Industry-first automation controller for dbt models accelerates deployment and reduces engineering maintenance work by up to 80%Ascend.io, the leader in data pipeline automation, 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 DataAware™ intelligence, Ascend helps data teams deliver data products faster and more efficiently.Marketing Technology News: Microsoft Unveils New Generative AI and Data Solutions Across the Shopper Journey, Offering Copilot Experiences Through Microsoft Cloud for Retail“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

Ascend.io earns placements on Built In's Best Places to Work list.

The integration of Ascend's data management capabilities with Databricks' Lakehouse Platform caters to the diverse needs of today's data professionals while eliminating the cost of loading and syncing data.