Community Discussions

Hello Cognite Team,I am not sure whether this should be reported as a bug, a configuration issue, or an intended behavior change, but I would like to ask for your help in understanding the situation.We are using alert notifications in Charts to monitor several items. In the past, alert emails were successfully sent not only to the chart creator but also to additional subscribers that were registered for the alert.Recently, however, only the chart creator appears to be receiving alert emails.When I checked the subscription settings, only the creator was displayed in the subscriber list. I tried adding additional subscribers multiple times, and the system indicated that the changes were saved successfully. However, the added subscribers do not appear in the list after saving, and they do not receive any alert emails.The same process worked correctly before, so I am not sure whether this could be related to a recent change, a configuration issue on our side, or a potential product issue.C

Please suggest a proven way to bring SAP ECC data changes into CDF within seconds. I have come across with below solution:SAP ECC→ IDoc or Change Document→ Kafka / Azure Event Hub→ Azure Function / Microservice→ Cognite Ingestion API→ CDFIs this correct?

I used the Atlas AI agent (default) to locate two timeseries, and then clicked on the “add to dashboard” button. The chart got added, and the title indicates both are added, but the chart only has a single visible line. If I move the mouse pointer down, it snaps to another value (notice the location of the blue point), but not sure if this is the other timeseries or if it is a spike from the raw data that is not shown due to downsampling on the chart. Also, the time range selection do not change the timerange for the chart  

If you have tests that assume the a specific result ordering in instance list or query results without specifying a sort order when querying for recently upserted nodes, you may in the future see that APIs can return results in a different order than they were upserted, and possibly see your tests fail.The change in ordering happens when we insert data in the DM graph, and is required to make sure we correctly serialise operations and avoid deadlocks when processing upsert operations. The previous implementation is prone to rare race conditions that, while not having been a frequent problem before, may become a problem now as we work to optimize DM API performance and add support for more sophisticated access control in instance upsert operations.While this change is observable if your test hard codes an assumption that items are returned in the same order that they were inserted, this behavior was never specified, let alone guaranteed. The API behavior remains in line with previously

Hi everyone,While querying the CogniteAsset view, I encountered an error when including the activities field in the selection. The API returns the following message: "Cannot traverse lists of direct relations inwards." Upon investigating, I noticed that the activities field in the CogniteAsset view is defined as a @reverseDirectRelation: activities: [CogniteActivity] @reverseDirectRelation(throughProperty: "assets") In turn, the assets field in the CogniteActivity view is defined as a list of direct relations:assets: [CogniteAsset] @directRelationThis makes sense as the root cause, since reverse traversal over a list of direct relations is not allowed, which explains the error.Given that this field leads to an invalid query pattern and always results in an error, should the activities field be removed from the CogniteAsset view to avoid confusion and runtime errors? Here’s a minimal query that reproduces the issue:query MyQuery {  listCogniteAsset {    items {      aliases      activit

Dedicated CDF environment for AI agent & app developmentWe've been working through how to give AI agent and application developers a proper home in CDF, and wanted to share the approach we're exploring in case it's useful to others facing the same thing.The problem we hitA standard dev / test / prod setup works well for governed data pipelines, but it gets awkward for AI agent and app development, which needs two things that are hard to provide together in those environments:Representative production data — dev is typically fed by a non-representative subset, so agents and apps built there don't behave the same once they meet real data. Broad, globally-scoped rights to create and edit agents and apps. Since creation rights are global within a project and can't be scoped down to a space — even with Row-Level Security — granting them in a shared dev project exposes every other workload there.The approach we're exploringA dedicated CDF environment (separate project) running parallel t

Hi community!Cognite has just announced Flows Custom Applications (Dune). If you’ve been using the Dune documentation, please note that it’s now available at the updated link below:https://docs.cognite.com/cdf/flows 

