Databases & Storage · core
Data Warehouses & Lakehouses
Columnar files, table formats, storage-compute separation, batch execution, metadata, governance, and lakehouse architecture.
Mental model
Warehouses optimize governed analytical execution; lakehouses place open table metadata over object storage. Both depend on pruning, columnar scans, and reliable metadata.
How to study Data Warehouses & Lakehouses
Begin by restating the mental model in your own words, then connect it to a concrete system you have built or operated. Name the mechanism, the constraint it addresses, and the trade-off it introduces. Use Delta Lake: High-Performance ACID Table Storage over Cloud Object Stores to check details, but close the source before writing your explanation. Retrieval is the learning step; rereading is only preparation.
Next, compare Data Warehouses & Lakehouses with the neighboring concepts in its roadmap. Ask what changes in correctness, latency, resource use, operability, and failure recovery. Complete Design exercise: Data Warehouses & Lakehouses and preserve the command, input, output, and one failed attempt as evidence. Finish by explaining the idea without jargon to someone who has not studied the track.
Proof of understanding
- Explain the mechanism from first principles and identify the state it reads or changes.
- Give one situation where the concept is the right choice and one where it is not.
- Predict a realistic failure mode before running the drill, then compare the prediction with evidence.
- Connect the result to a roadmap or build artifact instead of treating the concept as isolated trivia.
Learn from primary sources
Practice and explain it back
Design exercise: Data Warehouses & Lakehouses
Columnar files, table formats, storage-compute separation, batch execution, metadata, governance, and lakehouse architecture. Implement designOutline() returning non-empty values for: storageFormat, metadataLayer, queryExecution. Each value must name a concrete mechanism or decision.
Expected evidence: A design outline with storageFormat, metadataLayer, queryExecution plus an explicit failure mode or trade-off.
Open the interactive drill →Review prompts
- What does an open table format (Iceberg, Delta) add on top of Parquet files in object storage?
Build evidence
Object-storage-backed index
Store index segments in object storage with a hot in-memory cache.
- Index segments persisted as immutable objects
- A cache in front of object storage
- Cold-read latency measured and noted
Prerequisites
None assigned yet.
Related concepts
None assigned yet.