Last updated: July 22, 2025
Column count match data quality checks, SQL examples
Table level comparison check that compares the column count of the current (parent) table with the column count of the reference table.
The column count match data quality check has the following variants for each type of data quality checks supported by DQOps.
profile column count match
Check description
Verifies that the column count of the tested (parent) table matches the column count of the reference table. Only one comparison result is returned, without data grouping.
| Data quality check name | Friendly name | Category | Check type | Time scale | Quality dimension | Sensor definition | Quality rule | Standard |
|---|---|---|---|---|---|---|---|---|
profile_column_count_match |
Maximum percentage of difference between column count of compared tables | comparisons | profiling | Accuracy | column_count | diff_percent |
Command-line examples
Please expand the section below to see the DQOps command-line examples to run or activate the profile column count match data quality check.
Managing profile column count match check from DQOps shell
Activate this data quality using the check activate CLI command, providing the connection name, table name, check name, and all other filters. Activates the warning rule with the default parameters.
dqo> check activate -c=connection_name -t=schema_name.table_name -ch=profile_column_count_match --enable-warning
You can also use patterns to activate the check on all matching tables and columns.
dqo> check activate -c=connection_name -t=schema_prefix*.fact_* -ch=profile_column_count_match --enable-warning
Additional rule parameters are passed using the -Wrule_parameter_name=value.
Activate this data quality using the check activate CLI command, providing the connection name, table name, check name, and all other filters. Activates the error rule with the default parameters.
dqo> check activate -c=connection_name -t=schema_name.table_name -ch=profile_column_count_match --enable-error
You can also use patterns to activate the check on all matching tables and columns.
dqo> check activate -c=connection_name -t=schema_prefix*.fact_* -ch=profile_column_count_match --enable-error
Additional rule parameters are passed using the -Erule_parameter_name=value.
Run this data quality check using the check run CLI command by providing the check name and all other targeting filters. The following example shows how to run the profile_column_count_match check on all tables on a single data source.
It is also possible to run this check on a specific connection and table. In order to do this, use the connection name and the full table name parameters.
You can also run this check on all tables on which the profile_column_count_match check is enabled using patterns to find tables.
YAML configuration
The sample schema_name.table_name.dqotable.yaml file with the check configured is shown below.
# yaml-language-server: $schema=https://cloud.dqops.com/dqo-yaml-schema/TableYaml-schema.json
apiVersion: dqo/v1
kind: table
spec:
table_comparisons:
compare_to_source_of_truth_table:
reference_table_connection_name: <source_of_truth_connection_name>
reference_table_schema_name: <source_of_truth_schema_name>
reference_table_name: <source_of_truth_table_name>
check_type: profiling
grouping_columns:
- compared_table_column_name: country
reference_table_column_name: country_column_name_on_reference_table
- compared_table_column_name: state
reference_table_column_name: state_column_name_on_reference_table
profiling_checks:
comparisons:
compare_to_source_of_truth_table:
profile_column_count_match:
warning:
max_diff_percent: 0.0
error:
max_diff_percent: 1.0
fatal:
max_diff_percent: 5.0
columns:
country:
labels:
- column used as the first grouping key for calculating aggregated values used
for the table comparison
state:
labels:
- column used as the first grouping key for calculating aggregated values used
for the table comparison
Samples of generated SQL queries for each data source type
Please expand the database engine name section to see the SQL query rendered by a Jinja2 template for the column_count data quality sensor.
daily column count match
Check description
Verifies that the column count of the tested (parent) table matches the column count of the reference table. Only one comparison result is returned, without data grouping. Stores the most recent captured value for each day when the data quality check was evaluated.
| Data quality check name | Friendly name | Category | Check type | Time scale | Quality dimension | Sensor definition | Quality rule | Standard |
|---|---|---|---|---|---|---|---|---|
daily_column_count_match |
Maximum percentage of difference between column count of compared tables | comparisons | monitoring | daily | Accuracy | column_count | diff_percent |
Command-line examples
Please expand the section below to see the DQOps command-line examples to run or activate the daily column count match data quality check.
Managing daily column count match check from DQOps shell
Activate this data quality using the check activate CLI command, providing the connection name, table name, check name, and all other filters. Activates the warning rule with the default parameters.
dqo> check activate -c=connection_name -t=schema_name.table_name -ch=daily_column_count_match --enable-warning
You can also use patterns to activate the check on all matching tables and columns.
dqo> check activate -c=connection_name -t=schema_prefix*.fact_* -ch=daily_column_count_match --enable-warning
Additional rule parameters are passed using the -Wrule_parameter_name=value.
Activate this data quality using the check activate CLI command, providing the connection name, table name, check name, and all other filters. Activates the error rule with the default parameters.
dqo> check activate -c=connection_name -t=schema_name.table_name -ch=daily_column_count_match --enable-error
You can also use patterns to activate the check on all matching tables and columns.
dqo> check activate -c=connection_name -t=schema_prefix*.fact_* -ch=daily_column_count_match --enable-error
Additional rule parameters are passed using the -Erule_parameter_name=value.
