Resources and insights
Our Blog
Explore insights and practical tips on mastering Databricks Data Intelligence Platform and the full spectrum of today's modern data ecosystem.
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.
Explore More Content
ML & AI
DABs: Referencing Your Resources
Databricks bundle lookups failing with "does not exist" errors? Resource references solve timing issues and create strong dependencies. Complete guide with examples.
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.