Starburst Integrates With dbt Cloud to Unlock Cross-Platform Data Transformations

Starburst, the data lake analytics platform,announced a new integration with dbt Cloud, the fastest and most reliable way to deploy dbt. With the integration, which includes an enhanced adapter between dbt Cloud and Starburst’s SaaS offering, Starburst Galaxy, dbt users can now easily build data pipelines spanning multiple data sources on one central plane.

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As data becomes increasingly distributed, the ability to federate queries across disparate data sources has become critical for conducting lakehouse and data lake analytics. While migrating to centralize on a single cloud data warehouse is one option, most enterprise data is still spread across multiple platforms, including on-prem databases and object storage. With this integration, dbt users can easily federate that data across multiple disparate sources or access new data sources before it lands in their central data lake or warehouse.

“Combining the power of Starburst’s data lake analytics platform with dbt Cloud, enterprise customers can more easily transform data wherever it lives without suffering through cumbersome and expensive ETL processes,” said Harrison Johnson, Head of Technology Partnerships at Starburst. “This integration addresses the needs of the enterprise customer base, helping them get the most out of their existing systems and extending dbt’s world class analytics engineering workflow platform to new cloud-first use cases without additional operational overhead.”

Using a legacy ETL solution to transform and move data around using brittle, manually configured data pipelines is cumbersome, expensive, and can introduce risk. Yet, using a central cloud data warehouse for some use cases while other data exists in silos means organizations are not getting the most out of all their data. With this integration, dbt Cloud customers can get the most value out of all of their data with confidence, regardless of where it currently resides without adding the complexity of data ingestion (ETL) pipelines. This is a major benefit for dbt users who would need to otherwise rely on data engineering pipelines for ingestion.

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“dbt enables data teams to work faster and more efficiently to bring order to organizational knowledge,” said Nikhil Kothari, Head of Technology Partnerships at dbt Labs. “By combining the power of dbt Cloud with the flexibility of Starburst, we’re empowering a new segment of users to easily create analytical data assets, without having to be constrained by where the data lives.”

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