Databricks Performance Optimization

This get started 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.

Through hands-on labs, you will:

Create and explore a Lakebase project using autoscaling compute

• Navigate the Lakebase UI, including branching, monitoring, and configuration settings

• Create and query tables using the Lakebase SQL Editor

• Query Lakebase data from Databricks using Lakehouse Federation and foreign catalogs

• Perform Reverse ETL by synchronizing Delta tables to Lakebase

• Connect to Lakebase from Python and perform basic CRUD operations

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.

Note: For SCORM lecture files, please ensure that you close the SCORM window after completing the content. Do not click the ‘Next Lesson’ button, as doing so may prevent the SCORM module from being marked as complete.

This course teaches students how to systematically evaluate AI agents using MLflow's evaluation framework, addressing the unique challenges of non-deterministic AI systems that traditional software testing cannot handle. Students learn to implement various evaluation approaches including built-in judges for common criteria like correctness and safety, guideline judges for business-specific requirements, and custom judges for specialized needs. The course covers both offline evaluation using curated datasets and online production monitoring, with hands-on experience using MLflow's tracing capabilities to understand agent execution patterns and collect human feedback from different stakeholder types. Through practical demonstrations and labs, students develop skills in creating evaluation workflows that drive continuous quality improvements throughout the AI agent development lifecycle.

In this course, you will explore core SQL programming and procedural logic skills within Databricks, focusing on how to build modular, maintainable, and production-ready analytics workflows. You’ll start by learning about key SQL constructs—such as Common Table Expressions, temporary views, and User Defined Functions—that make your SQL development cleaner and more reusable. The course then guides you through advanced SQL scripting techniques using compound statements, variables, control flow, conditionals, loops, and robust error handling to create structured and flexible SQL processes. Finally, you’ll discover how to orchestrate and modernize complex workflows with Lakeflow Jobs and migration strategies, transforming legacy tasks into modular, automated pipelines that leverage the full capabilities of the Databricks platform.

Note: Databricks Academy is transitioning from video lectures to a more streamlined PDF format with slides and notes for all self-paced courses. Please note that demo videos will still be available in their original format. We would love to hear your thoughts on this change, so please share your feedback through the course survey at the end. Thank you for being a part of our learning community!