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What's your approach to data versioning in a data lake?

System Design/Architecturehard0.3 min read

Reviewed by Aditya Kumar · Last reviewed 2026-03-24

**Approaches:** (1) Delta/Iceberg—ACID, time travel, schema evolution. (2) Partition-based—daily partitions as versions; retain N days. (3) Snapshots—point-in-time views. (4) Git-like—DVC (ML), lakeFS (tables)....

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Frequency
Low
Asked at 1 company
Category
179
questions in System Design/Architecture
Difficulty Split
15E|6M|158H
in this category
Total Bank
1,863
across 7 categories
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Interview Pro Tip

Pro-Move: Delta time travel for debugging. Red Flag: No versioning strategy for mutable data.

Key Concepts Tested
partition
Expert Answer
53 words

Approaches: (1) Delta/Iceberg—ACID, time travel, schema evolution. (2) Partition-based—daily partitions as versions; retain N days. (3) Snapshots—point-in-time views. (4) Git-like—DVC (ML), lakeFS (tables).

Architecture Diagram:

[Raw Ingestion] --> [Bronze/Delta]
| |
v v
[S3 Versioning] [Time Travel API]
| |
+--------+-----------+
v
[Silver/Gold]
|
[Schema Evolution]
[Compaction/VACUUM]

Production: Delta/Iceberg for mutable; retention; compaction.

⚡
Pro Tip

Pro-Move: Delta time travel for debugging. Red Flag: No versioning strategy for mutable data.

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