Why did Spark evolve from RDDs to DataFrames and Datasets?

Spark started with Resilient Distributed Datasets (RDDs), which provide fault tolerance and parallelism but lack built‑in optimizations. As workloads grew, developers needed higher performance and easier data manipulation. DataFrames were introduced as a tabular abstraction with a schema, enabling Spark to apply the Catalyst optimizer and Tungsten execution engine. They offered SQL‑like expressiveness, automatic query optimization, and better memory management. Datasets added compile‑time type safety on top of DataFrames, allowing developers to work with strongly‑typed objects while still benefiting from the same optimizations. The evolution was driven by the need for faster execution, richer APIs, and integration with existing data sources (e.g., Hive, Parquet). Together, DataFrames and Datasets provide a higher‑level, more efficient alternative to RDDs for most analytical workloads.