
✅ PySpark Fundamentals - RDD vs DataFrames, Lazy Evaluation, Narrow/Wide Transformations
✅ Advanced Optimization - Partitioning Strategies, Broadcast Joins, Tungsten Optimizations
✅ Real-World Scenarios - ETL Pipelines, Delta Lake Integrations, Handling Skewed Data
✅ Performance Tuning - Spark UI Debugging, Memory Management, Cluster Configs
✅ Bonus Materials - Spark SQL Cheat Sheet & Common Error Solutions
✔ Data Engineers preparing for Big Data interviews
✔ Spark Developers transitioning to senior roles
✔ ETL Developers needing PySpark optimization skills
🔹 Actual Interview Questions from Amazon, Databricks, Uber & Netflix
🔹 Not Just Theory - Every answer includes optimized code samples
🔹 Avoid Costly Mistakes - Learn how to prevent OOM errors and data skew
🔹 2024-Relevant - Covers Spark 3.x, Delta Lake, and AWS Glue