Last updated: July 22, 2025
Column exists data quality checks, SQL examples
A column-level check that reads the metadata of the monitored table and verifies if the column still exists in the data source. The data quality sensor returns a value of 1.0 when the column is found or 0.0 when the column is not found.
The column exists data quality check has the following variants for each type of data quality checks supported by DQOps.
profile column exists
Check description
Checks the metadata of the monitored table and verifies if the column exists.
| Data quality check name | Friendly name | Category | Check type | Time scale | Quality dimension | Sensor definition | Quality rule | Standard |
|---|---|---|---|---|---|---|---|---|
profile_column_exists |
Verify if the column exists | schema | profiling | Completeness | column_exists | equals_1 |
Command-line examples
Please expand the section below to see the DQOps command-line examples to run or activate the profile column exists data quality check.
Managing profile column exists 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 -col=column_name -ch=profile_column_exists --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 -col=column_name -ch=profile_column_exists --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_exists check on all tables and columns 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 (and columns) on which the profile_column_exists 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:
columns:
target_column:
profiling_checks:
schema:
profile_column_exists:
error: {}
labels:
- This is the column that is analyzed for data quality issues
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_exists data quality sensor.
daily column exists
Check description
Checks the metadata of the monitored table and verifies if the column exists. Stores the most recent 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_exists |
Verify if the column exists | schema | monitoring | daily | Completeness | column_exists | equals_1 |
Command-line examples
Please expand the section below to see the DQOps command-line examples to run or activate the daily column exists data quality check.
Managing daily column exists 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 -col=column_name -ch=daily_column_exists --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 -col=column_name -ch=daily_column_exists --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_exists check on all tables and columns 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 (and columns) on which the daily_column_exists 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:
columns:
target_column:
monitoring_checks:
daily:
schema:
daily_column_exists:
error: {}
labels:
- This is the column that is analyzed for data quality issues
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_exists data quality sensor.
monthly column exists
Check description
Checks the metadata of the monitored table and verifies if the column exists. Stores the most recent 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_exists |
Verify if the column exists | schema | monitoring | monthly | Completeness | column_exists | equals_1 |
Command-line examples
Please expand the section below to see the DQOps command-line examples to run or activate the monthly column exists data quality check.
Managing monthly column exists 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 -col=column_name -ch=monthly_column_exists --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 -col=column_name -ch=monthly_column_exists --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_exists check on all tables and columns 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 (and columns) on which the monthly_column_exists 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:
columns:
target_column:
monitoring_checks:
monthly:
schema:
monthly_column_exists:
error: {}
labels:
- This is the column that is analyzed for data quality issues
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_exists 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