
            delete_incidents=True,
            delete_checks_configuration=False
        ),
        advanced_properties={

        },
        can_edit=True,
        can_collect_statistics=True,
        can_run_checks=True,
        can_delete_data=True
    ),
    TableListModel(
        connection_name='sample_connection',
        table_hash=7188561880498907939,
        target=PhysicalTableName(
            schema_name='sample_schema',
            table_name='sample_table'
        ),
        disabled=False,
        do_not_collect_error_samples_in_profiling=False,
        always_collect_error_samples_in_monitoring=False,
        has_any_configured_checks=True,
        has_any_configured_profiling_checks=True,
        has_any_configured_monitoring_checks=False,
        has_any_configured_partition_checks=False,
        partitioning_configuration_missing=False,
        run_checks_job_template=CheckSearchFilters(
            connection='sample_connection',
            full_table_name='sample_schema.sample_table',
            enabled=True
        ),
        run_profiling_checks_job_template=CheckSearchFilters(
            check_type=CheckType.PROFILING,
            connection='sample_connection',
            full_table_name='sample_schema.sample_table',
            enabled=True
        ),
        run_monitoring_checks_job_template=CheckSearchFilters(
            check_type=CheckType.MONITORING,
            connection='sample_connection',
            full_table_name='sample_schema.sample_table',
            enabled=True
        ),
        run_partition_checks_job_template=CheckSearchFilters(
            check_type=CheckType.PARTITIONED,
            connection='sample_connection',
            full_table_name='sample_schema.sample_table',
            enabled=True
        ),
        data_clean_job_template=DeleteStoredDataQueueJobParameters(
            connection='sample_connection',
            full_table_name='sample_schema.sample_table',
            delete_errors=True,
            delete_statistics=True,
            delete_check_results=True,
            delete_sensor_readouts=True,
            delete_error_samples=True,
            delete_incidents=True,
            delete_checks_configuration=False
        ),
        advanced_properties={

        },
        can_edit=True,
        can_collect_statistics=True,
        can_run_checks=True,
        can_delete_data=True
    )
]

Execution

from dqops import client
from dqops.client.api.tables import get_tables
from dqops.client.models import CheckType

dqops_client = client.Client(
    'http://localhost:8888/',
    raise_on_unexpected_status=True
)

call_result = await get_tables.asyncio(
    'sample_connection',
    'sample_schema',
    client=dqops_client
)
Expand to see the returned result 
[
    TableListModel(
        connection_name='sample_connection',
        table_hash=7188561880498907939,
        target=PhysicalTableName(
            schema_name='sample_schema',
            table_name='sample_table'
        ),
        disabled=False,
        do_not_collect_error_samples_in_profiling=False,
        always_collect_error_samples_in_monitoring=False,
        has_any_configured_checks=True,
        has_any_configured_profiling_checks=True,
        has_any_configured_monitoring_checks=False,
        has_any_configured_partition_checks=False,
        partitioning_configuration_missing=False,
        run_checks_job_template=CheckSearchFilters(
            connection='sample_connection',
            full_table_name='sample_schema.sample_table',
            enabled=True
        ),
        run_profiling_checks_job_template=CheckSearchFilters(
            check_type=CheckType.PROFILING,
            connection='sample_connection',
            full_table_name='sample_schema.sample_table',
            enabled=True
        ),
        run_monitoring_checks_job_template=CheckSearchFilters(
            check_type=CheckType.MONITORING,
            connection='sample_connection',
            full_table_name='sample_schema.sample_table',
            enabled=True
        ),
        run_partition_checks_job_template=CheckSearchFilters(
            check_type=CheckType.PARTITIONED,
            connection='sample_connection',
            full_table_name='sample_schema.sample_table',
            enabled=True
        ),
        data_clean_job_template=DeleteStoredDataQueueJobParameters(
            connection='sample_connection',
            full_table_name='sample_schema.sample_table',
            delete_errors=True,
            delete_statistics=True,
            delete_check_results=True,
            delete_sensor_readouts=True,
            delete_error_samples=True,
            delete_incidents=True,
            delete_checks_configuration=False
        ),
        advanced_properties={

        },
        can_edit=True,
        can_collect_statistics=True,
        can_run_checks=True,
        can_delete_data=True
    ),
    TableListModel(
        connection_name='sample_connection',
        table_hash=7188561880498907939,
        target=PhysicalTableName(
            schema_name='sample_schema',
            table_name='sample_table'
        ),
        disabled=False,
        do_not_collect_error_samples_in_profiling=False,
        always_collect_error_samples_in_monitoring=False,
        has_any_configured_checks=True,
        has_any_configured_profiling_checks=True,
        has_any_configured_monitoring_checks=False,
        has_any_configured_partition_checks=False,
        partitioning_configuration_missing=False,
        run_checks_job_template=CheckSearchFilters(
            connection='sample_connection',
            full_table_name='sample_schema.sample_table',
            enabled=True
        ),
        run_profiling_checks_job_template=CheckSearchFilters(
            check_type=CheckType.PROFILING,
            connection='sample_connection',
            full_table_name='sample_schema.sample_table',
            enabled=True
        ),
        run_monitoring_checks_job_template=CheckSearchFilters(
            check_type=CheckType.MONITORING,
            connection='sample_connection',
            full_table_name='sample_schema.sample_table',
            enabled=True
        ),
        run_partition_checks_job_template=CheckSearchFilters(
            check_type=CheckType.PARTITIONED,
            connection='sample_connection',
            full_table_name='sample_schema.sample_table',
            enabled=True
        ),
        data_clean_job_template=DeleteStoredDataQueueJobParameters(
            connection='sample_connection',
            full_table_name='sample_schema.sample_table',
            delete_errors=True,
            delete_statistics=True,
            delete_check_results=True,
            delete_sensor_readouts=True,
            delete_error_samples=True,
            delete_incidents=True,
            delete_checks_configuration=False
        ),
        advanced_properties={

        },
        can_edit=True,
        can_collect_statistics=True,
        can_run_checks=True,
        can_delete_data=True
    ),
    TableListModel(
        connection_name='sample_connection',
        table_hash=7188561880498907939,
        target=PhysicalTableName(
            schema_name='sample_schema',
            table_name='sample_table'
        ),
        disabled=False,
        do_not_collect_error_samples_in_profiling=False,
        always_collect_error_samples_in_monitoring=False,
        has_any_configured_checks=True,
        has_any_configured_profiling_checks=True,
        has_any_configured_monitoring_checks=False,
        has_any_configured_partition_checks=False,
        partitioning_configuration_missing=False,
        run_checks_job_template=CheckSearchFilters(
            connection='sample_connection',
            full_table_name='sample_schema.sample_table',
            enabled=True
        ),
        run_profiling_checks_job_template=CheckSearchFilters(
            check_type=CheckType.PROFILING,
            connection='sample_connection',
            full_table_name='sample_schema.sample_table',
            enabled=True
        ),
        run_monitoring_checks_job_template=CheckSearchFilters(
            check_type=CheckType.MONITORING,
            connection='sample_connection',
            full_table_name='sample_schema.sample_table',
            enabled=True
        ),
        run_partition_checks_job_template=CheckSearchFilters(
            check_type=CheckType.PARTITIONED,
            connection='sample_connection',
            full_table_name='sample_schema.sample_table',
            enabled=True
        ),
        data_clean_job_template=DeleteStoredDataQueueJobParameters(
            connection='sample_connection',
            full_table_name='sample_schema.sample_table',
            delete_errors=True,
            delete_statistics=True,
            delete_check_results=True,
            delete_sensor_readouts=True,
            delete_error_samples=True,
            delete_incidents=True,
            delete_checks_configuration=False
        ),
        advanced_properties={

        },
        can_edit=True,
        can_collect_statistics=True,
        can_run_checks=True,
        can_delete_data=True
    )
]

Execution

from dqops import client
from dqops.client.api.tables import get_tables
from dqops.client.models import CheckType

token = 's4mp13_4u7h_70k3n'

dqops_client = client.AuthenticatedClient(
    'http://localhost:8888/',
    token=token,
    raise_on_unexpected_status=True
)

call_result = get_tables.sync(
    'sample_connection',
    'sample_schema',
    client=dqops_client
)
Expand to see the returned result 
[
    TableListModel(
        connection_name='sample_connection',
        table_hash=7188561880498907939,
        target=PhysicalTableName(
            schema_name='sample_schema',
            table_name='sample_table'
        ),
        disabled=False,
        do_not_collect_error_samples_in_profiling=False,
        always_collect_error_samples_in_monitoring=False,
        has_any_configured_checks=True,
        has_any_configured_profiling_checks=True,
        has_any_configured_monitoring_checks=False,
        has_any_configured_partition_checks=False,
        partitioning_configuration_missing=False,
        run_checks_job_template=CheckSearchFilters(
            connection='sample_connection',
            full_table_name='sample_schema.sample_table',
            enabled=True
        ),
        run_profiling_checks_job_template=CheckSearchFilters(
            check_type=CheckType.PROFILING,
            connection='sample_connection',
            full_table_name='sample_schema.sample_table',
            enabled=True
        ),
        run_monitoring_checks_job_template=CheckSearchFilters(
            check_type=CheckType.MONITORING,
            connection='sample_connection',
            full_table_name='sample_schema.sample_table',
            enabled=True
        ),
        run_partition_checks_job_template=CheckSearchFilters(
            check_type=CheckType.PARTITIONED,
            connection='sample_connection',
            full_table_name='sample_schema.sample_table',
            enabled=True
        ),
        data_clean_job_template=DeleteStoredDataQueueJobParameters(
            connection='sample_connection',
            full_table_name='sample_schema.sample_table',
            delete_errors=True,
            delete_statistics=True,
            delete_check_results=True,
            delete_sensor_readouts=True,
            delete_error_samples=True,
            delete_incidents=True,
            delete_checks_configuration=False
        ),
        advanced_properties={

        },
        can_edit=True,
        can_collect_statistics=True,
        can_run_checks=True,
        can_delete_data=True
    ),
    TableListModel(
        connection_name='sample_connection',
        table_hash=7188561880498907939,
        target=PhysicalTableName(
            schema_name='sample_schema',
            table_name='sample_table'
        ),
        disabled=False,
        do_not_collect_error_samples_in_profiling=False,
        always_collect_error_samples_in_monitoring=False,
        has_any_configured_checks=True,
        has_any_configured_profiling_checks=True,
        has_any_configured_monitoring_checks=False,
        has_any_configured_partition_checks=False,
        partitioning_configuration_missing=False,
        run_checks_job_template=CheckSearchFilters(
            connection='sample_connection',
            full_table_name='sample_schema.sample_table',
            enabled=True
        ),
        run_profiling_checks_job_template=CheckSearchFilters(
            check_type=CheckType.PROFILING,
            connection='sample_connection',
            full_table_name='sample_schema.sample_table',
            enabled=True
        ),
        run_monitoring_checks_job_template=CheckSearchFilters(
            check_type=CheckType.MONITORING,
            connection='sample_connection',
            full_table_name='sample_schema.sample_table',
            enabled=True
        ),
        run_partition_checks_job_template=CheckSearchFilters(
            check_type=CheckType.PARTITIONED,
            connection='sample_connection',
            full_table_name='sample_schema.sample_table',
            enabled=True
        ),
        data_clean_job_template=DeleteStoredDataQueueJobParameters(
            connection='sample_connection',
            full_table_name='sample_schema.sample_table',
            delete_errors=True,
            delete_statistics=True,
            delete_check_results=True,
            delete_sensor_readouts=True,
            delete_error_samples=True,
            delete_incidents=True,
            delete_checks_configuration=False
        ),
        advanced_properties={

        },
        can_edit=True,
        can_collect_statistics=True,
        can_run_checks=True,
        can_delete_data=True
    ),
    TableListModel(
        connection_name='sample_connection',
        table_hash=7188561880498907939,
        target=PhysicalTableName(
            schema_name='sample_schema',
            table_name='sample_table'
        ),
        disabled=False,
        do_not_collect_error_samples_in_profiling=False,
        always_collect_error_samples_in_monitoring=False,
        has_any_configured_checks=True,
        has_any_configured_profiling_checks=True,
        has_any_configured_monitoring_checks=False,
        has_any_configured_partition_checks=False,
        partitioning_configuration_missing=False,
        run_checks_job_template=CheckSearchFilters(
            connection='sample_connection',
            full_table_name='sample_schema.sample_table',
            enabled=True
        ),
        run_profiling_checks_job_template=CheckSearchFilters(
            check_type=CheckType.PROFILING,
            connection='sample_connection',
            full_table_name='sample_schema.sample_table',
            enabled=True
        ),
        run_monitoring_checks_job_template=CheckSearchFilters(
            check_type=CheckType.MONITORING,
            connection='sample_connection',
            full_table_name='sample_schema.sample_table',
            enabled=True
        ),
        run_partition_checks_job_template=CheckSearchFilters(
            check_type=CheckType.PARTITIONED,
            connection='sample_connection',
            full_table_name='sample_schema.sample_table',
            enabled=True
        ),
        data_clean_job_template=DeleteStoredDataQueueJobParameters(
            connection='sample_connection',
            full_table_name='sample_schema.sample_table',
            delete_errors=True,
            delete_statistics=True,
            delete_check_results=True,
            delete_sensor_readouts=True,
            delete_error_samples=True,
            delete_incidents=True,
            delete_checks_configuration=False
        ),
        advanced_properties={

        },
        can_edit=True,
        can_collect_statistics=True,
        can_run_checks=True,
        can_delete_data=True
    )
]

Execution

from dqops import client
from dqops.client.api.tables import get_tables
from dqops.client.models import CheckType

token = 's4mp13_4u7h_70k3n'

dqops_client = client.AuthenticatedClient(
    'http://localhost:8888/',
    token=token,
    raise_on_unexpected_status=True
)

call_result = await get_tables.asyncio(
    'sample_connection',
    'sample_schema',
    client=dqops_client
)
Expand to see the returned result 
[
    TableListModel(
        connection_name='sample_connection',
        table_hash=7188561880498907939,
        target=PhysicalTableName(
            schema_name='sample_schema',
            table_name='sample_table'
        ),
        disabled=False,
        do_not_collect_error_samples_in_profiling=False,
        always_collect_error_samples_in_monitoring=False,
        has_any_configured_checks=True,
        has_any_configured_profiling_checks=True,
        has_any_configured_monitoring_checks=False,
        has_any_configured_partition_checks=False,
        partitioning_configuration_missing=False,
        run_checks_job_template=CheckSearchFilters(
            connection='sample_connection',
            full_table_name='sample_schema.sample_table',
            enabled=True
        ),
        run_profiling_checks_job_template=CheckSearchFilters(
            check_type=CheckType.PROFILING,
            connection='sample_connection',
            full_table_name='sample_schema.sample_table',
            enabled=True
        ),
        run_monitoring_checks_job_template=CheckSearchFilters(
            check_type=CheckType.MONITORING,
            connection='sample_connection',
            full_table_name='sample_schema.sample_table',
            enabled=True
        ),
        run_partition_checks_job_template=CheckSearchFilters(
            check_type=CheckType.PARTITIONED,
            connection='sample_connection',
            full_table_name='sample_schema.sample_table',
            enabled=True
        ),
        data_clean_job_template=DeleteStoredDataQueueJobParameters(
            connection='sample_connection',
            full_table_name='sample_schema.sample_table',
            delete_errors=True,
            delete_statistics=True,
            delete_check_results=True,
            delete_sensor_readouts=True,
            delete_error_samples=True,
            delete_incidents=True,
            delete_checks_configuration=False
        ),
        advanced_properties={