Run this data quality check using the check run CLI command by providing the check name and all other targeting filters. The following example shows how to run the daily_column_count_match check on all tables on a single data source.
It is also possible to run this check on a specific connection and table. In order to do this, use the connection name and the full table name parameters.
You can also run this check on all tables on which the daily_column_count_match check is enabled using patterns to find tables.
YAML configuration
The sample schema_name.table_name.dqotable.yaml file with the check configured is shown below.
# yaml-language-server: $schema=https://cloud.dqops.com/dqo-yaml-schema/TableYaml-schema.json
apiVersion: dqo/v1
kind: table
spec:
table_comparisons:
compare_to_source_of_truth_table:
reference_table_connection_name: <source_of_truth_connection_name>
reference_table_schema_name: <source_of_truth_schema_name>
reference_table_name: <source_of_truth_table_name>
check_type: profiling
grouping_columns:
- compared_table_column_name: country
reference_table_column_name: country_column_name_on_reference_table
- compared_table_column_name: state
reference_table_column_name: state_column_name_on_reference_table
monitoring_checks:
daily:
comparisons:
compare_to_source_of_truth_table:
daily_column_count_match:
warning:
max_diff_percent: 0.0
error:
max_diff_percent: 1.0
fatal:
max_diff_percent: 5.0
columns:
country:
labels:
- column used as the first grouping key for calculating aggregated values used
for the table comparison
state:
labels:
- column used as the first grouping key for calculating aggregated values used
for the table comparison
Samples of generated SQL queries for each data source type
Please expand the database engine name section to see the SQL query rendered by a Jinja2 template for the column_count data quality sensor.
monthly column count match
Check description
Verifies that the column count of the tested (parent) table matches the column count of the reference table. Only one comparison result is returned, without data grouping. Stores the most recent captured value for each month when the data quality check was evaluated.
| Data quality check name | Friendly name | Category | Check type | Time scale | Quality dimension | Sensor definition | Quality rule | Standard |
|---|---|---|---|---|---|---|---|---|
monthly_column_count_match |
Maximum percentage of difference between column count of compared tables | comparisons | monitoring | monthly | Accuracy | column_count | diff_percent |
Command-line examples
Please expand the section below to see the DQOps command-line examples to run or activate the monthly column count match data quality check.
Managing monthly column count match check from DQOps shell
Activate this data quality using the check activate CLI command, providing the connection name, table name, check name, and all other filters. Activates the warning rule with the default parameters.
dqo> check activate -c=connection_name -t=schema_name.table_name -ch=monthly_column_count_match --enable-warning
You can also use patterns to activate the check on all matching tables and columns.
dqo> check activate -c=connection_name -t=schema_prefix*.fact_* -ch=monthly_column_count_match --enable-warning
Additional rule parameters are passed using the -Wrule_parameter_name=value.
Activate this data quality using the check activate CLI command, providing the connection name, table name, check name, and all other filters. Activates the error rule with the default parameters.
dqo> check activate -c=connection_name -t=schema_name.table_name -ch=monthly_column_count_match --enable-error
You can also use patterns to activate the check on all matching tables and columns.
dqo> check activate -c=connection_name -t=schema_prefix*.fact_* -ch=monthly_column_count_match --enable-error
Additional rule parameters are passed using the -Erule_parameter_name=value.
Run this data quality check using the check run CLI command by providing the check name and all other targeting filters. The following example shows how to run the monthly_column_count_match check on all tables on a single data source.
It is also possible to run this check on a specific connection and table. In order to do this, use the connection name and the full table name parameters.
You can also run this check on all tables on which the monthly_column_count_match check is enabled using patterns to find tables.
YAML configuration
The sample schema_name.table_name.dqotable.yaml file with the check configured is shown below.
# yaml-language-server: $schema=https://cloud.dqops.com/dqo-yaml-schema/TableYaml-schema.json
apiVersion: dqo/v1
kind: table
spec:
table_comparisons:
compare_to_source_of_truth_table:
reference_table_connection_name: <source_of_truth_connection_name>
reference_table_schema_name: <source_of_truth_schema_name>
reference_table_name: <source_of_truth_table_name>
check_type: profiling
grouping_columns:
- compared_table_column_name: country
reference_table_column_name: country_column_name_on_reference_table
- compared_table_column_name: state
reference_table_column_name: state_column_name_on_reference_table
monitoring_checks:
monthly:
comparisons:
compare_to_source_of_truth_table:
monthly_column_count_match:
warning:
max_diff_percent: 0.0
error:
max_diff_percent: 1.0
fatal:
max_diff_percent: 5.0
columns:
country:
labels:
- column used as the first grouping key for calculating aggregated values used
for the table comparison
state:
labels:
- column used as the first grouping key for calculating aggregated values used
for the table comparison
Samples of generated SQL queries for each data source type
Please expand the database engine name section to see the SQL query rendered by a Jinja2 template for the column_count data quality sensor.
What's next
- Learn how to configure data quality checks in DQOps
- Look at the examples of running data quality checks, targeting tables and columns