Community Discussions

Workflow to Connect Power BI to FT DataMosaixI am currently experiencing issues connecting Power BI to FT DataMosaix for the project ai200prodtesting-44-datamosaix. I would like to highlight that for the essc-sandbox-44 project, the connection works as expected, as shown in the evidence below.Below is the step-by-step procedure I followed to connect to the ai200prodtesting-44 project:Steps:1. Select Get Data > Cognite Data Fusion (REST API) connector.2. Set up CDF Project and CDF Organization.3. Choose Sign in as a different user.4. Enter user email address.5. Enter user password.6. Click Connect.7. Review the error message: Unable to connect. the message with the error "Unable to connect" attached.

Instead of storing String data as timeseries, we would like to store data in Staging and then transform to Events with addition metadata.Can the existing OPCUA extractor support that?

HiI am testing out event based triggers to see if its a good fit for a use case I am working on. When deploying the trigger the workflow is triggered many times until it has processed the whole input query, however as very many workflow executions are started at the same time, some of them will fail as the limit for running workflows has been reached.How is this handled by the trigger, will the failing workflow runs be retried? If not, is there a setting I can use to limit the amount of concurrent runs?I think this would be good both for distributing the workflow runs out and avoid the failures and also reduce the risk of failing function calls because of high load on the API.Thank you!Sebastian

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

Sideways scrolling is really cumbersome, why have you made the UI significantly worse? 

Hi,I want to set up monitoring of timeseries wrt reservoir levels. Yesterday I also set up a dummy monitoring, simply to test the functionality. I set the monitoring to alert when timeseries is over 1462.88 meters, which was crossed yesterday evening, but no alarms were triggered.Please see screenshot of my setup below, does this seem right? BR,Jørgen Aarstad  

Interested in understanding cognite product line, how it is used with industrial automation and IoT system, Very much interested in how GenAI has been integrated. 

This is sample codehow can i give custom input to this python code # Note that the docstring is how the agent knows how to call the function. It follows the Google Python Style Guide:# https://google.github.io/styleguide/pyguide.html#383-functions-and-methods from cognite.client import CogniteClientfrom cognite.client.data_classes.data_modeling import NodeIdfrom cognite.client.data_classes.data_modeling.cdm.v1 import CogniteTimeSeries client = CogniteClient() # Retrieve latest data point by time series node iddef handle(time_series_id: NodeId):    """    Retrieves the latest data point for a given time series.       Args:        time_series_id: The node id of the time series to retrieve the latest data point for.       Returns:        The latest data point for the given time series.    """    datapoint = client.time_series.data.retrieve_latest(instance_id=time_series_id)       # Only json serializable data is supported, so we convert the created_data to a dict with .dump().    return d

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, We are planning to create Business Continuity and Disaster Recovery document for CDF in our environment and using https://docs.cognite.com/cdf/trust/security/reliability_intro/ But, we are missing some information regarding the RTO and RPO for different scenarios,Full cluster restore  CDF project restoreCould you please help us on this.ThanksAbhra

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!      

app.cognite.service: Found 1 data modelIn the publicdatacdm space, only one data model is currently available, which is related to 3D scenes.However, our goal is to create a knowledge graph, which requires asset-related data models. Since the available model is focused on 3D scene configuration, it doesn't cover the necessary asset relationships.To move forward with building a knowledge graph, we need: Asset-centric data models. A clustering mechanism to visualize and structure the knowledge graph meaningfully. Is there any plan to include asset-related models in publicdatacdm, or any recommended approach to achieve this with the current setup?

Hi,In our CDF project, how can we use GraphQL and how  we can connect to project in our react appCould you please guide me?Thanks!

Hi Cognite Community,I'm building a React + TypeScript app using Apollo Client to connect to the CDF GraphQL API. It shows a Knowledge Graph of assets, time series, P&ID diagrams, and 3D models using vis-network.✅ I already did this successfully using the Python SDK.Now, I need help with:Setting up the GraphQL endpoint in ReactHandling authentication from the frontendAny simple examples or tips would be greatly appreciated. Thanks!

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.

The Ghost mode in the red box doesn’t effect

Datasets have an “Access Control” page where you can see which groups have access. Is there a similar way to quickly identify which groups have access to Data Models / Spaces?If not, can this be implemented as a feature?

Hello,I’m looking to compute the standard deviation of a timeseries on the fly with synthetic timeseries.I expected to use this pseudocode formula : sqrt(avg(pow(TS{externalid}-avg(TS{externalid}),2))) with endpoint :client.time_series.data.synthetic.query(    expressions=expression,    start="2w-ago",    end="now")Unfortunately, avg expect at least 2 inputs, I try to switch to aggregate feature but I found it available only for timeseries, not synthetic timeseries.expression = '''sqrt( avg( pow( ts{ID} - ts{ID, aggregate="average", granularity="14d"}, 2 ) ))'''Do you have any tips or workaround to compute this value when “start” value changes ? Dont hesitate to explain I'm open to any opportunity to calculate this metric using another method.Thanks in advance,Pierre edit : I find this function in additionnal library : Rolling standard deviation of data points time delta — indsl 8.7.0 documentation but i’m looking for a answer without additionn

Weird formatting of labels in Extraction Pipelines web app interface. Flowing beyond it’s container making it not fit on the page. 

The data grid object has some issues when changing the grid like removing a row or adding a column etc. The attached video shows the behavior. A browser refresh clears up the “jitter” but changes do not take effect.

I need help getting attached error(Error: no gl) in chart page

Hello,I lost the account in the authenticator app while not changing phones. How do I get this back?Thank you in advance,Regards.

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!

Hi, I am on Data Scientist Learn training. I´m getting the 404 Not Found error whenever I try to make a GET request for publicdata project. That´s the URL I´m trying to use{{baseurl}}/api/v1/projects/{{project}}/assets I also replaced {{project}} with ‘publicdata’ with same result.

Hello,I am working with the Cognite Data Modeling API to validate edges in a large data set. Our edges do not have a dedicated "edge view" defined in the model. When I attempt to use the instances.query() API to fetch edges filtered by their type, I encounter errors or unexpected behavior indicating that selected properties do not match the view schema.Specifically, filtering or selecting on properties such as "externalId" fails because the queried view does not explicitly contain those properties, as the edges exist only as instances without a separate view.Is there a recommended approach or best practice for querying and validating edges in scenarios where no dedicated edge view is defined? Should we rely exclusively on the instances.list() API with filters, or is there a way to construct valid queries for edges in the current model setup?Any guidance on how to effectively query and validate edges in such cases, especially for large-scale data, would be appreciated.Thank you!