• Apache Spark addresses limitations of MapReduce by leveraging in-memory processing and lazy evaluation, reducing costly disk I/O and enabling complex workflows.
  • Lazy evaluation optimizes performance by consolidating operations into stages, minimizing intermediate data shuffling between cluster nodes.
  • Spark’s execution model uses narrow (partition-local) and wide (shuffle-dependent) transformations, with stages created at shuffle boundaries impacting job efficiency.
  • Data immutability and incremental processing are emphasized for cost-effective reprocessing, auditability, and historical data retention in production pipelines.
  • Partitioning strategies critically affect performance: skewed data distribution can cause bottlenecks, while proper repartitioning improves parallelism.
  • Spark SQL and DataFrame APIs simplify distributed computation by abstracting low-level complexities, though understanding execution plans remains vital for optimization.
  • Cluster memory management and spill-to-disk mechanisms balance large dataset processing with hardware constraints, requiring careful monitoring.