This project demonstrates key PySpark performance optimization techniques using a synthetic banking transactions dataset (~5,000 records). Built using Databricks and Delta Lake.
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Updated
Mar 3, 2026 - Python
This project demonstrates key PySpark performance optimization techniques using a synthetic banking transactions dataset (~5,000 records). Built using Databricks and Delta Lake.
Battle-tested Apache Spark tuning patterns with reproducible benchmarks. 10 techniques (partition pruning, broadcast joins, AQE, skew handling, Z-ORDER, and more) — each paired with measured before/after speedups runnable on a laptop.
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