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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
Building Production-Ready Databricks Projects with Bundles
Most Databricks teams using Bundles are only scratching the surface. The real value isn't in the deployment syntax — it's in the engineering discipline Bundles makes enforceable: explicit dependency management, reproducible local environments, automated quality gates, and CI/CD as the only path to production. This post breaks down what a production-ready Databricks project structure actually looks like, and the software engineering practices that make it ship with confidence.