Marcelino Mayorga

In this hands-on course, you’ll learn how to optimize workloads and physical layout with Spark and Delta Lake and and analyze the Spark UI to assess performance and debug applications. We’ll cover topics like streaming, liquid clustering, data skipping, caching, Photon, and more. 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 Databricks development skills, including creating clusters, running notebooks, and importing repos from Git - Intermediate PySpark experience, including data extraction, transformations, and use of advanced built-in functions - Intermediate Delta Lake experience, including creating tables, incremental updates, file compaction, and version restoration

This practitioner-focused course is a deep dive into designing data models within the Databricks Lakehouse. The course explores three leading data warehousing approaches — Inmon's Corporate Information Factory, Kimball's dimensional modeling, and Data Vault 2.0 — and shows how each maps to the medallion architecture. Participants will also learn how to extend these foundations to modern use cases such as data products for scalable, governed data sharing across domains. Prerequisites - Working knowledge of SQL - Conceptual understanding of relational databases - Completion of an introductory Databricks or Lakehouse Fundamentals course - Optional: prior exposure to Python and PySpark Note: Hands-on training courses will be updated to reflect the newest product and feature announcements from Data + AI Summit in June 2026.