Community Discussions

Whose idea was it to hide the dropdown behind the filters? 

Good news, innovators!We've heard your requests and have extended the deadline for registration, team formation, and idea submission for the Impact Challenge 2025. You now have until this Friday, June 13th, at the end of the day (US time) to get your ideas in.Don't miss this opportunity to solve real-world challenges with Cognite Data Fusion®, win amazing prizes, and present your solution at the Impact Conference in Houston.No coding required!👉 Join the Challenge Now: https://hub.cognite.com/p/hack-for-impactLet's turn your ideas into impact!

Dear Cognite Hub community: We are very happy to announce that our Cognite SAP Extractor is now generally available to CDF users!The extractor connects to OData V2.0 endpoints in the SAP NetWeaver Gateway, making use of many pre-built integrations available in S/4HANA and saving considerable implementation time, while also natively connecting to the SAP data you need in a standardised manner.To get started,  download the SAP Extractor from the “Extract Data” page directly from CDF. The documentation, including Server Requirements and how to set up your SAP extractor is available here.We have also prepared a short demo (less than 4 minutes long) showing the steps needed to setup a data extraction from SAP S/4HANA OnPremise to CDF from scratch, including how to find the standard service from SAP, enable it in S/4HANA, configure and run the extractor and then see the SAP data in CDF.   Any feedback is very welcome, after testing the extractor and/or watching the quick demo please make sur

ContextQuerying views with a large number of instances (>1 million), we frequently encounter query timeout issues. This has become a critical bottleneck affecting application performance and user experience. To mitigate this, we introduced on the app layer a pre-query caching strategy:Before sending a query to Cognite, we aggregate the number of instance spaces for a given view using the endpoint /models/instances/aggregate. This result is stored in a cache layer. When a query is initiated, we check if the user included a space filter. If not, we append the known relevant spaces from the cache to the query filter. This approach has significantly reduced timeouts across our applications. However, it introduces new challenges:One request per view is still needed to fetch associated spaces. Cache invalidation must be managed periodically, especially as user capabilities may change. This workaround does not help with timeouts in the CDF UI or Infield tools, where we cannot control the q

HiI am looking into using the toolkit more actively for deploying resources to CDF. One question that was raised when researching how to use the toolkit is what kind of validations actually happens when doing a dry run for deploying data modeling resources. I do not really have any specific issue I want answered, but rather want to learn more about the tool so prepare for a lot of questions from my notes:)Does it test that the configuration of views and containers work together?Does a successful dry run mean that I can be sure that the deployment will always work?Are there anything I need to consider even after getting a successful dry run?What kind of responses do I get if the dry run finds that something is wrong? Do I get any hints about how to fix an issue?Does it consider what is already deployed into the CDF environment?Will it tell me about any issues that can happen with new breaking changes? Appreciate all kinds of insights and experiences around this topic :)Sebastian

Aker Solutions Verdal Production Line (VPL)Last week I had the chance to visit the Aker Solutions team at their impressive yard in Verdal, fabricating fit-for-purpose steel substructures and jackets for offshore developments, and a great example of industrial innovation done right.At the heart of the yard is the Verdal Production Line (VPL), a fully robotized line that opened in 2024. With automated welding, sandblasting, and painting, VPL is delivering production speeds up to 10x faster than traditional methods, cutting costs and improving safety by keeping people away from hazardous tasks.To push things even further, the team has developed their own software for weld planning. The robots scan each pipe, create a 3D model, and then autonomously plan and carry out the welding.Excited to follow VPL further. Aker Robotics 🦾Aker Solutions - Yards and Fabrication📸 Johan Arnt Nesgård 

Hi Drilling & Wells team! CDF & Atlas AI for Drilling & WellsOur aim is to revolutionise how operators, rig contractors and service companies plan and execute wells, by breaking down data silos that exist between companies, technologies and data sources to create a unified data layer that contextualizes all data across the well delivery process.  We’re also leading the way when it comes to applying GenAI to Drilling & Wells, through our industrial agent workbench - Atlas AI. With Atlas AI, you can build a suite of custom AI agents tailored to specific tasks focused on your data, whether it be for Well Design, Planning or operations. Atlas AI Example Use Cases For D&WOffset Well Analysis in particular is an area where we see agents having a significant impact:Find relevant offset well info in seconds rather hours Reduce reliance on engineer experience/knowledge Find and implement all relevant lessons learned and best practicesThe below video is a demo of Cognite’s be

