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.

For reporting issues, feature requests and contributions, refer to issues in the main repo.

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 and run results to your tables in your data warehouse.

Here you can find additional details.

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

Data observability report

Checkout the live demo.

Slack alerts

High level architecture

Data warehouse support

This package has been tested on Snowflake, BigQuery and Redshift. Additional integrations coming soon!

Community & Support

• Slack (Talk to us, support, etc.)
• GitHub issues (Bug reports, feature requests)

Contributions

Thank you 🧡 Whether it’s a bug fix, new feature, or additional documentation - we greatly appreciate contributions!

Check out the contributions guide and open issues in the main repo.