Reviewed by Aditya Kumar · Last reviewed 2026-03-24
**Why distinction matters**: Immutable vs mutable; different operational model. **Parquet**: Columnar format; immutable; no built-in transactions. Append = new files. **Delta**: Parquet data files + JSON transaction log. Adds ACID, time travel, schema evolution, upsert/merge....
Why distinction matters: Immutable vs mutable; different operational model. Parquet: Columnar format; immutable; no built-in transactions. Append = new files. Delta: Parquet data files + JSON transaction log. Adds ACID, time travel, schema evolution, upsert/merge. Scalability trade-offs: Parquet = simple, append-only; Delta = mutable, more features. Cost implications: Delta = log storage overhead; Parquet = just files. Best practice: Delta for production lakehouse; Parquet for simple append-only or external exchange.
Red Flag: 'Delta is just Parquet'—missing transaction log. Pro-Move: 'Delta = Parquet + log; we use merge for CDC—Parquet can't do that.'
According to DataEngPrep.tech, this is one of the most frequently asked Spark/Big Data interview questions, reported at 1 company. DataEngPrep.tech maintains an editor-reviewed database of 1,863 data engineering interview questions across 7 categories.