data engineering
FDE interview questions tagged data engineering, across every topic.
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Concepts behind "data engineering"
The curriculum that explains the ideas these questions test.
Core
Idempotent Data PipelinesPipelines retry, get re-run, and get backfilled, and every one of those re-runs must produce the same result as running once. Idempotency is the property that makes that true: write by key with upsert or partition overwrite, never blind append, so a retry cannot double-count. It is the single property that makes a pipeline safe to operate, because the alternative is a 2 a.m. page where you cannot tell if it is safe to run the job again.🗄️ Data & SQL EngineeringSign in
Advanced
Change Data Capture (CDC)Change Data Capture streams row-level inserts, updates, and deletes out of a source database so downstream systems stay in near-real-time sync without full reloads. The strong form reads the database transaction log rather than polling tables, which captures deletes, preserves commit order, and adds almost no load to the source. The hard parts are ordering, tombstones for deletes, and applying the stream idempotently so a replay does not corrupt the target.🗄️ Data & SQL Engineering🔒 Premium
Advanced
ETL vs ELTETL transforms data before it lands; ELT loads it raw and transforms inside the warehouse. Cheap elastic compute made ELT the default, but ETL is still correct when data must never land in raw form, which is exactly the situation in most regulated customer environments an FDE deploys into.🗄️ Data & SQL Engineering🔒 Premium
Advanced
Lakehouse, Delta and the Medallion ArchitectureA lakehouse puts a transactional table format over cheap object storage, so a data lake gets ACID writes, schema enforcement and time travel without becoming a warehouse. The medallion layering on top (bronze, silver, gold) is less about the metaphor than about giving each layer an owner and a contract.🗄️ Data & SQL Engineering🔒 Premium