Community Discussions

We are excited to announce the Q1 2026 release of Cognite Data Fusion (CDF). This quarter, our focus is on breaking down technical barriers for Subject Matter Experts and providing a more immersive, high-precision data experience.Empowering SMEs: The New Simulators UI (Beta) Immersive Data Exploration & 3D Context Smarter Search & Precision Tools Atlas AI: Continuity & Control Performance & Readiness Looking Forward: Production-Grade Tailored SolutionsEmpowering SMEs: The New Simulators UI (Beta)We are transitioning from a "Code-First" to a "User-First" approach for industrial simulations. Feature Problem (Challenge) Solution: What You Can Do Now Simulators UI (Beta) SMEs are currently blocked by a "technical wall," where simulator connectors remain locked behind APIs that can be configured only by developers. Empower SMEs to independently configure their own models and use a no-code "Routine Board" for drag-and-drop visual logic

Button to close window disappears when browser window is too small. It is quite inconvienient that the “close” button is one that disappears. Is this by design or a UI bug?

Hello, I was playing with this project (https://github.com/blurrah/mcp-graphql) and created an MCP server on top of a test CDF data model. You can then use an LLM that supports MCP clients to register your server and have the help of the LLM to analyse your data :) Here I used Claude desktop for example:  ​​​​​​This is a great way if we want to integrate CDF data models with in-house or local LLM models.Do you plan to release an official MCP server for CDF data models? Thank you,

Hi, I’m Enikö Farkas from Cognite. With my team, we create e-learning for Cognite Academy. In our courses, we put a lot of emphasis on providing hands-on experiences so you can learn to use Cognite Data Fusion through interacting with real industrial data.In most of our courses, the training data we use comes from the Open Industrial Data (OID) project which is the result of a collaboration between Aker BP, one of Europe’s largest independent oil companies, and Cognite. While the data is from the oil and gas industry, its relevance is much broader: it gives us insight into dynamic industrial processes and can inspire all industries.In this article, I’m excited to share more about this project.About the dataThe data originates from a single compressor on Aker BP’s Valhall oil platform in the North Sea. Aker BP selected the first stage compressor on the Valhall because it is a subsystem with clearly defined boundaries, rich in time series and maintenance data.AkerBP uses Cognite Data Fus

Cognite documentation powers human and agent workflows with new AI functionalityCognite now offers several ways to bring our official CDF product documentation into your AI-powered workflows.A docs MCP server for live documentation access in your IDE.  SKILL.md capability files so AI assistants understand what Cognite Data Fusion (CDF) can do. An integrated experience on docs.cognite.com that includes an AI assistant that answers questions and cites sources, plus one-click options to send any page to Cursor, VS Code, ChatGPT, or Claude.To use the MCP server and the SKILL.md file, you'll need a compatible AI-powered IDE or assistant (for example, Cursor, Claude, Windsurf, or VS Code with Copilot). To install the SKILL.md file with the skills CLI, you also need Node.js. Docs MCP server: live documentation in your IDEThe Model Context Protocol (MCP) is an open standard that lets AI assistants securely access external data and tools. Cognite's docs MCP server gives your IDE live access to

 Dears,Would you please advise how can we hide specific layers (for ex. the crane) in the 3D model and then to save the updated or customised revision?is the revision editing for 3D models doable inside CDF itself or there is a specific workflow needed for it, as you can see it the snapshot below: Thank you! Fatima 

Hi everyone!We know that for an end-user, finding data shouldn't feel like a "needle in a haystack" mission. Last summer, we launched Per-Project Search Configuration, and if you haven't set it up yet, your team might be missing out on a much cleaner experience.As a Project Admin, you can curate what your users see when they sign in to Search. Instead of a generic view of data that maps 1:1 to your detailed data model definition, you can hand-deliver the most relevant data. Why configure your categories? Instant clarity: Define exactly which columns appear first. No more side-scrolling to find the "Status" or "ID." Relevant filtering: Hide the noise. Display only the filters that actually matter for that specific category. Context at a glance: Tailor the "Properties" card so the most crucial metadata is front and center. Quick guide: Check access: Ensure you have appconfig:read/write and app-scope = Search. Your users also need appconfig:read to make the config take effect for them

On the platform, when we have more than one annotation on a file, if they are very close to each other (overlapping by a pixel or two), the platform shows the overlapping box so we can select the correct annotation.I understand this as a bug.See the images below:The tags PB2885C and XA2885C are very close to each other. The overlap between them is barely noticeable.However, when you click on either one, both tags appear in the overlapping box:I’ve seen this happen every time we have annotations that are very close to each other.For a better user experience, it would be ideal if, at the moment of the click, the platform could understand that only one annotation is actually inside the click area.This would prevent the overlapping box from appearing and would solve the issue.Is this something we can have fixed?

Hi everyone,I’d like to share industrial-model — a Python ORM built on top of the Cognite Data Modeling Service API.It lets you define DMS views using Pydantic models, query them with a fluent, expressive API (filter, search, aggregate), and get fully typed results with IDE autocomplete out of the box.The SDK also supports upserts, deletes, and async workflows, making it a natural fit for modern Python stacks. In addition, it includes configuration for injecting instance spaces directly into queries, which can significantly improve query performance when working with large or complex data models.We’ve been using industrial-model for the past couple of months, and it has significantly reduced boilerplate when querying complex graphs, while also speeding up onboarding for new developers.Documentation:https://github.com/lucasrosaalves/industrial-model Feedback and feature requests are very welcome!Sample code:from pathlib import Pathfrom industrial_model import ( Engine, ViewInstanc

The documentation on this page should be updated to reflect the product behavior.https://api-docs.cognite.com/20230101/tag/Spaces/operation/deleteSpacesV3> If an existing data model references a space, you cannot delete that space. Nodes, edges and other data types that are part of a space will no longer be available. I tried deleting a space with a single node and I get this error:cognite.client.exceptions.CogniteAPIError: Unable to delete spaces because they contain nodes or edges: [some-test-space] | code: 400 This does not exactly contradict the documentation, but it means that the documentation is misleading. As it is written, it suggests a space can be used to manage lifecycle of ephemeral test data.

Description / Problem StatementWe are using the Cognite 3D File Extractor to migrate 3D model files from an on-premises Windows VM to Cognite Data Fusion (CDF).The majority of 3D files are ingested successfully with multiple sizes mostly > 10 GB. However, four specific files consistently fail ingestion, even after multiple retries.Key observations:All failing files are close to 5 GB in size Example sizes (as shown in Windows Explorer): 4.99 GB 4.94 GB 4.91 GB When checked via terminal/command line, these same files report a larger actual size (~5.3 GB) It appears that based on the Windows VM file size, the 3D extractor determines it should use a single-part upload. However, when it attempts to upload the actual file, the process fails because the size exceeds 5 GB      

Understanding the inner workings of your data pipelines is key to taking action to improve performance and debug issues. To give you better visibility, we’ve unlocked the full library of transformation metrics directly in the Transformations Run History UI.Previously, the UI only showed a few key metrics. Now, you can access everything the API tracks-from specific rate limits to granular resource updates-right from the graph.Navigate to the Run history tab in the Transformations UI, and select more metrics as shown in the images below.  What’s New?Searchable Metric Selector: Use the new dropdown to find exactly what you need. With the new search bar, you can quickly filter through long lists of metrics if you're working with multiple tables or data models. Smart Defaults: To keep things clean, your most important metrics (like reads, updates, and total requests) are still shown by default. Everything else is just a click away.Identifying Efficiency GainsThis update makes it easier to t