Serving Lakehouse Data in Real Time with Lakebase
Serves ML features from Lakebase on the hot path. Following the lecture-demo pattern, learners measure why Delta cannot serve single-row reads, read features from a synced table with millisecond point lookups, avoid the loop-vs-IN trap, reason about same-DB joins versus federation, and publish features to a managed online feature store.
The content was developed for participants with these skills/knowledge/abilities:
• Comfort with SQL and Python, plus basic ML feature concepts
• Ability to connect to Lakebase and read from a synced table (from LB-103, LB-302)
Suggested prior courses: LB-302 Reverse ETL (the synced table that holds the features) and LB-103 First Steps (connecting and querying). LB-202 Autoscaling helps for reasoning about read-path scale.
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