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Most teams that move to Databricks get the hard part right. They migrate the processing engine, rebuild the transformation logic, and stand up Unity Catalog. Then they leave Azure Data Factory running in the background: connected to everything, owned by nobody, and quietly accumulating cost and complexity. In this entry, that’s the gap we address.
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ML & AI
Managing Databricks CLI Versions in Your DAB Projects
Prevent Databricks deployment failures caused by CLI version conflicts. Step-by-step guide to version management in DAB projects with CI/CD automation.
CI/CD Best Practices: Passing tests isn't enough
CI/CD pipelines can pass all jobs yet still deploy broken functionality. This blog covers smoke testing, regression testing, and critical validation strategies: especially useful for data projects where data quality is as important as code quality.