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Hi Community,Has anyone successfully configured the Cognite DB Extractor to connect to Databricks via ODBC using a service principal?I was able to connect to Databricks using a Personal Access Token (PAT), but I am experiencing issues when trying to authenticate with a service principal.Any guidance or examples would be greatly appreciated.Thanks in advance,Andre

We have an app deployed in abp-sandbox for “offshore regulatory assistance” (Flows + KG-RAG agent) on the Agent API (`az-ams-sp-002`, `cdf-version: beta`). While developing document export, I've encountered a reproducible failure pattern:Generation calls with complex instructions and/or long input intermittently return HTTP 200 with empty `content.text` — no error signal. The failure threshold shifted within hours on identical configuration: a 28.7K prompt succeeded at 18:15, 25K failed 12/12 an hour later, 15K succeeded at 21:49. Compression/summarization calls of similar size succeeded in the same window where generation calls failed.A report with the full test matrix is attached.Questions for Cognite (prepared with AI assistance):1. Could the endpoint return an explicit error/finish-reason instead of an empty 200?2. Are there documented practical limits for input size × instruction complexity?3. What is the roadmap status for streaming or async chat?4. Does a plain LLM endpoint with

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 Team,We are working as a data platform team and there are multiple data product teams using our application. Each data product team has a different space to isolate their data. Is there a possibility to see the storage statistics restricted to space level? Clients also want to see “max number of concurrent requests”.

I’m getting the below error details when trying to access a new sandbox environment that has been setup.  What details do I need to share w/my team to unblock access?Your network administrator has blocked accessExternal access is blocked by policy. Contact your IT department for access. Troubleshooting detailsIf you contact your administrator, send this info to them.Copy info to clipboardRequest Id: f3b6e072-9289-427c-97e4-766a40af0700Correlation Id: e24e2dc7-9a3a-488c-92a3-fb333dc35281Timestamp: 2026-06-01T21:34:21ZMessage: AADSTS500021: Access to 'XXXXXXXX' tenant is denied. App name: Cognite Data FusionDevice platform: Windows 10Device state: UnregisteredIP address: 35.199.59.75Signed in as: XXXXXXXXXXFlag sign-in errors for review: Enable flaggingIf you plan on getting help for this problem, enable flagging and try to reproduce the error within 20 minutes. Flagged events make diagnostics available and are raised to admin attention.

Hi experts, In order to decide whether to use an aggregation strategy (or no aggregation at all), I need to know the number of data points between two timestamps without aggregation (server-side calculation). I’m looking for this code of call :client.time_series.data.count(external_id=<str>, start=<int | datetime>, end=<int | datetime>) -> intI'm trying to avoid having to perform a granularity calculation Thanks !Regards,Pierre

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

Hello, We have around ~10/15 CDF functions. We deploy these functions via cdfToolkit.We notice that deployment of new version of a function takes around ~10min in order for it to be fully available. In the worst case where we modify a common module used by all function, redeploying of all functions can reach ~30-40mins. During this time these functions are not available and results in a failure in the dependent high frequency workflows. We wonder if there is a way to accelerate deployment of new function versions. Thank you!  

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 Team,Our observation while storing duplicate entries in Cognite Streams:Mutable Streams - It discards the duplicate entries consistently.Immutable Streams - It sometimes allow exact duplicate entries (externalId and other fields) and sometimes it doesn’t.Even when we checked in the Cognite AI, it says it usually doesn’t allow duplicate entries but in some rare scenarios, it allows.Could you please let us know the behavior. If it allows in rare scenarios, please let us know the exact scenarios.Thanks,Rahul

Help! I am currently facing an issue where the dataset data type is set as string = true, whereas it should be false (numeric). As a result, the chart is not displaying any data due to the field being treated as a string.However, when I download the dataset as a CSV file, the data is present. Could you advise if there is a quick fix on your side to convert the data type from string to numeric?  

I am currently facing an issue where the dataset data type is set as string = true, whereas it should be false (numeric). As a result, the chart is not displaying any data due to the field being treated as a string.However, when I download the dataset as a CSV file, the data is present. Could you advise if there is a quick fix on your side to convert the data type from string to numeric?  

Hi,We're using the Cognite DB extractor (version 3.9.2) with a MongoDB database (Azure Cosmos DB via the MongoDB API) and are trying to set up incremental loading.The documentation for mongodb (https://docs.cognite.com/cdf/integration/guides/extraction/configuration/db#databases.mongodb) doesn't mention the use of start_at and incremental_field but after testing them and looking at the debug logs, we can see the extractor sends the {start_at} placeholder literally without substituting the state value. My questions are:    1. Is start_at substitution supported at all for MongoDB JSON queries, or is it SQL-only?    2. If incremental loading is not supported for MongoDB, is there a recommended workaround?Thanks.

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 

When we’ve tried to update to python sdk v8 we get a 400 validation issue when we try to create a container.We’re currently using v7.92.0, where we have no problems. We’ve tested versions v8.0.7 and v8.2.0.client.config.api_subversion is ‘20230101’ for both 7.92.0 and 8.2.0.Here is an example of a request that fails with: Unexpected field - items[0].properties..constraintState.constraintState seems to be a new property for v8 of the api.ContainerApply( space="Space", external_id="Comp", name="Comp", used_for="node", properties={ "manufacturer_name": ContainerProperty( type=Text(), nullable=True, ) })I notice also that the CDF Jupyter notebook in browser is still on v7.92.0. Is there potentially any other issues preventing an upgrade to v8? Best RegardsDaniel Rasmussen

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

Hello, In our project, we need to use TIMESTAMP_LTZ data type for our timestamps. This data type is only available in Spark starting from 3.4 version. Is it possible to upgrade your version (which is 3.3 I believe) to 3.4 at least please? Thank you

I am new to extending CogniteCore data model to further extend. How to Import CogniteCore datamodel to further extend in a new datamodel space. 

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  

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

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.  

We have requirement to update capabilities in existing groups in CDF using Python SDK/API, we can add capabilities while creating group using python SDK, but if we want to updated that created group no provision for that. we tried client.iam.groups.create(group) but it creates new group with same name, in this case how we can update capabilities in group?