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

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

Model Development at Scale

In this course, you will develop an in-depth understanding of how to design, implement, and govern scalable machine learning systems that operate effectively at enterprise scale. The curriculum is organized into three experiential modules: developing distributed ML workflows with frameworks such as Apache SparkML and Ray, transitioning local ML development to distributed compute using tools like Pandas on Spark, and operationalizing and governing production models with Databricks’ MLOps ecosystem.

Through hands-on projects, you will construct end-to-end distributed ML pipelines using the SparkML workflow, applying Transformers, Estimators, and the fit/transform paradigm for both classification and regression tasks. You will version, compare, and manage experiments using MLflow 3.0 to ensure reproducibility and governance, capturing lineage between data, features, and model artifacts. Additionally, you will apply scalable Hyperparameter Optimization frameworks to improve model performance at scale.

The course concludes by demonstrating complete lifecycle management, from experimentation to production deployment, using Unity Catalog and Model Serving. You will learn to operationalize trained models, monitor their performance, and implement strong governance over models, features, and Delta assets within the Databricks environment.

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Professional

Data Warehousing Practitioner

Delivery Specialization: CDW Migration Best Practices

This course is meant to provide guidance on best practices for executing a successful migration from Cloud Data Warehouses (CDWs) - e.g. Amazon Redshift, Snowflake, and Synapse Analytics - to Databricks. It covers a wide range of migration processes starting from initial discovery all the way through to delivery.

Data Warehousing Practitioner

Delivery Specialization: EDW Migration Best Practices

This course is meant to provide guidance on best practices for executing a successful migration from Enterprise Data Warehouses (EDWs) - e.g. Netezza, Teradata, Oracle, and Microsoft SQL Server - to Databricks. It covers a wide range of migration processes starting from initial discovery all the way through to delivery.

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!