Introduction to Databricks Lakebase
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Learning Track
Data Engineering
Delivery methods
On-Site, Virtual
Duration
1 day
A hands-on workshop introducing Databricks Lakebase, the serverless Postgres-based OLTP database for real-time, AI-driven applications. Learners will explore architecture, setup, integration, governance, and use cases through guided labs and applied exercises.
Learning Objectives
- Explain Lakebase architecture and how it differs from traditional OLTP
- Provision and configure a Lakebase instance
- Sync data from the Lakehouse and build real-time applications
- Apply Unity Catalog security and monitor Lakebase
- Explore industry use cases and best practices
Audience
- Developers, data engineers, solution architects
- Databricks users building AI and real-time apps
Prerequisites
Basic Databricks, SQL (Postgres), and Unity Catalog knowledgeCourse outline
- What is Lakebase and why it matters
- How Lakebase differs from other OLTP systems
- Key benefits: Serverless, integrated, scalable
- Lab: Explore Lakebase in the Databricks workspace
- Separation of compute and storage (Neon)
- Features: Low latency, high concurrency, autoscaling
- Advanced: Branching, HA, recovery
- Lab: Create a database branch and test queries
- Provisioning a Lakebase instance
- Basic settings and best practices
- Lab: Create an instance and run SQL queries
- Sync modes: One-off, triggered, continuous
- Reverse ETL with Lakebase
- Lab: Continuous sync from a Unity Catalog table
- Use cases: Feature serving, real-time apps, AI agents
- Integration with Databricks Apps
- Developer tools: SQL editor, notebooks, branching
- Lab: Build a simple app connected to Lakebase
- Unity Catalog integration
- Access controls, network security
- Monitoring and metrics
- Lab: Configure permissions and review audit logs
- Examples in e-commerce, healthcare, and finance
- Best practices for adoption
- Discussion: Participant-specific scenarios
