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Hello All,I’m very eager to learn about cognate data fusion. Hope you guys will help me if I got stuck.

 Trying to upload assets and asset hierarchy following the documentation available online is not working as shown.Trying to upload an ndjson with assets and hierarchy with the following files:myHierarchy.Manifest.yamlkind: Assetstype: dataSetdataSetExternalId: compressor_demo_datasetmyHierarchy.Assets.yaml{"externalId": "Compressor_Demo", "name": "Compressor Demo", "depth": 0}{"externalId": "Compressor_Demo_Geonosis","name": "Geonosis", "parentExternalId": "Compressor_Demo", "depth": 1}{"externalId": "Compressor_Demo_Geonosis_Central-Utilities","name": "Central Utilities", "parentExternalId": "Compressor_Demo_Geonosis", "depth": 2}{"externalId": "Central-UtilitiesCompressedAir_Cell","name": "Compressed Air", "parentExternalId": "Compressor_Demo_Geonosis_Central-Utilities", "depth": 3}{"externalId": "AirCompressor1_Central-UtilitiesCompressedAir_Cell", "name": "Air Compressor 1", "parentExternalId": "Central-UtilitiesCompressedAir_Cell", "depth": 4}{"externalId": "AirCompressor2_Central

 SummaryEnable aggregation operations (such as step_interpolation) on requests containing both numeric and string time series in the same SDK call.Currently, aggregation works correctly for numeric time series, but the request fails when a string/alphanumeric time series is included alongside numeric series. This limitation prevents users from retrieving synchronized operational datasets that combine process measurements and categorical/status information in a single query.Problem DescriptionWhen using the Cognite SDK method retrieve_dataframe() with aggregation enabled, requests containing mixed data types fail if at least one time series is of type string.Example:Numeric-only aggregation → works correctly Numeric + string aggregation → failsThe expected behavior for string time series using step_interpolation is to replicate the last known value across the aggregation interval, similar to how industrial historians such as PI System behave.Example Use CaseOperational dashboards and an

Hi Team,Our observation while storing duplicate entries in Cognite Streams:Mutable Streams - It discards the duplicate entries consistently.Immutable Streams - It sometimes allow exact duplicate entries (externalId and other fields) and sometimes it doesn’t.Even when we checked in the Cognite AI, it says it usually doesn’t allow duplicate entries but in some rare scenarios, it allows.Could you please let us know the behavior. If it allows in rare scenarios, please let us know the exact scenarios.Thanks,Rahul

Hi everyone!I'm starting the CDF course and I'm eager to learn more about this platform.Happy studying everyone!

Hi,We're the Drilling & Wells Data Analytics Platform team at TotalEnergies. We use CDF as the backbone for our drilling data for ingesting, transforming, and exposing data across multiple countries and data products. We manage our CDF resources with cognite-toolkit and deploy through GitHub Actions.As our data model grows and our team scales, we keep running into the same question: how allow all our team members to safely test and validate a change before it hits production? The tricky part is that CDF resources are shared. Two PRs deploying to the same test environment at the same time will overwrite each other's containers, views, and data, making it impossible to validate either change in isolation.Our plan is to deploy a fully isolated copy of the data model for each open PR, then tear it down automatically when the PR close, orchestrated end-to-end by a GitHub Actions workflow. This is achievable because all our resources and space naming are handled in the config.<env>

Hi Team,We are currently exploring migration of our existing content to use Flexible Data Modeling in CDF, and we are facing some challenges while configuring the OPC UA Extractor.At the moment, we are still pointing the extractor to the traditional Asset-Centric Time Series (dataset-based approach) and then transforming the data into our Data Model types afterward. However, we would like to ingest data directly into a view that implements the CogniteTimeSeries type.We would appreciate your guidance on the following: Do you have any example .yml configuration for the OPC UA Extractor that ingests data directly into a view implementing CogniteTimeSeries? Are there any best practices or recommended approaches for this setup? Is the following documentation the correct reference for this use case?https://docs.cognite.com/cdf/integration/guides/extraction/configuration/opcua#data-models Additionally, we are also evaluating OPC Events ingestion: Should OPC Events be modeled using Cognite

Maybe this will be solved with the release of Adaptive Experiences, but for now the overview of the published apps is a bit cumbersome to navigate. Here is an example of an app, “Data Quality Dashboard”, that shows up twice, this is the same app but with different versions. The only way to tell from here is the updated timestamp. Would be nice with some kind of indicator showing which is the current version of the app, and maybe some kind of grouping that indicates that it is in fact the same app and not just two apps that happen to have the same name. The apps have a label that shows whether it is a “Flows app” or a “Streamlit app”, but these labels are not searchable, nor is there any functionality that allows for filtering. Would be nice if it was possible to add labels/tags to apps that were searchable allowing us to filter and group apps together. When we have 50+ apps it can be somewhat difficult to navigate.Markus PettersenAker BP - Data Platform Architecht

I am currently working on oil and gas engineering projects, primarily focusing on document control. My responsibilities include managing document control activities, creating workflows in the EDMS tool, generating reports, and performing data analysis on document metadata and workflow status. I ensure the timely delivery of accurate overdue reports and validate customer and contractor submissions against project standards. I am enthusiastic about continuous learning, which has helped me improve the effectiveness, accuracy, and presentation quality of my work.

Hi there!My background is strongly connected to critical operations, industrial systems, troubleshooting, reliability, and human decision-making under pressure. Over the past few years, I’ve been increasingly focused on the intersection between AI, operational excellence, industrial digitalization, and human-centered decision support systems for the energy industry.What interests me most about this community is understanding how industrial data platforms can move beyond dashboards and become true operational intelligence environments — helping teams improve decision quality, knowledge retention, reliability, safety, and execution in real-world operations. Looking forward to learning from everyone here and contributing with an offshore operational perspective whenever possible.Excited to be part of this journey.Best,Thiago Araújohttps://www.linkedin.com/in/araujotja/

Currently if I perform a search, in equipment for example, I can only display the first 100 results with no ability to see the remaining results.  There is also no way for me to export the results to an excel spreadsheet. We can work around the export issue but there doesn’t appear to be a workaround for viewing more than the 100 first results.   

The documentation link takes me to a 404 page: 

Hi,I am discovering and learning CDF, I am following the Fundamentals course and I have an issue with contextualizing the P&ID: when I run the parser I get the error below:    Is there something I missed or is is related to my computer or browser? Thank you for your help

The color in the status bar doesn’t match the legend: 

I come from a data analysis background, specifically working on anomaly detection in power plant equipment operations. I find Cognite to be a great learning platform.I would like to suggest whether it is possible to have a sandbox environment where we can practice using data and apply our own logic. Recently, I received communication from Cognite encouraging users to implement their own anomaly detection ideas, which is a great opportunity to learn and contribute.Having access to a sandbox setup with relevant data would allow us to build and test data models for anomaly detection. This would be beneficial for both learning and practical application, especially since the platform is closely aligned with real plant operations.

Hello, We have around ~10/15 CDF functions. We deploy these functions via cdfToolkit.We notice that deployment of new version of a function takes around ~10min in order for it to be fully available. In the worst case where we modify a common module used by all function, redeploying of all functions can reach ~30-40mins. During this time these functions are not available and results in a failure in the dependent high frequency workflows. We wonder if there is a way to accelerate deployment of new function versions. Thank you!