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Delta Lake & Apache Iceberg Interview Questions Interview Guide

10 interview questions with sample answers

12-15 hours
Prep Time
$150K-$240K
Salary
10
Questions

About This Role

Master open table formats: Delta Lake, Apache Iceberg - schema evolution, ACID transactions, time travel, and modern data lakes.

Behavioral Questions (2)

Q1

Tell me about a project where you chose Delta Lake or Iceberg. Why that choice?

Sample Answer:

Chose Iceberg for data lake with multiple engines (Spark, Trino, Flink). Iceberg's hidden partitioning and engine-agnostic design fit better. Delta Lake was Spark-centric.

Q2

How have you leveraged time travel in Delta Lake or Iceberg?

Sample Answer:

Used Delta time travel for: data audit (query previous version), debugging (compare before/after), rollback (restore incorrect data). Saved hours on forensic analysis.

Technical & Situational Questions (4)

Q3

Explain Delta Lake vs Iceberg: similarities and trade-offs.

Sample Answer:

Both: ACID, schema evolution, time travel. Delta: Spark-native, better Databricks integration. Iceberg: engine-agnostic, hidden partitioning, better for complex partitioning. Choose Delta for Spark teams, Iceberg for multi-engine.

Q4

How do you implement schema evolution in Iceberg/Delta?

Sample Answer:

Both support adding/removing columns, renaming. Delta: schema inference from data. Iceberg: explicit schema management. Test compatibility in lower environment first.

Q5

Explain Iceberg hidden partitioning. Why is it better than traditional partitioning?

Sample Answer:

Hidden partitioning: metadata managed automatically, no directory pruning complexity. Partition-independent queries, better partition pruning. Queries automatically benefit without code changes.

Q6

How do you handle ACID transactions in a data lake?

Sample Answer:

Delta/Iceberg handle ACID via transaction logs. Serializable isolation, atomic writes. Enables safe concurrent writes, guarantees consistency.

FAQ

Can I migrate from Parquet to Iceberg/Delta?
Yes, use COPY INTO for Delta, or re-write Parquet to Iceberg. Plan for downtime or dual-write during migration.
How do I optimize Iceberg for analytics queries?
Use z-ordering for multi-column predicates, clustering for high-cardinality columns, compact small files, analyze query patterns.
What's the impact of time travel on storage?
Time travel uses snapshots (metadata, not data copy). Minimal storage overhead. Deletion can remove old snapshots to reclaim storage.
How do I handle concurrent writes with Delta/Iceberg?
Both support serializable isolation. Concurrent writes succeed if no conflicts. Implement conflict resolution: retry or fail.

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Last updated on 2026-03-07