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The five levels of Apache Spark - Data Engineering

Apache Spark has levels to it: Level 0 You can run spark-shell or pyspark, it means you can start Level 1 You understand the Spark execution model: • RDDs vs DataFrames vs Datasets • Transformations (map, filter, groupBy, join) vs Actions (collect, count, show) • Lazy execution & DAG (Directed Acyclic Graph) Master these concepts, and you’ll have a solid foundation Level 2 Optimizing Spark Queries • Understand Catalyst Optimizer and how it rewrites queries for efficiency. • Master columnar storage and Parquet vs JSON vs CSV. • Use broadcast joins to avoid shuffle nightmares • Shuffle operations are expensive. Reduce them with partitioning and good data modeling • Coalesce vs Repartition—know when to use them. • Avoid UDFs unless absolutely necessary (they bypass Catalyst optimization). Level 3 Tuning for Performance at Scale • Master spark.sql.autoBroadcastJoinThreshold. • Understand how Task Parallelism works and set spark.sql.shuffle.partitions properly. • Skewed Data? Use adaptive execution! • Use EXPLAIN and queryExecution.debug to analyze execution plans. Level 4 Deep Dive into Cluster Resource Management • Spark on YARN vs Kubernetes vs Standalone—know the tradeoffs. • Understand Executor vs Driver Memory—tune spark.executor.memory and spark.driver.memory. • Dynamic allocation (spark.dynamicAllocation.enabled=true) can save costs. • When to use RDDs over DataFrames (spoiler: almost never). What else did I miss for mastering Spark and distributed compute?