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Delta Lake: ACID compliance, time travel, streaming support

Spark/Big Datahard0.4 min read

**Why Delta matters**: Parquet + transaction log = mutable lakehouse with reliability. **ACID**: Transaction log (_delta_log/) records commits; concurrent reads/writes serialized. **Time travel**: Query by version or timestamp; `VERSION AS OF n` or `TIMESTAMP AS OF '...'`....

🤖 Analyze Your Answer
Frequency
Low
Asked at 1 company
Category
452
questions in Spark/Big Data
Difficulty Split
88E|81M|283H
in this category
Total Bank
1,863
across 7 categories
Asked at these companies
Kaseya
Key Concepts Tested
lakehouse

Why This Question Matters

This hard-level Spark/Big Data question appears frequently in data engineering interviews at companies like Kaseya. While less common, it tests deeper understanding that distinguishes strong candidates. Mastering the underlying concepts (lakehouse) will help you answer variations of this question confidently.

How to Approach This

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.

Expert Answer
85 words

Why Delta matters: Parquet + transaction log = mutable lakehouse with reliability. ACID: Transaction log (_delta_log/) records commits; concurrent reads/writes serialized. Time travel: Query by version or timestamp; VERSION AS OF n or TIMESTAMP AS OF '...'. Streaming: Read/write as stream; merge support; exactly-once with checkpoint. Scalability trade-offs: Log grows; VACUUM removes old files. Time travel retention = storage. Cost implications: Delta = storage + log; VACUUM reduces cost but limits time travel. Best practice: OPTIMIZE + VACUUM; set retention; use time travel for audits.

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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 a curated database of 1,863+ real data engineering interview questions across 7 categories, verified by industry professionals.

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