Data Engineering
Lakehouse & Table Formats
6 practice questions. Free questions open a full answer guide; the rest unlock with Pro.
- On an Iceberg or Delta table, what's the difference between copy-on-write and merge-on-read for updates and deletes, and why does the table still get slow over time if you only ever write to it?
- Why would a team store a data lake table in an open table format like Apache Iceberg or Delta Lake instead of just writing plain Parquet files to a directory?Go Pro
- You're running a lakehouse table that takes a steady stream of upserts and deletes from a CDC feed, and reads are starting to slow down. Walk me through how you'd reason about copy-on-write versus merge-on-read for this table, and what ongoing maintenance the choice commits you to.Go Pro
- Your team stores its analytics data as Parquet files in object storage with Hive-style directory partitioning, and you're being pushed to adopt an open table format like Iceberg, Delta, or Hudi. What concrete problems do these formats actually solve over plain Parquet, and how would you decide whether the migration is worth it?Go Pro
- What is time travel in a table format like Iceberg or Delta Lake, and how does the format make it possible?Go Pro
- Your team is moving raw Parquet files in cloud storage onto an open table format, and someone asks why you'd pick Iceberg over Delta Lake or Hudi. Walk me through the trade-offs and how you'd decide.Go Pro
Want questions matched to your role? Paste a job title, job description, or CV for a personalized set, or go Pro to unlock the full bank.