9 interactive modules covering 170+ real interview questions — with visual explanations, quizzes, and scenario-based practice.
From basics to brain-teasers — structured for interview success
What Spark does, why it exists, how it compares to Hadoop MapReduce, and the key features interviewers love to ask about.
Driver, Executors, Cluster Managers, SparkContext, DAG scheduler — the internal machinery and how pieces talk to each other.
Resilient Distributed Datasets explained: creating them, transformations, actions, lazy evaluation, lineage graphs, and fault tolerance.
Structured data in Spark — DataFrames vs RDDs vs Datasets, Catalyst optimizer, Parquet files, schema inference, and JDBC.
Persistence levels, cache vs persist, shuffle operations, broadcast variables, accumulators, partitioning, and memory tuning.
Spark Streaming, DStreams, Structured Streaming, MLlib for machine learning, GraphX for graph processing, and Pipelines.
Tricky scenario questions, "what would you choose" debates, debugging puzzles, rapid-fire rounds, and the questions that trip up 90% of candidates.
Navigate the Jobs, Stages, SQL, Executors, and Storage tabs like a pro. Learn to read query plans, spot data skew, and interpret every key metric.
Systematic bottleneck triage, shuffle & join optimization, data skew fixes, AQE, predicate pushdown, and a complete decision table for diagnosing slow Spark jobs.