        },
        can_edit=True,
        can_collect_statistics=True,
        can_run_checks=True,
        can_delete_data=True
    ),
    TableListModel(
        connection_name='sample_connection',
        table_hash=7188561880498907939,
        target=PhysicalTableName(
            schema_name='sample_schema',
            table_name='sample_table'
        ),
        disabled=False,
        do_not_collect_error_samples_in_profiling=False,
        always_collect_error_samples_in_monitoring=False,
        has_any_configured_checks=True,
        has_any_configured_profiling_checks=True,
        has_any_configured_monitoring_checks=False,
        has_any_configured_partition_checks=False,
        partitioning_configuration_missing=False,
        run_checks_job_template=CheckSearchFilters(
            connection='sample_connection',
            full_table_name='sample_schema.sample_table',
            enabled=True
        ),
        run_profiling_checks_job_template=CheckSearchFilters(
            check_type=CheckType.PROFILING,
            connection='sample_connection',
            full_table_name='sample_schema.sample_table',
            enabled=True
        ),
        run_monitoring_checks_job_template=CheckSearchFilters(
            check_type=CheckType.MONITORING,
            connection='sample_connection',
            full_table_name='sample_schema.sample_table',
            enabled=True
        ),
        run_partition_checks_job_template=CheckSearchFilters(
            check_type=CheckType.PARTITIONED,
            connection='sample_connection',
            full_table_name='sample_schema.sample_table',
            enabled=True
        ),
        data_clean_job_template=DeleteStoredDataQueueJobParameters(
            connection='sample_connection',
            full_table_name='sample_schema.sample_table',
            delete_errors=True,
            delete_statistics=True,
            delete_check_results=True,
            delete_sensor_readouts=True,
            delete_error_samples=True,
            delete_incidents=True,
            delete_checks_configuration=False
        ),
        advanced_properties={

        },
        can_edit=True,
        can_collect_statistics=True,
        can_run_checks=True,
        can_delete_data=True
    ),
    TableListModel(
        connection_name='sample_connection',
        table_hash=7188561880498907939,
        target=PhysicalTableName(
            schema_name='sample_schema',
            table_name='sample_table'
        ),
        disabled=False,
        do_not_collect_error_samples_in_profiling=False,
        always_collect_error_samples_in_monitoring=False,
        has_any_configured_checks=True,
        has_any_configured_profiling_checks=True,
        has_any_configured_monitoring_checks=False,
        has_any_configured_partition_checks=False,
        partitioning_configuration_missing=False,
        run_checks_job_template=CheckSearchFilters(
            connection='sample_connection',
            full_table_name='sample_schema.sample_table',
            enabled=True
        ),
        run_profiling_checks_job_template=CheckSearchFilters(
            check_type=CheckType.PROFILING,
            connection='sample_connection',
            full_table_name='sample_schema.sample_table',
            enabled=True
        ),
        run_monitoring_checks_job_template=CheckSearchFilters(
            check_type=CheckType.MONITORING,
            connection='sample_connection',
            full_table_name='sample_schema.sample_table',
            enabled=True
        ),
        run_partition_checks_job_template=CheckSearchFilters(
            check_type=CheckType.PARTITIONED,
            connection='sample_connection',
            full_table_name='sample_schema.sample_table',
            enabled=True
        ),
        data_clean_job_template=DeleteStoredDataQueueJobParameters(
            connection='sample_connection',
            full_table_name='sample_schema.sample_table',
            delete_errors=True,
            delete_statistics=True,
            delete_check_results=True,
            delete_sensor_readouts=True,
            delete_error_samples=True,
            delete_incidents=True,
            delete_checks_configuration=False
        ),
        advanced_properties={

        },
        can_edit=True,
        can_collect_statistics=True,
        can_run_checks=True,
        can_delete_data=True
    )
]

update_table

Updates an existing table specification, changing all the fields

Follow the link to see the source code on GitHub.

PUT 

http://localhost:8888/api/connections/{connectionName}/schemas/{schemaName}/tables/{tableName}

Parameters of this method are described below

 Property name   Description                       Data type   Required 
connection_name Connection name string
schema_name Schema name string
table_name Table name string

Request body

 Description                       Data type   Required 
Full table specification TableSpec

Usage examples

Execution

curl -X PUT http://localhost:8888/api/connections/sample_connection/schemas/sample_schema/tables/sample_table^
    -H "Accept: application/json"^
    -H "Content-Type: application/json"^
    -d^
    "{\"timestamp_columns\":{\"event_timestamp_column\":\"col1\",\"ingestion_timestamp_column\":\"col2\",\"partition_by_column\":\"col3\"},\"incremental_time_window\":{\"daily_partitioning_recent_days\":7,\"daily_partitioning_include_today\":true},\"profiling_checks\":{\"volume\":{\"profile_row_count\":{\"error\":{\"min_count\":1}}}},\"columns\":{}}"

Execution

from dqops import client
from dqops.client.api.tables import update_table
from dqops.client.models import ColumnSpecMap, \
                                DataGroupingConfigurationSpecMap, \
                                MinCountRule1ParametersSpec, \
                                PartitionIncrementalTimeWindowSpec, \
                                TableComparisonConfigurationSpecMap, \
                                TableMonitoringCheckCategoriesSpec, \
                                TablePartitionedCheckCategoriesSpec, \
                                TableProfilingCheckCategoriesSpec, \
                                TableRowCountCheckSpec, \
                                TableSpec, \
                                TableVolumeProfilingChecksSpec, \
                                TableVolumeRowCountSensorParametersSpec, \
                                TimestampColumnsSpec

dqops_client = client.Client(
    'http://localhost:8888/'
)

request_body = TableSpec(
    disabled=False,
    do_not_collect_error_samples_in_profiling=False,
    always_collect_error_samples_in_monitoring=False,
    timestamp_columns=TimestampColumnsSpec(
        event_timestamp_column='col1',
        ingestion_timestamp_column='col2',
        partition_by_column='col3'
    ),
    incremental_time_window=PartitionIncrementalTimeWindowSpec(
        daily_partitioning_recent_days=7,
        daily_partitioning_include_today=True,
        monthly_partitioning_include_current_month=False
    ),
    groupings=DataGroupingConfigurationSpecMap(),
    table_comparisons=TableComparisonConfigurationSpecMap(),
    profiling_checks=TableProfilingCheckCategoriesSpec(
        volume=TableVolumeProfilingChecksSpec(
            profile_row_count=TableRowCountCheckSpec(
                parameters=TableVolumeRowCountSensorParametersSpec(),
                error=MinCountRule1ParametersSpec(min_count=1),
                disabled=False,
                exclude_from_kpi=False,
                include_in_sla=False,
                always_collect_error_samples=False,
                do_not_schedule=False
            )
        )
    ),
    monitoring_checks=TableMonitoringCheckCategoriesSpec(),
    partitioned_checks=TablePartitionedCheckCategoriesSpec(),
    columns=ColumnSpecMap(),
    advanced_properties={

    }
)

call_result = update_table.sync(
    'sample_connection',
    'sample_schema',
    'sample_table',
    client=dqops_client,
    json_body=request_body
)

Execution

from dqops import client
from dqops.client.api.tables import update_table
from dqops.client.models import ColumnSpecMap, \
                                DataGroupingConfigurationSpecMap, \
                                MinCountRule1ParametersSpec, \
                                PartitionIncrementalTimeWindowSpec, \
                                TableComparisonConfigurationSpecMap, \
                                TableMonitoringCheckCategoriesSpec, \
                                TablePartitionedCheckCategoriesSpec, \
                                TableProfilingCheckCategoriesSpec, \
                                TableRowCountCheckSpec, \
                                TableSpec, \
                                TableVolumeProfilingChecksSpec, \
                                TableVolumeRowCountSensorParametersSpec, \
                                TimestampColumnsSpec

dqops_client = client.Client(
    'http://localhost:8888/'
)

request_body = TableSpec(
    disabled=False,
    do_not_collect_error_samples_in_profiling=False,
    always_collect_error_samples_in_monitoring=False,
    timestamp_columns=TimestampColumnsSpec(
        event_timestamp_column='col1',
        ingestion_timestamp_column='col2',
        partition_by_column='col3'
    ),
    incremental_time_window=PartitionIncrementalTimeWindowSpec(
        daily_partitioning_recent_days=7,
        daily_partitioning_include_today=True,
        monthly_partitioning_include_current_month=False
    ),
    groupings=DataGroupingConfigurationSpecMap(),
    table_comparisons=TableComparisonConfigurationSpecMap(),
    profiling_checks=TableProfilingCheckCategoriesSpec(
        volume=TableVolumeProfilingChecksSpec(
            profile_row_count=TableRowCountCheckSpec(
                parameters=TableVolumeRowCountSensorParametersSpec(),
                error=MinCountRule1ParametersSpec(min_count=1),
                disabled=False,
                exclude_from_kpi=False,
                include_in_sla=False,
                always_collect_error_samples=False,
                do_not_schedule=False
            )
        )
    ),
    monitoring_checks=TableMonitoringCheckCategoriesSpec(),
    partitioned_checks=TablePartitionedCheckCategoriesSpec(),
    columns=ColumnSpecMap(),
    advanced_properties={

    }
)

call_result = await update_table.asyncio(
    'sample_connection',
    'sample_schema',
    'sample_table',
    client=dqops_client,
    json_body=request_body
)

Execution

from dqops import client
from dqops.client.api.tables import update_table
from dqops.client.models import ColumnSpecMap, \
                                DataGroupingConfigurationSpecMap, \
                                MinCountRule1ParametersSpec, \
                                PartitionIncrementalTimeWindowSpec, \
                                TableComparisonConfigurationSpecMap, \
                                TableMonitoringCheckCategoriesSpec, \
                                TablePartitionedCheckCategoriesSpec, \
                                TableProfilingCheckCategoriesSpec, \
                                TableRowCountCheckSpec, \
                                TableSpec, \
                                TableVolumeProfilingChecksSpec, \
                                TableVolumeRowCountSensorParametersSpec, \
                                TimestampColumnsSpec

token = 's4mp13_4u7h_70k3n'

dqops_client = client.AuthenticatedClient(
    'http://localhost:8888/',
    token=token
)

request_body = TableSpec(
    disabled=False,
    do_not_collect_error_samples_in_profiling=False,
    always_collect_error_samples_in_monitoring=False,
    timestamp_columns=TimestampColumnsSpec(
        event_timestamp_column='col1',
        ingestion_timestamp_column='col2',
        partition_by_column='col3'
    ),
    incremental_time_window=PartitionIncrementalTimeWindowSpec(
        daily_partitioning_recent_days=7,
        daily_partitioning_include_today=True,
        monthly_partitioning_include_current_month=False
    ),
    groupings=DataGroupingConfigurationSpecMap(),
    table_comparisons=TableComparisonConfigurationSpecMap(),
    profiling_checks=TableProfilingCheckCategoriesSpec(
        volume=TableVolumeProfilingChecksSpec(
            profile_row_count=TableRowCountCheckSpec(
                parameters=TableVolumeRowCountSensorParametersSpec(),
                error=MinCountRule1ParametersSpec(min_count=1),
                disabled=False,
                exclude_from_kpi=False,
                include_in_sla=False,
                always_collect_error_samples=False,
                do_not_schedule=False
            )
        )
    ),
    monitoring_checks=TableMonitoringCheckCategoriesSpec(),
    partitioned_checks=TablePartitionedCheckCategoriesSpec(),
    columns=ColumnSpecMap(),
    advanced_properties={

    }
)

call_result = update_table.sync(
    'sample_connection',
    'sample_schema',
    'sample_table',
    client=dqops_client,
    json_body=request_body
)

Execution

from dqops import client
from dqops.client.api.tables import update_table
from dqops.client.models import ColumnSpecMap, \
                                DataGroupingConfigurationSpecMap, \
                                MinCountRule1ParametersSpec, \
                                PartitionIncrementalTimeWindowSpec, \
                                TableComparisonConfigurationSpecMap, \
                                TableMonitoringCheckCategoriesSpec, \
                                TablePartitionedCheckCategoriesSpec, \
                                TableProfilingCheckCategoriesSpec, \
                                TableRowCountCheckSpec, \
                                TableSpec, \
                                TableVolumeProfilingChecksSpec, \
                                TableVolumeRowCountSensorParametersSpec, \
                                TimestampColumnsSpec

token = 's4mp13_4u7h_70k3n'

dqops_client = client.AuthenticatedClient(
    'http://localhost:8888/',
    token=token
)

request_body = TableSpec(
    disabled=False,
    do_not_collect_error_samples_in_profiling=False,
    always_collect_error_samples_in_monitoring=False,
    timestamp_columns=TimestampColumnsSpec(
        event_timestamp_column='col1',
        ingestion_timestamp_column='col2',
        partition_by_column='col3'
    ),
    incremental_time_window=PartitionIncrementalTimeWindowSpec(
        daily_partitioning_recent_days=7,
        daily_partitioning_include_today=True,
        monthly_partitioning_include_current_month=False
    ),
    groupings=DataGroupingConfigurationSpecMap(),
    table_comparisons=TableComparisonConfigurationSpecMap(),
    profiling_checks=TableProfilingCheckCategoriesSpec(
        volume=TableVolumeProfilingChecksSpec(
            profile_row_count=TableRowCountCheckSpec(
                parameters=TableVolumeRowCountSensorParametersSpec(),
                error=MinCountRule1ParametersSpec(min_count=1),
                disabled=False,
                exclude_from_kpi=False,
                include_in_sla=False,
                always_collect_error_samples=False,
                do_not_schedule=False
            )
        )
    ),
    monitoring_checks=TableMonitoringCheckCategoriesSpec(),
    partitioned_checks=TablePartitionedCheckCategoriesSpec(),
    columns=ColumnSpecMap(),
    advanced_properties={

    }
)

call_result = await update_table.asyncio(
    'sample_connection',
    'sample_schema',
    'sample_table',
    client=dqops_client,
    json_body=request_body
)

update_table_basic

Updates the basic field of an existing table, changing only the most important fields.

