Clickstream lakehouse upserts
Design a lakehouse for clickstream + ad-impression data: ~20 TB/day landing as ~80M small JSON files/day from edge collectors, queried by ad-hoc Spark/Trino analysts, a daily attribution batch job, and near-real-time spend dashboards. Requirements: ACID upserts (late and corrected impressions arrive for up to 14 days), time-travel for audit, and analyst queries that scan a single day shouldn't read the whole table. Storage is object storage (S3/GCS). Design the table format, layout, and compaction strategy.
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.
Clarify5:30 left
Estimate5:30 planned
Design16:30 planned
Deep dive13:30 planned
Failure9:00 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.