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?