Compare a data warehouse, data lake, and lakehouse
Tests your grasp of data architectures for BI vs. ML workloads. Contrast warehouses (structured) and lakes (raw), then explain how a lakehouse adds ACID/schema features to a lake's storage. A red flag is confusing schema-on-write vs. schema-on-read.
This tests your grasp of modern data architectures and their trade-offs for BI vs. ML. Start by contrasting warehouses (structured data, schema-on-write) with lakes (raw data, schema-on-read). Then, introduce the lakehouse as a hybrid that adds warehouse features like ACID transactions and schema enforcement (via Delta Lake) onto the scalable, low-cost storage of a data lake. A common red flag is failing to explain *how* a lakehouse achieves its goals, just saying it 'combines the best of both'.
Read the original → docs.databricks.com
- #data architecture
- #data lake
- #data warehouse
- #analytics
- #big data
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