As a Cognite admin responsible for operating extractors at PBF, I want Cognite extractors and tooling to support CyberArk as a secrets provider (in addition to or instead of Azure Key Vault), so that we can reuse our standardized enterprise secrets management platform without introducing Azure Key Vault as a parallel solution, avoiding architectural disruption and policy exceptions. CyberArk: https://docs.cyberark.com/privilege-cloud-standard/latest/en/content/privilege%20cloud/privcloud-introduction.htmhttps://docs.cyberark.com/privilege-cloud-standard/latest/en/content/privilege%20cloud/privcloud-introduction.htm
In Charts, when choosing a core data model as your source, Events are disabled preventing the choosing of core model “CogniteActivity” as “events” to overlay timeseries data from CogniteTimeSeries.Is this functionality on the roadmap and if so, when can we expect?
Hi community,I’m learning to use the /models/instances/query endpoint (with Open Industrial Data, public project) and experiencing some confusion about how cursors and pagination work.{ "with": { "cities": { "nodes": { "filter": { "and": [ { "exists": { "property": [ "Geography", "City", "name" ] } } ] } } }, "continents": { "nodes": { "filter": { "and": [ { "exists": { "property": [ "Geography", "Continent",
In our 360 images, the circles indicating shooting positions are quite large, and when multiple markers are displayed, they tend to obstruct the view.I have several ideas, but the most practical and system-friendly suggestion would be to make the “invisible distance” setting stricter.It would be even better if this parameter could be configured on the system side. Currently, I feel like markers are visible from a very long distance.Other ideas include enabling size adjustments for the circles or adding an option to toggle their visibility on and off. However, I believe the first suggestion would be the easiest to implement. Note: Due to internal security reasons, we cannot share 360 plant images online. For illustrations, I’m using a black image during loading. Please imagine your own plant with your mind’s eye! 😊
An area, section, or banner detailing changes that have happened, within a user’s domain, since the last time they logged in. For example, a top 5 list of newest/hottest/top canvas’s, charts, custom apps. Akin to “what you have missed since you last logged in”. This provides information to the user on critical work they may have been involved in (a collaborative canvas they have been involved in has been updated) or a notification about new work posted (a canvas + chart detailing insights an overnight anomaly). This would not cover subjects that would be noted under Cognite’s “Release notes”.
Activities and Operations currently both support the isInApp flag. Would be great to have this implemented for Materials as well. Would be very much helpful in managing the materials that need to be hidden from Maintain.
Need (NEED) to be able to take pictures OFFLINE. This is critical for operators to use since their routes are in areas where signal can be lost.
As per current condition, there is no labelling capability can be made in CDF Charts. Many charts have been created to support Canvas but to navigate to each chart to monitor the data or calculation that has been done is difficult since there is no labelling can be done on Charts (like canvas).
Once a calculation created in Charts is saved and scheduled (thereby converting it into a time series), the associated metadata becomes fixed. This includes attributes such as units, name, and refresh interval (e.g., 1 minute, 1 hour, 1 day, 1 month).For example: Some calculations were saved and scheduled with a 1-minute refresh frequency. However, no data appears after scheduling because the underlying PI tag does not produce data at a 1-minute interval. To correct this, the refresh frequency would need to be changed to 1 hour or 1 month—but this is currently not possible. The only workaround is to delete the generated time series, redo the calculation, and save and schedule it again.
As of now, CDF are unable to change the value of x-axis to other than time. This is an issue as many engineering analyses involve plotting variables against pressure, temperature, flow rate, or composition,
User story:As a developerI want to produce documented and transparent codeso that my end users and colleagues can understand what is going on. Current stateFor deployed Cognite Function we see three tabs: Calls, Schedules, and Details. Documentation is limited to the description field. Desired stateI would like to see four tabs: Calls, Schedules, Details, and Documentation/ReadmeI would like to be able to write a README markdown file that I package together with the handler, requirements and all the rest. This should be picked up and rendered in a dedicated tab for the function. I think this would really increase transparency in the deployed functions. All employees have access to CDF, but not all have access to source code repositories where documentation usually lives. The deployed Function is a black box at the moment.
When using the search function in industrial tools, the system currently checks whether the search term appears in any metadata fields such as Name, Description, and many others, then returns the results accordingly. This is a great feature; however, there are cases where users want to search only within specific metadata fields, such as Name or Description.Is it possible for users to manually specify which metadata fields to include in the search? In a previous tool (Data Explorer), we had a similar capability, so we are hoping for an enhanced version of that functionality.
We’ve setup an MQTT Hosted Extractor that is recieving a SparkPlug B payload which some times contains a couple of null values, this values cannot be removed from the SparkPlug B payload. We believe that whenever the message contains null values the transformation for the datapoints fails.No more information is provided on the Transform Error, only the “Failed to transform data: Missing required field value”, could the null values just be ignored so the other values be inserted in the time series? We’re getting several empty time series that were created via the hosted extractor.
The InField QS Deployment Pack helps you configure the InField application for your project by providing the required data model, environment setup, and standard configuration aligned with InField 2.0 best practices This guide walks you through the purpose of InField, prerequisites, configuration steps, data-model setup, and how to deploy your InField QS module using the Cognite Toolkit!--> What is InField?InField is Cognite’s Industrial application for field execution and workforce coordination. It replaces paper-based data gathering and manual reporting with a structured, digital workflow.For field workersUse InField to:View and execute tasks Capture measurements, inspections, and observations Log field findings with traceabilityFor supervisors & plannersUse InField to:Plan, schedule, and assign work activities Manage locations, templates, and execution flows Monitor operational progressInField relies heavily on the data model configuration in CDF. As InField QS transitions fr
Is there an alert or notification that can be configured when the MQTT hosted extractor is down? Been having issues with Request to CDF failed with error Error in middleware: Failed to authenticate request This error is resolved by creating a new sink, however we don’t get a notification of any kind when the extractor is down.
