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The MQTT hosted extractor is erroring out and showing the following message: Internal database error: Session has expired: Session was not foundI’ve tried pausing and resuming the extractor, and modifying the configured sink, without success.

Dear Community,I am facing an issue when attempting to pull data from CDF into Excel through OData connector,Main purpose is to have data pulled into excel & get periodically updated (+ historian)As the Data from InField is required to be pushed into a specific Excel format for external reports.However, the main issue is that after adding URL :https:// westeurope-1.cognitedata.com/odata/v1/projects/{myProductionProject}I immediately get the Navigator view ( no prompt to sign in yet) Then once I try to get any data, the login pop-up window is there, but is rejecting all authentication methods:for both current & alternate credentials  and this is for all levels of the URL -For Web API Key Value, I wasn’t too sure where to get it fromTo note, I know I have the required access, as I can pull data normally in PowerBI REST API,Please advise, does the organization that has that  project need to add anything on azure ENTRAID side to allow this? or is it a different mistake I am making?

Description / Problem StatementWe are using the Cognite 3D File Extractor to migrate 3D model files from an on-premises Windows VM to Cognite Data Fusion (CDF).The majority of 3D files are ingested successfully with multiple sizes mostly > 10 GB. However, four specific files consistently fail ingestion, even after multiple retries.Key observations:All failing files are close to 5 GB in size Example sizes (as shown in Windows Explorer): 4.99 GB 4.94 GB 4.91 GB When checked via terminal/command line, these same files report a larger actual size (~5.3 GB) It appears that based on the Windows VM file size, the 3D extractor determines it should use a single-part upload. However, when it attempts to upload the actual file, the process fails because the size exceeds 5 GB      

Hi colleagues!I was seeking the course “Data Engineer Basics: Transformation and Contextualize” but the original link is broken: http://learn.cognite.com/path/data-engineer-basics-transform-and-contextualizeReference: Cognite Hub What happened?

Hi , I have the CogniteActivity in the CDM/EDM data model which the assets is part of the model: Below is the sample data of CogniteActivity:This is the data model for Event in PDM:I tried to transform from CDM/EDM to PDM using below transformation, but i am not sure what is the script to transform CogniteActivity to PDM Event:And i got below error: Anyone know how can I transform CDM/EDM asset node reference to Event entities attribute? Thank you.

Hello everyone. I’m looking to use a package from github the same way I already use it in requirements.txt for Cognite Function.The format is :git+https://<token>@<url>@<branch>When I add this line in settings :Error during booting upTraceback (most recent call last):  File "/lib/python3.12/site-packages/packaging/requirements.py", line 35, in __init__    parsed = _parse_requirement(requirement_string)             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^  File "/lib/python3.12/site-packages/packaging/_parser.py", line 64, in parse_requirement    return _parse_requirement(Tokenizer(source, rules=DEFAULT_RULES))           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^  File "/lib/python3.12/site-packages/packaging/_parser.py", line 82, in _parse_requirement    url, specifier, marker = _parse_requirement_details(tokenizer)                             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^  File "/lib/python3.12/site-packages/packaging/_parser.py", line 126, in _

Hi Guys,I tried to use a transformation from the SDK but i keep getting error on Transformation credentials. Do you guys know what I need to do to solve this credentials issue. I have check the current credential for the Service Account that I use and the access for tranformation has been granted. def fetch_raw_table_via_transformation(    client: CogniteClient,    db_name: str,    table_name: str,    calculation_date: Optional[str] = None,    columns: Optional[List[str]] = None) -> pd.DataFrame:    """    Fetch data by running a temporary CDF Transformation (uses Spark SQL engine).        This is the RECOMMENDED approach for production workloads:    - Runs on CDF's managed Spark infrastructure    - Handles billions of rows efficiently    - Server-side filtering and aggregation    - Can write results to temp Raw table or return directly        Note: Requires transformation permissions in CDF        Args:        client: CogniteClient instance        db_name: Name of the RAW database 

