What are the advantages of converting a CSV file to Parquet in Spark, and how do worker types, partitions, and Adaptive Query Execution (AQE) affect this process?

Converting CSV to Parquet in Spark brings several performance and storage benefits. Parquet is a columnar format that compresses data more efficiently, reduces I/O, and allows predicate push‑down, which speeds up queries that filter on specific columns. Because Parquet stores schema metadata, Spark can skip entire files that do not match a filter, further reducing scan time. Partitioning the output by a key (e.g., date or region) creates many small files that can be pruned during reads, so only relevant partitions are read. Adaptive Query Execution (AQE) can dynamically adjust shuffle partitions based on runtime statistics, merge small partitions, and prune partitions at runtime, which reduces shuffle overhead and improves join performance. In practice, you would read the CSV with a schema, repartition or coalesce to an optimal number of partitions, write Parquet with partitionBy, enable AQE (spark.sql.adaptive.enabled=true), and choose a compression codec like snappy or zstd. This combination yields faster ingestion, smaller storage footprint, and more efficient downstream analytics.