Community Discussions

On the platform, when we have more than one annotation on a file, if they are very close to each other (overlapping by a pixel or two), the platform shows the overlapping box so we can select the correct annotation.I understand this as a bug.See the images below:The tags PB2885C and XA2885C are very close to each other. The overlap between them is barely noticeable.However, when you click on either one, both tags appear in the overlapping box:I’ve seen this happen every time we have annotations that are very close to each other.For a better user experience, it would be ideal if, at the moment of the click, the platform could understand that only one annotation is actually inside the click area.This would prevent the overlapping box from appearing and would solve the issue.Is this something we can have fixed?

Hi everyone,I’d like to share industrial-model — a Python ORM built on top of the Cognite Data Modeling Service API.It lets you define DMS views using Pydantic models, query them with a fluent, expressive API (filter, search, aggregate), and get fully typed results with IDE autocomplete out of the box.The SDK also supports upserts, deletes, and async workflows, making it a natural fit for modern Python stacks. In addition, it includes configuration for injecting instance spaces directly into queries, which can significantly improve query performance when working with large or complex data models.We’ve been using industrial-model for the past couple of months, and it has significantly reduced boilerplate when querying complex graphs, while also speeding up onboarding for new developers.Documentation:https://github.com/lucasrosaalves/industrial-model Feedback and feature requests are very welcome!Sample code:from pathlib import Pathfrom industrial_model import ( Engine, ViewInstanc

The documentation on this page should be updated to reflect the product behavior.https://api-docs.cognite.com/20230101/tag/Spaces/operation/deleteSpacesV3> If an existing data model references a space, you cannot delete that space. Nodes, edges and other data types that are part of a space will no longer be available. I tried deleting a space with a single node and I get this error:cognite.client.exceptions.CogniteAPIError: Unable to delete spaces because they contain nodes or edges: [some-test-space] | code: 400 This does not exactly contradict the documentation, but it means that the documentation is misleading. As it is written, it suggests a space can be used to manage lifecycle of ephemeral test data.

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      

Understanding the inner workings of your data pipelines is key to taking action to improve performance and debug issues. To give you better visibility, we’ve unlocked the full library of transformation metrics directly in the Transformations Run History UI.Previously, the UI only showed a few key metrics. Now, you can access everything the API tracks-from specific rate limits to granular resource updates-right from the graph.Navigate to the Run history tab in the Transformations UI, and select more metrics as shown in the images below.  What’s New?Searchable Metric Selector: Use the new dropdown to find exactly what you need. With the new search bar, you can quickly filter through long lists of metrics if you're working with multiple tables or data models. Smart Defaults: To keep things clean, your most important metrics (like reads, updates, and total requests) are still shown by default. Everything else is just a click away.Identifying Efficiency GainsThis update makes it easier to t

Dears, How can we get the filled checklist in Infield exported for example in excel format for reporting purposes or archiving, InField's filled Checklist reports (including messages, Notes, and checkboxes) as excels is there a way to have it though an API, or is this feature coming soon? Appreciate you help in this since it’s urgent need.  Thanks!Fatima  

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.

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 

Hello,  Some capabilities, such as dataModelInstancesAcl or dataModelsAcl, support the spaceIdScope scope. Currently, we can only specify the spaces to which we want to grant access.To better support certain use cases, would it be possible to introduce support for exclusion rules or regex-based expressions within this scope? Thanks 

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

As part of the hands on part of the CDF Fundamentals course, I have parsed the pdf but cannot preview it or see its contents when adding it to canvas. After parsing I could also not see the Mapping tab and hence also not “verify” the matches in the diagram. Can anyone assist?

My site has pumps that have two vibration sensors mounted on each pump. Due to harsh cryogenic environment, these sensors often throw off spurious readings.I wrote a fx calculation to develop a 2oo2 voting logic system for vibration alerts on a pump. The code includes logic to weed out the initial high vibration recorded during startup. I saved and scheduled the calculation and chose it to run once every minute (vibration sensor scan rate is 1 second on the chart).The problem is that although the calculation works correctly on the chart display and my calculation ‘flag’ goes to 1 when both sensors exceed my defined threshold value, MONITORING ALERTS configured on the scheduled calculation NEVER GET TRIGGERED and no email alert notifications are received. I need to know what I am doing wrong and how to fix it. 

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?

We have multiple Asset Centric Location Filters created which are used in Industrial Tools search to filter site specific assets.When using multiple filters, we observed that the Search returns 0 assets.We inspected the Network requests and observed the Cognite API call to assets/list fails with below error:```{  "error": {    "code": 400,    "message": "Asset subtrees exceed the limit of 100000 assets."  }}```There are similar cases where a location filter is setup with asset subtree and the subtree has more than 100K assets. Is there a way we can configure Location Filters to bypass the limit of 100k assets.

We have configured Asset Centric Location Filter based on the required Dataset Ids. No Asset Subtrees or External Id Prefix are used for the Filter. On the applying the Location Filter on Industrial Tool search, the UI list all the required assets. However, when we try to switch from LIST to HIERARCHY view the UI is unable to render the hierarchy view correctly.The error message in network logs states - <Request had 1 constraint violations. Please fix the request and try again. [<list element> must not be null]>In the request payload could see the parentIds are set to null which could be causing this error.NOTE: If I manually select all the hierarchy levels using the filters option - the hierarchy view works.

Wow—was I in for a challenge!I just received my first proper assignment after joining Cognite: building a custom extractor to move documents and their metadata from a Document Management System (DMS) into Cognite Data Fusion (CDF).Diving headfirst into this, I found myself at the intersection of a steep learning curve, the high-stakes data needs of our industrial customers, and my own belief in Agile engineering practices. Coming from a background where Test-Driven Development (TDD) is the heartbeat of quality, I realized I didn't just want to build a script; I wanted to build a process I could trust.In this series, I want to share a practice that served me well while learning the ropes: The "Twin Auditor" Pattern.The Challenge: Beyond Logic, It’s IntegrationWhen you build an extractor, you aren't just writing code in a vacuum. You are building a bridge between two distinct worlds: your source system (the DMS) and your data platform (CDF).Extraction is inherently an integration challen

We have released a new course dedicated to building reliable, industry-grade agents within Cognite Atlas AI.This course is your starting point for building, configuring, and deploying Atlas AI agents in Cognite Data Fusion. You will gain a comprehensive overview of how agents function, how they connect to industrial data, and how to leverage them for smarter automation and decision-making.This course focuses specifically on how to build agents that adhere to strict operational logic suitable for your use-case and industry standards. We demonstrate how to properly configure tools, select the right models based on reasoning benchmarks, and use the instructions box to enforce standard operating procedures.Key topics covered in this course:Tool Configuration: How to grant agents access to CDF data through specific tools, creating an assistant that genuinely understands physical assets. Model Selection: Utilizing industrial benchmarks to select the right models for complex reasoning and pre

An old version(v1.0.something) of PI extractor was setup in a production environment. We stopped that version for about a month and then installed the latest version v4.14 in a new VM. We have specified the backfilling and front filling settings as below but we still can’t see the historical data in CDF, any ideas what would have happened?frontfill: skip: false streaming-interval: 5 delete-data-points: false use-data-pipe: truebackfill: skip: false step-size-hours: 0 to: 0 

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?   

How can we get access to work hands-on with the learning path athttps://cognite-learn.fusion.cognite.com/cdf-fundamentals/data-models?cluster=api.cognitedata.com&workspace=data-management?If not, how can we practice hands-on with Cognite Learn?

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