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Data Engineering

Databricks Performance Checklist

A practical checklist for debugging and improving Spark and Delta Lake jobs.

This is a starting checklist you can customize as you write more detailed technical posts.

1. Understand the symptom

Is the job slow, expensive, failing, skewed, or producing too many small files?

2. Look at Spark UI

Start with stages, tasks, shuffle read/write, spill, executor time, and data skew.

3. Check Delta table layout

Review partitioning, file sizes, table statistics, clustering, and whether filters can skip data.

4. Fix one bottleneck at a time

Avoid random tuning. Change one thing, measure, and keep notes.