When would you choose a lakehouse with Delta Lake or Iceberg over a cloud warehouse like Snowflake or BigQuery, or use both?
What the interviewer wants to hear
- Warehouses give managed SQL performance, simplicity and governance
- Lakehouses store open table formats on cheap object storage
- Open formats let many engines (Spark, Trino, warehouses) share one copy
- Lakehouses suit ML, unstructured data and very large raw volumes
- Team skills, cost model and operational effort matter
- Many companies combine both, for example Iceberg tables read by the warehouse
Model answer
A managed warehouse is the fastest way to give analysts reliable SQL with little operational work: storage, tuning, security and concurrency are handled for you. I would pick it for a small team focused on BI. A lakehouse keeps data as Parquet with a table format like Iceberg or Delta on object storage, adding ACID transactions, time travel and schema evolution. Its strengths are cheap storage at large volume, one open copy that Spark, Trino, Flink and even warehouses can read, and a natural fit for machine learning and semi-structured data. The cost is more engineering for compaction, catalogs and access control. In practice in 2026 many teams use both: raw and large data in Iceberg tables in the lake, and the warehouse querying those tables or holding curated marts. I decide based on data volume, use cases, team skills and how much vendor lock-in matters.
Can you answer it under time pressure?
Take a timed medium round and get scored on every answer.
More medium Data Engineer questions
- Your source system sometimes sends the same event twice. How do you handle duplicates in your pipeline?
- A dbt model that used to take 5 minutes now takes 2 hours. How do you troubleshoot it?
- A stakeholder asks for a "real-time" dashboard. How do you decide whether to build streaming?
- Your Spark job fails with executor out-of-memory errors. What do you check and change?
- How would you design an incremental load for a large orders table that also receives updates to old orders?
- Your main nightly Airflow DAG failed at 3 a.m. and dashboards are due at 8 a.m. Walk me through how you handle it.
- How would you build a pipeline that maintains an SCD Type 2 customer dimension from a daily source extract?
- A Spark job that used to take 20 minutes now takes 2 hours. How do you investigate?
- Product engineers keep breaking your pipelines by changing database columns without warning. How would you fix this?