Course
Databricks with the Python SDK
AdvancedSkill Level
Updated 02/2026DatabricksArtificial Intelligence3 hr8 videos24 Exercises1,950 XPStatement of Accomplishment
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Getting Started with the Databricks Python SDK
This course introduces the Databricks platform through the Python SDK, focusing on practical, developer-friendly workflows. You’ll learn the essential tools and techniques to interact with Databricks resources and AI models, while managing clusters and jobs programmatically. The emphasis is on hands-on, reproducible steps you can reuse across projects.Authenticating and Exploring the Workspace Client
Begin by setting up and authenticating a Workspace Client in Python. You’ll practice core API operations to create, list, and delete resources, building confidence with the request–response patterns used throughout the SDK. Along the way, you’ll understand how authentication and environment variables enable secure, scriptable access to your Databricks Workspace.Querying AI Models with the Databricks SDK
Dive into AI by querying foundation models with the SDK. You’ll learn how chat completion models work, how to pass “System” and “User” messages, and how to integrate external data into your workflows. You’ll also explore the Foundation Models API and practice generating and summarizing text programmatically.Managing Clusters and Jobs Programmatically
Conclude by orchestrating compute and automation. You’ll create, start, validate, and delete clusters; then create, run, schedule, list, and remove jobs—all from Python. By the end, you’ll have practical skills to manage resources, query AI models, and automate repeatable workflows in Databricks with confidence.Feels like what you want to learn?
Start Course for FreeWhat you'll learn
- Describe the purpose and capabilities of the Databricks SDK.
- Implement workflows to create, delete, and list Databricks jobs and clusters.
- Authenticate a Python client to securely connect with Databricks workspaces.
- Query and interact with AI models via the Databricks Python SDK.
Prerequisites
Intermediate Python for Developers1
Introduction to the Databricks Python SDK
Learn how to set up the Databricks workspace client using Python and authenticate it to your Databricks Workspace. You will explore fundamental API operations, gaining familiarity with key functions for interacting with Databricks clusters and jobs, including creating, listing, and deleting resources.
2
Querying LLM Models with the Databricks SDK
Dive into querying AI models using the Databricks Python SDK. You will discover how to integrate external data sources like CSV files, query the Databricks AI model called DBRX LLM through the Foundation Models API, and understand the roles of different chat message types in AI models, including “System” and “User” roles. You will also learn how to use Databricks catalog data as a data source that that AI agents use to answer your queries.
3
Using the Databricks SDK to Manage Clusters and Jobs
Discover how to manage Databricks clusters and jobs programmatically. You will learn how to create, delete, start, and validate the status of Databricks clusters and jobs using the Python SDK.
Databricks with the Python SDK
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FAQs
What can I do with the Databricks Python SDK after this course?
You will be able to authenticate workspaces, manage clusters and jobs, query AI models, create intelligent agents, and automate workflows programmatically using Python.
Do I need prior Databricks experience to take this course?
No Databricks experience is required, but you should have intermediate Python developer skills. The course teaches Databricks SDK setup and authentication from scratch.
Does the course cover querying AI models through Databricks?
Yes. The second chapter teaches you to query the DBRX large language model through the Foundation Models API, integrate external data sources, and work with chat message roles.
Will I learn to build AI agents with the Databricks SDK?
Yes. You will create intelligent agents that can analyze data from your Databricks catalog, combining SDK operations with AI model capabilities in the final chapter.
How many exercises does this course include?
The course has 25 exercises across three chapters. Most learners complete it in about one and a half hours of focused study time.
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