I am able to Delete data using python code using Primary key of the table in CDF staging/RAW. But I need help with deleting data based on where condition for columns other than primary key. I am following the below documentation for deleting based on primary key. Data Ingestion — cognite-sdk 7.74.5 documentation  Delete rows from table:>>> from cognite.client import CogniteClient>>> client = CogniteClient()>>> keys_to_delete = ["k1", "k2", "k3"]>>> client.raw.rows.delete("db1", "table1", keys_to_delete)

Hello Team,I have a modeling question.We want to add some well information to all object types in our knowledge graph. At the same time, we don't want to create a link from the objects to the well.So, to avoid data duplication and updating these pieces of information, I thought about using the mapping functionality.For example, I have a casing object with two attributes: name and diameter. And I added a name_well attribute using the mapping functionality, which, as I understand, under the hood, performs a join.type Well { name: String}type Casing { name: String name_well: String @mapping(container: "Well", property: "name") diameter: Float32}Well transformation:select wh.IDWELL as externalId, wh.WELLNAME as namefrom well-data as whCasing transformationselect cas.IDCASING AS externalId, cas.NAME AS name, cas.DIAMETER as diameter, wh.WELLNAME as name_wellfrom casing-data as cas join well-data as wh on cas.IDWELL == cas.IDWELLWhat I don’t understand is how exactly this join i

can someone please explain the this procedure for me with details ? because I got really confused. so first we register an app  and create a client secret and add api read all permission n Microsoft azure, after that we create an app in SharePoint  and we add the “permission request” to it later. after that what is next before downloading the extractor ? how are the two apps linked ? I saw someone at my work using Microsoft graph to link them but did not understand the logic behind it. and if this how they are linked , how ?  when in the Microsoft when doing the post each app dose not mention the other  

I am starting the Cognite Data Engineer Learning Path. How to get the access to Cognite Data Fusion platform to practice the learning.

The new CDF Power BI REST connector has been certified by Microsoft and is now included in the latest Power BI desktop version and deployed in the Power BI online service.What's new with the Power BI REST connector:Flexible authentication: Connect Power BI with any IdP supported by CDF (the legacy OData connector only worked with Azure Entra ID) Broader data access: Fetch data from OData services (just like the legacy connector) Access data from Data Models using GraphQL Connect to any GA CDF API endpoint Significant performance boost: Up to 10x faster when using regular REST endpoints compared to fetching the same data via ODataThe connector is currently in Beta, and we're eager to hear customer feedback before promoting it to GA. The documentation for the new connector is available here, and we're working on a new set of micro learning modules in Academy based on the new connector.

In Open Industrial Data (OID), we have moved away from API keys. Open Industrial Data currently supports Open ID connect. You can check out more on how to configure OpenID Connect on Open Industrial Data.If you are planning to use OID and authenticate using client credentials flow, you will need a client secret from the app registration on the Azure Active Directory. Go to Open Industrial Data and you will observe there is a widget for generating a new client secret IDIn the drop down, you can select two options:Other: Use this if you are using Postman or Python SDK Javascript: Select this option if your app is in JavascriptOnce you click on Create client secret, this will be display just once. Make sure to save it somewhere safe.Let me know if you have any questions 🙂

Is anyone using Cognite as their main timeseries historian? We are always exploring alternatives and would be interested to hear if Cognite has fit this use case for any users. 

Here are the features and functionalities that you can expect to find in Industrial Canvas as of June release:Work with several related data types:You can search and add the data you need in the canvas such as files, images, time series, assets, events. Use the button “Add data”. When adding P&IDs to the canvas, each highlighted area is clickable and can be used to add additional data to the canvas. Add personal files and images from your local machine by just using drag-and-drop. You can also add data to the canvas while being in Data Explorer, in Cognite Data Fusion by clicking on “Open in Industrial Canvas”.Add related data directly from the data already selected in the canvas  User interactivity:Create/Rename/Delete a canvas. Free form experience, canvas-like experience. You have access to different shapes, text box, sticky notes and connected lines. Easily add insights on top of selected data.Collaborate with your peers:Share the canvas with your colleagues by either sharing t

I have changed phones and am not able to setup Microsoft authenticator on the new one.Please reset the MFA Access so that I can set up the authenticator again.

