With column-level lineage and dbt metrics as a first-class citizen in Atlan, this partnership increases context, visibility, and self-service for diverse data teams.
Atlan, the active metadata platform for modern data teams, announced a partnership with dbt Labs, the pioneer in analytics engineering. As part of this, joint customers will have access to an end-to-end governance framework for data models and metrics in the modern data stack.
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“We’re excited to partner with dbt Labs to make metrics a first class citizen for data teams”
dbt Labs’ new Semantic Layer enables organizations to centrally define key business metrics like “revenue,” “customer count,” or “churn rate” in dbt, and query them in downstream analytics tools. This allows everyone in the business to feel confident that they are working from the same assumptions as their colleagues, regardless of their data tooling of choice. If a metric definition is updated in dbt, it is seamlessly updated everywhere, ensuring consistency throughout the business.
Atlan’s integration with the dbt Semantic Layer brings dbt’s rich metrics into the rest of the data stack. With this integration, company metrics are now a part of column-level lineage, spanning from source systems and data storage to transformation and BI.
“We’re excited to partner with dbt Labs to make metrics a first class citizen for data teams,” said Varun Banka, Co-Founder of Atlan. “Metrics are the language through which the business understands data. For far too long, data teams have dealt with endless chaos about metrics definitions and accuracy. Now, with Atlan and dbt, diverse data people can cut through the chaos and work together better with easier collaboration and alignment.”
Atlan’s native dbt Cloud integration ingests all dbt metrics and metadata about dbt models, merges it with metadata from all other tools in the data stack, creates column-level lineage from source to BI, and sends that unified context back into tools like Snowflake and the BI tools where people work daily.
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With this, when questions arise about company data, data teams can quickly find the correct metric, backtrack through changes via version control, assess exactly what changed at every layer (i.e. the data, definition, and operational layers), and trace how downstream assets were affected. This powerful impact and root cause analysis finally gives modern data teams the tools they need for end-to-end data governance and change management at every stage of the data lifecycle.
“The dbt Semantic Layer gives customers a central source of truth for their business-critical metrics, and the ability to query them from tools like Atlan,” said dbt Labs Chief Product Officer Margaret Francis. “Through this partnership between dbt Labs, Atlan, and other industry leaders, organizations will be able to benefit from unprecedented consistency and precision in their key metrics.”
Here is how joint customers benefit from Atlan and dbt Labs’ partnership:
- 360° metric profiles: Just like a data asset, every dbt metric is now a first-class citizen with a complete profile in Atlan. Data teams can find and verify metrics, assign owners, personalize access, attach documentation, track changes, find downstream assets, and more.
- Metrics querying: With Atlan’s Visual Query Builder, non-technical data users can now query dbt metrics — democratizing data and reducing dependencies on analytical and data engineers.
- End-to-end, column-level data lineage: Atlan uses automated SQL parsing to create end-to-end, column-level lineage for all dbt transformations. This shows how each dbt model affects not just upstream warehouses but also downstream BI reports and dashboards.
- Activating metric context into BI: With Atlan’s Chrome extension, dbt metadata is now accessible in downstream BI tools like Looker and Tableau.
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