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Hi, I have defined a Data Model with a Source View having a DirectRelation to a Target View as a simplified version below, which Pygen created an SDK successfully.  But when I added a ReverseDirectRelationApply to the Target View, Pygen returned the captioned error.  Please help.source_container = ContainerApply(       space =”myspace”,       external_id=”sourceContainer”,       properties={            “sourceName”: ContainerProperty(type=Text, name=”sourceName”),            “target”: ContainerProperty(type=DirectRelation(is_list=False, container=ContainerId(space=”myspace”, external_id=”targetcontainer”) } target_container = ContainerApply(       space =”myspace”,       external_id=”targetContainer”,       properties={            “targetName”: ContainerProperty(type=Text, name=”targetName”)} source_view = ViewApply(     space = “myspace”,     external_id = “souceView”,     version = “1.0”,     properties = {           “sourceName”: MappedPropertyApply(container=ContainerId(“myspace”,

Hello, we are facing an SSL issue while trying to connect to cognite even if we’re disabling SSL verification.Could you please help ?from cognite.client import ClientConfig, CogniteClientfrom cognite.client.credentials import OAuthClientCredentialsfrom cognite.client.config import global_configglobal_config.disable_ssl = Truecreds = OAuthClientCredentials( **{ "client_id": "", "client_secret": "", "token_url": "", "scopes": ["https://westeurope-1.cognitedata.com/.default"], } )def get_cognite_client(project: str) -> CogniteClient: return CogniteClient( ClientConfig( client_name="Python-jupyter", project=project, credentials=creds, base_url="https://westeurope-1.cognitedata.com", ) )client = get_cognite_client("totalenergies-sandbox")res = client.units.list().to_pandas()------------------------------------------------------------------------------------

Hello everyone,I observe a difference in behavior that I cannot explain.My input data, few datapoints, and before the value is 0, timeseries.is_step = TrueInput datapointsWith client.time_series.data.retrieve, behavior is as expected :And with every granularity results are okay as expected.Nevertheless, with client.time_series.data.synthetic.query it’s… different.that’s okthat’s okNot ok, why the result is not 2 groups : from 46 to 49 then 49 to 52 ? another wrong exampleWhy the start timestamp is 48 and not at 46 ? Again, the timeseries endoint is good : I have lost hope.If anyone has anything to try or an explanation, i’ll be grateful.Thanks in advance,Pierre 

The current naming convention in the OPCUA server node structure naming is not intuitive for user to be able to find the tag in Cognite.From the example below, we will have 3 timeseries tags with Message and also Data.It is not intuitive for user to look into the path or externalid to find which it is belongs to.How do we configure the OPCUA extractor to construct the timeseries name based on certain criteria or to based on the complete path of the node to be the timeseries name (e.g. Root\Temperature1\Message as the name)?E.g. OPCUA Node structureRoot|_ Temperature1   |_ Message   |_ Data|_ Temperature2   |_ Message   |_ Data|_ Pressure1    |_ Message   |_ Data 

I am trying to update column value in view using upsert method, but it's not working and giving 400 error code.  adding screenshot of upsert api, as its not matching with the input structure provided in document.

I need some help with the SDK (Python). I'm trying to list active sessions in Cognite using the following resource: client.iam.sessions.list(status="ACTIVE" However, I notice that even my client_id doesn't return in the listing (I'm logged in via browser with my user). Am I misunderstanding this feature?

Hello experts,I have a question about Cognite Functions. I know there’s a limit on the size of the input data, as discussed here : Cognite Hub and documented there : Cloud provider limitations - Cognite Docs But is there a limit on the size of the function’s output? I have one that works perfectly fine but crashes without any explicit error message when handling a large output payload (>500 KB). I’m on Azure provider.Is there any information on this? Thank you,Best regards,Pierre Rambourg edit : the initial error was on my side, but i’m still interested for output limits !

I’m using the CogniteSDK. I have seen the following behavior several times: assets = client.assets.list()the variable assets can contain deleted assets if they have been recetly deleted. If I find the external_ids of these assets, the client.assets.retrieve_multiple(external_ids=deleted_xids) correcly do not return the assets. The assets cannot be found in fusion either. I suspect that client.assets.list() is using cached values. This also applies with arguments, f.ex LabelFilter. I have seen the issue many times, but now it really got painfull, so therefore I’m writing here. It returns assets that were deleted several days ago.  What to do? 

I need help getting attached error(Error: no gl) in chart page

The python extractor utils requires the config to be stored as a local file there is not way to pass it directly as a string/yaml object. This makes it quite cumbersome to use as not all our deployments have access to local file storage for config files.Is there a way to add support for the config to be passed as an environment variable? Reading config from env example:  

I am stumbling through the Beta documentation. When I try to run the http post to create a Stream (as described here), I get this error:The feature can only be used with [alpha, beta] headersI found documentation that seemed relevant, but it seems the format of the header is this cdf-version: 20230101-beta https://api-docs.cognite.com/20230101/#section/API-versions/Beta-versionsHow do I know what date should I put into the header to gain access to the Streams beta feature?

