I'm already using the Databricks UI for governance. Why do I need the SDK or CLI?

While the UI is great for one-off tasks, managing governance at scale requires automation. This course teaches you how to use the SDK and CLI to programmatically manage users, permissions, and data assets, which is essential for integrating governance into your CI/CD pipelines and Infrastructure as Code practices.

I'm a data engineer, not a machine learning expert. Will the ML module be too advanced?

The ML module is designed to give data engineers the necessary context for working with ML teams. It focuses on the operational aspects—like setting up a serving endpoint and the overall MLOps architecture—that are relevant for integrating and supporting ML models within governed data pipelines.

When will I have access to the lectures and assignments?

To access the course materials, assignments and to earn a Certificate, you will need to purchase the Certificate experience when you enroll in a course. You can try a Free Trial instead, or apply for Financial Aid. The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.

What will I get if I subscribe to this Specialization?

When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile.

Is financial aid available?

Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.

Production Governance and MLOps on Databricks

This course is part of Enterprise AI and Data Engineering with Databricks Specialization

Instructors: Noah Gift

Included with

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3 modules

Gain insight into a topic and learn the fundamentals.

Intermediate level

Recommended experience

4 hours to complete

Flexible schedule

Learn at your own pace

3 modules

Gain insight into a topic and learn the fundamentals.

Intermediate level

Recommended experience

4 hours to complete

Flexible schedule

Learn at your own pace

What you'll learn

Navigate and manage the Unity Catalog hierarchy (metastores, catalogs, schemas, tables) using the SDK, CLI, and VS Code
Implement access control by creating service principals and writing GRANT/REVOKE statements in SQL
Implement access control by creating service principals and writing GRANT/REVOKE statements in SQL

Skills you'll gain

Tools you'll learn

Details to know

Shareable certificate

Add to your LinkedIn profile

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Build your subject-matter expertise

This course is part of the Enterprise AI and Data Engineering with Databricks Specialization

When you enroll in this course, you'll also be enrolled in this Specialization.

Learn new concepts from industry experts
Gain a foundational understanding of a subject or tool
Develop job-relevant skills with hands-on projects
Earn a shareable career certificate

There are 3 modules in this course

This intermediate course provides a practical, hands-on exploration of Databricks Governance, focusing on the essential tools and workflows for managing and securing your data lakehouse. You will learn to navigate and control access to your data assets using Unity Catalog, the foundation of Databricks governance. The course covers the core hierarchy of metastores, catalogs, schemas, and tables, and teaches you how to manage them programmatically using the Databricks Python SDK, CLI, and VS Code extension.

Beyond foundational access control, you will master the skills to implement modern CI/CD and MLOps practices directly within the Databricks environment. You'll learn to integrate Databricks Repos with GitHub, automate notebook testing and deployment with GitHub Actions, and understand the architectural considerations for managing machine learning models in production. Finally, you will explore how to ensure ongoing data reliability by setting up and understanding Lakehouse Monitoring for data quality and freshness. This course is unique because it moves beyond theory, demonstrating how to apply these governance concepts with the actual tools and code used by data professionals. By the end, you'll be equipped to build, deploy, and monitor secure and reliable data pipelines and AI applications on the Databricks platform

Module details

This module establishes the foundation of Databricks governance through Unity Catalog. You'll navigate the metastore-catalog-schema- table hierarchy, set up role-based access control using service principals and GRANT/REVOKE statements, and learn to manage your governance setup programmatically with the Databricks Python SDK, CLI, and VS Code extension.

What's included

16 videos9 readings1 assignment

16 videosTotal 48 minutes

Course Introduction1 minute
Introduction0 minutes
Unity Catalog overview5 minutes
Navigating the catalog hierarchy5 minutes
Setting up your first Unity Catalog6 minutes
Summary0 minutes
Introduction0 minutes
Introducing the Databricks Python SDK7 minutes
Setting up the Databricks VS Code extension3 minutes
Overview of the Databricks CLI5 minutes
Summary0 minutes
Introduction0 minutes
Principals and configurations3 minutes
Using the SDK to create a Service Principal5 minutes
Writing REVOKE and GRANT statements4 minutes
Summary1 minute

9 readingsTotal 17 minutes

About this course and your instructors1 minute
Key terms1 minute
Lab5 minutes
Reflection1 minute
Key terms1 minute
Lab5 minutes
Reflection1 minute
Key terms1 minute
Reflection1 minute

1 assignmentTotal 30 minutes

Quiz: Governance 30 minutes

This module covers the workflows that take Databricks code from a developer's laptop to production. You'll integrate Databricks Repos with GitHub using branching strategies and code review, automate notebook testing and deployment with GitHub Actions, and build a complete MLOps pipeline that serves a GenAI application through a model serving endpoint.

What's included

16 videos9 readings1 assignment

16 videosTotal 52 minutes

Introduction0 minutes
Connecting Databricks to GitHub5 minutes
Authenticating to GitHub4 minutes
Branching strategies and code review4 minutes
Summary1 minute
Introduction0 minutes
Running notebooks as jobs6 minutes
Challenges with notebooks6 minutes
Automating tests and runs with GitHub Actions6 minutes
Summary1 minute
Introduction0 minutes
Overview of ML and AI capabilities4 minutes
Creating a GenAI application5 minutes
Creating a serving endpoint6 minutes
MLOps architectural overview4 minutes
Summary0 minutes

9 readingsTotal 17 minutes

Key terms1 minute
Lab5 minutes
Reflection1 minute
Databricks Free Edition1 minute
Key terms1 minute
Lab5 minutes
Reflection1 minute
Key terms1 minute
Reflection1 minute

1 assignmentTotal 30 minutes

Quiz: CI/CD and MLOps30 minutes

This module closes the production loop with Lakehouse Monitoring. You'll enable quality and freshness monitoring on Unity Catalog tables, interpret monitoring results to detect data anomalies and drift, and review the recommendations that turn a working pipeline into a production-ready governance setup.