Compare data warehouses and data lakes. How does a lakehouse merge benefits?
Tests schema tradeoffs. Warehouses enforce ACID for BI but cost more; lakes store raw cheaply but lack governance. Lakehouses add ACID metadata on object storage to unify ML and BI.
Tests schema-on-read versus schema-on-write tradeoffs and cost-performance tension. A warehouse enforces ACID schemas at write time for fast BI and governance, but storage is expensive and unstructured data is often rejected. A data lake stores raw data cheaply with schema-on-read flexibility for ML, yet lacks transactions and quality enforcement. A lakehouse merges both by adding an ACID metadata layer like Delta Lake on object storage, enabling schema enforcement and unified governance while keeping costs low and supporting BI and ML.
Read the original → docs.databricks.com
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- #analytics
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- #delta lake
- #system design
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