A diagnostic-first guide to ETL optimization covering how to baseline, diagnose, and fix real bottlenecks in production pipelines running Spark, Flink, and Airflow. Covers the full pipeline lifecycle: extraction patterns (CDC, incremental loads, pushdown), transformation wins (skew handling, AQE, set-based ops, engine selection), load best practices (bulk writes, file sizing, idempotency), Airflow orchestration pitfalls, ETL vs ELT trade-offs, cost optimization tactics, and observability. Includes a concrete 90-day optimization plan and a list of common mistakes teams make when tuning data pipelines.
Table of contents
What "Optimization" Actually MeansBaseline First. Always.Diagnose Before You PrescribeYour Data Is Dirty. Plan For It.Extraction: Pull Less, Pull SmarterTransformation: Where the Wins CompoundLoad: Bulk, Partitioned, IdempotentOrchestration: Airflow, Without Shooting Yourself in the FootWhen ETL Should Become ELTCost Optimization: The Metric Most Teams Do Not TrackObservability: How You Know the Fix HeldA 90-Day Optimization PlanCommon Mistakes We SeeWhen to Bring in Outside HelpFrequently Asked QuestionsSort: