Product User Community

We have customers using InfluxDB as a local on-prem storage mechanism. What’s the best extractor to use to push this data to CDF? I’ve heard OPC-UA is the way to do it, but what I find online are ways to store OPC-UA data into Influx, but not ways to get InfluxDB data into a OPC-UA server so I could use the OPC-UA extractor to get it into CDF.

Our team is handling 3D models in various file formats and applying colors to different models before importing them into CDF. Additionally, we are applying textures to models using Navisworks Simulate and exporting them in .nwd or .fbx formats. All related files are zipped before uploading them in CDF.According to the Cognite 3D documentation, this should be possible. However, when we import the models, the colors do not appear as expected. Could you provide guidance on the correct procedure to ensure that colors and textures are retained? Is there a specific workflow or setting we should follow?

The new CDF Power BI REST connector has been certified by Microsoft and is now included in the latest Power BI desktop version and deployed in the Power BI online service.What's new with the Power BI REST connector:Flexible authentication: Connect Power BI with any IdP supported by CDF (the legacy OData connector only worked with Azure Entra ID) Broader data access: Fetch data from OData services (just like the legacy connector) Access data from Data Models using GraphQL Connect to any GA CDF API endpoint Significant performance boost: Up to 10x faster when using regular REST endpoints compared to fetching the same data via ODataThe connector is currently in Beta, and we're eager to hear customer feedback before promoting it to GA. The documentation for the new connector is available here, and we're working on a new set of micro learning modules in Academy based on the new connector.

Hi Just wondering if CDF extractors/connectors has a support to fetch DFOS data, say for example to measure temperature along the line. Access may perhaps be through the third party SDK or a native connector. Also interesting would be how such high frequency data can be modelled in CDF core and solution models correctly. Any pointers would be of great help!  RegardsAlex 

Hello, While testing data gouvernance feature per space in the data model service, we noticed that it does work on the instance level but not on the data models level. Here are the tests conducted: Test 1:We tested giving access to users to only instances that are in a specific space but applying the following ACL :  - dataModelInstancesAcl: actions: - READ scope: spaceIdScope: { spaceIds: ['test'] }When requesting the data model, only instances in the test space are returned. ACL works on instance level. Perfect!Test 2: When applying a similar ACL on the data model level :  - dataModelsAcl: actions: - READ scope: spaceIdScope: { spaceIds: ['not_that_important_data_models'] }In this case, we notice that we get revoked access to all data models, even data model in not_that_important_data_models space.  We expected that we would be able to access only data models in not_that_important_data_models. Could you please check ?  Thanks

Hi,I would like to check whether there is any CDF Admin UI or available APIs that provide the telemetry and usage-related insights such:​​​​​ User activity logs such logins, user actions, dataset access, frequency of use API usage metering such calls made by service accounts or integrations, volume over time Application usage tracking such as which integrations/apps are active, which endpoints are being used and etc. Quota and resource usage tracking, like number of API calls, storage/compute loads. Are there any CDF admin dashboards, telemetry APIs, or audit logs available for this purpose?Please advise.  Thanks

Hi!Is there a way to enable cascading deletes for instances for both direct relations and edges? Edit: I posted in the wrong group, but i don’t think i can delete. 

I would like to know what to fill in BI to be able to connect on CDF. I tried to use  that how on the course, but gives a error to add the organization name. publicdata?tenantID=48d5043c-cf70-4c49-881c-c638f5796997

 Hello, I’m having this error while using CDF latest Spark datasource. I was only able to make it work using 2.1.10 version (com.cognite.spark.datasource:cdf-spark-datasource_2.12:2.1.10).  Could you please take a look?  Thank you ! 

In Cognite Academy Fundamentals : Working With CDF: Contextualize, I have followed all of the instructions several times for Entity Matching and, in all instances, get “No matches found”, so there is nothing to confirm or write to CDF.  What am I missing here?

Using PBI Desktop and Cognites Rest API Connector betaWhen configuring the GraphQL parameters of Space, Datamodel, version and queryThere is a problem when pasting a query that was copied from the Query Explorer or even notepad into the query input boxIt is truncating the query string at the first carriage return CR and or Line Feed LFIf all CR and LF are removed from the query string, it will paste properly and run properly.   

I am new bee for this mammal, to Setup the Cognite file extractor for the local file upload to CDF, how to I config the environment variable in the yaml?according to the example-local.yaml I need to read:COGNITE_BASE_URLCOGNITE_PROJECTCOGNITE_CLIENT_IDCOGNITE_TOKEN_URLCOGNITE_CLIENT_SECRETI am little confuse, should I create a .env file to have all of those environment variables in that file and put it in the same folder as the yaml config file or can I inject those variables in the configuration file itself, if so any example?  need someone lighting me up.

it says that the project is not valid and do not give me the hello world output. 

got 401 "Unauthorized" error when using Postman and also  sometimes a bad request error while I did everything  as they did in the course. please help it took a lot of time from me.

How can we configure the OPCUA data node (in string) as event in Cognite instead of timeseries (by default)?P.S. The existing OPCUA Server does not support to configure it as Event.

I m working cognite hosted rest extractor and i m not able to perform incremental load and getting Kuiper http error while making a request. Can someone explain what the key name is when we use query params for incremental load and how should value look like in json having a conditional statement to pick a constant value in first execution and last_run from context after that? (Assume we have to modify startindex and lastindex query params after first excution)

so in the Learn to Use the Cognite Python SDK  in the data engineer course , I got stuck on the hands on test.as   in the readme file after I created a dataset and a root assets I just do not know how can I  do this section : - Read the `all_countries.csv` file as a dataframe and list all the unique regions in the world.- For each geographical region, create a corresponding CDF asset that is under the "global" root asset and is associated with the "world_info" data set.- Next, create country-level assets using the data from the CSV and link them to their corresponding region-level assets. 

I have a data model with a many to many relation between view 1 and view 2. This is modeled as an edge and stored as columnEdgeView2 in view 1 and has an edge pointing the other direction in the other view. I have been digging through the Python SDK documentation but cannot figure out how to retrieve this information. Can anyone help me with this? What I need to do is query either of the edge properties and do something similar to the instance.list of regular views. If possible, getting the columnEdgeView2 included in the result when listing instances the normal way would also work. I have tried to replicate my data model structure below.type View1 { column1: String columnEdgeView2: [View2]}type View2 { column1: String columnEdgeView1: [View1] @relation( type: { space: "space", externalId: "View1.columnEdgeView2" } direction: INWARDS )}Thank you!

Hello, we're using pygen to generate a Full Data Model and instances. One of our containers can have multiple connections to objects in another container (see the pic below).As a result, definitions are lists of objects. It is then difficult to query data using tuples: bays = dm_client.bay.list( bay_to_line=(space, id), retrieve_connections="identifier", limit=None, ).to_pandas()We can overcome this by reading all bays with retreive_connections=”full” and then querying the data. But we would like to generate a model, where such tricks are not needed and the definitions are not a list. Is it possible? To generate a model we do this workflow:  neat.read.rdf("lines_bays.jsonld")neat.infer(max_number_of_instance=-1)neat.prepare.data_model.cdf_compliant_external_ids()neat.verify()neat.convert("dms")neat.set.data_model_id(("lin", "lines", "v1"))neat.to.cdf.data_model()neat.to.cdf.instances() 

Hello,  We are currently the Pygen (cognite-pygen==1.0.2) to generate an SDK of our data model. While testing a use case where we want to query all Casings of all Onshore Wells we notice that we get different results using the `List` and `Select` methods. # First method of queryingwells = dm_client.well.list(product_line='Onshore', retrieve_connections='identifier',limit=-1)direct_relations = []for w in wells: if w.wellbores: for wb in w.wellbores: wb_ext=wb.external_id inst = dm.DirectRelationReference(wb.space, wb_ext) direct_relations.append(inst)wb_sections = dm_client.wellbore_section.list(wellbore=direct_relations, retrieve_connections='full', limit=-1)casings = wb_sections.casingprint(f'casings: {len(casings)}') # casings: 645# Second method of queryingcasings= dm_client.well.select().product_line.equals('Onshore').wellbores.wellbore_sections.casing.list_casing(limit=-1)print(f'casings: {len(casings)}') # casings: 94Any thing we missing

Is anyone using Cognite as their main timeseries historian? We are always exploring alternatives and would be interested to hear if Cognite has fit this use case for any users. 

As owner of a canvas, the Canvas UI identifies me as expected such as in version history or in comments. When another user makes edits and comments they are not identified and show up as seen in the attached screenshot. Just in case it matters, this is a Rockwell Automation DataMosaix project. Thanks!

After creating a new calculated time series in Charts, how can I replicate the same calculation across multiple similar assets?Let’s say I calculate [ampers / Fluid rate] in one pump; I’m now interesetd on repeating the same calculation across 10 other pumps (let’s assume timeseries have the same name for all pumps, i.e.: PumpX:AMP).Am I obliged to go to SDK and code that? Shouldn’t be an easier way to do that? Thanks!  

Hi,I have a datamodel in CDF and I want to run a GraphQL query through the SDK to retrieve specific data from the model. When I try to run response = client.data_modeling.graphql.query( id = ('sp_watercourse_data_model', 'Watercourse', 'v1'), query = query )I receive the following error message:CogniteGraphQLError: [GraphQLErrorSpec(message=Could not find data model with space=sp_watercourse_data_model, externalId=Watercourse and version=v1, locations=[], extensions={'classification': 'DataFetchingException'})]However, when I list my models usingmodels = client.data_modeling.data_models.list(limit=100).to_pandas()the model is in the dataframe as expected. What causes this problem and how can I fix it? thanks

I had gotten a number of rest extractor pipe lines running the other day and then paused them. I am now trying to resume them and I am getting an Startup Error message “Missing required field session key”. I do not see a field session key referenced anywhere in the documentation on the rest extractor. Are you able to give any additional inform of where I can find this field?