Data Governance at Scale

In this course, you will learn how to implement data governance at scale on Databricks using Unity Catalog, with a focus on attribute-based access control, observability, and federated sharing. You will configure ABAC with governed tags, migrate from legacy fine-grained controls, enable and use system tables for audit and cost monitoring, deploy Lakehouse Monitoring for data and model quality, interpret lineage for impact and compliance, and apply federated governance and Delta Sharing patterns for secure cross-cloud collaboration.

Skill Level

Associate

Duration

Prerequisites

Complete the following course before taking this course:

• Databricks Fundamentals (or equivalent introductory Databricks course)

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

• Familiarity with the Databricks platform and basic workspace operations (creating and attaching clusters, running notebooks, managing basic job runs).

• Working knowledge of core data governance concepts such as access control, permissions, and security policies in a data platform.

• Intermediate SQL experience, including creating and managing tables, views, and functions, and granting/revoking privileges on database objects.

• Understanding of Unity Catalog’s basic object model (metastore, catalogs, schemas, tables, volumes, functions, models).

• Basic understanding of data lineage and how data moves between sources, transformations, and downstream analytics or ML assets.

• Familiarity with fine-grained security techniques like row-level filters and column masking, even if not yet implemented in Unity Catalog.

• Beginner-level knowledge of cloud concepts (compute, storage, identities/groups) on at least one major cloud provider.

• Basic awareness of metadata management and data discovery practices in modern data platforms.

Self-Paced

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

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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

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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

Get Started with Lakebase

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.

Paid & Subscription

Lab

Onboarding

Data Warehousing Practitioner

Building Semantic Models in Databricks with UC Metric Views

In this course, you’ll learn how to empower your data consumers to get validated business insights with less burden on you and the rest of your enterprise's data and AI team. Through building a semantic layer on top of your gold layer, you'll offer decision makers and business analysts the opportunity to explore key business performance indicators in novel ways, while still maintaining efficient data operations and enterprise-vetted metrics.

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