Why is Spark faster than normal Python computations?

Spark is faster than plain Python for large‑scale data processing because it distributes work across many nodes and keeps data in memory. In a typical Python script, data is processed sequentially on a single machine, and intermediate results are written to disk, which is slow. Spark, on the other hand, partitions data into Resilient Distributed Datasets (RDDs) or DataFrames that are spread across a cluster. Each partition is processed in parallel by worker executors. Spark’s lazy evaluation builds a DAG of transformations; only when an action is called does Spark trigger execution, allowing it to optimize the plan (e.g., pipelining map and filter operations). Additionally, Spark’s in‑memory columnar storage (Tungsten) and code generation (WholeStageCodeGen) reduce serialization overhead and CPU usage. These features together give Spark a significant speed advantage for large datasets compared to a single‑machine Python loop.