Migrating to Lakebase

Migrates an existing Postgres workload to Lakebase. Following the lecture-demo pattern, learners use a decision matrix to confirm fit, assemble a pre-migration checklist, execute a pg_dump/pg_restore migration (and its alternatives), and design a cutover strategy with validation queries that catch subtle drift.

Skill Level

Associate

Duration

Prerequisites

The content was developed for participants with these skills/knowledge/abilities:

• Comfort with Postgres administration (pg_dump/pg_restore) and SQL

• Understanding of Lakebase architecture and limits (from LB-102)

• Familiarity with synced tables as a migration path (from LB-302)

Suggested prior courses: LB-102 Architecture Deep Dive (limits and extension parity that drive fit) and LB-302 Reverse ETL (synced tables, one of the migration paths). LB-201 branching underpins the blue-green cutover.

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Upcoming Public Classes

Production Operations: HA, Read Replicas, Instant Restore

Hardens Lakebase for production. Following the lecture-demo pattern and framed around an incident, learners configure High Availability across zones, add read replicas and route analytical traffic to them, tune the restore window, and recover from data loss with a point-in-time branch.

Paid & Subscription

Lab

Associate

Vector Search and RAG with pgvector on Lakebase

Builds a semantic retrieval layer on pgvector inside Lakebase Postgres, taught against a live "Ask the docs" app that Classroom Setup deploys. Following the lecture-demo pattern, learners decide when pgvector beats Databricks AI Search, design a document/chunk schema, choose and build HNSW and IVFFlat indexes, generate embeddings in batch with ai_query(), and write a hybrid search combining vector similarity, a metadata filter, and full-text ranking (ts_rank_cd cover-density) in one query. An optional closing section surveys Lakebase Search (Beta) and its lakebase_text (BM25) and lakebase_vector (ANN) extensions.

Paid & Subscription

Lab

Associate

AI Agents and Long-Term Memory on Lakebase

Gives AI agents durable memory on Lakebase. Following the lecture-demo pattern, learners distinguish the three memory categories, choose among framework paths, persist a LangGraph conversation with a checkpointer, resume a thread after a restart, isolate per-tier state with branches, and recall facts across threads with a semantic store.

Paid & Subscription

Lab

Associate