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Delta Lake: ACID compliance, time travel, streaming support

Spark/Big Datahard0.4 min readPremium
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
Expert AnswerPremium
85 wordsInterview-ready
**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....
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