Learning Paths: Build skills step by step

New learning paths bring related topics together so you can build a solid understanding of key CDF concepts and workflows, progressing from fundamentals to practical implementation.

Data Modeling: Schema Design and Structure: 

Learn how to create Containers, Views, and Data Models. 
This learning path focuses on the schema design and structure of Data Modeling in CDF. It covers the foundational concepts - how to design Containers and Views, structure your model, and apply basic naming and governance practices before writing code or ingesting data.

 This is a fundamental-level path for users who are new to Data Modeling but already have some familiarity with Cognite Data Fusion. 

Data Modeling: knowledge graph creation: 

Understand how to load data into an existing data model in CDF. This learning path focuses on building a Knowledge Graph by ingesting nodes and edges, covering common data sources and the tools used to populate a schema.

This is a foundational path for users new to Data Modeling who already have some familiarity with Cognite Data Fusion.

Courses: Focused learning on key CDF capabilities

New courses dive deeper into specific CDF features and workflows, helping you strengthen your technical understanding and apply them effectively.

An Overview of Cognite Extractors

Understand the role of extractors in Cognite Data Fusion. This course covers extractor architecture, types, security considerations, and deployment approaches used for industrial data ingestion.

You’ll learn what extractors are, why they are critical for bringing industrial data into CDF, and how they work at a conceptual level. The course builds a technical foundation that prepares you for later courses focused on configuration, deployment, and operations.

Mastering Extraction Pipelines in Cognite Data Fusion

Learn how to build, secure, and monitor reliable data integrations in CDF. This course focuses on designing extraction pipelines that provide visibility into data ingestion and help prevent silent failures.

You’ll explore how extraction pipelines work, how to separate extraction logic from monitoring, and how to implement mechanisms such as heartbeat monitoring, remote configuration, and secure authentication using OIDC service principals. By the end of the course, you’ll understand how to move from basic extraction scripts to more robust, observable data pipelines.

Microlearning videos: Small lessons, big impact

We’ve added a new set of bite-sized microlearning videos focused on simulator integrations in CDF. These short lessons cover key concepts and practical steps, making it easier to learn at your own pace.

Activities in charts 
You can now add Activities to your graph view in Charts! Activities can be shown together with the time series data.

Data workflows from the UI:
Learn the fundamentals of creating and managing Data Workflows directly in the CDF user interface. This microlearning shows how to create, run, and monitor workflow tasks and process runs to automate data operations.

How-To Guide articles: Practical Tips from the Experts

We’ve added a fresh set of how-to guides. These articles are designed to help you troubleshoot faster, work more efficiently, and get the most out of CDF.

Enabling the alpha API Subversion and DEBUG mode in the Cognite Python SDK
With this guide, learn how to work with alpha API versions in Cognite Data Fusion and use the Python SDK debug flag to capture detailed HTTP logs for troubleshooting and testing.

It also explains which configuration parameters must be set during client initialization for consistent behavior across environments (Jupyter, local scripts, CI/CD) and which ones can be safely modified later.

How to capture x-request-id for debugging CDF Functions 

With this guide, learn how to trace and debug your CDF API calls using the x-request-id returned with every request. It shows how to log the request ID from failed calls using CogniteAPIError, how to temporarily enable client.config.debug = True to capture it for successful calls, and best practices for using debug logging. It also highlights how extractors (OPC UA, PI, PIAF) can be configured to automatically include x-request-id in logs.

Master Cognite Data Fusion with new training on Cognite Academy. We’d love to hear your thoughts and help you connect with other experts. Come join the conversation on Cognite Hub.