Follow the link to see the source code on GitHub.

PUT 

http://localhost:8888/api/connections/{connectionName}/schemas/{schemaName}/tables/{tableName}/basic

Parameters of this method are described below

 Property name   Description                       Data type   Required 
connection_name Connection name string
schema_name Schema name string
table_name Table name string

Request body

 Description                       Data type   Required 
Table basic model with the updated settings TableListModel

Usage examples

Execution

curl -X PUT http://localhost:8888/api/connections/sample_connection/schemas/sample_schema/tables/sample_table/basic^
    -H "Accept: application/json"^
    -H "Content-Type: application/json"^
    -d^
    "{\"connection_name\":\"sample_connection\",\"table_hash\":7188561880498907939,\"target\":{\"schema_name\":\"sample_schema\",\"table_name\":\"sample_table\"},\"do_not_collect_error_samples_in_profiling\":false,\"always_collect_error_samples_in_monitoring\":false,\"has_any_configured_checks\":true,\"has_any_configured_profiling_checks\":true,\"run_checks_job_template\":{\"connection\":\"sample_connection\",\"fullTableName\":\"sample_schema.sample_table\",\"enabled\":true},\"run_profiling_checks_job_template\":{\"connection\":\"sample_connection\",\"fullTableName\":\"sample_schema.sample_table\",\"enabled\":true,\"checkType\":\"profiling\"},\"run_monitoring_checks_job_template\":{\"connection\":\"sample_connection\",\"fullTableName\":\"sample_schema.sample_table\",\"enabled\":true,\"checkType\":\"monitoring\"},\"run_partition_checks_job_template\":{\"connection\":\"sample_connection\",\"fullTableName\":\"sample_schema.sample_table\",\"enabled\":true,\"checkType\":\"partitioned\"},\"data_clean_job_template\":{\"connection\":\"sample_connection\",\"fullTableName\":\"sample_schema.sample_table\",\"deleteErrors\":true,\"deleteStatistics\":true,\"deleteCheckResults\":true,\"deleteSensorReadouts\":true,\"deleteErrorSamples\":true,\"deleteIncidents\":true,\"deleteChecksConfiguration\":false},\"advanced_properties\":{},\"can_edit\":true,\"can_collect_statistics\":true,\"can_run_checks\":true,\"can_delete_data\":true}"

Execution

from dqops import client
from dqops.client.api.tables import update_table_basic
from dqops.client.models import CheckSearchFilters, \
                                DeleteStoredDataQueueJobParameters, \
                                PhysicalTableName, \
                                TableListModel

dqops_client = client.Client(
    'http://localhost:8888/'
)

request_body = TableListModel(
    connection_name='sample_connection',
    table_hash=7188561880498907939,
    target=PhysicalTableName(
        schema_name='sample_schema',
        table_name='sample_table'
    ),
    disabled=False,
    do_not_collect_error_samples_in_profiling=False,
    always_collect_error_samples_in_monitoring=False,
    has_any_configured_checks=True,
    has_any_configured_profiling_checks=True,
    has_any_configured_monitoring_checks=False,
    has_any_configured_partition_checks=False,
    partitioning_configuration_missing=False,
    run_checks_job_template=CheckSearchFilters(
        connection='sample_connection',
        full_table_name='sample_schema.sample_table',
        enabled=True
    ),
    run_profiling_checks_job_template=CheckSearchFilters(
        check_type=CheckType.PROFILING,
        connection='sample_connection',
        full_table_name='sample_schema.sample_table',
        enabled=True
    ),
    run_monitoring_checks_job_template=CheckSearchFilters(
        check_type=CheckType.MONITORING,
        connection='sample_connection',
        full_table_name='sample_schema.sample_table',
        enabled=True
    ),
    run_partition_checks_job_template=CheckSearchFilters(
        check_type=CheckType.PARTITIONED,
        connection='sample_connection',
        full_table_name='sample_schema.sample_table',
        enabled=True
    ),
    data_clean_job_template=DeleteStoredDataQueueJobParameters(
        connection='sample_connection',
        full_table_name='sample_schema.sample_table',
        delete_errors=True,
        delete_statistics=True,
        delete_check_results=True,
        delete_sensor_readouts=True,
        delete_error_samples=True,
        delete_incidents=True,
        delete_checks_configuration=False
    ),
    advanced_properties={

    },
    can_edit=True,
    can_collect_statistics=True,
    can_run_checks=True,
    can_delete_data=True
)

call_result = update_table_basic.sync(
    'sample_connection',
    'sample_schema',
    'sample_table',
    client=dqops_client,
    json_body=request_body
)

Execution

from dqops import client
from dqops.client.api.tables import update_table_basic
from dqops.client.models import CheckSearchFilters, \
                                DeleteStoredDataQueueJobParameters, \
                                PhysicalTableName, \
                                TableListModel

dqops_client = client.Client(
    'http://localhost:8888/'
)

request_body = TableListModel(
    connection_name='sample_connection',
    table_hash=7188561880498907939,
    target=PhysicalTableName(
        schema_name='sample_schema',
        table_name='sample_table'
    ),
    disabled=False,
    do_not_collect_error_samples_in_profiling=False,
    always_collect_error_samples_in_monitoring=False,
    has_any_configured_checks=True,
    has_any_configured_profiling_checks=True,
    has_any_configured_monitoring_checks=False,
    has_any_configured_partition_checks=False,
    partitioning_configuration_missing=False,
    run_checks_job_template=CheckSearchFilters(
        connection='sample_connection',
        full_table_name='sample_schema.sample_table',
        enabled=True
    ),
    run_profiling_checks_job_template=CheckSearchFilters(
        check_type=CheckType.PROFILING,
        connection='sample_connection',
        full_table_name='sample_schema.sample_table',
        enabled=True
    ),
    run_monitoring_checks_job_template=CheckSearchFilters(
        check_type=CheckType.MONITORING,
        connection='sample_connection',
        full_table_name='sample_schema.sample_table',
        enabled=True
    ),
    run_partition_checks_job_template=CheckSearchFilters(
        check_type=CheckType.PARTITIONED,
        connection='sample_connection',
        full_table_name='sample_schema.sample_table',
        enabled=True
    ),
    data_clean_job_template=DeleteStoredDataQueueJobParameters(
        connection='sample_connection',
        full_table_name='sample_schema.sample_table',
        delete_errors=True,
        delete_statistics=True,
        delete_check_results=True,
        delete_sensor_readouts=True,
        delete_error_samples=True,
        delete_incidents=True,
        delete_checks_configuration=False
    ),
    advanced_properties={

    },
    can_edit=True,
    can_collect_statistics=True,
    can_run_checks=True,
    can_delete_data=True
)

call_result = await update_table_basic.asyncio(
    'sample_connection',
    'sample_schema',
    'sample_table',
    client=dqops_client,
    json_body=request_body
)

Execution

from dqops import client
from dqops.client.api.tables import update_table_basic
from dqops.client.models import CheckSearchFilters, \
                                DeleteStoredDataQueueJobParameters, \
                                PhysicalTableName, \
                                TableListModel

token = 's4mp13_4u7h_70k3n'

dqops_client = client.AuthenticatedClient(
    'http://localhost:8888/',
    token=token
)

request_body = TableListModel(
    connection_name='sample_connection',
    table_hash=7188561880498907939,
    target=PhysicalTableName(
        schema_name='sample_schema',
        table_name='sample_table'
    ),
    disabled=False,
    do_not_collect_error_samples_in_profiling=False,
    always_collect_error_samples_in_monitoring=False,
    has_any_configured_checks=True,
    has_any_configured_profiling_checks=True,
    has_any_configured_monitoring_checks=False,
    has_any_configured_partition_checks=False,
    partitioning_configuration_missing=False,
    run_checks_job_template=CheckSearchFilters(
        connection='sample_connection',
        full_table_name='sample_schema.sample_table',
        enabled=True
    ),
    run_profiling_checks_job_template=CheckSearchFilters(
        check_type=CheckType.PROFILING,
        connection='sample_connection',
        full_table_name='sample_schema.sample_table',
        enabled=True
    ),
    run_monitoring_checks_job_template=CheckSearchFilters(
        check_type=CheckType.MONITORING,
        connection='sample_connection',
        full_table_name='sample_schema.sample_table',
        enabled=True
    ),
    run_partition_checks_job_template=CheckSearchFilters(
        check_type=CheckType.PARTITIONED,
        connection='sample_connection',
        full_table_name='sample_schema.sample_table',
        enabled=True
    ),
    data_clean_job_template=DeleteStoredDataQueueJobParameters(
        connection='sample_connection',
        full_table_name='sample_schema.sample_table',
        delete_errors=True,
        delete_statistics=True,
        delete_check_results=True,
        delete_sensor_readouts=True,
        delete_error_samples=True,
        delete_incidents=True,
        delete_checks_configuration=False
    ),
    advanced_properties={

    },
    can_edit=True,
    can_collect_statistics=True,
    can_run_checks=True,
    can_delete_data=True
)

call_result = update_table_basic.sync(
    'sample_connection',
    'sample_schema',
    'sample_table',
    client=dqops_client,
    json_body=request_body
)

Execution

from dqops import client
from dqops.client.api.tables import update_table_basic
from dqops.client.models import CheckSearchFilters, \
                                DeleteStoredDataQueueJobParameters, \
                                PhysicalTableName, \
                                TableListModel

token = 's4mp13_4u7h_70k3n'

dqops_client = client.AuthenticatedClient(
    'http://localhost:8888/',
    token=token
)

request_body = TableListModel(
    connection_name='sample_connection',
    table_hash=7188561880498907939,
    target=PhysicalTableName(
        schema_name='sample_schema',
        table_name='sample_table'
    ),
    disabled=False,
    do_not_collect_error_samples_in_profiling=False,
    always_collect_error_samples_in_monitoring=False,
    has_any_configured_checks=True,
    has_any_configured_profiling_checks=True,
    has_any_configured_monitoring_checks=False,
    has_any_configured_partition_checks=False,
    partitioning_configuration_missing=False,
    run_checks_job_template=CheckSearchFilters(
        connection='sample_connection',
        full_table_name='sample_schema.sample_table',
        enabled=True
    ),
    run_profiling_checks_job_template=CheckSearchFilters(
        check_type=CheckType.PROFILING,
        connection='sample_connection',
        full_table_name='sample_schema.sample_table',
        enabled=True
    ),
    run_monitoring_checks_job_template=CheckSearchFilters(
        check_type=CheckType.MONITORING,
        connection='sample_connection',
        full_table_name='sample_schema.sample_table',
        enabled=True
    ),
    run_partition_checks_job_template=CheckSearchFilters(
        check_type=CheckType.PARTITIONED,
        connection='sample_connection',
        full_table_name='sample_schema.sample_table',
        enabled=True
    ),
    data_clean_job_template=DeleteStoredDataQueueJobParameters(
        connection='sample_connection',
        full_table_name='sample_schema.sample_table',
        delete_errors=True,
        delete_statistics=True,
        delete_check_results=True,
        delete_sensor_readouts=True,
        delete_error_samples=True,
        delete_incidents=True,
        delete_checks_configuration=False
    ),
    advanced_properties={

    },
    can_edit=True,
    can_collect_statistics=True,
    can_run_checks=True,
    can_delete_data=True
)

call_result = await update_table_basic.asyncio(
    'sample_connection',
    'sample_schema',
    'sample_table',
    client=dqops_client,
    json_body=request_body
)

update_table_comments

Updates the list of comments on an existing table.

Follow the link to see the source code on GitHub.

PUT 

http://localhost:8888/api/connections/{connectionName}/schemas/{schemaName}/tables/{tableName}/comments

Parameters of this method are described below

 Property name   Description                       Data type   Required 
connection_name Connection name string
schema_name Schema name string
table_name Table name string

Request body

 Description                       Data type   Required 
List of comments to attach (replace) on a table or an empty object to clear the list of comments on a table List[CommentSpec]

Usage examples

Execution

curl -X PUT http://localhost:8888/api/connections/sample_connection/schemas/sample_schema/tables/sample_table/comments^
    -H "Accept: application/json"^
    -H "Content-Type: application/json"^
    -d^
    "[{\"date\":\"2007-12-03T10:15:30\",\"comment_by\":\"sample_user\",\"comment\":\"Sample comment\"},{\"date\":\"2007-12-03T10:15:30\",\"comment_by\":\"sample_user\",\"comment\":\"Sample comment\"},{\"date\":\"2007-12-03T10:15:30\",\"comment_by\":\"sample_user\",\"comment\":\"Sample comment\"}]"

Execution

from dqops import client
from dqops.client.api.tables import update_table_comments
from dqops.client.models import CommentSpec

dqops_client = client.Client(
    'http://localhost:8888/'
)

request_body = [
    CommentSpec(
        date=Some date/time value: [2007-12-03T10:15:30],
        comment_by='sample_user',
        comment='Sample comment'
    ),
    CommentSpec(
        date=Some date/time value: [2007-12-03T10:15:30],
        comment_by='sample_user',
        comment='Sample comment'
    ),
    CommentSpec(
        date=Some date/time value: [2007-12-03T10:15:30],
        comment_by='sample_user',
        comment='Sample comment'
    )
]

call_result = update_table_comments.sync(
    'sample_connection',
    'sample_schema',
    'sample_table',
    client=dqops_client,
    json_body=request_body
)

Execution

from dqops import client
from dqops.client.api.tables import update_table_comments
from dqops.client.models import CommentSpec

dqops_client = client.Client(
    'http://localhost:8888/'
)

request_body = [
    CommentSpec(
        date=Some date/time value: [2007-12-03T10:15:30],
        comment_by='sample_user',
        comment='Sample comment'
    ),
    CommentSpec(
        date=Some date/time value: [2007-12-03T10:15:30],
        comment_by='sample_user',
        comment='Sample comment'
    ),
    CommentSpec(
        date=Some date/time value: [2007-12-03T10:15:30],
        comment_by='sample_user',
        comment='Sample comment'
    )
]

call_result = await update_table_comments.asyncio(
    'sample_connection',
    'sample_schema',
    'sample_table',
    client=dqops_client,
    json_body=request_body
)

Execution

from dqops import client
from dqops.client.api.tables import update_table_comments
from dqops.client.models import CommentSpec

token = 's4mp13_4u7h_70k3n'

dqops_client = client.AuthenticatedClient(
    'http://localhost:8888/',
    token=token
)

request_body = [
    CommentSpec(
        date=Some date/time value: [2007-12-03T10:15:30],
        comment_by='sample_user',
        comment='Sample comment'
    ),
    CommentSpec(
        date=Some date/time value: [2007-12-03T10:15:30],
        comment_by='sample_user',
        comment='Sample comment'
    ),
    CommentSpec(
        date=Some date/time value: [2007-12-03T10:15:30],
        comment_by='sample_user',
        comment='Sample comment'
    )
]

call_result = update_table_comments.sync(
    'sample_connection',
    'sample_schema',
    'sample_table',
    client=dqops_client,
    json_body=request_body
)

Execution

from dqops import client
from dqops.client.api.tables import update_table_comments
from dqops.client.models import CommentSpec

token = 's4mp13_4u7h_70k3n'

dqops_client = client.AuthenticatedClient(
    'http://localhost:8888/',
    token=token
)

request_body = [
    CommentSpec(
        date=Some date/time value: [2007-12-03T10:15:30],
        comment_by='sample_user',
        comment='Sample comment'
    ),
    CommentSpec(
        date=Some date/time value: [2007-12-03T10:15:30],
        comment_by='sample_user',
        comment='Sample comment'
    ),
    CommentSpec(
        date=Some date/time value: [2007-12-03T10:15:30],
        comment_by='sample_user',
        comment='Sample comment'
    )
]

call_result = await update_table_comments.asyncio(
    'sample_connection',
    'sample_schema',
    'sample_table',
    client=dqops_client,
    json_body=request_body
)

update_table_daily_monitoring_checks

Updates the list of daily table level data quality monitoring on an existing table.

