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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("lin

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

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?

I have created a function that was configured to 5G of memory and 2 CPUs to run huggingface AI model (py311). Deploying the function went fine. However, when running the function it throws this error.  RuntimeError: [enforce fail at alloc_cpu.cpp:118] err == 0. DefaultCPUAllocator: can't allocate memory: you tried to allocate 9437184 bytes. Error code 12 (Cannot allocate memory)  

Are there any documented use cases or papers on integrating MLflow with Cognite, or is it something we need to implement ourselves?For example, if we aim to seamlessly integrate the MLflow UI with Cognite to evaluate and select the top-performing models, we could leverage SQLite, which operates on the local file system (e.g., mlruns.db) and provides a built-in client, sqlite3. However, our preference is to seamlessly integrate it with Cognite.

Hi,I’ve encountered a bit of issue with CDF when using Microsoft Edge. The full screen button doesn’t do anything. In Chrome and Firefox it does as expected and opens the document in full screen mode.Regards,Markus

Hi, i have set notification to alert when runs failed. However, i noticed that am still receiving email notifications wherever the extraction pipeline is successfully executed. Could you please advice. thanks  

charts Daily average of the previous dayIf anyone knows a good way to do this, please let me know.I would like to see the previous day's 24-hour values by averaging the data over the day with a mean of 0 days 24:00:00 (resampling to granularity), but due to the UTC+9:00 specification, the previous day's processing does not occur until 9:00 AM in japan.I would like to see the previous day's nightly processing volume average at 8:00 AM.The only way I can think of is to use the “shift time series” to force it forward, i would be appriciated if anyone have a better idea.. 

I would like to know the benefitS of implementing UNS architecture in CDF. A detailed guide would be excellent to show how to implement it in the oil and gas refinery with all the prerequisites and third-party platforms along with that.

Hii Team,I'm looking solution for below problem.Currently we have build our custom model using core data model. we have timeseries available in cdf.timeseries resource. Now i want to provide timeseries reference in below view using transformation. Can anyone suggest how timeseries concept is worked in core data model using transformation. type TimeseriesProperty implements Property & CogniteTimeSeries & CogniteDescribable & CogniteSourceable  @view(version: "2_1") {  name: String  source: CogniteSourceSystem  entity: Entity  sourceId: String  sourceContext: String  sourceCreatedTime: Timestamp  sourceUpdatedTime: Timestamp  sourceCreatedUser: String  sourceUpdatedUser: String  isStep: Boolean!  type: CogniteTimeSeries_type  sourceUnit: String  unit: CogniteUnit  assets: [CogniteAsset]  equipment: [Cognit

I have set up the Cognite Pi Extractor on a virtual machine and am running into errors in the log:  Fatal error in request of type "CreateTimeSeries": Unauthorized. Unauthorized RequestId: , CDF Message: UnauthorizedI have checked that the client_id and client_secret being used in the config.yml have the ‘time_series: write’ capability for this project. Additionally, I have confirmed that the client_id and client_secret can be used with the python sdk to create timeseries in the project I am working with.Any ideas where I am going wrong?

I’ve had the OPC-UA extractor running for a couple of months with no issues. On Feb 19 it stopped pushing data to Cognite properly. According to Charts, it started pushing again on March 6. As I write this it is March 3rd. The data goes out thru June 2025,The host machine w/extractor has been restarted.The OS date/time looks correct. The log file has the correct date/time. I’d appreciate some advice.  

How is Agentic AI used in Cognite. Are there any use cases where Agentic AL is being used in the oil and gas industry?

Hi,we have quite a number of timeseries which have externalIds with spaces. Unfortunately this throws off the search algorithm of the data explorer (and also in charts when adding new data). Disallowing fuzzy search (which should not be necessary since we *know* the externalid we are looking for) yields no results. Why allowing fuzzy search for the name will give back almost all timeseries not even remotely related to the timeseries we are looking for. I tried placing quotes around the externalId without any success. Is there any way to protect the spaces in the name or to get the search to match externalIds including the spaces?