dbt-data-reliability: A Python repository from yvathrey

Data Observability for Analytics Engineers

Elementary enables you to monitor your data and dbt operation.

To learn more, refer to our main repo, and live demo.

Quick start

After adding the package to packages.yml and running dbt deps, add to your dbt_project.yml:

models:

## elementary models will be created in the schema '<your_schema>_elementary'
## see docs: https://docs.elementary-data.com/
  elementary:
    +schema: 'elementary'

And run dbt run --select elementary.

Check out the full documentation.

Run Results and dbt artifacts

The package automatically uploads the dbt artifacts artifacts and run results to your tables in your data warehouse.

Data anomalies detection as dbt tests

Elementary dbt tests collect metrics and metadata over time, such as freshness, volume, schema changes, distribution, cardinality, etc. Executed as any other dbt tests, the Elementary tests alert on anomalies and outliers.

Elementary tests are configured and executed like native tests in your project!

Example of Elementary test config in properties.yml:

models:
  - name: your_model_name
    config:
      elementary:
        timestamp_column: updated_at
    tests:
        - elementary.table_anomalies
        - elementary.all_columns_anomalies