This dbt package is part of Elementary, the dbt-native data observability solution for data and analytics engineers. Set up in minutes, gain immediate visibility, detect data issues, send actionable alerts, and understand impact and root cause. Available as self-hosted or Cloud service with premium features.

• Quick start - dbt package
• Get more out of Elementary
• Run results and dbt artifacts
• Data anomaly detection as dbt tests
• How Elementary works?
• Community & Support
• Contribution

1. Add to your packages.yml:

packages:
  - package: elementary-data/elementary
    version: 0.14.1
    ## Docs: https://docs.elementary-data.com

2. Run dbt deps

3. Add to your dbt_project.yml:

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

4. Run dbt run --select elementary

Check out the full documentation.

Elementary has 3 offerings: This dbt package, Elementary Community (OSS) and Elementary (cloud service).

• dbt package
  • For basic data monitoring and dbt artifacts collection, Elementary offers a dbt package. The package adds logging, artifacts uploading, and Elementary tests (anomaly detection and schema) to your project.
• Elementary Community
  • An open-source CLI tool you can deploy and orchestrate to send alerts and self-host the Elementary report. Best for data and analytics engineers that require basic observability capabilities or for evaluating features without vendor approval. Our community can provide great support on Slack if needed.
• Elementary Cloud
  • Ideal for teams monitoring mission-critical data pipelines, requiring guaranteed uptime and reliability, short-time-to-value, advanced features, collaboration, and professional support. The solution is secure by design, and requires no access to your data from cloud. To learn more, book a demo or start a trial.

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

Run results tables:

• dbt_run_results
• model_run_results
• snapshot_run_results
• dbt_invocations
• elementary_test_results (all dbt test results)

Metadata tables:

• dbt_models
• dbt_tests
• dbt_sources
• dbt_exposures
• dbt_metrics
• dbt_snapshots

Here you can find additional details about the tables.

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

Read about the available Elementary tests and configuration.

Elementary dbt package creates tables of metadata and test results in your data warehouse, as part of your dbt runs.

The cloud service or the CLI tool read the data from these tables, send alerts and present the results in the UI.

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

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.