A Decision Framework for ETL Migration to Databricks
Databricks, Friday, June 26th, 2026
A framework for choosing Lakehouse, Spark Declarative Pipelines, or PySpark when migrating ETL workloads to Databricks.
This post by Rafael Aielo argues against a one-size-fits-all data warehouse migration, recommending teams evaluate each workload and pick the right tool, whether SQL-based Lakehouse tasks, declarative pipelines, or Python notebooks.
It outlines a phased strategy spanning assessment, quick wins, modernization, and optimization, and leans on automated migration tools for mechanical translation.
It warns against forcing all workloads into one approach, measuring success by migration percentage instead of actual legacy retirement, and recreating outdated scheduling layers rather than adopting modern orchestration.