Reviewed by Aditya Kumar · Last reviewed 2026-03-25
**Why Delta Over Raw Parquet**: Parquet is immutable; no deletes, no transactional writes. Concurrent writers create corrupt state. Delta adds transaction log and metadata. **Delta Advantages**: (1) **ACID**—concurrent writes; readers see consistent snapshot. (2) **Time...
This easy-level Spark/Big Data question appears frequently in data engineering interviews at companies like Puma. While less common, it tests deeper understanding that distinguishes strong candidates.
Start by clearly defining the core concept being asked about. Interviewers want to see that you understand the fundamentals before diving into implementation details. Structure your answer with a definition, then explain the practical application with a concise example.
Why Delta Over Raw Parquet: Parquet is immutable; no deletes, no transactional writes. Concurrent writers create corrupt state. Delta adds transaction log and metadata.
Delta Advantages: (1) ACID—concurrent writes; readers see consistent snapshot. (2) Time travel—versionAsOf, timestampAsOf for audits and rollback. (3) MERGE/UPDATE/DELETE—CDC, upserts without overwrite. (4) Schema enforcement/evolution—reject bad data; add columns safely. (5) OPTIMIZE/VACUUM—compact small files; reclaim storage.
Scalability Trade-offs: Transaction log can grow; checkpoint files and VACUUM manage it. Z-ordering helps reads but costs write time.
Cost Implications: Delta adds ~5–10% storage for log; read performance often better via compaction. Enables use cases (CDC, time travel) impossible with raw Parquet.
Pro-Move: 'Time travel saved us when a bad merge went to prod; rolled back in 5 min.' Red Flag: Never running OPTIMIZE—thousands of small files, slow reads.
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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.