Data lake columnar storage
Design the storage layer for a data lake holding petabytes of analytical data as columnar (Parquet) files on object storage, queried by Spark/Presto-style engines. Workloads are append-heavy ingestion plus large analytical scans that read a few columns across billions of rows, with occasional row-level updates/deletes (GDPR erasure). How do you organize files, make queries fast, and support mutation on an immutable object store?
What a strong answer looks like
Clarify scale and constraints first. Propose a clean component breakdown, then go deep on the hard parts (data model, bottlenecks, consistency, failure modes) and name the trade-offs you are making.
Clarify5:00 left
Estimate5:00 planned
Design15:00 planned
Deep dive12:00 planned
Failure8:00 planned
Which questions mattered is sealed until you submit. Telling you now would just be handing over the edge cases.
Run or narrate your approach, then ask the coach.