We have multiple Asset Centric Location Filters created which are used in Industrial Tools search to filter site specific assets.When using multiple filters, we observed that the Search returns 0 assets.We inspected the Network requests and observed the Cognite API call to assets/list fails with below error:```{  "error": {    "code": 400,    "message": "Asset subtrees exceed the limit of 100000 assets."  }}```There are similar cases where a location filter is setup with asset subtree and the subtree has more than 100K assets. Is there a way we can configure Location Filters to bypass the limit of 100k assets.

cdf version is 0.7.236.i cannot find the same quickstart mentioned in the training material. below is the screenshot from my environment.   the instruction mentions two times about “would you like to make changes to the selection”. the action is inconsistent.  The second question should be updated to be the real one.  

first of all i created a new environment with below codes.python -m venv envenv\scripts\activatepip install poetry poetry config virtualenvs.in-project truepip install pandas numpypip install cognite-sdkpip install "cognite-sdk[pandas, geo]"pip install cognite-extractor-manageronce all above code completed. I started running cogex init.I am getting below error.cogex initEnter extractor name: csv extractorEnter description: descEnter author: --I provided the cognite login mail id in name <mail id>Which template should be loaded?  simple: loads a generic template suitable for most source systems, using          a simple function as the main entrypoint for the extractor.          Most suitable for small and simple extractors.  class:  loads a generic template suitable for most source systems, using          a run method on a class as the main entrypoint for the extractor.          Most suitable for slightly bigger and more complicated extractors.  rest:   loads a template for extrac

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

In the process of initializing a new CDF tenant for one of our customers I am getting the following error when adding Assets Read/Write capabilities: 1 invalid capabilitie(s) are present: {"assetsAcl":{"actions":["READ","WRITE"],"scope":{"all":{}}}} (invalid capability - write access to the legacy Assets API is restricted) | code: 400 | Are core assets now restricted?

Hi , I have the CogniteActivity in the CDM/EDM data model which the assets is part of the model: Below is the sample data of CogniteActivity:This is the data model for Event in PDM:I tried to transform from CDM/EDM to PDM using below transformation, but i am not sure what is the script to transform CogniteActivity to PDM Event:And i got below error: Anyone know how can I transform CDM/EDM asset node reference to Event entities attribute? Thank you.

Had a chance to experiment with streams and records and it seems to be working well.  Couple of questions based on what I found:Are any of the filtering options that are available for general data modeling queries but not available with records likely to be supported in future?  Specifically, I am thinking of queries like Fetch me all of the records in the last week where the `asset` property is below `Facility-ABC` (i.e. it contains `Facility-ABC` in asset.path).  I think this would require us to use the nested filter unless the total number of assets that were below Facility-ABC was small enough to pass them directly into the filter.  I can imagine that this kind of filter is more difficult to implement and a more expensive operation, but I think it could be valuable. Is there a reason why containers rather than views must be used when creating/querying records?  It seems like a view with a specific version is nothing more than a set of containers with a (possibly incomplete) list of

Hello,  Is it planned to support RAW querying via CDF Grafana Datasource ?  Thanks ! 

With Tableau 2026.x introduced the native REST API connector, I wanted to check if anyone has explored using it to connect Cognite Data Fusion (CDF) APIs.Has anyone successfully connected CDF data via the Tableau REST API connector, or evaluated this approach?Any insights, limitations, or best practices would be greatly appreciated. Thanks in advance!

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 All,I am working with hosted extractors for kafka and it works pretty well for me with transformations when we have plain json data in kafka topics.Now I am trying to check if we can work with zlib gzip compressed data coming in topic, I have json string and messagepayload attribute of json string will be holding compressed data instead of whole message as compressed one, is it possible to write transformation for such data. e.g., in kafka topics{    "Header": {        "MessageId": 133367162,        "MessageType": "DATA_REPORT",        "Timestamp": 1741122422,        "PayloadCompression": "Z_LIB_COMPRESSION"            },    "MessagePayload": "eJyqVnJJLctMTi1WsoquVvJLzE1VslIyVNJRckksSQxJTIeIhySmQ6WCA3wVfFMTi0uLUlNgqioLQDIu/qFOPq7xYY4+oa5KOkphiTmlMLNCMnNTi0sScwuUrAzNTQwNjYxMjAwtzA11lAJLE3MySyqVrAxqY2tjawEBAAD//x5aKt0="}

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

Hi Guys,I tried to use a transformation from the SDK but i keep getting error on Transformation credentials. Do you guys know what I need to do to solve this credentials issue. I have check the current credential for the Service Account that I use and the access for tranformation has been granted. def fetch_raw_table_via_transformation(    client: CogniteClient,    db_name: str,    table_name: str,    calculation_date: Optional[str] = None,    columns: Optional[List[str]] = None) -> pd.DataFrame:    """    Fetch data by running a temporary CDF Transformation (uses Spark SQL engine).        This is the RECOMMENDED approach for production workloads:    - Runs on CDF's managed Spark infrastructure    - Handles billions of rows efficiently    - Server-side filtering and aggregation    - Can write results to temp Raw table or return directly        Note: Requires transformation permissions in CDF        Args:        client: CogniteClient instance        db_name: Name of the RAW database 

I'm experiencing an issue with the Workflows editor where the bottom toolbar buttons (zoom in, zoom out, pan to center, auto layout) stop responding in certain conditions.Steps to reproduce:1. Open Workflows in CDF2. Create or edit a workflow3. Open the right panel (e.g., click on a task/agent to configure it)4. Try clicking the zoom in/out/pan buttons in the bottom toolbar5. Observe: Buttons don't respond when right panel is close to the left toolbar6. Maximize browser window or close right panel → buttons work againExpected: Toolbar buttons should work regardless of panel positionsActual: Buttons appear to be blocked, clicks don't register when panels are close together Workaround: Maximize browser window to create space between panels before using toolbar buttonsHas anyone else experienced this? Is there a better workaround or is this a known issue?