Koji Kawamura

In this hands-on course, you’ll learn how to empower your data consumers to get validated business insights with less burden on you and the rest of your enterprise's data and AI team. Through building a semantic layer on top of your gold layer, you'll offer decision makers and business analysts the opportunity to explore key business performance indicators in novel ways, while still maintaining efficient data operations and enterprise-vetted metrics. Prerequisites: Proficiency in Databricks SQL Understanding of Databricks data modeling concepts Note: Hands-on training courses will be updated to reflect the newest product and feature announcements from Data + AI Summit in June 2026.

This course introduces Databricks Lakebase, a fully managed PostgreSQL service built into the Databricks Data Intelligence Platform that brings operational (OLTP) and analytical (OLAP) workloads closer together. The course begins with a conceptual lecture that compares OLTP and OLAP systems, explaining their different performance characteristics, storage models, and typical use cases. You will also explore the challenges organizations face when maintaining separate transactional databases and analytical platforms, including data movement, latency, and architectural complexity. You will then learn how Databricks Lakebase helps address these challenges by providing a PostgreSQL-compatible operational database that integrates directly with the Databricks Lakehouse, enabling operational applications and analytics to work together within a unified platform. This is a Get Started course, so the focus is on understanding the core concepts and basic workflows for working with Lakebase. Building full production applications on top of Lakebase is outside the scope of this course.

This hands-on course teaches Databricks application developers how to use Lakebase as the operational database for intelligent, data-driven applications on Databricks. Learners explore how Lakebase enables applications powered by analytics, BI, and AI. Through guided exercises, the course covers Lakebase core concepts and architecture, provisioning and managing database instances, connecting from Databricks SQL, notebooks, and external clients, and applying best practices for connection management, permissions, and environment configuration. By the end of the course, developers will be able to confidently integrate Lakebase into production-ready data and AI applications, safely manage database resources, and unlock unified analytics and AI on live operational data. Note: Hands-on training courses will be updated to reflect the newest product and feature announcements from Data + AI Summit in June 2026. Prerequisites - Familiarity with the Databricks Data Intelligence Platform, including workspaces and notebooks - Intermediate SQL proficiency, including SELECT, INSERT, UPDATE, and DELETE statements - Intermediate Python programming experience, including functions, exceptions, and…