Last updated: July 22, 2025
Column types changed data quality checks, SQL examples
A table-level check that detects if the column names or column types have changed since the last time the check was run. This check calculates a hash of the column names and all the components of the column's data type: the data type name, length, scale, precision and nullability. A data quality issue will be detected if the hash of the column data types has changed. This check does not depend on the order of columns, the columns can be reordered as long as all columns are still present and the data types match since the last time they were tested.
The column types changed data quality check has the following variants for each type of data quality checks supported by DQOps.
profile column types changed
Check description
Detects if new columns were added, removed or their data types have changed. Retrieves the metadata of the monitored table and calculates an unordered hash of the column names and the data types (including the length, scale, precision, nullability). Compares the current hash to the previously known hash to detect any changes to the list of columns or their types.
| Data quality check name | Friendly name | Category | Check type | Time scale | Quality dimension | Sensor definition | Quality rule | Standard |
|---|---|---|---|---|---|---|---|---|
profile_column_types_changed |
Detect if the column list or data type has changed | schema | profiling | Consistency | column_types_hash | value_changed |
Command-line examples
Please expand the section below to see the DQOps command-line examples to run or activate the profile column types changed data quality check.
Managing profile column types changed 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_types_changed --enable-warning
You can also use patterns to activate the check on all matching tables and columns.
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_types_changed --enable-error
You can also use patterns to activate the check on all matching tables and columns.
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_types_changed 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_types_changed 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:
profiling_checks:
schema:
profile_column_types_changed:
error: {}
columns: {}
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_types_hash data quality sensor.
daily column types changed
Check description
Detects if new columns were added, removed or their data types have changed since the most recent day. Retrieves the metadata of the monitored table and calculates an unordered hash of the column names and the data types (including the length, scale, precision, nullability). Compares the current hash to the previously known hash to detect any changes to the list of columns or their types.
| Data quality check name | Friendly name | Category | Check type | Time scale | Quality dimension | Sensor definition | Quality rule | Standard |
|---|---|---|---|---|---|---|---|---|
daily_column_types_changed |
Detect if the column list or data type has changed | schema | monitoring | daily | Consistency | column_types_hash | value_changed |
Command-line examples
Please expand the section below to see the DQOps command-line examples to run or activate the daily column types changed data quality check.
Managing daily column types changed 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_types_changed --enable-warning
You can also use patterns to activate the check on all matching tables and columns.
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_types_changed --enable-error
You can also use patterns to activate the check on all matching tables and columns.
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_types_changed 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_types_changed 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:
monitoring_checks:
daily:
schema:
daily_column_types_changed:
error: {}
columns: {}
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_types_hash data quality sensor.
monthly column types changed
Check description
Detects if new columns were added, removed or their data types have changed since the last month. Retrieves the metadata of the monitored table and calculates an unordered hash of the column names and the data types (including the length, scale, precision, nullability). Compares the current hash to the previously known hash to detect any changes to the list of columns or their types.
| Data quality check name | Friendly name | Category | Check type | Time scale | Quality dimension | Sensor definition | Quality rule | Standard |
|---|---|---|---|---|---|---|---|---|
monthly_column_types_changed |
Detect if the column list or data type has changed | schema | monitoring | monthly | Consistency | column_types_hash | value_changed |
Command-line examples
Please expand the section below to see the DQOps command-line examples to run or activate the monthly column types changed data quality check.
Managing monthly column types changed 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_types_changed --enable-warning
You can also use patterns to activate the check on all matching tables and columns.
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_types_changed --enable-error
You can also use patterns to activate the check on all matching tables and columns.
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_types_changed 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_types_changed 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:
monitoring_checks:
monthly:
schema:
monthly_column_types_changed:
error: {}
columns: {}
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_types_hash 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