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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.
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.