I have a bit of a conundrum here. I am trying to visualize the daily and weekly averages of a time series in Azure Managed Grafana v11.6.3 using the CDF datasource. Raw data is strictly on hourly resolution, and provided in CET/CEST. Grafana is set to browser tz (i.e. CET/CEST). I need the daily average to align with CET midnight (=23:00 UTC). I use these synthetic series parameters: aggregate='average', granularity='24h', alignment=82800000 (82800000 aligns the datapoints to the correct hour if I do aggregate='step_interpolation', granularity='1h'). The returned value is neither the UTC day average or the CET day average. It is exactly the average of the two. So from what I see it seems to be a 30-minute shift/interpolation in the backend. Case in point: CET day average for a specific day is 158,67, while its UTC equivalent is 159,83 (see screenshot). Neither value is found in Grafana for that day at 00:00 - here I see 159,25. Which coincidentally is equal to (158,67+159,83)/2. The ti

Hi Team, Is there any possibility that I can register for Bootcamp virtually or it can be in India location as I am from India.Also, want to know is the Bootcamp is for individual or group and the cost for attending the Bootcamp? Thanks,Navyasri Indupalli

Hello! I have got a new phone and am not able to restore microsoft authenticator from its backup.Could someboday please reset the MFA Access so that I can set up the authenticator again?

Dear Community,I have questions regarding the required access control for CDF  Canvas drag & drop featureand CDF Charts scheduling & creating a timeseries from calculation feature,As the people who use these features are usually granted read only capabilities (business users)But those two features I would assume require capability of writing to a dataset and files & timeseries create .I want to know what group permissions exactly need to be added for these users ( on the canvas&charts dataset)so that the new dataset start showing for them ( when needing to scheduel a timeseries in charts and drag/drop in canvas)So far we made an additional group, connected to the same azure group that includes the business users that have read only access,the permissions added to the new group are: timeseries read & write, files read & write ( scoped to the new dedicated dataset )but then, the new dataset still didn’t show in the drop down list for the business users ( in canvas

When doing the exercise “Valve mileage use case” of the Domain Expert training in CDF, this error appears (in Portuguese):It says:Competências necessáriasEntre em contato com seu administrador do CDF para solicitar as seguintes competências: timeSeriesAcl:WRITEIn English:Required SkillsContact your CDF administrator to request the following skills: timeSeriesAcl:WRITE Basically, someone should give me the timeSeriesAcl:WRITE permission. But who?   

Hello everyone,I observe a difference in behavior that I cannot explain.My input data, few datapoints, and before the value is 0, timeseries.is_step = TrueInput datapointsWith client.time_series.data.retrieve, behavior is as expected :And with every granularity results are okay as expected.Nevertheless, with client.time_series.data.synthetic.query it’s… different.that’s okthat’s okNot ok, why the result is not 2 groups : from 46 to 49 then 49 to 52 ? another wrong exampleWhy the start timestamp is 48 and not at 46 ? Again, the timeseries endoint is good : I have lost hope.If anyone has anything to try or an explanation, i’ll be grateful.Thanks in advance,Pierre 

Hi Everyone,We're currently managing multiple CDF Toolkit solutions in a mono-repo and evaluating whether to maintain this approach or migrate to a multi-repo structure. We'd love to learn from teams who've navigated through this decision.Current Setup (Mono-repo):1. Single repository housing multiple solutions with shared infrastructure. 2. Shared auth-governance across all solutions.3. Teams need independence but also need to prevent cross-team overwrites.We're Looking For: 1. Real-world examples: How did you decide between mono/multi-repo? What factors mattered most?2. Tooling patterns: How do you handle shared resources (auth governance) in multi-repo? How do you manage CDF Toolkit versioning across repos?3. At how many solutions/teams did you reach the limit of mono-repo and decide to switch to multi-repo?4. How do teams with multi-repos handle toolkit version synchronization? Do you enforce a minimum/maximum version across repos?5. Documentation (runbooks or migration guidelines)

Are there any extractors or connectors to seamlessly integrate Cognite CDF and Aspentech Mtell?If not, have you discussed approaches for that integration, and whether one of them should involve using Aspentech DataSource Interfaces to connect via Aspentech ODBC Datasource, Kafka Datasource, or MQTT Datasource?Please also share any other approaches you may have discussed for seamlessly integrating CDF and Aspentech Mtell.