Hello all,We are trying to establish connectivity to a source which has cassadra as its backend database. The system allows OPCUA connection but we will have only live data feed that way, without access to historical data.Has anyone explored or tested data extarction from Cassandra. Any inputs would be valuable. Thanks 

Hello,I am running a function ‘dq_validate_model_integrity’ and ‘dq_validate_broken_references’ which give a 403 unauthorized error for a timeseries intermittently. There has been no change in access or data yet its failing sometimes with a 403 error.Could you please let us know why this issue is coming up frequently?PS: I have attached the screenshots of the passed and failed statuses of both functions with the timestamp as well as the logs of both failed functions. Do let me know if there’s any more information required from my end. Thanks alot :)

Hello,  Since function deployment using cdf tk is asynchronous, function can fail without any error feedback and stay in failing state until next deployment..Any recommendation to avoid this?Thanks  

KepServerEx 6.10 OPC UA Configuration Manager : KepServerEx 6.10 OPC UA Node ID :config.yml :source:    endpoint-url: opc.tcp://127.0.0.1:49320      #This is your OPC UA server url    auto-accept: true    queue-length: 10    username:     password:     browse-chunk: 1000    attributes-chunk: 1000    x509-certificate:            browse-throttling:        max-node-parallelism: 10   extraction:    id-prefix:     # Delay in ms between each push of data points to targets    # Alternatively, use N[timeunit] where timeunit is w, d, h, m, s or ms.    data-push-delay: 5000        # Source node in the OPC-UA server. Leave empty to use the top level Objects node.    # If root-nodes is set, this is added to the list of root nodes.    root-node:        # Full name of the namespace of the root node.        namespace-uri: "Simulation"        # Id of the root node, on the form "i=123" or "s=stringid" etc.        node-id: "s=Simulation"            # List of proto-node-ids similar to root-node.    # The

Hello, Currently using cdf-tk version 0.5.111. The bySpace param on btree indexes is not yet supported in this version.We are trying to migrate to version 0.7.220. Although, this version sets correctly the bySpace param on indexes it does not seem to resolve correctly the view dependencies in our data model.Error trace:  Deploying 375 views to CDF...WARNING [MEDIUM]: Found a strongly interdependent set of 75 views: sp_dm_dap_knowledge_graph:BHAComponent(version=4.3), sp_dm_dap_knowledge_graph:BHARun(version=4.3), (...)sp_dm_dap_knowledge_graph:WellPath(version=4.3), sp_dm_dap_knowledge_graph:Wellbore(version=4.3) and sp_dm_dap_knowledge_graph:WellboreSection(version=4.3). This might indicate a data model design issue, and the deployment might fail due to API batch size limits.Traceback (most recent call last):  File "pypoetry/virtualenvs/drillx-dwdap-cdf-toolkit-fxS2v_xU-py3.13/bin/cdf-tk", line 8, in <module>    sys.exit(app())             ~~~^^  File "pypoetry/virtualenvs/drill

I posted it in Github, but cross-posting here too.https://github.com/cognitedata/cognite-sdk-python/issues/2525 System information (please complete the following information):OS: Windows11 Python Version: 3.12 SDK Version: 8.Describe the bugThis code previously worked:NodeId.load_if(None)But since v8, I am getting this error:AttributeError: type object 'NodeId' has no attribute 'load_if'. Did you mean: '_load_if'?To ReproduceRunnable code reproducing the error.NodeId.load_if(None)Expected behaviorIn Cognite SDK v7 this returns None.  