Follow the link to see the source code on GitHub.

PUT 

http://localhost:8888/api/connections/{connectionName}/schemas/{schemaName}/tables/{tableName}/monitoring/daily

Parameters of this method are described below

 Property name   Description                       Data type   Required 
connection_name Connection name string
schema_name Schema name string
table_name Table name string

Request body

 Description                       Data type   Required 
Configuration of daily table level data quality monitoring to store or an empty object to remove all data quality monitoring on the table level (column level monitoring are preserved). TableDailyMonitoringCheckCategoriesSpec

Usage examples

Execution

curl -X PUT http://localhost:8888/api/connections/sample_connection/schemas/sample_schema/tables/sample_table/monitoring/daily^
    -H "Accept: application/json"^
    -H "Content-Type: application/json"^
    -d^
    "{\"volume\":{\"daily_row_count\":{\"error\":{\"min_count\":1}}}}"

Execution

from dqops import client
from dqops.client.api.tables import update_table_daily_monitoring_checks
from dqops.client.models import MinCountRule1ParametersSpec, \
                                TableComparisonDailyMonitoringChecksSpecMap, \
                                TableDailyMonitoringCheckCategoriesSpec, \
                                TableRowCountCheckSpec, \
                                TableVolumeDailyMonitoringChecksSpec, \
                                TableVolumeRowCountSensorParametersSpec

dqops_client = client.Client(
    'http://localhost:8888/'
)

request_body = TableDailyMonitoringCheckCategoriesSpec(
    volume=TableVolumeDailyMonitoringChecksSpec(
        daily_row_count=TableRowCountCheckSpec(
            parameters=TableVolumeRowCountSensorParametersSpec(),
            error=MinCountRule1ParametersSpec(min_count=1),
            disabled=False,
            exclude_from_kpi=False,
            include_in_sla=False,
            always_collect_error_samples=False,
            do_not_schedule=False
        )
    ),
    comparisons=TableComparisonDailyMonitoringChecksSpecMap()
)

call_result = update_table_daily_monitoring_checks.sync(
    'sample_connection',
    'sample_schema',
    'sample_table',
    client=dqops_client,
    json_body=request_body
)

Execution

from dqops import client
from dqops.client.api.tables import update_table_daily_monitoring_checks
from dqops.client.models import MinCountRule1ParametersSpec, \
                                TableComparisonDailyMonitoringChecksSpecMap, \
                                TableDailyMonitoringCheckCategoriesSpec, \
                                TableRowCountCheckSpec, \
                                TableVolumeDailyMonitoringChecksSpec, \
                                TableVolumeRowCountSensorParametersSpec

dqops_client = client.Client(
    'http://localhost:8888/'
)

request_body = TableDailyMonitoringCheckCategoriesSpec(
    volume=TableVolumeDailyMonitoringChecksSpec(
        daily_row_count=TableRowCountCheckSpec(
            parameters=TableVolumeRowCountSensorParametersSpec(),
            error=MinCountRule1ParametersSpec(min_count=1),
            disabled=False,
            exclude_from_kpi=False,
            include_in_sla=False,
            always_collect_error_samples=False,
            do_not_schedule=False
        )
    ),
    comparisons=TableComparisonDailyMonitoringChecksSpecMap()
)

call_result = await update_table_daily_monitoring_checks.asyncio(
    'sample_connection',
    'sample_schema',
    'sample_table',
    client=dqops_client,
    json_body=request_body
)

Execution

from dqops import client
from dqops.client.api.tables import update_table_daily_monitoring_checks
from dqops.client.models import MinCountRule1ParametersSpec, \
                                TableComparisonDailyMonitoringChecksSpecMap, \
                                TableDailyMonitoringCheckCategoriesSpec, \
                                TableRowCountCheckSpec, \
                                TableVolumeDailyMonitoringChecksSpec, \
                                TableVolumeRowCountSensorParametersSpec

token = 's4mp13_4u7h_70k3n'

dqops_client = client.AuthenticatedClient(
    'http://localhost:8888/',
    token=token
)

request_body = TableDailyMonitoringCheckCategoriesSpec(
    volume=TableVolumeDailyMonitoringChecksSpec(
        daily_row_count=TableRowCountCheckSpec(
            parameters=TableVolumeRowCountSensorParametersSpec(),
            error=MinCountRule1ParametersSpec(min_count=1),
            disabled=False,
            exclude_from_kpi=False,
            include_in_sla=False,
            always_collect_error_samples=False,
            do_not_schedule=False
        )
    ),
    comparisons=TableComparisonDailyMonitoringChecksSpecMap()
)

call_result = update_table_daily_monitoring_checks.sync(
    'sample_connection',
    'sample_schema',
    'sample_table',
    client=dqops_client,
    json_body=request_body
)

Execution

from dqops import client
from dqops.client.api.tables import update_table_daily_monitoring_checks
from dqops.client.models import MinCountRule1ParametersSpec, \
                                TableComparisonDailyMonitoringChecksSpecMap, \
                                TableDailyMonitoringCheckCategoriesSpec, \
                                TableRowCountCheckSpec, \
                                TableVolumeDailyMonitoringChecksSpec, \
                                TableVolumeRowCountSensorParametersSpec

token = 's4mp13_4u7h_70k3n'

dqops_client = client.AuthenticatedClient(
    'http://localhost:8888/',
    token=token
)

request_body = TableDailyMonitoringCheckCategoriesSpec(
    volume=TableVolumeDailyMonitoringChecksSpec(
        daily_row_count=TableRowCountCheckSpec(
            parameters=TableVolumeRowCountSensorParametersSpec(),
            error=MinCountRule1ParametersSpec(min_count=1),
            disabled=False,
            exclude_from_kpi=False,
            include_in_sla=False,
            always_collect_error_samples=False,
            do_not_schedule=False
        )
    ),
    comparisons=TableComparisonDailyMonitoringChecksSpecMap()
)

call_result = await update_table_daily_monitoring_checks.asyncio(
    'sample_connection',
    'sample_schema',
    'sample_table',
    client=dqops_client,
    json_body=request_body
)

update_table_default_grouping_configuration

Updates the default data grouping configuration at a table level.

Follow the link to see the source code on GitHub.

PUT 

http://localhost:8888/api/connections/{connectionName}/schemas/{schemaName}/tables/{tableName}/defaultgroupingconfiguration

Parameters of this method are described below

 Property name   Description                       Data type   Required 
connection_name Connection name string
schema_name Schema name string
table_name Table name string

Request body

 Description                       Data type   Required 
Default data grouping configuration to store or an empty object to clear the data grouping configuration on a table level DataGroupingConfigurationSpec

Usage examples

Execution

curl -X PUT http://localhost:8888/api/connections/sample_connection/schemas/sample_schema/tables/sample_table/defaultgroupingconfiguration^
    -H "Accept: application/json"^
    -H "Content-Type: application/json"^
    -d^
    "{\"level_3\":{\"source\":\"column_value\",\"column\":\"sample_column\"}}"

Execution

from dqops import client
from dqops.client.api.tables import update_table_default_grouping_configuration
from dqops.client.models import DataGroupingConfigurationSpec, \
                                DataGroupingDimensionSource, \
                                DataGroupingDimensionSpec

dqops_client = client.Client(
    'http://localhost:8888/'
)

request_body = DataGroupingConfigurationSpec(
    level_3=DataGroupingDimensionSpec(
        source=DataGroupingDimensionSource.COLUMN_VALUE,
        column='sample_column'
    )
)

call_result = update_table_default_grouping_configuration.sync(
    'sample_connection',
    'sample_schema',
    'sample_table',
    client=dqops_client,
    json_body=request_body
)

Execution

from dqops import client
from dqops.client.api.tables import update_table_default_grouping_configuration
from dqops.client.models import DataGroupingConfigurationSpec, \
                                DataGroupingDimensionSource, \
                                DataGroupingDimensionSpec

dqops_client = client.Client(
    'http://localhost:8888/'
)

request_body = DataGroupingConfigurationSpec(
    level_3=DataGroupingDimensionSpec(
        source=DataGroupingDimensionSource.COLUMN_VALUE,
        column='sample_column'
    )
)

call_result = await update_table_default_grouping_configuration.asyncio(
    'sample_connection',
    'sample_schema',
    'sample_table',
    client=dqops_client,
    json_body=request_body
)

Execution

from dqops import client
from dqops.client.api.tables import update_table_default_grouping_configuration
from dqops.client.models import DataGroupingConfigurationSpec, \
                                DataGroupingDimensionSource, \
                                DataGroupingDimensionSpec

token = 's4mp13_4u7h_70k3n'

dqops_client = client.AuthenticatedClient(
    'http://localhost:8888/',
    token=token
)

request_body = DataGroupingConfigurationSpec(
    level_3=DataGroupingDimensionSpec(
        source=DataGroupingDimensionSource.COLUMN_VALUE,
        column='sample_column'
    )
)

call_result = update_table_default_grouping_configuration.sync(
    'sample_connection',
    'sample_schema',
    'sample_table',
    client=dqops_client,
    json_body=request_body
)

Execution

from dqops import client
from dqops.client.api.tables import update_table_default_grouping_configuration
from dqops.client.models import DataGroupingConfigurationSpec, \
                                DataGroupingDimensionSource, \
                                DataGroupingDimensionSpec

token = 's4mp13_4u7h_70k3n'

dqops_client = client.AuthenticatedClient(
    'http://localhost:8888/',
    token=token
)

request_body = DataGroupingConfigurationSpec(
    level_3=DataGroupingDimensionSpec(
        source=DataGroupingDimensionSource.COLUMN_VALUE,
        column='sample_column'
    )
)

call_result = await update_table_default_grouping_configuration.asyncio(
    'sample_connection',
    'sample_schema',
    'sample_table',
    client=dqops_client,
    json_body=request_body
)

update_table_incident_grouping

Updates the configuration of incident grouping on a table.

Follow the link to see the source code on GitHub.

PUT 

http://localhost:8888/api/connections/{connectionName}/schemas/{schemaName}/tables/{tableName}/incidentgrouping

Parameters of this method are described below

 Property name   Description                       Data type   Required 
connection_name Connection name string
schema_name Schema name string
table_name Table name string

Request body

 Description                       Data type   Required 
New configuration of the table's incident grouping TableIncidentGroupingSpec

Usage examples

Execution

curl -X PUT http://localhost:8888/api/connections/sample_connection/schemas/sample_schema/tables/sample_table/incidentgrouping^
    -H "Accept: application/json"^
    -H "Content-Type: application/json"^
    -d^
    "{\"grouping_level\":\"table_dimension\",\"minimum_severity\":\"warning\",\"divide_by_data_group\":true,\"disabled\":false}"

Execution

from dqops import client
from dqops.client.api.tables import update_table_incident_grouping
from dqops.client.models import IncidentGroupingLevel, \
                                MinimumGroupingSeverityLevel, \
                                TableIncidentGroupingSpec

dqops_client = client.Client(
    'http://localhost:8888/'
)

request_body = TableIncidentGroupingSpec(
    grouping_level=IncidentGroupingLevel.TABLE_DIMENSION,
    minimum_severity=MinimumGroupingSeverityLevel.WARNING,
    divide_by_data_group=True,
    disabled=False
)

call_result = update_table_incident_grouping.sync(
    'sample_connection',
    'sample_schema',
    'sample_table',
    client=dqops_client,
    json_body=request_body
)

Execution

from dqops import client
from dqops.client.api.tables import update_table_incident_grouping
from dqops.client.models import IncidentGroupingLevel, \
                                MinimumGroupingSeverityLevel, \
                                TableIncidentGroupingSpec

dqops_client = client.Client(
    'http://localhost:8888/'
)

request_body = TableIncidentGroupingSpec(
    grouping_level=IncidentGroupingLevel.TABLE_DIMENSION,
    minimum_severity=MinimumGroupingSeverityLevel.WARNING,
    divide_by_data_group=True,
    disabled=False
)

call_result = await update_table_incident_grouping.asyncio(
    'sample_connection',
    'sample_schema',
    'sample_table',
    client=dqops_client,
    json_body=request_body
)

Execution

from dqops import client
from dqops.client.api.tables import update_table_incident_grouping
from dqops.client.models import IncidentGroupingLevel, \
                                MinimumGroupingSeverityLevel, \
                                TableIncidentGroupingSpec

token = 's4mp13_4u7h_70k3n'

dqops_client = client.AuthenticatedClient(
    'http://localhost:8888/',
    token=token
)

request_body = TableIncidentGroupingSpec(
    grouping_level=IncidentGroupingLevel.TABLE_DIMENSION,
    minimum_severity=MinimumGroupingSeverityLevel.WARNING,
    divide_by_data_group=True,
    disabled=False
)

call_result = update_table_incident_grouping.sync(
    'sample_connection',
    'sample_schema',
    'sample_table',
    client=dqops_client,
    json_body=request_body
)

Execution

from dqops import client
from dqops.client.api.tables import update_table_incident_grouping
from dqops.client.models import IncidentGroupingLevel, \
                                MinimumGroupingSeverityLevel, \
                                TableIncidentGroupingSpec

token = 's4mp13_4u7h_70k3n'

dqops_client = client.AuthenticatedClient(
    'http://localhost:8888/',
    token=token
)

request_body = TableIncidentGroupingSpec(
    grouping_level=IncidentGroupingLevel.TABLE_DIMENSION,
    minimum_severity=MinimumGroupingSeverityLevel.WARNING,
    divide_by_data_group=True,
    disabled=False
)

call_result = await update_table_incident_grouping.asyncio(
    'sample_connection',
    'sample_schema',
    'sample_table',
    client=dqops_client,
    json_body=request_body
)

update_table_labels

Updates the list of assigned labels of an existing table.

