Introduction - Performance Tuning & Caching
"Master Apache Spark and Big Data Engineering from first principles."
What You'll Master
Caching & Persistence
Differentiating cache() and persist(), choosing JVM Storage Levels, and avoiding cache memory leaks.
Adaptive Query Execution
Tracing Spark's automatic self-tuning engine: dynamic partition merging, join switching, and skew join salting.
Runtime Statistics-Driven Tuning
How AQE re-optimizes query plans dynamically at runtime based on live partition statistics rather than static estimates.
Hands-on Cache Workbook
Applying caching strategy and AQE tuning decisions to real memory-pressure and skew scenarios.
Learning Path & Course Syllabus
Tracing Spark's automatic self-tuning engine: dynamic partition merging, join switching, and skew join salting.
Differentiating cache() and persist(), choosing JVM Storage Levels, and avoiding cache memory leaks.
A hands-on workbook applying caching strategy and AQE tuning decisions to real memory-pressure and skew scenarios.
Scenario questions covering storage level selection, cache memory leaks, and AQE's runtime re-optimization behavior.
What's Included in This Module
| Component | Coverage Details |
|---|---|
| Core Topics | Driver & Executor Architecture, Cluster Managers, Datasets |
| Practical Exercises | Interactive Hands-on Labs & Spark Tasks |
| Assessments | 1 Practical Assignment + 1 System Design Interview Quiz |