Data Mesh and Lakehouse
summary
Lakehouse systems implement warehouse features on open file formats using metadata layers (Delta Lake, Apache Hudi) that add ACID transactions, time travel, zero‑copy clone, check constraints, and let any engine read Parquet files. New engines (Databricks Photon) and metadata optimizations deliver state‑of‑the‑art SQL: a TPC‑DS entry runs ~2.5× faster than Snowflake at 12× lower cost. Delta Sharing and connectors move petabytes/week between platforms and enable cross‑cloud data mesh sharing.
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chapters (18)
- 0:00Intro
- 1:23Top Challenges for Enterprise Data Us
- 2:42kaggle Data Scientist Survey
- 3:07Sources of Data Challenges
- 5:161980s: Data Warehouse Systems
- 6:072010s: New Problems for Data Wareho
- 7:202010s: Data Lake Systems
- 10:26Lakehouse Systems Implement data warehouse management and performance features on top of directly-accessible data in open formats
- 11:47Key Technologies Enabling Lakehouse
- 12:57Metadata Layers on Data Lakes
- 14:25Management Features with A DELTA LAKI
- 14:59DELTA LAKE Adoption
- 15:42Enabling Lakehouse Performance
- 17:05Result: Best-in-class Performance on DW Workl
- 18:23How to Share Data Between Orgs?
- 20:15How to Manage Governance in Lakeho
- 21:19Summary Lakehouse systems combine the benefits of data warehouses & lakes • Management features • Performance
- 22:01Putting it All Together
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