Post by DEEPESH KUMAR

Spark can process billions of records—but writing fast and optimized Spark code is what sets great Data Engineers apart. I created this handwritten cheat sheet covering the most important Spark Optimization Techniques that are frequently discussed in Data Engineer interviews. 📌 It includes: ✅ Repartition vs Coalesce ✅ Cache vs Persist ✅ Broadcast Joins ✅ Partitioning Strategies ✅ Adaptive Query Execution (AQE) ✅ Data Skew Handling ✅ Filter Pushdown ✅ Avoiding UDFs ✅ Shuffle Optimization ✅ Spark Configuration Tuning 💡 One lesson I learned: Optimization isn't about writing more code—it's about making Spark do less work. A small optimization can reduce execution time from minutes to seconds when working with large datasets. #ApacheSpark #PySpark #DataEngineering #BigData #SparkOptimization #DataEngineer #InterviewPrep #ETL #PerformanceTuning #TechCommunity #Learning #CareerGrowth