Write a PySpark script to process data stored in Delta format and transform it into Parquet.

Why It Matters (Architectural Logic): Delta→Parquet conversion enables format migration while preserving ACID and time-travel during transition. Partition preservation is critical for query performance.

Reading Delta and writing Parquet is straightforward with Delta Lake's native APIs. Read: delta_df = spark.read.format("delta").load("s3://bucket/path/to/delta"). Write Parquet: delta_df.write.format("parquet").mode("overwrite").save("s3://bucket/output/parquet"). For incremental processing, use spark.read.format("delta").option("readChangeFeed", "true").load(...) for CDC, or option("versionAsOf", N) for time travel. Best practices: preserve partitioning during conversion for query performance; use coalesce/partitionBy if output size matters; validate row counts match; consider using Delta's convertToDelta for existing Parquet if you need Delta features. For large tables, process in batches by partition to avoid OOM.