Follow the link to see the source code on GitHub.

PUT 

http://localhost:8888/api/connections/{connectionName}/schemas/{schemaName}/tables/{tableName}/labels

Parameters of this method are described below

 Property name   Description                       Data type   Required 
connection_name Connection name string
schema_name Schema name string
table_name Table name string

Request body

 Description                       Data type   Required 
List of labels to attach (replace) on a table or an empty object to clear the list of labels on a table List[string]

Usage examples

Execution

curl -X PUT http://localhost:8888/api/connections/sample_connection/schemas/sample_schema/tables/sample_table/labels^
    -H "Accept: application/json"^
    -H "Content-Type: application/json"^
    -d^
    "[\"sampleString_1\",\"sampleString_2\",\"sampleString_3\"]"

Execution

from dqops import client
from dqops.client.api.tables import update_table_labels

dqops_client = client.Client(
    'http://localhost:8888/'
)

request_body = [
    'sampleString_1',
    'sampleString_2',
    'sampleString_3'
]

call_result = update_table_labels.sync(
    'sample_connection',
    'sample_schema',
    'sample_table',
    client=dqops_client,
    json_body=request_body
)

Execution

from dqops import client
from dqops.client.api.tables import update_table_labels

dqops_client = client.Client(
    'http://localhost:8888/'
)

request_body = [
    'sampleString_1',
    'sampleString_2',
    'sampleString_3'
]

call_result = await update_table_labels.asyncio(
    'sample_connection',
    'sample_schema',
    'sample_table',
    client=dqops_client,
    json_body=request_body
)

Execution

from dqops import client
from dqops.client.api.tables import update_table_labels

token = 's4mp13_4u7h_70k3n'

dqops_client = client.AuthenticatedClient(
    'http://localhost:8888/',
    token=token
)

request_body = [
    'sampleString_1',
    'sampleString_2',
    'sampleString_3'
]

call_result = update_table_labels.sync(
    'sample_connection',
    'sample_schema',
    'sample_table',
    client=dqops_client,
    json_body=request_body
)

Execution

from dqops import client
from dqops.client.api.tables import update_table_labels

token = 's4mp13_4u7h_70k3n'

dqops_client = client.AuthenticatedClient(
    'http://localhost:8888/',
    token=token
)

request_body = [
    'sampleString_1',
    'sampleString_2',
    'sampleString_3'
]

call_result = await update_table_labels.asyncio(
    'sample_connection',
    'sample_schema',
    'sample_table',
    client=dqops_client,
    json_body=request_body
)

update_table_monitoring_checks_model

Updates the data quality monitoring from a model that contains a patch with changes.

Follow the link to see the source code on GitHub.

PUT 

http://localhost:8888/api/connections/{connectionName}/schemas/{schemaName}/tables/{tableName}/monitoring/{timeScale}/model

Parameters of this method are described below

 Property name   Description                       Data type   Required 
connection_name Connection name string
schema_name Schema name string
table_name Table name string
time_scale Time scale CheckTimeScale

Request body

 Description                       Data type   Required 
Model with the changes to be applied to the data quality monitoring configuration. CheckContainerModel

Usage examples

Execution

curl -X PUT http://localhost:8888/api/connections/sample_connection/schemas/sample_schema/tables/sample_table/monitoring/daily/model^
    -H "Accept: application/json"^
    -H "Content-Type: application/json"^
    -d^
    "{\"categories\":[{\"category\":\"sample_category\",\"help_text\":\"Sample help text\",\"checks\":[{\"check_name\":\"sample_check\",\"help_text\":\"Sample help text\",\"sensor_parameters\":[],\"sensor_name\":\"sample_target/sample_category/table/volume/row_count\",\"quality_dimension\":\"sample_quality_dimension\",\"supports_error_sampling\":false,\"supports_grouping\":false,\"default_severity\":\"error\",\"disabled\":false,\"exclude_from_kpi\":false,\"include_in_sla\":false,\"configured\":false,\"can_edit\":false,\"can_run_checks\":false,\"can_delete_data\":false}]}],\"can_edit\":false,\"can_run_checks\":false,\"can_delete_data\":false}"

Execution

from dqops import client
from dqops.client.api.tables import update_table_monitoring_checks_model
from dqops.client.models import CheckContainerModel, \
                                CheckModel, \
                                CheckTimeScale, \
                                DefaultRuleSeverityLevel, \
                                FieldModel, \
                                QualityCategoryModel

dqops_client = client.Client(
    'http://localhost:8888/'
)

request_body = CheckContainerModel(
    categories=[
        QualityCategoryModel(
            category='sample_category',
            help_text='Sample help text',
            checks=[
                CheckModel(
                    check_name='sample_check',
                    help_text='Sample help text',
                    sensor_parameters=[

                    ],
                    sensor_name='sample_target/sample_category/table/volume/row_count',
                    quality_dimension='sample_quality_dimension',
                    supports_error_sampling=False,
                    supports_grouping=False,
                    standard=False,
                    default_check=False,
                    default_severity=DefaultRuleSeverityLevel.ERROR,
                    disabled=False,
                    exclude_from_kpi=False,
                    include_in_sla=False,
                    configured=False,
                    always_collect_error_samples=False,
                    do_not_schedule=False,
                    can_edit=False,
                    can_run_checks=False,
                    can_delete_data=False
                )
            ]
        )
    ],
    can_edit=False,
    can_run_checks=False,
    can_delete_data=False
)

call_result = update_table_monitoring_checks_model.sync(
    'sample_connection',
    'sample_schema',
    'sample_table',
    CheckTimeScale.daily,
    client=dqops_client,
    json_body=request_body
)

Execution

from dqops import client
from dqops.client.api.tables import update_table_monitoring_checks_model
from dqops.client.models import CheckContainerModel, \
                                CheckModel, \
                                CheckTimeScale, \
                                DefaultRuleSeverityLevel, \
                                FieldModel, \
                                QualityCategoryModel

dqops_client = client.Client(
    'http://localhost:8888/'
)

request_body = CheckContainerModel(
    categories=[
        QualityCategoryModel(
            category='sample_category',
            help_text='Sample help text',
            checks=[
                CheckModel(
                    check_name='sample_check',
                    help_text='Sample help text',
                    sensor_parameters=[

                    ],
                    sensor_name='sample_target/sample_category/table/volume/row_count',
                    quality_dimension='sample_quality_dimension',
                    supports_error_sampling=False,
                    supports_grouping=False,
                    standard=False,
                    default_check=False,
                    default_severity=DefaultRuleSeverityLevel.ERROR,
                    disabled=False,
                    exclude_from_kpi=False,
                    include_in_sla=False,
                    configured=False,
                    always_collect_error_samples=False,
                    do_not_schedule=False,
                    can_edit=False,
                    can_run_checks=False,
                    can_delete_data=False
                )
            ]
        )
    ],
    can_edit=False,
    can_run_checks=False,
    can_delete_data=False
)

call_result = await update_table_monitoring_checks_model.asyncio(
    'sample_connection',
    'sample_schema',
    'sample_table',
    CheckTimeScale.daily,
    client=dqops_client,
    json_body=request_body
)

Execution

from dqops import client
from dqops.client.api.tables import update_table_monitoring_checks_model
from dqops.client.models import CheckContainerModel, \
                                CheckModel, \
                                CheckTimeScale, \
                                DefaultRuleSeverityLevel, \
                                FieldModel, \
                                QualityCategoryModel

token = 's4mp13_4u7h_70k3n'

dqops_client = client.AuthenticatedClient(
    'http://localhost:8888/',
    token=token
)

request_body = CheckContainerModel(
    categories=[
        QualityCategoryModel(
            category='sample_category',
            help_text='Sample help text',
            checks=[
                CheckModel(
                    check_name='sample_check',
                    help_text='Sample help text',
                    sensor_parameters=[

                    ],
                    sensor_name='sample_target/sample_category/table/volume/row_count',
                    quality_dimension='sample_quality_dimension',
                    supports_error_sampling=False,
                    supports_grouping=False,
                    standard=False,
                    default_check=False,
                    default_severity=DefaultRuleSeverityLevel.ERROR,
                    disabled=False,
                    exclude_from_kpi=False,
                    include_in_sla=False,
                    configured=False,
                    always_collect_error_samples=False,
                    do_not_schedule=False,
                    can_edit=False,
                    can_run_checks=False,
                    can_delete_data=False
                )
            ]
        )
    ],
    can_edit=False,
    can_run_checks=False,
    can_delete_data=False
)

call_result = update_table_monitoring_checks_model.sync(
    'sample_connection',
    'sample_schema',
    'sample_table',
    CheckTimeScale.daily,
    client=dqops_client,
    json_body=request_body
)

Execution

from dqops import client
from dqops.client.api.tables import update_table_monitoring_checks_model
from dqops.client.models import CheckContainerModel, \
                                CheckModel, \
                                CheckTimeScale, \
                                DefaultRuleSeverityLevel, \
                                FieldModel, \
                                QualityCategoryModel

token = 's4mp13_4u7h_70k3n'

dqops_client = client.AuthenticatedClient(
    'http://localhost:8888/',
    token=token
)

request_body = CheckContainerModel(
    categories=[
        QualityCategoryModel(
            category='sample_category',
            help_text='Sample help text',
            checks=[
                CheckModel(
                    check_name='sample_check',
                    help_text='Sample help text',
                    sensor_parameters=[

                    ],
                    sensor_name='sample_target/sample_category/table/volume/row_count',
                    quality_dimension='sample_quality_dimension',
                    supports_error_sampling=False,
                    supports_grouping=False,
                    standard=False,
                    default_check=False,
                    default_severity=DefaultRuleSeverityLevel.ERROR,
                    disabled=False,
                    exclude_from_kpi=False,
                    include_in_sla=False,
                    configured=False,
                    always_collect_error_samples=False,
                    do_not_schedule=False,
                    can_edit=False,
                    can_run_checks=False,
                    can_delete_data=False
                )
            ]
        )
    ],
    can_edit=False,
    can_run_checks=False,
    can_delete_data=False
)

call_result = await update_table_monitoring_checks_model.asyncio(
    'sample_connection',
    'sample_schema',
    'sample_table',
    CheckTimeScale.daily,
    client=dqops_client,
    json_body=request_body
)

update_table_monitoring_checks_monthly

Updates the list of monthly table level data quality monitoring on an existing table.

Follow the link to see the source code on GitHub.

PUT 

http://localhost:8888/api/connections/{connectionName}/schemas/{schemaName}/tables/{tableName}/monitoring/monthly

Parameters of this method are described below

 Property name   Description                       Data type   Required 
connection_name Connection name string
schema_name Schema name string
table_name Table name string

Request body

 Description                       Data type   Required 
Configuration of monthly table level data quality monitoring to store or an empty object to remove all data quality monitoring on the table level (column level monitoring are preserved). TableMonthlyMonitoringCheckCategoriesSpec

Usage examples

Execution

curl -X PUT http://localhost:8888/api/connections/sample_connection/schemas/sample_schema/tables/sample_table/monitoring/monthly^
    -H "Accept: application/json"^
    -H "Content-Type: application/json"^
    -d^
    "{\"volume\":{\"monthly_row_count\":{\"error\":{\"min_count\":1}}}}"

Execution

from dqops import client
from dqops.client.api.tables import update_table_monitoring_checks_monthly
from dqops.client.models import MinCountRule1ParametersSpec, \
                                TableComparisonMonthlyMonitoringChecksSpecMap, \
                                TableMonthlyMonitoringCheckCategoriesSpec, \
                                TableRowCountCheckSpec, \
                                TableVolumeMonthlyMonitoringChecksSpec, \
                                TableVolumeRowCountSensorParametersSpec

dqops_client = client.Client(
    'http://localhost:8888/'
)

request_body = TableMonthlyMonitoringCheckCategoriesSpec(
    volume=TableVolumeMonthlyMonitoringChecksSpec(
        monthly_row_count=TableRowCountCheckSpec(
            parameters=TableVolumeRowCountSensorParametersSpec(),
            error=MinCountRule1ParametersSpec(min_count=1),
            disabled=False,
            exclude_from_kpi=False,
            include_in_sla=False,
            always_collect_error_samples=False,
            do_not_schedule=False
        )
    ),
    comparisons=TableComparisonMonthlyMonitoringChecksSpecMap()
)

call_result = update_table_monitoring_checks_monthly.sync(
    'sample_connection',
    'sample_schema',
    'sample_table',
    client=dqops_client,
    json_body=request_body
)

Execution

from dqops import client
from dqops.client.api.tables import update_table_monitoring_checks_monthly
from dqops.client.models import MinCountRule1ParametersSpec, \
                                TableComparisonMonthlyMonitoringChecksSpecMap, \
                                TableMonthlyMonitoringCheckCategoriesSpec, \
                                TableRowCountCheckSpec, \
                                TableVolumeMonthlyMonitoringChecksSpec, \
                                TableVolumeRowCountSensorParametersSpec

dqops_client = client.Client(
    'http://localhost:8888/'
)

request_body = TableMonthlyMonitoringCheckCategoriesSpec(
    volume=TableVolumeMonthlyMonitoringChecksSpec(
        monthly_row_count=TableRowCountCheckSpec(
            parameters=TableVolumeRowCountSensorParametersSpec(),
            error=MinCountRule1ParametersSpec(min_count=1),
            disabled=False,
            exclude_from_kpi=False,
            include_in_sla=False,
            always_collect_error_samples=False,
            do_not_schedule=False
        )
    ),
    comparisons=TableComparisonMonthlyMonitoringChecksSpecMap()
)

call_result = await update_table_monitoring_checks_monthly.asyncio(
    'sample_connection',
    'sample_schema',
    'sample_table',
    client=dqops_client,
    json_body=request_body
)

