Get Started with Data Governance on Databricks

In this course, you will explore how Unity Catalog enables secure, centralized data governance and fine-grained access control on Databricks. You will learn about table and volume types, catalog and schema configuration, group-based access management, and strategies for migrating existing access controls into Unity Catalog. The course also explains how to design and apply fine-grained controls such as row-level security, column masking, and attribute-based access control, how to combine these mechanisms across data and AI assets, and how to align them with broader governance requirements for compliant, scalable access management.

Languages Available: English | 日本語 | 한국어

Skill Level

Onboarding

Duration

Prerequisites

Complete the following course before taking up this course:

• Databricks Fundamentals

In this course, the content was developed for participants with these skills/knowledge/abilities:

• Familiarity with the Databricks Data Intelligence Platform and basic workspace operations (create clusters, run code in notebooks, use basic notebook operations)

• Basic understanding of data governance concepts, including access control, permissions management, and security policies

• Intermediate experience with SQL concepts such as creating tables, views, functions, and managing database objects and permissions

• Understanding of Unity Catalog's hierarchical object model (metastore, catalogs, schemas, tables, volumes, models)

• Basic knowledge of data lineage concepts and understanding of data flows and dependencies between tables and assets

• Familiarity with security and compliance principles, including row-level security, column masking, and fine-grained access controls

• Beginner familiarity with cloud computing concepts (virtual machines, object storage, identity management)

• Basic understanding of metadata management and data discovery principles

• Completion of a foundational Databricks course (such as Fundamentals of the Databricks Data Intelligence Platform) is beneficial but not mandatory

Self-Paced

Custom-fit learning paths for data, analytics, and AI roles and career paths through on-demand videos

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Databricks has a delivery method for wherever you are on your learning journey

Self-Paced

Custom-fit learning paths for data, analytics, and AI roles and career paths through on-demand videos

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Public and private courses taught by expert instructors across half-day to two-day courses

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Upcoming Public Classes

Data Analyst

SQL Analytics on Databricks

In this course, you'll learn how to effectively use Databricks for data analytics, with a specific focus on Databricks SQL. As a Databricks Data Analyst, your responsibilities will include finding relevant data, analyzing it for potential applications, and transforming it into formats that provide valuable business insights. You will also understand your role in managing data objects and how to manipulate them within the Databricks Data Intelligence Platform, using tools such as Notebooks, the SQL Editor, and Databricks SQL. Additionally, you will learn about the importance of Unity Catalog in managing data assets and the overall platform. Finally, the course will provide an overview of how Databricks facilitates performance optimization and teach you how to access Query Insights to understand the processes occurring behind the scenes when executing SQL analytics on Databricks.

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!

Languages Available: English | 日本語 | Português BR | 한국어

Paid & Subscription

Lab

Associate

Data Warehousing Practitioner

SQL Programming and Procedural Logic

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 (including advanced recursive CTEs), session-scoped objects like temporary views and tables, 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. You will also learn how to guarantee data consistency across these multi-step pipelines using ACID-compliant multi-statement transactions. Finally, you’ll discover how to encapsulate logic into SQL Stored Procedures and orchestrate complex workflows with Lakeflow Jobs and migration strategies, transforming legacy tasks into modular, automated pipelines that leverage the full capabilities of the Databricks platform.

Paid & Subscription

Lab

Associate

Agent Evaluation on Databricks

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.

Paid & Subscription

Lab

Associate