Reviewed by Aditya Kumar · Last reviewed 2026-03-25
**Why it matters**: At scale, design choices directly impact reliability, latency, and cost. Wrong decisions compound across jobs and teams. Delta Lake stores transaction history in the `_delta_log` folder within the table directory (e.g., `s3://bucket/table/_delta_log/`). Each...
This hard-level Spark/Big Data question appears frequently in data engineering interviews at companies like Meesho. While less common, it tests deeper understanding that distinguishes strong candidates. Mastering the underlying concepts (optimization, partition) will help you answer variations of this question confidently.
This is a senior-level question that tests architectural thinking. Lead with the high-level design, then drill into specifics. Discuss trade-offs explicitly - there is rarely one correct answer. Show awareness of scale, fault tolerance, and operational complexity.
Why it matters: At scale, design choices directly impact reliability, latency, and cost. Wrong decisions compound across jobs and teams.
Delta Lake stores transaction history in the _delta_log folder within the table directory (e.g., s3://bucket/table/_delta_log/). Each transaction is a JSON file (e.g., 00000000000000000000.json) containing: add/remove file actions, metadata, protocol version. Old log files are compacted via VACUUM (default 7 days). The checkpoint file (.checkpoint.parquet) aggregates log entries for faster reads. Best practice: Run OPTIMIZE and VACUUM periodically; retain history as needed with delta.logRetentionDuration; monitor _delta_log size.
Scalability trade-offs: Partition/parallelism limits; single points of failure; horizontal vs vertical scaling. Cost implications: Sizing, spot vs reserved, optimization ROI.
Red Flag: VACUUM without retention check. Pro-Move: 'Checkpoint; retention; monitor _delta_log.'
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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.