Execution

from dqops import client
from dqops.client.api.tables import update_table_monitoring_checks_monthly
from dqops.client.models import MinCountRule1ParametersSpec, \
                                TableComparisonMonthlyMonitoringChecksSpecMap, \
                                TableMonthlyMonitoringCheckCategoriesSpec, \
                                TableRowCountCheckSpec, \
                                TableVolumeMonthlyMonitoringChecksSpec, \
                                TableVolumeRowCountSensorParametersSpec

token = 's4mp13_4u7h_70k3n'

dqops_client = client.AuthenticatedClient(
    'http://localhost:8888/',
    token=token
)

request_body = TableMonthlyMonitoringCheckCategoriesSpec(
    volume=TableVolumeMonthlyMonitoringChecksSpec(
        monthly_row_count=TableRowCountCheckSpec(
            parameters=TableVolumeRowCountSensorParametersSpec(),
            error=MinCountRule1ParametersSpec(min_count=1),
            disabled=False,
            exclude_from_kpi=False,
            include_in_sla=False,
            always_collect_error_samples=False,
            do_not_schedule=False
        )
    ),
    comparisons=TableComparisonMonthlyMonitoringChecksSpecMap()
)

call_result = update_table_monitoring_checks_monthly.sync(
    'sample_connection',
    'sample_schema',
    'sample_table',
    client=dqops_client,
    json_body=request_body
)

Execution

from dqops import client
from dqops.client.api.tables import update_table_monitoring_checks_monthly
from dqops.client.models import MinCountRule1ParametersSpec, \
                                TableComparisonMonthlyMonitoringChecksSpecMap, \
                                TableMonthlyMonitoringCheckCategoriesSpec, \
                                TableRowCountCheckSpec, \
                                TableVolumeMonthlyMonitoringChecksSpec, \
                                TableVolumeRowCountSensorParametersSpec

token = 's4mp13_4u7h_70k3n'

dqops_client = client.AuthenticatedClient(
    'http://localhost:8888/',
    token=token
)

request_body = TableMonthlyMonitoringCheckCategoriesSpec(
    volume=TableVolumeMonthlyMonitoringChecksSpec(
        monthly_row_count=TableRowCountCheckSpec(
            parameters=TableVolumeRowCountSensorParametersSpec(),
            error=MinCountRule1ParametersSpec(min_count=1),
            disabled=False,
            exclude_from_kpi=False,
            include_in_sla=False,
            always_collect_error_samples=False,
            do_not_schedule=False
        )
    ),
    comparisons=TableComparisonMonthlyMonitoringChecksSpecMap()
)

call_result = await update_table_monitoring_checks_monthly.asyncio(
    'sample_connection',
    'sample_schema',
    'sample_table',
    client=dqops_client,
    json_body=request_body
)

update_table_partitioned_checks_daily

Updates the list of daily table level data quality partitioned checks on an existing table.

Follow the link to see the source code on GitHub.

PUT 

http://localhost:8888/api/connections/{connectionName}/schemas/{schemaName}/tables/{tableName}/partitioned/daily

Parameters of this method are described below

 Property name   Description                       Data type   Required 
connection_name Connection name string
schema_name Schema name string
table_name Table name string

Request body

 Description                       Data type   Required 
Configuration of daily table level data quality partitioned checks to store or an empty object to remove all data quality partitioned checks on the table level (column level partitioned checks are preserved). TableDailyPartitionedCheckCategoriesSpec

Usage examples

Execution

curl -X PUT http://localhost:8888/api/connections/sample_connection/schemas/sample_schema/tables/sample_table/partitioned/daily^
    -H "Accept: application/json"^
    -H "Content-Type: application/json"^
    -d^
    "{\"volume\":{\"daily_partition_row_count\":{\"error\":{\"min_count\":1}}}}"

Execution

from dqops import client
from dqops.client.api.tables import update_table_partitioned_checks_daily
from dqops.client.models import MinCountRule1ParametersSpec, \
                                TableComparisonDailyPartitionedChecksSpecMap, \
                                TableDailyPartitionedCheckCategoriesSpec, \
                                TableRowCountCheckSpec, \
                                TableVolumeDailyPartitionedChecksSpec, \
                                TableVolumeRowCountSensorParametersSpec

dqops_client = client.Client(
    'http://localhost:8888/'
)

request_body = TableDailyPartitionedCheckCategoriesSpec(
    volume=TableVolumeDailyPartitionedChecksSpec(
        daily_partition_row_count=TableRowCountCheckSpec(
            parameters=TableVolumeRowCountSensorParametersSpec(),
            error=MinCountRule1ParametersSpec(min_count=1),
            disabled=False,
            exclude_from_kpi=False,
            include_in_sla=False,
            always_collect_error_samples=False,
            do_not_schedule=False
        )
    ),
    comparisons=TableComparisonDailyPartitionedChecksSpecMap()
)

call_result = update_table_partitioned_checks_daily.sync(
    'sample_connection',
    'sample_schema',
    'sample_table',
    client=dqops_client,
    json_body=request_body
)

Execution

from dqops import client
from dqops.client.api.tables import update_table_partitioned_checks_daily
from dqops.client.models import MinCountRule1ParametersSpec, \
                                TableComparisonDailyPartitionedChecksSpecMap, \
                                TableDailyPartitionedCheckCategoriesSpec, \
                                TableRowCountCheckSpec, \
                                TableVolumeDailyPartitionedChecksSpec, \
                                TableVolumeRowCountSensorParametersSpec

dqops_client = client.Client(
    'http://localhost:8888/'
)

request_body = TableDailyPartitionedCheckCategoriesSpec(
    volume=TableVolumeDailyPartitionedChecksSpec(
        daily_partition_row_count=TableRowCountCheckSpec(
            parameters=TableVolumeRowCountSensorParametersSpec(),
            error=MinCountRule1ParametersSpec(min_count=1),
            disabled=False,
            exclude_from_kpi=False,
            include_in_sla=False,
            always_collect_error_samples=False,
            do_not_schedule=False
        )
    ),
    comparisons=TableComparisonDailyPartitionedChecksSpecMap()
)

call_result = await update_table_partitioned_checks_daily.asyncio(
    'sample_connection',
    'sample_schema',
    'sample_table',
    client=dqops_client,
    json_body=request_body
)

Execution

from dqops import client
from dqops.client.api.tables import update_table_partitioned_checks_daily
from dqops.client.models import MinCountRule1ParametersSpec, \
                                TableComparisonDailyPartitionedChecksSpecMap, \
                                TableDailyPartitionedCheckCategoriesSpec, \
                                TableRowCountCheckSpec, \
                                TableVolumeDailyPartitionedChecksSpec, \
                                TableVolumeRowCountSensorParametersSpec

token = 's4mp13_4u7h_70k3n'

dqops_client = client.AuthenticatedClient(
    'http://localhost:8888/',
    token=token
)

request_body = TableDailyPartitionedCheckCategoriesSpec(
    volume=TableVolumeDailyPartitionedChecksSpec(
        daily_partition_row_count=TableRowCountCheckSpec(
            parameters=TableVolumeRowCountSensorParametersSpec(),
            error=MinCountRule1ParametersSpec(min_count=1),
            disabled=False,
            exclude_from_kpi=False,
            include_in_sla=False,
            always_collect_error_samples=False,
            do_not_schedule=False
        )
    ),
    comparisons=TableComparisonDailyPartitionedChecksSpecMap()
)

call_result = update_table_partitioned_checks_daily.sync(
    'sample_connection',
    'sample_schema',
    'sample_table',
    client=dqops_client,
    json_body=request_body
)

Execution

from dqops import client
from dqops.client.api.tables import update_table_partitioned_checks_daily
from dqops.client.models import MinCountRule1ParametersSpec, \
                                TableComparisonDailyPartitionedChecksSpecMap, \
                                TableDailyPartitionedCheckCategoriesSpec, \
                                TableRowCountCheckSpec, \
                                TableVolumeDailyPartitionedChecksSpec, \
                                TableVolumeRowCountSensorParametersSpec

token = 's4mp13_4u7h_70k3n'

dqops_client = client.AuthenticatedClient(
    'http://localhost:8888/',
    token=token
)

request_body = TableDailyPartitionedCheckCategoriesSpec(
    volume=TableVolumeDailyPartitionedChecksSpec(
        daily_partition_row_count=TableRowCountCheckSpec(
            parameters=TableVolumeRowCountSensorParametersSpec(),
            error=MinCountRule1ParametersSpec(min_count=1),
            disabled=False,
            exclude_from_kpi=False,
            include_in_sla=False,
            always_collect_error_samples=False,
            do_not_schedule=False
        )
    ),
    comparisons=TableComparisonDailyPartitionedChecksSpecMap()
)

call_result = await update_table_partitioned_checks_daily.asyncio(
    'sample_connection',
    'sample_schema',
    'sample_table',
    client=dqops_client,
    json_body=request_body
)

update_table_partitioned_checks_model

Updates the data quality partitioned checks from a model that contains a patch with changes.

Follow the link to see the source code on GitHub.

PUT 

http://localhost:8888/api/connections/{connectionName}/schemas/{schemaName}/tables/{tableName}/partitioned/{timeScale}/model

Parameters of this method are described below

 Property name   Description                       Data type   Required 
connection_name Connection name string
schema_name Schema name string
table_name Table name string
time_scale Time scale CheckTimeScale

Request body

 Description                       Data type   Required 
Model with the changes to be applied to the data quality partitioned checks configuration. CheckContainerModel

Usage examples

Execution

curl -X PUT http://localhost:8888/api/connections/sample_connection/schemas/sample_schema/tables/sample_table/partitioned/daily/model^
    -H "Accept: application/json"^
    -H "Content-Type: application/json"^
    -d^
    "{\"categories\":[{\"category\":\"sample_category\",\"help_text\":\"Sample help text\",\"checks\":[{\"check_name\":\"sample_check\",\"help_text\":\"Sample help text\",\"sensor_parameters\":[],\"sensor_name\":\"sample_target/sample_category/table/volume/row_count\",\"quality_dimension\":\"sample_quality_dimension\",\"supports_error_sampling\":false,\"supports_grouping\":false,\"default_severity\":\"error\",\"disabled\":false,\"exclude_from_kpi\":false,\"include_in_sla\":false,\"configured\":false,\"can_edit\":false,\"can_run_checks\":false,\"can_delete_data\":false}]}],\"can_edit\":false,\"can_run_checks\":false,\"can_delete_data\":false}"

Execution

from dqops import client
from dqops.client.api.tables import update_table_partitioned_checks_model
from dqops.client.models import CheckContainerModel, \
                                CheckModel, \
                                CheckTimeScale, \
                                DefaultRuleSeverityLevel, \
                                FieldModel, \
                                QualityCategoryModel

dqops_client = client.Client(
    'http://localhost:8888/'
)

request_body = CheckContainerModel(
    categories=[
        QualityCategoryModel(
            category='sample_category',
            help_text='Sample help text',
            checks=[
                CheckModel(
                    check_name='sample_check',
                    help_text='Sample help text',
                    sensor_parameters=[

                    ],
                    sensor_name='sample_target/sample_category/table/volume/row_count',
                    quality_dimension='sample_quality_dimension',
                    supports_error_sampling=False,
                    supports_grouping=False,
                    standard=False,
                    default_check=False,
                    default_severity=DefaultRuleSeverityLevel.ERROR,
                    disabled=False,
                    exclude_from_kpi=False,
                    include_in_sla=False,
                    configured=False,
                    always_collect_error_samples=False,
                    do_not_schedule=False,
                    can_edit=False,
                    can_run_checks=False,
                    can_delete_data=False
                )
            ]
        )
    ],
    can_edit=False,
    can_run_checks=False,
    can_delete_data=False
)

call_result = update_table_partitioned_checks_model.sync(
    'sample_connection',
    'sample_schema',
    'sample_table',
    CheckTimeScale.daily,
    client=dqops_client,
    json_body=request_body
)

Execution

from dqops import client
from dqops.client.api.tables import update_table_partitioned_checks_model
from dqops.client.models import CheckContainerModel, \
                                CheckModel, \
                                CheckTimeScale, \
                                DefaultRuleSeverityLevel, \
                                FieldModel, \
                                QualityCategoryModel

dqops_client = client.Client(
    'http://localhost:8888/'
)

request_body = CheckContainerModel(
    categories=[
        QualityCategoryModel(
            category='sample_category',
            help_text='Sample help text',
            checks=[
                CheckModel(
                    check_name='sample_check',
                    help_text='Sample help text',
                    sensor_parameters=[

                    ],
                    sensor_name='sample_target/sample_category/table/volume/row_count',
                    quality_dimension='sample_quality_dimension',
                    supports_error_sampling=False,
                    supports_grouping=False,
                    standard=False,
                    default_check=False,
                    default_severity=DefaultRuleSeverityLevel.ERROR,
                    disabled=False,
                    exclude_from_kpi=False,
                    include_in_sla=False,
                    configured=False,
                    always_collect_error_samples=False,
                    do_not_schedule=False,
                    can_edit=False,
                    can_run_checks=False,
                    can_delete_data=False
                )
            ]
        )
    ],
    can_edit=False,
    can_run_checks=False,
    can_delete_data=False
)

call_result = await update_table_partitioned_checks_model.asyncio(
    'sample_connection',
    'sample_schema',
    'sample_table',
    CheckTimeScale.daily,
    client=dqops_client,
    json_body=request_body
)

Execution

from dqops import client
from dqops.client.api.tables import update_table_partitioned_checks_model
from dqops.client.models import CheckContainerModel, \
                                CheckModel, \
                                CheckTimeScale, \
                                DefaultRuleSeverityLevel, \
                                FieldModel, \
                                QualityCategoryModel

token = 's4mp13_4u7h_70k3n'

dqops_client = client.AuthenticatedClient(
    'http://localhost:8888/',
    token=token
)

request_body = CheckContainerModel(
    categories=[
        QualityCategoryModel(
            category='sample_category',
            help_text='Sample help text',
            checks=[
                CheckModel(
                    check_name='sample_check',
                    help_text='Sample help text',
                    sensor_parameters=[

                    ],
                    sensor_name='sample_target/sample_category/table/volume/row_count',
                    quality_dimension='sample_quality_dimension',
                    supports_error_sampling=False,
                    supports_grouping=False,
                    standard=False,
                    default_check=False,
                    default_severity=DefaultRuleSeverityLevel.ERROR,
                    disabled=False,
                    exclude_from_kpi=False,
                    include_in_sla=False,
                    configured=False,
                    always_collect_error_samples=False,
                    do_not_schedule=False,
                    can_edit=False,
                    can_run_checks=False,
                    can_delete_data=False
                )
            ]
        )
    ],
    can_edit=False,
    can_run_checks=False,
    can_delete_data=False
)

call_result = update_table_partitioned_checks_model.sync(
    'sample_connection',
    'sample_schema',
    'sample_table',
    CheckTimeScale.daily,
    client=dqops_client,
    json_body=request_body
)

Execution

from dqops import client
from dqops.client.api.tables import update_table_partitioned_checks_model
from dqops.client.models import CheckContainerModel, \
                                CheckModel, \
                                CheckTimeScale, \
                                DefaultRuleSeverityLevel, \
                                FieldModel, \
                                QualityCategoryModel

token = 's4mp13_4u7h_70k3n'

dqops_client = client.AuthenticatedClient(
    'http://localhost:8888/',
    token=token
)

request_body = CheckContainerModel(
    categories=[
        QualityCategoryModel(
            category='sample_category',
            help_text='Sample help text',
            checks=[
                CheckModel(
                    check_name='sample_check',
                    help_text='Sample help text',
                    sensor_parameters=[

                    ],
                    sensor_name='sample_target/sample_category/table/volume/row_count',
                    quality_dimension='sample_quality_dimension',
                    supports_error_sampling=False,
                    supports_grouping=False,
                    standard=False,
                    default_check=False,
                    default_severity=DefaultRuleSeverityLevel.ERROR,
                    disabled=False,
                    exclude_from_kpi=False,
                    include_in_sla=False,
                    configured=False,
                    always_collect_error_samples=False,
                    do_not_schedule=False,
                    can_edit=False,
                    can_run_checks=False,
                    can_delete_data=False
                )
            ]
        )
    ],
    can_edit=False,
    can_run_checks=False,
    can_delete_data=False
)

call_result = await update_table_partitioned_checks_model.asyncio(
    'sample_connection',
    'sample_schema',
    'sample_table',
    CheckTimeScale.daily,
    client=dqops_client,
    json_body=request_body
)

update_table_partitioned_checks_monthly

Updates the list of monthly table level data quality partitioned checks on an existing table.

