Community Discussions

When dropping a file from your desktop/drives into Canvas, the file size increases. As one example, it went up 30 times; from 84kb --> 2520kb (without the canvas coverpage). The file size change makes it difficult to download the canvas, as it gets stuck. The end users cannot download the canvas they create and therefore are prone to stop using the platform. 

Hi Team,It was observed that List and Search queries have some functional limitations which are handled explicitly by teams. Below are the expectations: Search queries must support pagination, enabling users to retrieve results beyond the current 1000-record limit. Pagination should include a cursor or offset mechanism, allowing seamless navigation through large datasets without data loss or duplication. List queries must support case-insensitive filtering Users need substring search over large datasets, which is not fully supported. Can we provide feature like "Contains" which will allow substring search.Thanks,Rahul

 Cognite Data Fusion (CDF) appears to enforce a limit of approximately 200 Azure AD group memberships when resolving permissions for Service Principals. This limitation does not exist in Microsoft Graph, which supports retrieval of all group memberships associated with a user or Service Principal.As organizations scale their authorization models using Azure AD groups, this restriction can prevent access inheritance from functioning as expected and requires manual workarounds that increase operational overhead and governance complexity.Business ContextMany enterprise deployments use Azure AD groups to manage access to assets, plants, data products, and other resources within CDF.A common architecture relies on: Azure AD groups representing access domains. Group Object IDs mapped to corresponding Source IDs in CDF. Service Principals inheriting permissions through group membership. However, when the number of group memberships exceeds the current supported threshold, some memberships

SummaryWhen building workflows in Cognite Data Fusion to populate views in Data Models, there is often a need for intermediate, curated datasets between raw source data and the final target data model.Today, this intermediate data can be stored in RAW tables, but that requires customers to manage temporary tables, cleanup logic, naming conventions, and lifecycle handling manually. A native, workflow-managed temporary storage layer would make workflow development cleaner, reduce repetitive transformation logic, and simplify the overall implementation.  Business ContextWe are using CDF workflows to populate an Asset Hierarchy data model.The source data comes from multiple systems:SAP Functional locations Equipment AVEVA PI Tag metadata The target asset hierarchy data model contains the following views:Site Area Line Equipment System Subsystem TagThe source metadata arrives without the required treatment, standardization, or contextualization. Before writing to the final data model, the

Hi all,The point is to get the possibility to enlarge the All Categories column in the Search. This should be useful if views names are too long to facilitate the navigation.  

Atlas AI – agent building and chatbot notesThe GoodAble to switch between list and tile view as well as Search. Having a Description is helpful. Nice that there are sample questions. Loading feedback. Ability to stop generation. Legal disclaimer near prompt area. Nice that it shows reasoning steps. Like the suggestions with Show More Bot background color is fine. Reasonable area to write a prompt. Great to be able to view the details using the info icon of what each agent is good at before switching to it.Could be improvedPriority 1Starting a new chat is “risky” because there is no history, copy or chat download/export functionality. Part of the point is to aggregate information for analysis later, sharing or collaboration Chat history/multiple threads Need a way to set expectations up front so that when a new chat is started, we can orient users as to what can and cannot be done. Description helps but gets cut off. Sample questions as defaults are not injected but auto triggered. This

Hi Team,I would like to request for a new feature from Cognite to provide the hard delete options of instances in FDM.Currently, the instances are by default soft deleted and if the soft delete instances limit is reached, no further ingestion is allowed in the Project.Issues faced in the past - We have a shared Cognite Project, which is being used by multiple teams, and one team was in process of migrating their data from SQL to Cognite FDM. In the process, transformations run successfully but few data were corrupted. So, they deleted the entire FDM dataset and performed the transformation again. Meanwhile the soft delete instances limit was reached, making the ingestion failure for all the teams using that project.Here, the first team wants to hard delete the instances however, with no option currently available left them with no choices. And the similar incident has occurred again.Hence, we are requesting to provide a feature to perform hard delete of instances, considering the perfo

When adding time series data to industrial canvas, providing the name of the tag and the description field in the time series tile window would clarify what tag is being shown.   Submitted at the request of the Cognite project team. 

I would like to submit a product idea regarding Charts calculations.Currently, calculations in Charts are performed only within the time range that is visible on the screen. We would like to request an enhancement that allows users to define a specific calculation period (i.e., start and end points) directly within the calculation formula.For example, when using the “sample average” operator, the result is calculated only based on the data currently displayed. As a result, the calculated value changes when the user scrolls or shifts the visible time range.We would like to be able to calculate metrics such as average values over a fixed, explicitly defined time range, including data outside of the current view. This would ensure that the result remains consistent and is not affected by scrolling or changes in the displayed window.We believe this functionality would significantly improve usability and enable more stable and meaningful analysis.Thank you for your consideration.

While working with a selection of time seies in canvas, I would like the ability to select a number of these to send to canvas in one action for further analysis. The way it works now I can send one time series to canvas and then have to search for the other time series i want to add to charts manually.

Currently, the search function starts only after entering a keyword and pressing Enter.Would it be possible to support incremental search, where results are updated dynamically as the user types?I believe that the “Data explorer” in Data Fusion (prior to the current Search feature) used incremental search.With incremental search, users can see results immediately, which makes it easier to try different keywords—especially for users who do not remember exact equipment names or file names. It also reduces the need to retype words.In addition, with the current behavior, there are cases where pressing Enter does not seamlessly trigger the search. In such situations, users need to click the search box again before pressing Enter, which can be slightly frustrating.On the other hand, I understand that incremental search may increase system load and could have performance implications. If introducing incremental search negatively impacts overall search performance, then the current implementat

When searching for assets or timeseries, there’s a feature in the detail tab that shows a list of Canvases where they’re used.↓Would it be possible to extend this to also show a list of Charts that use a given timeseries?Some example use cases:・Charts tend to be created in large numbers, so it’s sometimes hard to remember which Chart was used to create which visualization. Being able to trace Charts from a timeseries would make it much easier to reuse existing work.・Especially for scheduled calculation tags, it can be difficult to find the Chart where the original calculation logic is defined. It would be really helpful if we could trace back from the timeseries to the corresponding Chart to review the calculation.