1 min lesson
Why lakehouse over a warehouse here
Explain your answer to "Why does Cursor prefer a lakehouse over a classic warehouse for this role's data?" Add one concrete detail from the lesson.
Step 1 of 3
Why lakehouse over a warehouse hereone copy, three very different consumers
In the scenario this role describes, the data serves BI dashboards, product analytics and ML/agent training from the same tables. A warehouse-only stack would force you to copy data out to a separate object store every time the ML side needed raw Parquet, doubling storage and creating two sources of truth that drift. The lakehouse keeps a single governed copy that Spark, SQL and training jobs all read directly.
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Advanced table
Reference table
- Feature
- Storage cost
- Raw data lake
- Lowest, on an object store
- Classic warehouse
- Highest, on proprietary storage
- Lakehouse with Delta and UC
- Low, on an object store
- Feature
- ACID / schema
- Raw data lake
- None
- Classic warehouse
- Strong
- Lakehouse with Delta and UC
- Strong, via the Delta log
- Feature
- Open formats
- Raw data lake
- Yes, Parquet
- Classic warehouse
- No, locked in
- Lakehouse with Delta and UC
- Yes, Parquet under Delta
- Feature
- Serves ML on raw files
- Raw data lake
- Yes, but ungoverned
- Classic warehouse
- Awkward, needs export
- Lakehouse with Delta and UC
- Yes, governed in place
- Feature
- Governance / lineage
- Raw data lake
- Bolt-on, weak
- Classic warehouse
- Strong but engine-bound
- Lakehouse with Delta and UC
- Unity Catalog, cross-engine
| Feature | Raw data lake | Classic warehouse | Lakehouse with Delta and UC |
|---|---|---|---|
| Storage cost | Lowest, on an object store | Highest, on proprietary storage | Low, on an object store |
| ACID / schema | None | Strong | Strong, via the Delta log |
| Open formats | Yes, Parquet | No, locked in | Yes, Parquet under Delta |
| Serves ML on raw files | Yes, but ungoverned | Awkward, needs export | Yes, governed in place |
| Governance / lineage | Bolt-on, weak | Strong but engine-bound | Unity Catalog, cross-engine |
The lakehouse is the only column that serves BI and ML from one governed copy at object-store cost.
When asked "why a lakehouse," don't list features. Name the consumer conflict it resolves. "We serve BI, product analytics and agent-training data from the same telemetry. A warehouse would make me export raw files for the ML side and run two sources of truth; Delta plus Unity Catalog gives one governed copy at object-store cost." That ties the choice to the workload the role describes, which is the senior signal.
Don't call Delta "just Parquet." The Parquet files are inert without the transaction log: drop the _delta_log and you've lost ACID, time travel and the file set itself. Conflating the two reads as surface knowledge; the log is what makes it a table.