Follow the link to see the source code on GitHub.

PUT 

http://localhost:8888/api/connections/{connectionName}/schemas/{schemaName}/tables/{tableName}/partitioned/monthly

Parameters of this method are described below

 Property name   Description                       Data type   Required 
connection_name Connection name string
schema_name Schema name string
table_name Table name string

Request body

 Description                       Data type   Required 
Configuration of monthly table level data quality partitioned checks to store or an empty object to remove all data quality partitioned checks on the table level (column level partitioned checks are preserved). TableMonthlyPartitionedCheckCategoriesSpec

Usage examples

Execution

curl -X PUT http://localhost:8888/api/connections/sample_connection/schemas/sample_schema/tables/sample_table/partitioned/monthly^
    -H "Accept: application/json"^
    -H "Content-Type: application/json"^
    -d^
    "{\"volume\":{\"monthly_partition_row_count\":{\"error\":{\"min_count\":1}}}}"

Execution

from dqops import client
from dqops.client.api.tables import update_table_partitioned_checks_monthly
from dqops.client.models import MinCountRule1ParametersSpec, \
                                TableComparisonMonthlyPartitionedChecksSpecMap, \
                                TableMonthlyPartitionedCheckCategoriesSpec, \
                                TableRowCountCheckSpec, \
                                TableVolumeMonthlyPartitionedChecksSpec, \
                                TableVolumeRowCountSensorParametersSpec

dqops_client = client.Client(
    'http://localhost:8888/'
)

request_body = TableMonthlyPartitionedCheckCategoriesSpec(
    volume=TableVolumeMonthlyPartitionedChecksSpec(
        monthly_partition_row_count=TableRowCountCheckSpec(
            parameters=TableVolumeRowCountSensorParametersSpec(),
            error=MinCountRule1ParametersSpec(min_count=1),
            disabled=False,
            exclude_from_kpi=False,
            include_in_sla=False,
            always_collect_error_samples=False,
            do_not_schedule=False
        )
    ),
    comparisons=TableComparisonMonthlyPartitionedChecksSpecMap()
)

call_result = update_table_partitioned_checks_monthly.sync(
    'sample_connection',
    'sample_schema',
    'sample_table',
    client=dqops_client,
    json_body=request_body
)

Execution

from dqops import client
from dqops.client.api.tables import update_table_partitioned_checks_monthly
from dqops.client.models import MinCountRule1ParametersSpec, \
                                TableComparisonMonthlyPartitionedChecksSpecMap, \
                                TableMonthlyPartitionedCheckCategoriesSpec, \
                                TableRowCountCheckSpec, \
                                TableVolumeMonthlyPartitionedChecksSpec, \
                                TableVolumeRowCountSensorParametersSpec

dqops_client = client.Client(
    'http://localhost:8888/'
)

request_body = TableMonthlyPartitionedCheckCategoriesSpec(
    volume=TableVolumeMonthlyPartitionedChecksSpec(
        monthly_partition_row_count=TableRowCountCheckSpec(
            parameters=TableVolumeRowCountSensorParametersSpec(),
            error=MinCountRule1ParametersSpec(min_count=1),
            disabled=False,
            exclude_from_kpi=False,
            include_in_sla=False,
            always_collect_error_samples=False,
            do_not_schedule=False
        )
    ),
    comparisons=TableComparisonMonthlyPartitionedChecksSpecMap()
)

call_result = await update_table_partitioned_checks_monthly.asyncio(
    'sample_connection',
    'sample_schema',
    'sample_table',
    client=dqops_client,
    json_body=request_body
)

Execution

from dqops import client
from dqops.client.api.tables import update_table_partitioned_checks_monthly
from dqops.client.models import MinCountRule1ParametersSpec, \
                                TableComparisonMonthlyPartitionedChecksSpecMap, \
                                TableMonthlyPartitionedCheckCategoriesSpec, \
                                TableRowCountCheckSpec, \
                                TableVolumeMonthlyPartitionedChecksSpec, \
                                TableVolumeRowCountSensorParametersSpec

token = 's4mp13_4u7h_70k3n'

dqops_client = client.AuthenticatedClient(
    'http://localhost:8888/',
    token=token
)

request_body = TableMonthlyPartitionedCheckCategoriesSpec(
    volume=TableVolumeMonthlyPartitionedChecksSpec(
        monthly_partition_row_count=TableRowCountCheckSpec(
            parameters=TableVolumeRowCountSensorParametersSpec(),
            error=MinCountRule1ParametersSpec(min_count=1),
            disabled=False,
            exclude_from_kpi=False,
            include_in_sla=False,
            always_collect_error_samples=False,
            do_not_schedule=False
        )
    ),
    comparisons=TableComparisonMonthlyPartitionedChecksSpecMap()
)

call_result = update_table_partitioned_checks_monthly.sync(
    'sample_connection',
    'sample_schema',
    'sample_table',
    client=dqops_client,
    json_body=request_body
)

Execution

from dqops import client
from dqops.client.api.tables import update_table_partitioned_checks_monthly
from dqops.client.models import MinCountRule1ParametersSpec, \
                                TableComparisonMonthlyPartitionedChecksSpecMap, \
                                TableMonthlyPartitionedCheckCategoriesSpec, \
                                TableRowCountCheckSpec, \
                                TableVolumeMonthlyPartitionedChecksSpec, \
                                TableVolumeRowCountSensorParametersSpec

token = 's4mp13_4u7h_70k3n'

dqops_client = client.AuthenticatedClient(
    'http://localhost:8888/',
    token=token
)

request_body = TableMonthlyPartitionedCheckCategoriesSpec(
    volume=TableVolumeMonthlyPartitionedChecksSpec(
        monthly_partition_row_count=TableRowCountCheckSpec(
            parameters=TableVolumeRowCountSensorParametersSpec(),
            error=MinCountRule1ParametersSpec(min_count=1),
            disabled=False,
            exclude_from_kpi=False,
            include_in_sla=False,
            always_collect_error_samples=False,
            do_not_schedule=False
        )
    ),
    comparisons=TableComparisonMonthlyPartitionedChecksSpecMap()
)

call_result = await update_table_partitioned_checks_monthly.asyncio(
    'sample_connection',
    'sample_schema',
    'sample_table',
    client=dqops_client,
    json_body=request_body
)

update_table_partitioning

Updates the table partitioning configuration of an existing table.

Follow the link to see the source code on GitHub.

PUT 

http://localhost:8888/api/connections/{connectionName}/schemas/{schemaName}/tables/{tableName}/partitioning

Parameters of this method are described below

 Property name   Description                       Data type   Required 
connection_name Connection name string
schema_name Schema name string
table_name Table name string

Request body

 Description                       Data type   Required 
Table partitioning model with the updated settings TablePartitioningModel

Usage examples

Execution

curl -X PUT http://localhost:8888/api/connections/sample_connection/schemas/sample_schema/tables/sample_table/partitioning^
    -H "Accept: application/json"^
    -H "Content-Type: application/json"^
    -d^
    "{\"connection_name\":\"sample_connection\",\"target\":{\"schema_name\":\"sample_schema\",\"table_name\":\"sample_table\"},\"timestamp_columns\":{\"event_timestamp_column\":\"col1\",\"ingestion_timestamp_column\":\"col2\",\"partition_by_column\":\"col3\"},\"incremental_time_window\":{\"daily_partitioning_recent_days\":7,\"daily_partitioning_include_today\":true},\"can_edit\":true}"

Execution

from dqops import client
from dqops.client.api.tables import update_table_partitioning
from dqops.client.models import PartitionIncrementalTimeWindowSpec, \
                                PhysicalTableName, \
                                TablePartitioningModel, \
                                TimestampColumnsSpec

dqops_client = client.Client(
    'http://localhost:8888/'
)

request_body = TablePartitioningModel(
    connection_name='sample_connection',
    target=PhysicalTableName(
        schema_name='sample_schema',
        table_name='sample_table'
    ),
    timestamp_columns=TimestampColumnsSpec(
        event_timestamp_column='col1',
        ingestion_timestamp_column='col2',
        partition_by_column='col3'
    ),
    incremental_time_window=PartitionIncrementalTimeWindowSpec(
        daily_partitioning_recent_days=7,
        daily_partitioning_include_today=True,
        monthly_partitioning_include_current_month=False
    ),
    can_edit=True
)

call_result = update_table_partitioning.sync(
    'sample_connection',
    'sample_schema',
    'sample_table',
    client=dqops_client,
    json_body=request_body
)

Execution

from dqops import client
from dqops.client.api.tables import update_table_partitioning
from dqops.client.models import PartitionIncrementalTimeWindowSpec, \
                                PhysicalTableName, \
                                TablePartitioningModel, \
                                TimestampColumnsSpec

dqops_client = client.Client(
    'http://localhost:8888/'
)

request_body = TablePartitioningModel(
    connection_name='sample_connection',
    target=PhysicalTableName(
        schema_name='sample_schema',
        table_name='sample_table'
    ),
    timestamp_columns=TimestampColumnsSpec(
        event_timestamp_column='col1',
        ingestion_timestamp_column='col2',
        partition_by_column='col3'
    ),
    incremental_time_window=PartitionIncrementalTimeWindowSpec(
        daily_partitioning_recent_days=7,
        daily_partitioning_include_today=True,
        monthly_partitioning_include_current_month=False
    ),
    can_edit=True
)

call_result = await update_table_partitioning.asyncio(
    'sample_connection',
    'sample_schema',
    'sample_table',
    client=dqops_client,
    json_body=request_body
)

Execution

from dqops import client
from dqops.client.api.tables import update_table_partitioning
from dqops.client.models import PartitionIncrementalTimeWindowSpec, \
                                PhysicalTableName, \
                                TablePartitioningModel, \
                                TimestampColumnsSpec

token = 's4mp13_4u7h_70k3n'

dqops_client = client.AuthenticatedClient(
    'http://localhost:8888/',
    token=token
)

request_body = TablePartitioningModel(
    connection_name='sample_connection',
    target=PhysicalTableName(
        schema_name='sample_schema',
        table_name='sample_table'
    ),
    timestamp_columns=TimestampColumnsSpec(
        event_timestamp_column='col1',
        ingestion_timestamp_column='col2',
        partition_by_column='col3'
    ),
    incremental_time_window=PartitionIncrementalTimeWindowSpec(
        daily_partitioning_recent_days=7,
        daily_partitioning_include_today=True,
        monthly_partitioning_include_current_month=False
    ),
    can_edit=True
)

call_result = update_table_partitioning.sync(
    'sample_connection',
    'sample_schema',
    'sample_table',
    client=dqops_client,
    json_body=request_body
)

Execution

from dqops import client
from dqops.client.api.tables import update_table_partitioning
from dqops.client.models import PartitionIncrementalTimeWindowSpec, \
                                PhysicalTableName, \
                                TablePartitioningModel, \
                                TimestampColumnsSpec

token = 's4mp13_4u7h_70k3n'

dqops_client = client.AuthenticatedClient(
    'http://localhost:8888/',
    token=token
)

request_body = TablePartitioningModel(
    connection_name='sample_connection',
    target=PhysicalTableName(
        schema_name='sample_schema',
        table_name='sample_table'
    ),
    timestamp_columns=TimestampColumnsSpec(
        event_timestamp_column='col1',
        ingestion_timestamp_column='col2',
        partition_by_column='col3'
    ),
    incremental_time_window=PartitionIncrementalTimeWindowSpec(
        daily_partitioning_recent_days=7,
        daily_partitioning_include_today=True,
        monthly_partitioning_include_current_month=False
    ),
    can_edit=True
)

call_result = await update_table_partitioning.asyncio(
    'sample_connection',
    'sample_schema',
    'sample_table',
    client=dqops_client,
    json_body=request_body
)

update_table_profiling_checks

Updates the list of table level data quality profiling checks on an existing table.

Follow the link to see the source code on GitHub.

