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Is there a way to configure a Hosted HiveMQ Extractor to extract the data into CogniteTimeSeries and not the traditional Time Series?

Hello, We are using cdf toolkit to deploy our data models. We have a relatively a big data models that uses 478 views. Each time we deploy a breaking change our data model architecture (well centric) obliges us to redeploy a new version for all views. We notice a huge deployment time at the dependencies resolution step: 2025-08-19T09:34:54+00:00 WARNING [LOW]: Failed to create 478 views: One or moreviews do not exist: 'sp_dm_mud_brines_for_ops_real_wview:Activity/0.10'.. Attempting to recover...The overall deployment time takes 35 mins, this way bigger than our standard CI/CD job duration where we try to keep it under 10min at worst. Is there a way to optimize this please?  Thank you!    

Hi all, looking for guidance on two issues we hit with CDF related to timeseries visibility and datamodel queries.403 error leaking timeseries externalIds:when a user without access requests a timeseries resource, a 403 error is returned as expected, but the error body contains the externalIds of the timeseries. Those externalIds are sensitive and should not be exposed to users who don’t have access.Is it expected behavior that externalIds are included in 403 error messages? If not, is there a setting / configuration / log-level that controls whether identifiers are returned in errors? Any recommended mitigation or planned fix? Datamodel query returns datapoints from unauthorized timeseriesSetup: Created dataset_1 and dataset_2 Created two timeseries, TS_A in dataset_1 and TS_B in dataset_2 Attached both TS_A and TS_B to a datamodel Granted the user only timeseries:READ scope:dataset_2 (so they should see TS_B only) Observed behavior: querying the datamodel returns datapoints for T

How can we get access to work hands-on with the learning path athttps://cognite-learn.fusion.cognite.com/cdf-fundamentals/data-models?cluster=api.cognitedata.com&workspace=data-management?If not, how can we practice hands-on with Cognite Learn?

Hello Team,I am trying to check like how many edges(entities in my case) are null in the view(event) is there any specific way to do so? I am trying right now sdk query api like below, but not working. Please give your insights. {    "with": {        "Event": {            "nodes": {                "filter": {                    "hasData": [                        {                            "type": "view",                            "space": "slb-pdm-dm-governed",                            "externalId": "Event",                            "version": "1_7"                        }                    ]                },                "chain_to": "destination",                "direction": "outwards"            },            "limit": 10000        },        "Event_2_entities.Entity": {            "edges": {                "from": "Event",                "direction": "outwards",                "filter": {                    "and": [                        {                            "equ

Hi,I have a view called "Entity" in an existing model with mandatory properties "name", "description" and "isVirtual".Now, I have a new version of "Entity" in which I have decided to implement CogniteDescribable from CDM for name and description, where these fields are not mandatory.When I try to ingest data into the new version of this view, it gives me a non-writable error since the mandatory properties that I had created for the older version is not getting populated.Is there any way to overcome this?Can I map the mandatory container to the container used in CogniteDescribable, so that when I ingest data in the new version, it automatically ingests the mandatory container as well?If yes, can you please share a YAML example of the same to be deployed through toolkit?Please note that both the mandatory and non-mandatory containers have the same name "name".

Hello,  We are heavely relying on transformations to transform our data from Raw (staging) service to Data Models. We notice that it has very high latency compared to a “simple” Spark job. As per our discussion with our Solution Architect we understood that the bottleneck is the Raw service. Any plans to improve this in the future? Thank you!

 Dears,Would you please advise how can we hide specific layers (for ex. the crane) in the 3D model and then to save the updated or customised revision?is the revision editing for 3D models doable inside CDF itself or there is a specific workflow needed for it, as you can see it the snapshot below: Thank you! Fatima 

When running the following lines of code I get an error with Pydantic:from cognite.neat import NeatSessionneat = NeatSession(client)The error is as follows:---------------------------------------------------------------------------ImportError Traceback (most recent call last)Cell In[13], line 1----> 1 from cognite.neat import NeatSession 2 neat = NeatSession(client)File ~\AppData\Local\Programs\Python\Python311\Lib\site-packages\cognite\neat\__init__.py:4 1 from cognite.neat.core._utils.auth import get_cognite_client 3 from ._version import __version__----> 4 from .session import NeatSession 6 __all__ = ["NeatSession", "__version__", "get_cognite_client"]File ~\AppData\Local\Programs\Python\Python311\Lib\site-packages\cognite\neat\session\__init__.py:1----> 1 from ._base import NeatSession 3 __all__ = ["NeatSession"]File ~\AppData\Local\Programs\Python\Python311\Lib\site-packages\cognite\neat\session\_base.py:9 7 from

Hi Team, I need help to write the unit test using pytest,below is the code:    row_list = cognite.raw.rows.list(file.destination.database, file.Extractor_Last_Run_Table, limit=-1,                               columns=["key", "LastUpdatedDatetime"]).to_pandas()    if not row_list.empty:        val = list(row_list[row_list.index.isin([file.Extractor_key])]['LastUpdatedDatetime'])Could you please help to write the unit test for above code?Thanks

I am using CogniteSdk c# to query data model from CDF. When I tried to execute it, it did not return row items. Instead it just returned below result:What is the correct way to use the DataModel in C# CogniteSdk to return rows of data as per below? 

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.