I’m working with the PI AF Extractor and facing an issue with Table Lookup attributes.The Table Lookup attribute itself is being extracted correctly into the attributes table, but the value column always comes as “no data”, even though the attribute resolves normally inside PI System Explorer.I would like to know whether there is any supported way to extract the evaluated Table Lookup value using the PI AF Extractor, or if the extractor does not currently resolve Table Lookup references during extraction.Has anyone faced this issue or found a recommended approach to retrieve these values through the extractor?
We are thrilled to announce the next evolution of your user experience. We’ve unified our ecosystem to help you master your industrial data without the friction.What’s new? One Identity: We’ve merged Cognite Academy and the Community. One login for everything. Unified Search: Find How-to guides, discussions, and courses in a single search. Guided Careers: New role-based learning paths, starting with the Data Engineer path! 👇 Read the full announcement here:
We are on a mission to help everyone build and deploy better, more scalable, and Atlas AI-ready data models in Cognite Data Fusion. Today, we introduce NEAT 1.0 as an official part of our product tooling. Why NEAT 1.0 Matters A reliable, scalable, and usable data model is the bedrock of the Knowledge Graph in Cognite Data Fusion. NEAT 1.0 is designed to give you confidence that your models are usable, scalable, extendable, and performant. We've taken years of deep product expertise and hard-earned experience from our most demanding customers and turned it into concrete feedback and guardrails you can leverage in your automated development workflow, giving you help exactly when you need it.What's New? Faster & Focused: We concentrated on physical data modeling and made NEAT significantly faster than previous versions. We also adopted a clean, object-oriented approach for interacting with features. Better Quality Checks: We've included a ~2x larger library of data model validators (
Hi everyone,We are thrilled to unveil the next evolution of your user experience: One Cognite Hub! Over the past 5 years, we’ve grown together into a network of over 15,000 industrial professionals. Today, we are taking a massive leap forward by breaking down the silos between learning and community.We have unified our ecosystem into a single, powerful platform designed to help you solve problems faster and master your industrial data.Here is what’s new for you: 1. Seamless Consolidation: One Identity, Zero FrictionWe have migrated Cognite Academy directly into the Community. One Login: No more switching platforms. Use a single account to access discussions, courses, and community support. Unified Experience: Your certifications, badges, and contributions are now tied to one user identity across community and academy. Unified Search: Search once and find everything - whether it’s a documentation article, a community discussion, or an Academy course - all in one place. 2. Guided L
OverviewThis deployment pack provides an ISA-95/ISA-88 based manufacturing domain model for Cognite Data Fusion (CDF). It defines spaces, containers, views, and a composed data model, along with optional RAW seed data, SQL transformations, and a workflow to orchestrate data loading. The model is not 100% complete for any customers, but contains place holders and structures where project can add properties add & remove views as required by customer.This module follows Cognite's best practices for data modeling, focusing on practical, operational design rather than academic theory. It is optimized for industrial scale, high performance, and flexibility. Key PrinciplesLeverages Cognite Data Modeling (Service + AI) Follows best practices for extending the Core Data Model (CDM) Supports both direct and reverse direct relationships Uses AI-friendly naming and descriptions to improve navigation and ensure accurate AI outputs Builds the data model around the Asset Hierarchy to simplify nav
Overview Dependencies Deployment Agents How It Works Support Overview The Root Cause Analysis (RCA) module provides intelligent Atlas AI agents for advanced root cause analysis capabilities in your Cognite Data Fusion (CDF) environment. This module is designed to work with the RMDM (Reliability Maintenance Data Model) v1 data model deployed in CDF.Dependencies ⚠️ Important: This module requires the data_models/rmdm_v1/ module to be deployed in your CDF project before deploying these agents.All agents in this module connect to and query the RMDM data model within CDF to access equipment, failure notifications, time series, and other maintenance-related data.Deployment Prerequisites Before you start, ensure you have the following:You already have a Cognite Toolkit project set up locally Your project contains the standard cdf.toml file You have valid authentication to your target CDF environment The RMDM v1 data model is already deployed (see data_models/rmdm_v1/)Step 1: Enable External
Overview Module Components Deployment Configuration Post-Deployment: Running the Function Launching the Dashboard Scheduling the Function Troubleshooting Metrics Reference Support Overview The Contextualization Quality Dashboard module provides a comprehensive solution for measuring, monitoring, and visualizing the contextualization quality of your data in Cognite Data Fusion (CDF). It consists of two main components:Contextualization Quality Metrics Function (external_id: context_quality_handler) - Computes all quality metrics and saves them as a JSON file in CDF Streamlit Dashboard (context_quality_dashboard) - Visualizes the pre-computed metrics with interactive gauges, charts, and tablesThis module helps data engineers and operations teams understand how well their data is contextualized across three key dimensions:Asset Hierarchy Quality - Structural integrity of the asset tree Equipment-Asset Relationships - Quality of equipment-to-asset mappings Time Series Contextualization -
My team and I were utilizing Canvas during a hands-on session, with the expectation that everyone would be able to access the link and contribute simultaneously. Unfortunately, we encountered an issue, receiving the following error message:The inability to collaborate in real-time significantly limits the effectiveness of the tool, particularly for team-based activities. Real-time editing is a critical feature for fostering dynamic collaboration, and without it, the user experience is somewhat hindered.Is there any plan to introduce this functionality in the near future? Enabling simultaneous editing would greatly enhance Canvas’s collaborative potential, making it a much more powerful tool for teams working together.
What are the licensing terms and conditions for using Cognite's Open Industrial Data? Could you please provide a link to these terms and conditions?Thank you.
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