PUT 

http://localhost:8888/api/connections/{connectionName}/schemas/{schemaName}/tables/{tableName}/profiling

Parameters of this method are described below

 Property name   Description                       Data type   Required 
connection_name Connection name string
schema_name Schema name string
table_name Table name string

Request body

 Description                       Data type   Required 
Configuration of table level data quality profiling checks to store or an empty object to remove all data quality profiling checks on the table level (column level profiling checks are preserved). TableProfilingCheckCategoriesSpec

Usage examples

Execution

curl -X PUT http://localhost:8888/api/connections/sample_connection/schemas/sample_schema/tables/sample_table/profiling^
    -H "Accept: application/json"^
    -H "Content-Type: application/json"^
    -d^
    "{\"volume\":{\"profile_row_count\":{\"error\":{\"min_count\":1}}}}"

Execution

from dqops import client
from dqops.client.api.tables import update_table_profiling_checks
from dqops.client.models import MinCountRule1ParametersSpec, \
                                TableProfilingCheckCategoriesSpec, \
                                TableRowCountCheckSpec, \
                                TableVolumeProfilingChecksSpec, \
                                TableVolumeRowCountSensorParametersSpec

dqops_client = client.Client(
    'http://localhost:8888/'
)

request_body = TableProfilingCheckCategoriesSpec(
    volume=TableVolumeProfilingChecksSpec(
        profile_row_count=TableRowCountCheckSpec(
            parameters=TableVolumeRowCountSensorParametersSpec(),
            error=MinCountRule1ParametersSpec(min_count=1),
            disabled=False,
            exclude_from_kpi=False,
            include_in_sla=False,
            always_collect_error_samples=False,
            do_not_schedule=False
        )
    )
)

call_result = update_table_profiling_checks.sync(
    'sample_connection',
    'sample_schema',
    'sample_table',
    client=dqops_client,
    json_body=request_body
)

Execution

from dqops import client
from dqops.client.api.tables import update_table_profiling_checks
from dqops.client.models import MinCountRule1ParametersSpec, \
                                TableProfilingCheckCategoriesSpec, \
                                TableRowCountCheckSpec, \
                                TableVolumeProfilingChecksSpec, \
                                TableVolumeRowCountSensorParametersSpec

dqops_client = client.Client(
    'http://localhost:8888/'
)

request_body = TableProfilingCheckCategoriesSpec(
    volume=TableVolumeProfilingChecksSpec(
        profile_row_count=TableRowCountCheckSpec(
            parameters=TableVolumeRowCountSensorParametersSpec(),
            error=MinCountRule1ParametersSpec(min_count=1),
            disabled=False,
            exclude_from_kpi=False,
            include_in_sla=False,
            always_collect_error_samples=False,
            do_not_schedule=False
        )
    )
)

call_result = await update_table_profiling_checks.asyncio(
    'sample_connection',
    'sample_schema',
    'sample_table',
    client=dqops_client,
    json_body=request_body
)

Execution

from dqops import client
from dqops.client.api.tables import update_table_profiling_checks
from dqops.client.models import MinCountRule1ParametersSpec, \
                                TableProfilingCheckCategoriesSpec, \
                                TableRowCountCheckSpec, \
                                TableVolumeProfilingChecksSpec, \
                                TableVolumeRowCountSensorParametersSpec

token = 's4mp13_4u7h_70k3n'

dqops_client = client.AuthenticatedClient(
    'http://localhost:8888/',
    token=token
)

request_body = TableProfilingCheckCategoriesSpec(
    volume=TableVolumeProfilingChecksSpec(
        profile_row_count=TableRowCountCheckSpec(
            parameters=TableVolumeRowCountSensorParametersSpec(),
            error=MinCountRule1ParametersSpec(min_count=1),
            disabled=False,
            exclude_from_kpi=False,
            include_in_sla=False,
            always_collect_error_samples=False,
            do_not_schedule=False
        )
    )
)

call_result = update_table_profiling_checks.sync(
    'sample_connection',
    'sample_schema',
    'sample_table',
    client=dqops_client,
    json_body=request_body
)

Execution

from dqops import client
from dqops.client.api.tables import update_table_profiling_checks
from dqops.client.models import MinCountRule1ParametersSpec, \
                                TableProfilingCheckCategoriesSpec, \
                                TableRowCountCheckSpec, \
                                TableVolumeProfilingChecksSpec, \
                                TableVolumeRowCountSensorParametersSpec

token = 's4mp13_4u7h_70k3n'

dqops_client = client.AuthenticatedClient(
    'http://localhost:8888/',
    token=token
)

request_body = TableProfilingCheckCategoriesSpec(
    volume=TableVolumeProfilingChecksSpec(
        profile_row_count=TableRowCountCheckSpec(
            parameters=TableVolumeRowCountSensorParametersSpec(),
            error=MinCountRule1ParametersSpec(min_count=1),
            disabled=False,
            exclude_from_kpi=False,
            include_in_sla=False,
            always_collect_error_samples=False,
            do_not_schedule=False
        )
    )
)

call_result = await update_table_profiling_checks.asyncio(
    'sample_connection',
    'sample_schema',
    'sample_table',
    client=dqops_client,
    json_body=request_body
)

update_table_profiling_checks_model

Updates the data quality profiling checks from a model that contains a patch with changes.

Follow the link to see the source code on GitHub.

PUT 

http://localhost:8888/api/connections/{connectionName}/schemas/{schemaName}/tables/{tableName}/profiling/model

Parameters of this method are described below

 Property name   Description                       Data type   Required 
connection_name Connection name string
schema_name Schema name string
table_name Table name string

Request body

 Description                       Data type   Required 
Model with the changes to be applied to the data quality profiling checks configuration. CheckContainerModel

Usage examples

Execution

curl -X PUT http://localhost:8888/api/connections/sample_connection/schemas/sample_schema/tables/sample_table/profiling/model^
    -H "Accept: application/json"^
    -H "Content-Type: application/json"^
    -d^
    "{\"categories\":[{\"category\":\"sample_category\",\"help_text\":\"Sample help text\",\"checks\":[{\"check_name\":\"sample_check\",\"help_text\":\"Sample help text\",\"sensor_parameters\":[],\"sensor_name\":\"sample_target/sample_category/table/volume/row_count\",\"quality_dimension\":\"sample_quality_dimension\",\"supports_error_sampling\":false,\"supports_grouping\":false,\"default_severity\":\"error\",\"disabled\":false,\"exclude_from_kpi\":false,\"include_in_sla\":false,\"configured\":false,\"can_edit\":false,\"can_run_checks\":false,\"can_delete_data\":false}]}],\"can_edit\":false,\"can_run_checks\":false,\"can_delete_data\":false}"

Execution

from dqops import client
from dqops.client.api.tables import update_table_profiling_checks_model
from dqops.client.models import CheckContainerModel, \
                                CheckModel, \
                                DefaultRuleSeverityLevel, \
                                FieldModel, \
                                QualityCategoryModel

dqops_client = client.Client(
    'http://localhost:8888/'
)

request_body = CheckContainerModel(
    categories=[
        QualityCategoryModel(
            category='sample_category',
            help_text='Sample help text',
            checks=[
                CheckModel(
                    check_name='sample_check',
                    help_text='Sample help text',
                    sensor_parameters=[

                    ],
                    sensor_name='sample_target/sample_category/table/volume/row_count',
                    quality_dimension='sample_quality_dimension',
                    supports_error_sampling=False,
                    supports_grouping=False,
                    standard=False,
                    default_check=False,
                    default_severity=DefaultRuleSeverityLevel.ERROR,
                    disabled=False,
                    exclude_from_kpi=False,
                    include_in_sla=False,
                    configured=False,
                    always_collect_error_samples=False,
                    do_not_schedule=False,
                    can_edit=False,
                    can_run_checks=False,
                    can_delete_data=False
                )
            ]
        )
    ],
    can_edit=False,
    can_run_checks=False,
    can_delete_data=False
)

call_result = update_table_profiling_checks_model.sync(
    'sample_connection',
    'sample_schema',
    'sample_table',
    client=dqops_client,
    json_body=request_body
)

Execution

from dqops import client
from dqops.client.api.tables import update_table_profiling_checks_model
from dqops.client.models import CheckContainerModel, \
                                CheckModel, \
                                DefaultRuleSeverityLevel, \
                                FieldModel, \
                                QualityCategoryModel

dqops_client = client.Client(
    'http://localhost:8888/'
)

request_body = CheckContainerModel(
    categories=[
        QualityCategoryModel(
            category='sample_category',
            help_text='Sample help text',
            checks=[
                CheckModel(
                    check_name='sample_check',
                    help_text='Sample help text',
                    sensor_parameters=[

                    ],
                    sensor_name='sample_target/sample_category/table/volume/row_count',
                    quality_dimension='sample_quality_dimension',
                    supports_error_sampling=False,
                    supports_grouping=False,
                    standard=False,
                    default_check=False,
                    default_severity=DefaultRuleSeverityLevel.ERROR,
                    disabled=False,
                    exclude_from_kpi=False,
                    include_in_sla=False,
                    configured=False,
                    always_collect_error_samples=False,
                    do_not_schedule=False,
                    can_edit=False,
                    can_run_checks=False,
                    can_delete_data=False
                )
            ]
        )
    ],
    can_edit=False,
    can_run_checks=False,
    can_delete_data=False
)

call_result = await update_table_profiling_checks_model.asyncio(
    'sample_connection',
    'sample_schema',
    'sample_table',
    client=dqops_client,
    json_body=request_body
)

Execution

from dqops import client
from dqops.client.api.tables import update_table_profiling_checks_model
from dqops.client.models import CheckContainerModel, \
                                CheckModel, \
                                DefaultRuleSeverityLevel, \
                                FieldModel, \
                                QualityCategoryModel

token = 's4mp13_4u7h_70k3n'

dqops_client = client.AuthenticatedClient(
    'http://localhost:8888/',
    token=token
)

request_body = CheckContainerModel(
    categories=[
        QualityCategoryModel(
            category='sample_category',
            help_text='Sample help text',
            checks=[
                CheckModel(
                    check_name='sample_check',
                    help_text='Sample help text',
                    sensor_parameters=[

                    ],
                    sensor_name='sample_target/sample_category/table/volume/row_count',
                    quality_dimension='sample_quality_dimension',
                    supports_error_sampling=False,
                    supports_grouping=False,
                    standard=False,
                    default_check=False,
                    default_severity=DefaultRuleSeverityLevel.ERROR,
                    disabled=False,
                    exclude_from_kpi=False,
                    include_in_sla=False,
                    configured=False,
                    always_collect_error_samples=False,
                    do_not_schedule=False,
                    can_edit=False,
                    can_run_checks=False,
                    can_delete_data=False
                )
            ]
        )
    ],
    can_edit=False,
    can_run_checks=False,
    can_delete_data=False
)

call_result = update_table_profiling_checks_model.sync(
    'sample_connection',
    'sample_schema',
    'sample_table',
    client=dqops_client,
    json_body=request_body
)

Execution

from dqops import client
from dqops.client.api.tables import update_table_profiling_checks_model
from dqops.client.models import CheckContainerModel, \
                                CheckModel, \
                                DefaultRuleSeverityLevel, \
                                FieldModel, \
                                QualityCategoryModel

token = 's4mp13_4u7h_70k3n'

dqops_client = client.AuthenticatedClient(
    'http://localhost:8888/',
    token=token
)

request_body = CheckContainerModel(
    categories=[
        QualityCategoryModel(
            category='sample_category',
            help_text='Sample help text',
            checks=[
                CheckModel(
                    check_name='sample_check',
                    help_text='Sample help text',
                    sensor_parameters=[

                    ],
                    sensor_name='sample_target/sample_category/table/volume/row_count',
                    quality_dimension='sample_quality_dimension',
                    supports_error_sampling=False,
                    supports_grouping=False,
                    standard=False,
                    default_check=False,
                    default_severity=DefaultRuleSeverityLevel.ERROR,
                    disabled=False,
                    exclude_from_kpi=False,
                    include_in_sla=False,
                    configured=False,
                    always_collect_error_samples=False,
                    do_not_schedule=False,
                    can_edit=False,
                    can_run_checks=False,
                    can_delete_data=False
                )
            ]
        )
    ],
    can_edit=False,
    can_run_checks=False,
    can_delete_data=False
)

call_result = await update_table_profiling_checks_model.asyncio(
    'sample_connection',
    'sample_schema',
    'sample_table',
    client=dqops_client,
    json_body=request_body
)

update_table_scheduling_group_override

Updates the overridden schedule configuration of an existing table for a named schedule group (named schedule for checks using the same time scale).

Follow the link to see the source code on GitHub.

PUT 

http://localhost:8888/api/connections/{connectionName}/schemas/{schemaName}/tables/{tableName}/schedulesoverride/{schedulingGroup}

Parameters of this method are described below

 Property name   Description                       Data type   Required 
connection_name Connection name string
schema_name Schema name string
table_name Table name string
scheduling_group Check scheduling group (named schedule) CheckRunScheduleGroup

Request body

 Description                       Data type   Required 
Table's overridden schedule configuration to store or an empty object to clear the schedule configuration on a table CronScheduleSpec

Usage examples

Execution

curl -X PUT http://localhost:8888/api/connections/sample_connection/schemas/sample_schema/tables/sample_table/schedulesoverride/partitioned_daily^
    -H "Accept: application/json"^
    -H "Content-Type: application/json"^
    -d^
    "{\"cron_expression\":\"0 12 1 * *\"}"

Execution

from dqops import client
from dqops.client.api.tables import update_table_scheduling_group_override
from dqops.client.models import CheckRunScheduleGroup, \
                                CronScheduleSpec

dqops_client = client.Client(
    'http://localhost:8888/'
)

request_body = CronScheduleSpec(
    cron_expression='0 12 1 * *',
    disabled=False
)

call_result = update_table_scheduling_group_override.sync(
    'sample_connection',
    'sample_schema',
    'sample_table',
    CheckRunScheduleGroup.partitioned_daily,
    client=dqops_client,
    json_body=request_body
)

Execution

from dqops import client
from dqops.client.api.tables import update_table_scheduling_group_override
from dqops.client.models import CheckRunScheduleGroup, \
                                CronScheduleSpec

dqops_client = client.Client(
    'http://localhost:8888/'
)

request_body = CronScheduleSpec(
    cron_expression='0 12 1 * *',
    disabled=False
)

call_result = await update_table_scheduling_group_override.asyncio(
    'sample_connection',
    'sample_schema',
    'sample_table',
    CheckRunScheduleGroup.partitioned_daily,
    client=dqops_client,
    json_body=request_body
)

Execution

from dqops import client
from dqops.client.api.tables import update_table_scheduling_group_override
from dqops.client.models import CheckRunScheduleGroup, \
                                CronScheduleSpec

token = 's4mp13_4u7h_70k3n'

dqops_client = client.AuthenticatedClient(
    'http://localhost:8888/',
    token=token
)

request_body = CronScheduleSpec(
    cron_expression='0 12 1 * *',
    disabled=False
)

call_result = update_table_scheduling_group_override.sync(
    'sample_connection',
    'sample_schema',
    'sample_table',
    CheckRunScheduleGroup.partitioned_daily,
    client=dqops_client,
    json_body=request_body
)

Execution

from dqops import client
from dqops.client.api.tables import update_table_scheduling_group_override
from dqops.client.models import CheckRunScheduleGroup, \
                                CronScheduleSpec

token = 's4mp13_4u7h_70k3n'

dqops_client = client.AuthenticatedClient(
    'http://localhost:8888/',
    token=token
)

request_body = CronScheduleSpec(
    cron_expression='0 12 1 * *',
    disabled=False
)

call_result = await update_table_scheduling_group_override.asyncio(
    'sample_connection',
    'sample_schema',
    'sample_table',
    CheckRunScheduleGroup.partitioned_daily,
    client=dqops_client,
    json_body=request_body
)