Apache Spark Interview Questions: Beginner to Advanced

Apache Spark is the de facto standard for large-scale data processing. If you're interviewing for a data engineering role at any scale-up or enterprise, you'll face Spark questions. Our data shows Spark/Big Data is the second most tested category, with questions about partitioning, shuffle optimization, and the difference between RDDs and DataFrames appearing most frequently.

Before diving into advanced topics, make sure you have rock-solid fundamentals: - Difference between repartition() and coalesce() - SparkSession vs SparkContext - Lazy evaluation and the DAG - Narrow vs Wide transformations - Catalyst Optimizer and Tungsten These concepts come up in screening rounds and are table stakes for any Spark role.

At the senior/staff level, you'll be asked about real-world optimization: - Data skew detection and mitigation strategies - Broadcast joins vs sort-merge joins - Dynamic partition pruning - Adaptive Query Execution (AQE) - Memory management: storage vs execution memory - Spill to disk and its performance impact

Most companies have shifted to PySpark, but understanding the performance implications is important. Know when the Python-to-JVM serialization overhead matters and when it doesn't (hint: with DataFrames, it rarely matters).