Define the name of the Profiler Workflow. While we only support a single workflow for the Metadata and Usage ingestion, users can define different schedules and filters for Profiler workflows.
As profiling is a costly task, this enables a fine-grained approach to profiling and running tests by specifying different filters for each pipeline.
**Database filter pattern**
regex expression to filter databases.
**Schema filter pattern**
regex expression to filter schemas.
**Table filter pattern**
regex expression to filter tables.
**Profile Sample**
Sampling percentage to apply for profiling tables.
Number of thread to use when computing metrics for the profiler. For Snowflake users we recommend setting it to 1. There is a known issue with one of the dependency (`snowflake-connector-python`) affecting projects with certain environments.
After clicking Next, you will be redirected to the Scheduling form. This will be the same as the Metadata and Usage Ingestions. Select your desired schedule and click on Deploy to find the usage pipeline being added to the Service Ingestions.
### 4. Updating Profiler setting at the table level
Once you have created your profiler you can adjust some behavior at the table level by going to the table and clicking on the profiler tab
In the [connectors](/connectors) section we showcase how to run the metadata ingestion from a JSON file using the Airflow SDK or the CLI via metadata ingest. Running a profiler workflow is also possible using a JSON configuration file.
This is a good option if you which to execute your workflow via the Airflow SDK or using the CLI; if you use the CLI a profile workflow can be triggered with the command `metadata profile -c FILENAME.yaml`. The `serviceConnection` config will be specific to your connector (you can find more information in the [connectors](/connectors) section), though the sourceConfig for the profiler will be similar across all connectors.