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Hi Cognite Community,we currently have a Streamlit Custom App hosted directly in Cognite Data Fusion that we would like to provide to end users for an upcoming testing phase.Our plan is to let the users test the current Streamlit version first, while in parallel we are working on migrating/rebuilding the application as a Flows app.However, this migration will still take some time, while the users are expected to start testing the Streamlit application in approximately two weeks.For the testing phase, we would therefore like to provide users with a direct link to the Streamlit application, but ideally without showing the surrounding CDF navigation/sidebar (Home, Search, Canvas, Charts, Custom Apps, etc.).The goal would be something similar to a standalone/full-screen application experience while still keeping the current Streamlit app hosted and authenticated through CDF.Is there currently any supported way for Streamlit Custom Apps to:hide the CDF shell/sidebar, open the application in

Hello!We have encountered some issues with backfilling on the PI Extractor. This usually goes automatically, and works fine. However, we do now have a 2 day gap in data on Cognite, however all that data is present in PI.Are there any way to “force” a backfill, or fix this issue somehow?Thanks!

HiWe've hit a bug in cdf-tk (reproduced on 0.8.147 and 0.8.155) when deploying containers to a brand new space.We have 98 containers in a new space. One container has no constraints, but others reference it via a requires constraint pointing to the same space. cdf-tk sends all containers in a single batch. The CDF API seems to validates requires constraints against what already exists in CDF not against other items in the same batch so it rejects the entire batch with this error:Cannot create requires constraint 'requireMetadata' in container'sp_xxx:ContainerB'.Target container 'sp_xxx:ContainerA' does not exist.cdf-tk should sort containers by their requires dependencies before batching, so dependency free containers like containerA are sent before the containers that depend on them.We'd appreciate a fix in an upcoming release, or any insights regarding this issue.Thanks

Hello,  the default value for autoCreateDirectRelations for node creation is True. Cdf toolkit do not expose this config at transformation level today. This results into uncontrolled ‘empty’ instance creation when direct relationship nodes does not exist.Do you plan to expose this config to cdf toolkit so we can change the default behaviour?ref: https://docs.cognite.com/20230101/instances/create-or-update-nodesedges#body-auto-create-direct-relations Thanks Best,

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

Need help ASAP since this is on a customer’s system Using Core Data Model + Extended Timeseries container/viewUsing Pi Extractor against Rockwell Historiansee attached video for problem description and some configuration

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

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

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!  

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.

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,  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. 

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?

I have used the db extractor before, but with version 4.0.0 it now comes with a connect.yml and a config.yml. The documentation still lists one config file to be used. I’m not sure how to use the connection file, which looks like it used to be the cognite portion of the of the config file.I tried combining the two into one file, but when running “ dbextract.exe -c config.yml “ I am getting errors that its not expecting inputs such as version, log-handlers, database, and queries.How am I supposed to use these 2 different files when I run as an executable?Thank you

Hello experts,I'm interested in the following function: the sum of all data points in a time series server-side.I know there are aggregation operations for a specific time interval, but I'm looking for either a cumulative or total sum.I'd like to be able to perform this operation on either a standard time series or a synthetic time series, but unfortunately, I haven't found what I'm looking for yet.Please see below for the operations I am performing; I would like a discrete summation between the multiplication and the output.Does anyone have a server-side solution? Regards,Pierre Rambourg

We have a Cognite Function in which we are retrieving rows from staging tables using below sdk commandclient.raw.rows.retrieve_dataframe(db_name, tbl_name, limit=-1)The problem we are facing is the above command returns an empty Dataframe (0 columns, 0 rows) even though the table exists with data. The code execution does not fail but returns an empty Dataframe.When I manually/locally run the above command on the same database and tables, I get the required data.However, when running inside a CDF function it returns a Dataframe with shape (0,0)