Ascend.io announced an integration that allows users to painlessly scale and run sophisticated dbt models in production with a single command. Ascend for dbt represents the industry’s first automation and optimization controller for dbt models, giving data and analytics engineers access to Ascend’s advanced orchestration capabilities with no added overhead.
Ascend for dbt allows analytics engineers to continue to build with dbt Core while deploying their models to Ascend for intelligent execution. This solves a major challenge with dbt Core, the open-source version of dbt, which requires users to bring in a separate orchestration tool to execute their models. By automating the process of deploying dbt models with DataAwareTM intelligence, Ascend helps data teams deliver data products faster and more efficiently.
“With data teams under pressure to innovate faster and generate more value for their organizations, they need systems to automate traditionally manual processes that deploy and maintain data products at scale,” said Sean Knapp, founder and CEO, Ascend.io. “This new integration eliminates the pain of operationalizing dbt models, providing a smooth journey from design to production.”
With Ascend for dbt, Ascend automatically tracks lineage across multiple dbt projects. By profiling the data against the generated dbt code, Ascend’s automation controller identifies every change in code or data that affects a pipeline. This enables Ascend to auto-generate the jobs required to operate a sophisticated network of pipelines with maximum efficiency. Its dynamic intelligence limits unnecessary data processing, reducing cloud bills by up to 30% and engineering operations work by up to 80%.
Ascend’s data pipeline automation allows engineers to continue to build ontop of dbt Core. Once the updated models are compiled and pushed into Ascend, the platform automatically identifies any change from previous versions and autonomously orchestrates pipeline operation in response to those changes.
“In addition to the cost and time saving benefits, we see this integration as a critical step in organizations’ AI readiness strategy,” said Knapp. “AI projects often require entirely new datasets at a speed most data engineering teams are currently not equipped to meet. By launching dbt pipelines in a fraction of the time it traditionally takes, data teams can create and manage new data products at scale and ultimately become more efficient and responsive.”