TL;DR Databricks ETL runs across three layers: Delta Lake for storage, Lakeflow for orchestration, and Unity Catalog for governance. ELT is the dominant pattern in 2026 because cloud storage is cheap and Databricks compute scales on demand. ETL applies when data needs anonymization or validation before it reaches the lakehouse. This guide covers the medallion architecture, native and third-party tooling,
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TL;DR Databricks Lakeflow is a unified framework for building, orchestrating, and monitoring data pipelines directly within the Databricks lakehouse, replacing the need to stitch together separate ingestion, transformation, and orchestration tools. It combines declarative pipeline definitions with built-in data quality checks and observability, so teams get pipeline reliability without managing infrastructure separately. This guide covers Lakeflow’s key features, the benefits
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