Jade Lauzon

This hands-on course is designed for data professionals who want to explore the data warehousing capabilities of Databricks. Assuming no prior knowledge of Databricks, it provides an introduction to leveraging Databricks as a modern cloud-based data warehousing solution. Learners will explore how use the Databricks Data Intelligence Platform to ingest, transform, govern, and analyze data efficiently. Learners will also explore Genie, an innovative Databricks feature that simplifies data exploration through natural language queries. By the end of this course, participants will be equipped with the foundational skills to implement and optimize a data warehouse using Databricks. Note: Hands-on training courses will be updated to reflect the newest product and feature announcements from Data + AI Summit in June 2026. Prerequisites - Basic SQL proficiency and understanding of data querying concepts - General knowledge of data warehousing concepts, including tables, schemas, and ETL/ELT processes - Some experience with BI or data visualization tools (recommended but not required)

This hands-on course teaches you how to build production-ready ETL pipelines using pure SQL on the Databricks Data Intelligence Platform. You'll learn the declarative building blocks — Streaming Tables, Materialized Views, and AUTO CDC — that replace complex procedural ingestion and transformation logic with concise, incremental, and fully managed pipeline definitions. Following a realistic retail dataset through the medallion architecture, you'll incrementally ingest files with Auto Loader, build pre-computed Silver-to-Gold transformations with incremental refresh, manage SCD Type 1 and Type 2 dimensions with AUTO CDC, and orchestrate the full pipeline using Lakeflow Jobs with SQL File tasks and DAG-based workflows. Prerequisites - Navigating the Databricks workspace — sidebar, Catalog Explorer, and SQL Editor - Unity Catalog basics — catalogs, schemas, tables, and volumes - Intermediate SQL — SELECT, JOIN, GROUP BY, CAST, COALESCE, CREATE TABLE, and INSERT - Data warehousing concepts — fact tables, dimension tables, star schemas, and the medallion architecture - Basic understanding of ETL — Extract, Transform, Load workflows and why incremental processing matters Note: Hands-on…

This hands-on lab covers building and deploying a stateful AI assistant with both short-term and long-term memory using an agent framework, Lakebase, and Databricks Apps. Attendees create Unity Catalog function tools, give the agent the ability to remember conversations and recall user preferences across sessions, and deploy it as a chat application. By the end, attendees will understand how both memory systems work together and have a deployed stateful agent.