Lineage doesn’t work. You click on it and it shows for 1 second, before you get this screen. Multiple people gets the same error. 

I’m currently working through the Data Engineer – Data Modeling learning course, specifically the hands-on section where we use the Cognite Toolkit to deploy and extend the provided cognite_dm_skilling repo repository.The course instructions state that, before starting, learners should have: VS Code set up CDF Toolkit installed The cognite_dm_skilling repo cloned Read and write access to a CDF project My question is: does Cognite provide a dedicated training or sandbox CDF project with read/write access for this course, or is access expected to be arranged separately (for example via an internal org project)?I can complete the local build steps with the Toolkit, but deploying and validating the model changes appears to require an actual CDF project with write access.Any guidance on the intended setup for learners here would be much appreciated.

I am working through Cognite Data Fusion Fundamentals: Part 2 – Hands-on, specifically the step to upload assets.csv to the staging area.When I select Upload CSV or JSON, choose the assets.csv file, and select “I don’t know the primary key”, I never get the Create button. The upload flow does not proceed after selecting the file.What I have tried so far: Deleting and recreating the RAW database Manually creating a blank table, then attempting to upload the CSV into it Repeating the process multiple times following the training steps exactly In all cases, the behavior is the same and the Create option never appears.Has anyone run into this issue during the training, or is there a known workaround or permission requirement I might be missing?

I’m using the CogniteSDK. I have seen the following behavior several times: assets = client.assets.list()the variable assets can contain deleted assets if they have been recetly deleted. If I find the external_ids of these assets, the client.assets.retrieve_multiple(external_ids=deleted_xids) correcly do not return the assets. The assets cannot be found in fusion either. I suspect that client.assets.list() is using cached values. This also applies with arguments, f.ex LabelFilter. I have seen the issue many times, but now it really got painfull, so therefore I’m writing here. It returns assets that were deleted several days ago.  What to do? 

There are some signals that in the names or the description have the words: alarm, warning, or fault. E.G. There is this alarm signal, (in red ) that I’m comparing with one of the vibration signals.  And the alarm seems to be on when the vibrations are close to 0 and the Compressor is off. I’m plotting the label as a combination of the name and the description, but the externalId of the alarm is: pi:160268In most cases that alarm is on when the compressor is off, except in this case, Where I do not see any problem with the vibration signal: There are these other two alarms, from the plots: one is on when the compressor is off and the other one when it is starting up. But I do not see any strange behavior in the signal when they occur. Besides the alarms, I’ve also looked into some of the fault signals but those seem to be on for very long times: Is there another way to use the alarm and fault signals to label anomalies or failures? I don’t know if using the vibration signals is the bes

Hi, I have defined a Data Model with a Source View having a DirectRelation to a Target View as a simplified version below, which Pygen created an SDK successfully.  But when I added a ReverseDirectRelationApply to the Target View, Pygen returned the captioned error.  Please help.source_container = ContainerApply(       space =”myspace”,       external_id=”sourceContainer”,       properties={            “sourceName”: ContainerProperty(type=Text, name=”sourceName”),            “target”: ContainerProperty(type=DirectRelation(is_list=False, container=ContainerId(space=”myspace”, external_id=”targetcontainer”) } target_container = ContainerApply(       space =”myspace”,       external_id=”targetContainer”,       properties={            “targetName”: ContainerProperty(type=Text, name=”targetName”)} source_view = ViewApply(     space = “myspace”,     external_id = “souceView”,     version = “1.0”,     properties = {           “sourceName”: MappedPropertyApply(container=ContainerId(“myspace”,

Is there a way to annotate 3D models towards instances from a data model. In the API documentation I only see linking to assets as an option.  

I have exhausted my 3 attempts of “Working with Cognite InField - Assessment”. Possible to help me with additional attempts or any other process to follow here?

Hi Team,Could you please provide 3 more attempts to clear working with Cognite Infield Assessment.

Is there a plan to support enumeration (or any way to restrict value of string properties) in the future?Enumerations are described here: https://graphql.org/learn/schema/, but seems like its currently not supported: