Working with Data

Tours the surfaces for working with data in Lakebase. Following the lecture-demo pattern, learners run queries in the SQL Editor, browse and edit rows and add indexes in the Tables view, connect from external clients and the PostgREST Data API, and query historical data through a point-in-time branch — choosing the right surface for each task and audience.

Skill Level

Associate

Duration

Prerequisites

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

• Comfort with SQL (SELECT, INSERT, UPDATE, DELETE) and basic Python

• Ability to connect to Lakebase and run basic CRUD (from LB-103)

Suggested prior course: LB-103 First Steps — establishes connecting and basic CRUD, which this course extends across every surface.

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Self-Paced

Custom-fit learning paths for data, analytics, and AI roles and career paths through on-demand videos

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Public and private courses taught by expert instructors across half-day to two-day courses

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Comprehensive training offering for large scale customers that includes learning elements for every style of learning. Inquire with your account executive for details

Upcoming Public Classes

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.

Paid & Subscription

Lab

Associate

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