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@Divyanshi Mangal I have added your user to the AAD Tenant for the appropriate CDF projects.
@Divyanshi Mangal I have responded on the SLB:Cognite Teams channel.
Users have been added!
@Neerajkumar Bhatewara. FDM is currently in beta and limits are in place. These limits will change and will be communicated when FDM is made generally available. I’ve sent a note kindly requesting followup on your requirements. :).
Ardash,I read 2 concerns here, One is refresh for a long running process and the other is secret rotation. For long running processes with the Python SDK, it’s my understanding that the token will (should) be refreshed via the SDK. The secret you use for running the workflow should be managed (stored and rotated) externally as any other secrets.Hope this helps :)Jason
Ardash,Yes, Cognite Functions have a finite runtime so the short answer is No. Workloads requiring execution resources (memory, cpu, runtime) larger than what Cognite Functions were designed for, it’s common to leverage native CSP infrastructure (ie. VM’s, K8s). These resources can be hosted in the same cloud region as your CDF project to minimize network latency. (ie. https://azure.microsoft.com/en-us/explore/global-infrastructure/geographies/#overview)Jason
Ardash,No, workflow orchestration is not currently available out of the box.Jason
Hi Ardash,The Cognite SDK’s are convenience packages that leverage CDF’s RESTful web APIs. So no, there is no difference. The benefit of the SDKs is to handle many cross cutting concerns and best practices when consuming the CDF APIs (auth, retry, web requests, ...). Limits and request sizes are documented here: https://docs.cognite.com/api/v1/Jason
Hi Ardash,I have reached out via email to respond to your request.Jason
Hi Ardash,CDF is deployed in many globally. CDF is designed to handle large numbers of concurrent users. In short, no you should not need to worry. Scaling is performed using the native capabilities of the cloud service providers’ infrastructure. e.g. Kubernetes.Jason
Hi Ardash,I have sent a request via email to discuss your requirements in more detail. In short, the answer would be to provision individual CDF projects for each Customer.Jason
Hi @Snehal Jagtap,You can consume via the beta APIs. As FDM is not currently GA, these APIs are subject to change. You can also refer to the following documentation: https://pr-ark-codegen-1771.specs.preview.cogniteapp.com/v1.json.html#tag/Data-modelsHope this helps!Jason
@Vikram Srinath Sriraman , there are many SLB sandboxes in use today. Please email me @Jason Dressel with details and I can set you up accordingly on an existing or new project 😀.Jason
Hi Niranjan,Can you share with me your entire transformation config yaml? Here or via Teams :).Jason
Eddy,Can you kindly share which instructions you followed? It should be as simple as ‘git clone’ and using out of the box VSCode git integration plugins.Jason
Hi Eddy,The sample code for that course reside in Cognite’s git repository: https://github.com/cognitedata/using-cognite-python-sdk. The instruction is asking to clone (fetch/pull) that code from the repository to your local machine. It appear the git commands are not installed on your windows machine. You can find several instructions to install. Here is one resource: https://git-scm.com/book/en/v2/Getting-Started-Installing-GitHope this helps,Jason
Mohit,Can you kindly elaborate? The dotnet SDK is a community SDK. It, and the dotnet extractor utils, are leveraged in cognite dotnet based extractors OSI PI and OPC UA (as examples)Jason
Xiaofeng,Looking at XiaofengTestDataModel, I don’t exactly know between which 2 types you are relating (creating edge for). There are 2 types of relations If your model looked something like:type SimulationModel { name: String! simulator: Simulator nodes: [Node]}type Simulator { name: String!}type Node { name: String!}You would need to create a Simulator instance first (I presume you have that). Let’s assume that the id of a Simulator node instance is “simulator-s”A SimulationModel Instance creation would look something like this: { "instanceType": "node", "existingVersion": 1, "space": "your-space", "externalId": "sim-model-id-x", "sources": [ { "source": { "type": "view", "space": "your-space", "externalId": "SimulationModel", "version": "1" }, "properties
Have a look at the sample I shared. The “Direct” relation is not established by creating an Edge. It’s established by setting the property on the Node { "instanceType": "node", "space": "XiaofengTest", "externalId": "externalId:simmodel1", "sources": [ { "source": { "type": "view", "space": "XiaofengTest", "externalId": "SimulationModel", "version": "1" }, "properties": { "modelName": "firstSimulationModelName", "modelId": "xxx-xxx-xxx-xxx-xxx"#DECLARE THE DIRECT RELATION "simulator": { "space": "XiaofengTest", "externalId": "externalId:simulator1"#### } } ] }
Hello,I presume you have followed the directions here: https://docs.cognite.com/cdf/dashboards/guides/grafana/admin_oidcIf you want user level authentication delegation, your Azure Active Directory (AAD) Administrator will need to provide the appropriate approval. It will be the same person that provided you the Client Id and Client Secret for grafana setup.If you want to use Client Credentials, you can follow instructions here: https://docs.cognite.com/cdf/dashboards/guides/grafana/admin_oidc#set-up-a-client-credentials-grant-flow, but you will still need an client id and secret that has permissions to read from your CDF project. Hope this helps,Jason
Hello Niranjan,I created a working sample I hope helps. I created an FDM targeted transformation in Fusion (version as of the date I wrote this) and used the export CLI button in the upper right. This created the following manifest. I updated authentication section with the credentials that are required when the transformation itself is going to run. This auth# Manifest file downloaded from fusionexternalId: tr-BSEEWell-Header-Asset-FDM-cliname: BSEE Well Header Asset - FDM - CLIquery: >- select cast(`API_WELL_NUMBER` as STRING) as externalId, cast(`WELL_NAME` as STRING) as name, cast(`API_WELL_NUMBER` as STRING) as apiNumber from `bsee`.`well_header`;destination: view: space: space-well-model-simple externalId: Well version: "1" instanceSpace: space-well-model-simple type: nodesignoreNullFields: trueshared: trueaction: upsertdataSetExternalId: ds-bsee# this is authentication credentials for Fusion, not the transformation-cliauthentication: clientId: myc
Snehal,A ticket has been created to address resolution of this issue. We will update you once resolved.Regards,Jason
Xaiofeng, Can you confirm this is resolved?Jason
@carriechung,I can provide a brief answer. Think of space as storage scoping. You can define your models in one space, but use another space to store instances. This way, you can have one model but use different spaces to partition your instances. Currently, to keep it simple, you can use the same space for data model and the instances. Jason
@Vaibhav Narain TimeSeries datatype is yet to be fully supported. What teams are typically doing is created a surrogate “TimeSeriesRef” type as a placeholder for the time being. You would populate TimeSeriesRef instances with your CDF native time series instances and metadata properties.
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