From ETL to Lakeflow: Shifting to Declarative Data Paradigm

▤ Summary

This article explains why teams are moving from imperative ETL pipelines to a declarative model in Databricks Lakeflow. It shows how Lakeflow removes orchestration boilerplate by inferring dependencies, handling quality checks, and generating lineage automatically. It also outlines the Lakeflow architecture across Connect, Pipelines, and Jobs, and compares a traditional Airflow-style pipeline with a declarative implementation. The piece closes with a practical migration path and the main limitations teams still need to watch.

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