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Databricks

Delta, medallion, and the lakehouse questions platform teams ask

10 questions with solutions

  1. Q1DatabricksAppleNVIDIA

    When do you pick Delta on Databricks versus Iceberg on a shared lake?

    Solution

    Delta is the default if Databricks is the compute and you want UniForm / deletion vectors / Liquid Clustering in that ecosystem. Iceberg if Trino, Snowflake, and Spark must read the same files without a vendor lock. Do not dual-write “just in case.”

  2. Q2ComcastShellRivian

    Explain bronze / silver / gold without the slide.

    Solution

    Bronze is raw as landed (schema-on-read, lineage). Silver is cleaned, typed, deduped, conformed keys. Gold is the grain a dashboard or feature can trust. Skipping silver is how gold lies. Optional: a serving layer that is just gold plus a rollup.

  3. Q3DatabricksFordGM

    OPTIMIZE and ZORDER versus Liquid Clustering. What do you actually run weekly?

    Solution

    OPTIMIZE compacts small files. ZORDER (or Liquid Clustering) colocates filter columns. VACUUM removes stale files after the retention window — and it is irreversible. Do not VACUUM 0 hours in prod. Quote the retention and the “time travel vs vacuum” trade.

  4. Q4NetflixUberLinkedIn

    A shuffle-heavy join OOMs the worker. Name three levers before you “just scale the cluster.”

    Solution

    Broadcast the small side if it fits. Repartition both on the join key. AQE / skew join hints. Filter before the join. If it is still huge, rethink the grain — you may be exploding a many-to-many. Scaling the cluster last is the expensive answer.

  5. Q5DatabricksAccentureDeloitte

    Unity Catalog vs Hive metastore. Why do interviewers care in 2026?

    Solution

    UC is the governance plane: identities, row filters, lineage, a 3-level namespace. HMS is the legacy catalog. “We still have two catalogs” is a real migration answer. Do not claim ACLs on a path-based table you never registered.

  6. Q6StripeShopifyBlock

    Auto Loader vs a COPY into a table every hour. When is streaming the simpler design?

    Solution

    Auto Loader (cloudFiles) tracks what was ingested with a checkpoint — good for landing buckets that grow all day. A scheduled COPY is fine for a nightly dump. The fail is “streaming” a file drop that arrives once a day, or batching a firehose with no watermark.

  7. Q7DatabricksComcastFord

    Job cluster vs all-purpose for a 20-minute twice-daily ETL.

    Solution

    Job cluster that dies when the run ends. An always-on all-purpose box is an idle bill.

  8. Q8DatabricksAppleNVIDIA

    UniForm vs dual-write Delta and Iceberg.

    Solution

    UniForm (or one Iceberg table) with a single writer. Dual-write is how the two copies lie to each other.

  9. Q9DatabricksShellRivian

    DLT expectations: warn vs fail vs drop.

    Solution

    Fail or quarantine when the SLA cannot tolerate bad keys. Warn-only is a blog post. Drop is a product decision you must document.

  10. Q10DatabricksComcastAccenture

    Instance pools — when are they worth idle VMs?

    Solution

    When job start time is in the SLA and cold VM spin is 8 minutes. Size the idle count to the SLA, not to max theoretically.

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