Hello experts,I’m looking to work with data workflows.I’m looking to trigger on node with a specific attribute (direct relation to another view) empty.I’m trying to work with the UI but I struggle with the Data modeling query validation.I don’t understand why the validation fails and what is the expected format for the query : JSON seems to be the expected format as seen here : But documentation uses YAML : https://docs.cognite.com/cdf/dm/dm_concepts/dm_querying#graph-query-examplesI’m a bit lost and I need your help. And my query : { "with":[ { "ts":{ "nodes":{ "filter":{ "exists":{ "property":[ "nameTranslations" ] } } }, "limit":1000 } } ], "select":[ { "ts":{ "sources":[ { "source":{ "type":"view", "sp

HiWe are testing out a new deletion transformation for a view in data modeling. The transformation looks something like this:select externalIdfrom cdf_data_models(...)where project in ("ProjectA","ProjectB")This transformation always times out and returns a “Graph query time out” error message. However, when changing the transformation to this the transformation just runs fine:select externalIdfrom cdf_data_models(...)where project = "ProjectA"unionselect externalIdfrom cdf_data_models(...)where project = "ProjectB"For context, the project column here is indexed and the view has around 10 properties and about 1.7 million rows. The transformations all query the same view.We are therefore wondering how the filtering in transformations handles filtering in data modeling and why these transformations are performing different. Learning more about this would benefit us greatly when working with transformations and data modeling in the future :)Thanks in advance!Sebastian

Hi Cognite Team,We are currently defining an integration pattern for a partner who will provide data from an OPC UA source into Cognite Data Fusion.Our preference is to use a managed MQTT broker in CDF and avoid deploying any self-hosted MQTT broker or additional bridge components on-prem.Based on the documentation, we understand that the OPC UA Extractor supports an mqtt: output mode, but it also references a requirement for the MQTT-CDF Bridge and a configured broker host/port.To ensure we define the correct and supported architecture, could you please clarify: Is it supported to configure the OPC UA Extractor to publish directly to the Cognite-hosted MQTT broker in CDF? If using the Cognite-managed MQTT broker, is the MQTT-CDF Bridge still required? Is a self-hosted MQTT broker mandatory when using the mqtt: configuration in the OPC UA Extractor? What is the recommended architecture today for a partner delivering OPC UA data into CDF using MQTT? Our intended target architectur

Hi Everyone,Previously, I was able to bring my Cognite chart into Canvas, and it displayed correctly without showing the calculation area at the bottom. However, starting today, I can still add the chart to Canvas, but nothing appears—it’s completely blank. We tested this across multiple profiles, and the issue occurs for all of them. Is anyone else experiencing this problem? Could the Cognite team help us resolve it?Thanks in advance!

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

When creating a Cognite Function you specify the dataset it belongs to, as an example lets say D2 LCI, then the files of the function is stored as files in that dataset. However, as D2 LCI is a dataset for file storage for engineering documents these function files cause noise, not much but still. If you were to list out all files for the dataset these “internal” CDF files would simply show as regular files and would have to be filtered out.Now, as we (AkerBP) are moving away from the asset-centric and over to data modelling this might not be an issue given how data models and spaces are. But in the future when datasets are a thing of the past, how will function and the like work, where will those files be stored. Storing the file used for compute with the data they compute might be a simple implementation, but not ideal as it does cause some noise. Note: this does not only apply to Cognite Functions, but to all CDF features where “internal” files are stored alongside the regular data.

I’m trying to update the is_string value of Time Series from false to true using CDF python class ognite.client.data_classes.time_series.TimeSeriesUpdate. But I’m unable to update it. The value in CDF remains false even though I updated it. Is this an expected behavior of Time Series Update?

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

Hi Team, we have data in format like list of externalIds and space as key and value.  Like list of Json object in data model. something like [{externalId:well,space:slb},{externalId:well1,space:slb2}]So can we keep a field in model like violatedEntities: [JsonObject]Is it possible to parse all the externalIds and store it like this?Thank you.

Hi, Data models names are limited to 37 chars today. Is this a hard limit? Is there a way to extend this so we can have more possibilities for internal naming conventions? Thank you!

Hello, We are facing an issue using private SSL certificate with Cognite REST extractor, we are receiving this error: While executing request to <URL>: HTTP response error: 400 Bad Request. Body: <html> <head><title>400 No required SSL certificate was sent</title></head> <body> <center><h1>400 Bad Request</h1></center> <center>No required SSL certificate was sent</center> It seems that the extractor does not send the certificate, could you please help? Thanks! 

This might be a basic question, but I often find myself uncertain about when to use CogniteDescribable in data modeling within Cognite Data Fusion.For example, let's say we have domain tables in our data model, such as MaterialType, which contains data from SAP and defines the types of materials we work with (e.g., code and description). In this case, would it be appropriate to implement CogniteDescribable to reuse common properties like name and description?What are the advantages of doing so? Does using CogniteDescribable provide any specific benefits—such as improved performance through indexing or partitioning—when used it containers?In summary, should I always use CogniteDescribable in these types of scenarios, or are there cases where it's better not to?

Hello,  We recently migrating the way of deployment of Streamlit App in our cdf toolkit project. Before we used to deploy our streamlit app by creating a file in a dedicated dataset. Now we use out of box way of deploying Streamlit Apps provided by cdf toolkit  (this was not available before).  When changing to this way, we notice that the app is only deployed using “--include streamlit” option.i.e: when not using the “--include streamlit” cdf toolkit the deploy summary is the following:    however, we notice it is a previous version that has been deployed!  when using “--include streamlit”: summary is the following And the App latest version is deployed as expected. Toolkit Version used is '0.6.20'. Are we missing something? Could you please take a look at this?  Thanks!