I was fetching data using token.I am encountering a CogniteProjectAccessError while fetching data:"You don't have access to the requested CDF project='**' | Code: 401 | X-Request-ID: 6530e2ad-ff30-9d91-af29-218a4c3c0e62 | Cluster: **"

Hello Developer Community,The Contextualization team at Cognite released an experimental interactive engineering diagram flow on fusion.cognite.com  . The old interactive engineering diagram flow still exists in fusion.cognite.com, however, it will be replaced with the new flow come August 1st. Thus, we highly encourage you to try out the new flow titled Explore and create interactive engineering diagrams. This new flow includes a plethora of new functionality and updates such as: Overview of all interactive diagrams in fusion.cognite.com  Easy test setup for relevant file and asset links Handling 1000 engineering diagrams in one go  New P&ID preview component Reducing and cleaning up technical debtAgain, we are still at the experimental stage, so any feedback we receive is beneficial to future iterations and to truly polish the interactive engineering diagrams flow. Please provide any feedback to the Contextualization Cognite hub group found here .   

Pygen v1 is now released and ready for use. This means there will be no more breaking changes of pygen, and it can be used in production. 

When we create a custom model which implements a few core concepts and features from CDM such as CogniteDescribable & CogniteAsset, the GraphQL shows these errors - "Unknown type "CogniteAsset"", "Unknown type "Cognite3DObject"". The Query Explorer also shows the errors as below:These errors were not at all expected since we are not even using “3DObject and other things”. To resolve this, we were suggested a workaround to add every core concepts and features in CDM as views into our data model.It did resolve the above errors, but results in all the CDM views being added to my model, majority of which I am not using.Two things about this:1) Does not feel correct to add all the CDM views into my model to remove these errors, we should only need to add the views that we are going to implement.2) I don't want to see all these additional views that are not being used even. Any suggestions? Anyone?

I see that the API is updated with AI capabilities for documents. We tried that on some PDFs, and worked nicely. However, I have a particular PDF, which seems to be a scanned version, and the API is not able to read that. I am trying to convert the file to text, but the vision API only supports jpg and png files. Do you know if it is possible to convert pdfs to text somehow using the Cognite libraries (for instance contextualization)? Any ideas?

A few highlightsSupport for filtering on direct relations in the .select method. The .select method is no longer in alpha. When calling .upsert pygen detects fields explicitly set to None and writes it to CDF. The .retrieve() method now (like .list()) have the retrieve_connections parameter to retrieve connections.Full changelog.

Hello team, I am trying to get the details of a reverse direct relation from my data model. In the SDK documentation I found some sdk methods. I have tried client.data_modeling.views.ReverseDirectRelation(source= "Entity", through="entityType") but I am getting the error that ReverseDirectRelation is not found in views could you please tell me what SDK method I should use to retrieve information on ReverseDirectRelations from my models.

HelloI was thinking Congite should create a comprehensive guide on best practices for a 3D walkthrough to enhance user experience and efficiency. Navigating 360 images,getting the best point cloud performance, and mixing in CAD models while leveraging the platform's full functionality can be a bit overwhelming with numerous buttons and features to explore. A detailed guide would streamline the process, providing clear instructions on entering and exiting 360 views, highlighting essential tools, and showcasing advanced features. This would empower users to make the most of the 3D walkthrough capabilities, reduce learning curves, and ensure consistent, optimal usage across teams.

Hi Everyone,I need to create query like it is creating on cognite UI’s  like   { "listEntity": { "with": { "0": { "limit": 50, "nodes": { "filter": { "and": [ { "matchAll": {} }, { "hasData": [ { "type": "view", "space": "slb-pdm-dm-governed", "externalId": "Entity", "version": "1_7" } ] }, { "or": [ { "and": [ { "nested": { "scope": [ "slb-pdm-dm-governed", "Entity/1_7", "parent" ],