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On September 16th, dbt_artifacts v2.0.0 was published. This is a major version release that we are really excited about as it adds more support for users and for contributors.

What is dbt_artifacts?

dbt_artifacts is a package for modeling a dbt project and its run metadata. It includes the following models to help you understand the current state of a dbt project and its performance over time.

  • dim_dbt__current_models
  • dim_dbt__exposures
  • dim_dbt__models
  • dim_dbt__seeds
  • dim_dbt__snapshots
  • dim_dbt__sources
  • dim_dbt__tests
  • fct_dbt__invocations
  • fct_dbt__model_executions
  • fct_dbt__seed_executions
  • fct_dbt__snapshot_executions
  • fct_dbt__test_executions

It has many use cases, from identifying flakey tests to understanding the slowest running models for performance optimization.

Going from version 1.0.0 to 2.0.0 added the following major features:

  • Support for `dbt-spark` adapter
  • Support for `dbt-bigquery` adapter
  • Source tables are now dbt models which means that new columns can be easily released in new versions, and dbt automatically will add them to the tables on the next run.

See here for the full list of releases, features and fixes since v1.0.0.

If you're an existing dbt_artifacts user, there's a straightforward process for migrating to v2, check it out here.

A huge thank you to the Brooklyn Data engineers who contributed to v2, Josh Devlin and Niall Woodward, as well as the open source community members who have contributed.

Have fun, and happy data modeling!

  • Data Strategy and Governance
  • Data and Analytics Engineering
  • dbt
  • dbt

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