Get Started with Data Governance on Databricks

In this course, you will explore Unity Catalog and fine-grained access controls on Databricks with hands-on demos and a capstone lab. You will learn about table types, catalog and schema configuration, group-based access management, and access control migration strategies. The course includes demos on applying Fine-Grained Access Controls with Row Level Security and Column Masking, Attribute based access control, combining controls, migrating controls, and a lab for comprehensive governance implementation.

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:

⇾ A basic understanding of cloud computing concepts (virtual machines, object storage, etc.)

⇾ Intermediate experience with SQL (commands, aggregate functions, filters, sorting, tables, views)

⇾ Familiarity with Python programming and the Jupyter notebook interface

⇾ Basic knowledge of PySpark fundamentals (for hands-on exercises)

⇾ 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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Registration options

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

Instructor-Led

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

Blended Learning

Self-paced and weekly instructor-led sessions for every style of learner to optimize course completion and knowledge retention. Go to Subscriptions Catalog tab to purchase

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Skills@Scale

Comprehensive training offering for large scale customers that includes learning elements for every style of learning. Inquire with your account executive for details

Upcoming Public Classes

Data Engineer

Data Interoperability with Unity Catalog

This course explores how to achieve seamless data interoperability between systems using Databricks Unity Catalog as a unified governance layer, using both Delta and Iceberg open table formats. Participants will learn to implement, manage and optimize a data environment where tables can be accessed by both Delta and Iceberg clients, enabling cross platform analytics and eliminating data silos.

Paid & Subscription

Lab

Associate

Machine Learning Practitioner

Feature Engineering at Scale

In this course, you will gain a comprehensive understanding of how to design, scale, and operationalize end-to-end feature engineering pipelines on the Databricks platform. The curriculum is structured across three progressive modules: mastering the fundamentals of Spark’s distributed execution and optimization, implementing scalable data ingestion with Auto Loader and declarative Lakeflow pipelines, and advancing to production-grade MLOps with the Databricks Feature Store.

You will engage in hands-on learning experiences such as debugging Spark performance with the Catalyst Optimizer and Spark UI, building robust Bronze-Silver-Gold medallion architectures with automated quality checks, and implementing scalable feature transformations using SparkML. The course culminates in deploying real-time feature serving through Online Feature Stores, defining FeatureSpecs with on-demand transformations, and applying governance and lineage tracking with Unity Catalog.

Paid & Subscription

Lab

Professional

Data Warehousing Practitioner

Data Warehousing with Databricks

This 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 using the industry-standard TCP-DI dataset as a reference. 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: 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