We are committed to continuously improving your experience in Industrial Canvas. Our goal is simple: help you generate value faster through a seamless, uninterrupted workflow—eliminating the friction of slow load times and performance lag. With our March release, Industrial Canvas now loads 50% to 70% faster on average. We’ve also reduced memory usage by up to 70% during loading, even for your most complex canvases. We look forward to hearing your feedback!Best,Andreea

How much time does it really take to build a full solution?We deployed a complete solution using Cognite CDF, Cursor, and Anthropic Claude Opus 4.6, powered heavily by Gen AI in just 4 hours.This was not just a prototype screen. It was a fully deployed solution for demo purpose.And here’s the most interesting part:All of the following steps were done using Gen AI:1️⃣ Creation of the data model in Cognite Data Fusion and the Terraform2️⃣ Deployment of the model in Cognite3️⃣ Data generation and ingestion into the Cognite Data Platform4️⃣ Development of a React application on top of Cognite using the Cognite SDK5️⃣ Creation of: Asset Map, KPIs, Production Time Series Dashboardand Well Intervention viewFrom data modeling to frontend application — accelerated with AI.This is not about replacing engineers.It’s about dramatically increasing speed, experimentation, and delivery capacity. The question is no longer “Can we build it?”It’s “How fast can we build it?”Post here!  

Hi everyone, At Cognite, we strive to ensure that every tool in the Cognite Data Fusion workspace meets our high standards for security, performance, and user experience.The Image and Video Management interface (found under the Data Management workspace) has reached a stage where it can no longer be maintained to those standards. To focus our efforts on more robust, integrated data exploration tools, we are initiating a 12-month deprecation period for this specific interface. This means that the Image and Video Management interface will be deprecated on 17 February 2027.What is changing?The standalone Image and Video Management app is entering its sunset phase. This includes: The specialized Map view for finding image and video data. The Annotation tool for images. The integrated Video streaming/viewing interface within this specific app. Note: This deprecation only affects the management interface. Your raw image and video files stored in CDF remain safe and accessible via the API

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 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.

Wow—was I in for a challenge!I just received my first proper assignment after joining Cognite: building a custom extractor to move documents and their metadata from a Document Management System (DMS) into Cognite Data Fusion (CDF).Diving headfirst into this, I found myself at the intersection of a steep learning curve, the high-stakes data needs of our industrial customers, and my own belief in Agile engineering practices. Coming from a background where Test-Driven Development (TDD) is the heartbeat of quality, I realized I didn't just want to build a script; I wanted to build a process I could trust.In this series, I want to share a practice that served me well while learning the ropes: The "Twin Auditor" Pattern.The Challenge: Beyond Logic, It’s IntegrationWhen you build an extractor, you aren't just writing code in a vacuum. You are building a bridge between two distinct worlds: your source system (the DMS) and your data platform (CDF).Extraction is inherently an integration challen

An old version(v1.0.something) of PI extractor was setup in a production environment. We stopped that version for about a month and then installed the latest version v4.14 in a new VM. We have specified the backfilling and front filling settings as below but we still can’t see the historical data in CDF, any ideas what would have happened?frontfill: skip: false streaming-interval: 5 delete-data-points: false use-data-pipe: truebackfill: skip: false step-size-hours: 0 to: 0 

Hello,Please reset my mfa account.nbhatewara@slb.com Appreciate.

Hello, We have a very big CDF workflow with many parrallel steps. When a step fails, all the steps fails with “404: Session was not found.” (because the failing step deleted the session) all steps are market as failed. It makes debugging and finding the real failing step very tedious ! Any improvement planned for this? Any workaround? Thanks 

 We are thrilled to announce the next evolution of your user experience. We’ve unified our ecosystem to help you master your industrial data without the friction.What’s new? One Identity: We’ve merged Cognite Academy and the Community. One login for everything. Unified Search: Find How-to guides, discussions, and courses in a single search. Guided Careers: New role-based learning paths, starting with the Data Engineer path! 👇 Read the full announcement here:  

We are thrilled to announce the next evolution of your user experience. We’ve unified our ecosystem to help you master your industrial data without the friction.What’s new? One Identity: We’ve merged Cognite Academy and the Community. One login for everything. Unified Search: Find How-to guides, discussions, and courses in a single search. Guided Careers: New role-based learning paths, starting with the Data Engineer path! 👇 Read the full announcement here: