Product Updates

Have you seen it? In our last release, we added support for File Upload through the Search application. The goal is to remove a hindrance to daily use by allowing single-file uploads for non-technical users of Cognite Data Fusion.While most data in Cognite Data Fusion is ingested through structured pipelines, there was a need to enable individual Subject Matter Experts (SMEs) to upload data directly. This allows non-technical users, such as Reliability Engineers, to add valuable information for future reference.As of earlier this year, we have support for file uploads through the Search application and for using Search as a resource selector (e.g., in Canvas or Charts). This is available for Data Modeling Files. We ensured to add governance around file uploads: Only available through use of Location(s) The Location needs to have File Upload space added to the Location configThe individual user will see the File Upload option for Files if the location they have selected has a File Upload space and they themselves have permission to write data to that spaceThe individual user will see Files uploaded by other users if they have permission to read from the File Upload spaceWe covered this feature in the March’26 release notes:  Upload files directly in Search. The upload option is available after your admin configures the space for file uploads in Admin > Project settings > Locations > Configure > Space for file upload. Only one space can be set per location. Once you upload files, they may take a moment to appear. Upload is available only for data modeling views. Let me know if you have questions regarding File Upload - we would love to see it in use!Best, Sofie, Product Manager 

We’re deprecating the totalVariation aggregate function for numeric time series in Cognite Data Fusion, effective April 20, 2027. This follows Cognite’s 12-month deprecation policy for GA features: between now and that date the aggregate will continue to function as documented, and after it, requests that include totalVariation will return an error. The change applies to requests to the time series data list endpoint (/timeseries/data/list).If your integrations rely on totalVariation, directly via the API, through an SDK, or in dashboards and derived pipelines, please review them before April 20, 2027 and migrate to another aggregate.If totalVariation is important to a workflow in your organization and none of the alternative aggregates fit, reply to this thread or reach out to your Cognite representative. It helps us most if you can share:The use case and what the output drives (dashboard, alert, calculation, report). The time series volume and aggregation granularity involved. Whether computing the value client-side from raw data points is feasible for your setup.

Today, we released a ton of improvements to the Cognite Toolkit. Here is what you should know about the fresh v0.8 version: Better performance: Substantial refactoring has led to faster execution, especially for build, deploy, and data transfer commands. Enhanced Deployment: The deploy command is now forward-compatible with the API, ensuring proper property handling. Clearer Validation: Output is now easier to read and provides richer guidance for both you and your agents.  NEAT Intelligence: You’ll notice more recommendations and auto-fixes for Data Modeling performance and scalability, powered by built-in NEAT logic. Module Scaling: Reusing modules is easier with the module repeat feature, allowing you to deploy a module multiple times using site-specific variables. New Plugins: To streamline the experience, we’ve moved development features to cdf dev ... and data features to cdf data ....Other nice-to-knows:Auth breaking changes: In cdf auth init, we have removed the --no-verify and --dry-run flags. Command Updates: cdf dump asset/timeseries has been retired. Use cdf data download assets/timeseries instead. CSV Handling: You can no longer build/deploy assets in .csv format; please use cdf data upload for those files. GA Status: Support for Streams and Simulators is now officially GA.We continue to release improvements and new resource types frequently. Most of these features have been available behind alpha flags for a while - thank you to our eager users for the constant feedback!

Learning Paths: Build skills step by stepNew learning paths bring related topics together so you can build a solid understanding of key CDF concepts and workflows, progressing from fundamentals to practical implementation.Data Modeling: Schema Design and Structure: Learn how to create Containers, Views, and Data Models. This learning path focuses on the schema design and structure of Data Modeling in CDF. It covers the foundational concepts - how to design Containers and Views, structure your model, and apply basic naming and governance practices before writing code or ingesting data. This is a fundamental-level path for users who are new to Data Modeling but already have some familiarity with Cognite Data Fusion. Data Modeling: knowledge graph creation: Understand how to load data into an existing data model in CDF. This learning path focuses on building a Knowledge Graph by ingesting nodes and edges, covering common data sources and the tools used to populate a schema.This is a foundational path for users new to Data Modeling who already have some familiarity with Cognite Data Fusion. Courses: Focused learning on key CDF capabilitiesNew courses dive deeper into specific CDF features and workflows, helping you strengthen your technical understanding and apply them effectively.An Overview of Cognite ExtractorsUnderstand the role of extractors in Cognite Data Fusion. This course covers extractor architecture, types, security considerations, and deployment approaches used for industrial data ingestion.You’ll learn what extractors are, why they are critical for bringing industrial data into CDF, and how they work at a conceptual level. The course builds a technical foundation that prepares you for later courses focused on configuration, deployment, and operations.Mastering Extraction Pipelines in Cognite Data FusionLearn how to build, secure, and monitor reliable data integrations in CDF. This course focuses on designing extraction pipelines that provide visibility into data ingestion and help prevent silent failures.You’ll explore how extraction pipelines work, how to separate extraction logic from monitoring, and how to implement mechanisms such as heartbeat monitoring, remote configuration, and secure authentication using OIDC service principals. By the end of the course, you’ll understand how to move from basic extraction scripts to more robust, observable data pipelines. Microlearning videos: Small lessons, big impactWe’ve added a new set of bite-sized microlearning videos focused on simulator integrations in CDF. These short lessons cover key concepts and practical steps, making it easier to learn at your own pace.Activities in charts You can now add Activities to your graph view in Charts! Activities can be shown together with the time series data.Data workflows from the UI: Learn the fundamentals of creating and managing Data Workflows directly in the CDF user interface. This microlearning shows how to create, run, and monitor workflow tasks and process runs to automate data operations.How-To Guide articles: Practical Tips from the ExpertsWe’ve added a fresh set of how-to guides. These articles are designed to help you troubleshoot faster, work more efficiently, and get the most out of CDF.Enabling the alpha API Subversion and DEBUG mode in the Cognite Python SDKWith this guide, learn how to work with alpha API versions in Cognite Data Fusion and use the Python SDK debug flag to capture detailed HTTP logs for troubleshooting and testing.It also explains which configuration parameters must be set during client initialization for consistent behavior across environments (Jupyter, local scripts, CI/CD) and which ones can be safely modified later. How to capture x-request-id for debugging CDF Functions With this guide, learn how to trace and debug your CDF API calls using the x-request-id returned with every request. It shows how to log the request ID from failed calls using CogniteAPIError, how to temporarily enable client.config.debug = True to capture it for successful calls, and best practices for using debug logging. It also highlights how extractors (OPC UA, PI, PIAF) can be configured to automatically include x-request-id in logs. Master Cognite Data Fusion with new training on Cognite Academy. We’d love to hear your thoughts and help you connect with other experts. Come join the conversation on Cognite Hub.  

We are committed to continuously improving your experience in Industrial Canvas. Our goal is simple: help you generate value faster through a seamless, uninterrupted workflow—eliminating the friction of slow load times and performance lag. With our March release, Industrial Canvas now loads 50% to 70% faster on average. We’ve also reduced memory usage by up to 70% during loading, even for your most complex canvases. We look forward to hearing your feedback!Best,Andreea

While most data in Cognite Data Fusion flows through structured pipelines, we know that critical insights often live with your experts.To empower non-technical users, like Reliability Engineers, we have introduced Direct File Upload. This allows Subject Matter Experts to instantly upload resources (such as a Root Cause Analysis template, a report etc.) directly into the Search UI or through resource selectors in tools like Canvas.This feature was released on 3 March 2026.   Admin governance & setupTo ensure these files remain organized and secure, Admins must first define a destination for each Location for the File Upload option to appear. The File Upload option will only appear for users once this is configured.To enable this feature:Go to Admin Workspace > Project Settings. Select Locations > Configure. Define which Space should be used for all file uploads in that location.How permissions workAccess is controlled through the permissions of the destination Space you’ve selected:Who can upload? Users who have Write access to the configured Space. Who can view? Users who have Read access to that Space.Where is it visible? In the Search UI and as a Resource Selector (specifically for Data Modeling File Views, e.g File upload to Asset-centric File resource is not supported).Looking forward to seeing this highly requested feature in use!

We are thrilled to announce the Beta release of our new Simulator UI within Cognite Data Fusion (CDF). For too long, high-fidelity simulation runs have been locked behind a "technical wall" of complex APIs and developer-only workflows. Not anymore.We are handing the keys to the Subject Matter Experts (SMEs). Whether you’re optimizing production or running custom models, you can now orchestrate the future of your assets without writing a single line of code. From API-First to SME-ReadyPreviously, our simulator connector integrations required deep technical knowledge to configure and run. Our new interface replaces manual API calls with a sleek, intuitive workspace. Visual Logic Orchestration: The Routine BoardThe heart of this release is the Routine Board. SMEs can now visually map out simulation sequences, setting inputs, defining calculation steps, and extracting results using a drag-and-drop interface. 

Hi everyone!Finding an asset is one thing; understanding its surroundings is another. In the upcoming release on the 3rd of March, we’ve upgraded the 3D Experience in Search to give you instant spatial context without leaving your search results.What’s New?In-Preview Scenes & 360-images: View 3D scenes and 360-degree images directly in the search preview pane. Seamless Transitions: Move from a search preview to the unified 3D world while staying immersed in your 360-degree view. Direct Access: Launch full Scenes immediately from the preview card.See your asset in it’s full real-world context byviewing your 3D Scenes in Search⚡ Quick Start:Prepare - Ensure you are using Scenes for 3D and that they are connected to your Location Search for an asset or location in CDF. Preview the result to see the 3D scene load automatically. Explore by opening the unified 3D view for full immersion.The preview also supports fully interactive 360 images Pro Tip 💡: Opening the full 3D view from a 360 preview maintains your exact orientation—no more "re-finding" the asset once the viewer loads!Full Guide: https://docs.cognite.com/cdf/explore/navigate_3dGive it a spin and let us know what you think!

We are excited to announce the Q1 2026 release of Cognite Data Fusion (CDF). This quarter, our focus is on breaking down technical barriers for Subject Matter Experts and providing a more immersive, high-precision data experience.Empowering SMEs: The New Simulators UI (Beta) Simulators UI (Beta) Immersive Data Exploration & 3D Context Chart - Activities 3D - MiniMap 3D - Experience in Search Smarter Search & Precision Tools Search -  Smarter Results Search - File Upload Records - Unit-Aware (Beta) Atlas AI: Continuity & Control Atlas - Conversation History Atlas - Per Agent Granular Access Control Performance & Readiness Canvas - Performance 3D - Browser Caching Diagram Parsing - Tag Detection Transformations - Full Metrics in Run History Looking Forward: Production-Grade Tailored SolutionsEmpowering SMEs: The New Simulators UI (Beta)We are transitioning from a "Code-First" to a "User-First" approach for industrial simulations. Feature Problem (Challenge) Solution: What You Can Do Now Simulators UI (Beta) SMEs are currently blocked by a "technical wall," where simulator connectors remain locked behind APIs that can be configured only by developers. Empower SMEs to independently configure their own models and use a no-code "Routine Board" for drag-and-drop visual logic orchestration.   Routine builder  Immersive Data Exploration & 3D ContextCorrelating data trends with the physical world is critical for efficient troubleshooting. Feature Problem (Challenge) Solution: What You Can Do Now Chart - Activities Industrial users struggle to correlate data trends with real-world operational events. Overlay activity types, such as work orders, directly onto time series in Charts to instantly connect anomalies with the "why". Documentation 3D - MiniMap When navigating 3D scenes in Search, it is easy to lose orientation and real-world context. Use customizable real-time minimaps to track camera position and "fast-travel" through the scene by clicking destinations on the map. Documentation 3D - Experience in Search Users previously lacked 3D scenes in search previews, leaving them without context for model placement. View 3D Scenes and 360-degree images directly in the search preview to gain immediate context for a selected Location. Documentation  Smarter Search & Precision ToolsWe’ve overhauled our search and resource management to prioritize accuracy and ease of use. Feature Problem (Challenge) Solution: What You Can Do Now Search -  Smarter Results (opt-in beta, and GA April 2026) Standard search often fails to handle partial industrial tags or returns broad, inaccurate results for complex strings. Benefit from advanced "Industrial Parsing" and prefix matching that prioritizes result accuracy over quantity. This is applicable for search on Core Data Model. Search - File Upload SMEs lacked a simple way to upload individual files, such as RCA templates, without relying on technical pipelines. Upload single files directly through the Search UI and use Search as a resource selector in tools like Industrial Canvas. Upload is available only for data modeling views. Records - Unit-Aware (Beta) Users must manually convert sensor values when querying, filtering, or displaying data across different unit systems. Perform unit-aware querying, filtering, and aggregation within Records to eliminate the need for custom unit-conversion code. Documentation   Atlas AI: Continuity & ControlOur AI agents are now more persistent and easier to manage within your existing workspace. Feature Problem (Challenge) Solution: What You Can Do Now Atlas - Conversation History Atlas agents didn't retain session history, forcing users to restart workflows and lose context. Continue past chats where you left off or work across multiple parallel conversations without losing context. Atlas - Per Agent Granular Access Control Previously, all agents were visible to every user, and only creators could modify them. Configure specific read, write, and run rights on a per-agent basis to ensure users only interact with authorized agents.  Performance & ReadinessWe continue to optimize our core tools to ensure they are production-ready and provide full transparency into pipeline health. Feature Problem (Challenge) Solution: What You Can Do Now Canvas - Performance Increased load on Industrial Canvas caused sub-optimal load times and high memory consumption. Experience a 50% average reduction in load times and up to 40% less memory usage during loading. 3D - Browser Caching Increasing 3D data volumes led to performance degradation and suboptimal load times for large scenes. Utilize client-side browser caching for CAD and point cloud data to enable 63% faster 360 image loading and 52% less data transfer, providing instant loading for returning users Diagram Parsing - Tag Detection Tag detection wasn't ready for Data Modeling, blocking users from migrating from asset-centric models. Use production-ready tag detection for Data Modeling with built-in per-file parsing status. Documentation  Transformations - Full Metrics in Run History The UI previously showed limited metrics, making it difficult to fully debug issues or track specific rate limits. Access the full library of API-tracked metrics, including searchable selectors, directly within the Transformations Run History UI.  Looking Forward: Production-Grade Tailored SolutionsBeyond this release, we are building a future where tailored industrial applications are delivered with unprecedented speed and precision. We are evolving our platform to support production-grade, tailored solutions that can be deployed natively in just days. Rapid Delivery of Industrial WorkflowsWe are targeting the "long tail" of industrial use cases, thousands of small and medium-sized workflows that deliver significant value but were previously too complex to build.Tailored Experiences: Moving from generic interfaces to AI-first, agentic user experiences designed for specific roles like reliability engineers or maintenance supervisors. Native Deployment: Fully featured applications deployed directly as part of the Cognite product stack. Immediate Value: Targeting 100X faster deployment times and up to 80% lower costs by automating the generation of industrial applications.Compounding the Value of Your Industrial DataThis new capability is built on the foundation of the Industrial Knowledge Graph and Atlas AI. Instead of creating new data silos, these tailored applications write insights and outcomes back into the knowledge graph. This ensures that every workflow contributes to a growing, unified body of industrial knowledge that can be scaled across the entire enterprise. Ready to dive deeper? Check out our Release Notes  

Hi everyone!We know that for an end-user, finding data shouldn't feel like a "needle in a haystack" mission. Last summer, we launched Per-Project Search Configuration, and if you haven't set it up yet, your team might be missing out on a much cleaner experience.As a Project Admin, you can curate what your users see when they sign in to Search. Instead of a generic view of data that maps 1:1 to your detailed data model definition, you can hand-deliver the most relevant data. Why configure your categories? Instant clarity: Define exactly which columns appear first. No more side-scrolling to find the "Status" or "ID." Relevant filtering: Hide the noise. Display only the filters that actually matter for that specific category. Context at a glance: Tailor the "Properties" card so the most crucial metadata is front and center. Quick guide: Check access: Ensure you have appconfig:read/write and app-scope = Search. Your users also need appconfig:read to make the config take effect for them.  Finding the config options: Go to Admin workspace > Project Settings > Categories. Configure: Pick a category and hit + Add.  Pro Tip 💡: After you update the columns, remind your users to hit "Reset" in their search column selector to see your shiny new layout!Ready to clean up your search? Full Guide here: https://docs.cognite.com/cdf/configure/project_settings/  Have you tried? Let me know if you have any feedback!

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 track the instances.upsertedNoop metric we recently introduced. By comparing "upserted" vs. "upsertedNoop," you can see exactly how much data is being re-written unnecessarily. High "No-op" counts are a clear sign that you can save time and compute costs by using smarter, incremental loading.Give it a try!Head over to your Transformations Run History today to explore these new insights. We’re always looking to improve, so please share your questions or feedback in the comments below! 

Microlearning videos: Small lessons, big impactWe’ve added a new set of bite-sized microlearning videos focused on simulator integrations in CDF. These short lessons cover key concepts and practical steps, making it easier to learn at your own pace.Simulator Integrations: Core ConceptsDiscover how simulator integrations support optimization and digital twins by connecting simulation tools to CDF, and how these building blocks work together to run and manage simulations effectively.The Simulators API: Key Resources and Data FlowExplore how simulations are created, controlled, and tracked in CDF- from defining models and routines to running simulations and reviewing results and logs.Configuring a Simulator ConnectorLearn how to set up a Windows-based simulator connector so CDF can securely communicate with your simulation tools, including authentication, network access, and service configuration.Configuring Simulator RoutinesLearn how to define reusable simulation setups that control how simulations run, covering inputs, outputs, scheduling, and execution logic.Data Sampling and ValidationFind out how to ensure your simulations run on reliable, high-quality data by configuring time windows, sampling strategies, and validation checks to catch unstable or invalid inputs.Triggering Simulation RunsUnderstand how to start simulations manually, automatically, or programmatically - and how to review outcomes using logs and simulation results. How-To Guide articles: Practical Tips from the ExpertsWe’ve added a fresh set of how-to guides. These articles are designed to help you troubleshoot faster, work more efficiently, and get the most out of CDF.How to fix Video Playback Failure – Error Code: 232011Get back to streaming smoothly by resolving the common 232011 playback error. This guide walks you through clearing cache and cookies, checking browser settings, and avoiding common conflicts.How to: Understanding CDF User Access and Entra ID ConnectivityTake a closer look at how CDF handles orphaned content and access loss, along with admin best practices to ensure critical data remains accessible.How to Build Efficient Transformations in CDFImprove performance and scalability in your CDF transformations using proven techniques like early data filtering, incremental logic with is_new, and CDF Workflows for complex jobs.How to Upload Large Files to CDFConfidently upload files larger than 5 GiB using Multipart Upload. Learn how to split data into chunks, enable parallel uploads with retries, and implement the flow using the Cognite Python SDK.How-To: Getting started with the CDF File Extractor from local folder to data modelSet up the CDF File Extractor on Windows to ingest local files into CDF. This guide covers downloading the extractor, configuring authentication, and mapping files to Core Data Model objects.How-To: Getting started with the CDF DB Extractor to populate staging (RAW) with records from a CSV fileIngest local CSV data into CDF Staging (RAW) on Windows using the Cognite DB Extractor. Learn how to configure authentication, treat CSVs as spreadsheet databases, and populate target RAW tables.How-To: Use a CSV file to populate data points for a time series (data model) with the CDF DB ExtractorLoad CSV data directly into CDF Time Series on Windows. This article explains how to map timestamps, values, and external IDs to create or update CogniteTimeSeries objects.How to: Transformation Execution Timestamps ExplainedDemystify CDF transformation timestamps - Created Time, Started Time, and Duration - so you can better understand performance without confusion from queuing delays.How to add filtering in Cognite OPC UA ExtractorGain more control over OPC UA data ingestion by defining effective filters. Learn how to use the Include transformation and full tag hierarchies for accurate extraction.How to resolve 'Bounding Box is empty' error when uploading a .LAZ fileFix this common 3D point cloud issue by re-saving LAZ files with compressor 2 or converting them to E57 using tools like CloudCompare.How to resolve 'This Location contains no files yet' error in diagram parsing for data modelingEnsure files are visible for Diagram Parsing in CDF Data Modeling by correctly including them in the data model and linking them to a Location with the right instance spaces. Master Cognite Data Fusion with new training on Cognite Academy. We’d love to hear your thoughts and help you connect with other experts. Come join the conversation on Cognite Hub. 

We’re excited to announce the release of Activities in Charts, a new capability that brings operational context directly into time series analysis on CDM and Hybrid Projects.With Activities in Charts, you can now visualize asset-related activities, such as work orders, maintenance events, or operational tasks, directly on your Charts, alongside your time-series data. This makes it easier to understand what happened, when it happened, and how it impacted performance.Why this mattersWhen investigating operational behavior, data alone is often not enough. Activities provide critical context that helps teams:Correlate events and activities with changes in sensor data Perform faster root-cause analysis Reduce the need to switch between tools during investigationsBy bringing activities into Charts, we enable a more unified and intuitive analysis experience.What you can do with Activities in ChartsView activities overlaid on time-series charts Align operational events with performance trends Use Charts as a central workspace for investigation and troubleshootingThis capability also replaces the legacy Events visualization previously available in Asset Centric projects, offering a more modern and integrated experience.Documentation 

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 precise filtering. Operational Instructions: Using the instructions box to implement SOPs that keep agents methodical and verified. Advanced Querying: How to use the Query Knowledge Graph tool to customize your agent for specific data types.By the end of this course, you’ll be equipped to design agents that rely on your data and documents as a source of truth. This approach ensures your AI assistants remain predictable and safe for industrial workflows.Access the full course on the Academy 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 (constantly growing at thisisneat.io/validation). This means more checks to ensure your model is viable, scalable, and maintainable. Deep Pre-Deployment Analysis: The dry-run feature was completely rebuilt to give you a deep, clear analysis of the changes that will occur, including a severity score, before you even push your model to CDF. Easy Issue Navigation: You get rich analysis of data model issues with interactive navigation, search, and clustering, making it simple to find and fix problems. Ready for Governance: We take a user-centric approach to anything graph related, offering four pre-built, configurable data model governance profiles to jumpstart your quality assurance.What About Legacy Features? NEAT 1.0 embraces focus and minimalism. To make this release more performant and easier to maintain, we removed some features from older, legacy versions that did not align with our new, streamlined approach. Legacy features continue to be available from cognite.neat.legacy import NeatSession while we continue to port these features to V1. Getting started?Watch the short video and check out https://cognite-neat.readthedocs-hosted.com/en/latest/installation.html to try for yourself!

Hi everyone,We are thrilled to announce that on Monday, December 8th, we will be releasing a highly requested feature: Search-in-file!This new functionality supports searches within individual .pdf files across Asset-centric/Classic and Data Modeling files.Problem: Finding content within a file can be a daunting task without the ability to perform a quick search across the file. Feature: Search box in the file viewer allowing the user to search for text (single word) within the file (.pdf). The text gets highlighted, and if there is more than one match, you get the option to browse through all matches. (Wanted) Impact: Faster exploration, saving time (and unnecessary frustration) working with files.We hope this update will greatly improve your workflow and productivity. As always, your feedback is invaluable to us. Please continue sharing your thoughts to help us improve.Best,Sofie, Product Manager Search

2025: Delivering Industrial Scale and ReliabilityThis year, we turned industrial potential into enterprise reality. Every release, from Q1 to Q4, was designed to help you build and run AI and data solutions at unprecedented scale.We focused on three pillars:Data Foundation at Scale: Handle billions of records with ease and accelerate model creation to eliminate bottlenecks. Workflow Automation: Introduced visual orchestration and enhanced validation for reliable, high-volume processes. AI You Can Trust: Moved AI agents from pilot to production with governance, monitoring, and precision tuning for enterprise-grade reliability.Our Q4 release completes this journey, delivering the critical building blocks that make CDF the only platform purpose-built for industrial scale.Operational Excellence & Field Work Replacement Faster, Cleaner Troubleshooting in Industrial Canvas (Beta) Get Immediate Context: Location Prompt (New User Experience) Seamless Planning for Frontline Teams (Beta) Smarter, Faster 3D Measurements (Replace Field Work) Customized 3D Experience (Reduced Friction) Full 3D Support for Hybrid Projects Unlocking Scalability and Optimization Massive Log and Event Data Storage: Records API Full-Field Production Optimization (Beta) Driving Data Quality & Model Confidence High-Quality Field Observations Reliable Workflow Development Total Process Visibility (Workflow Triggers) Transparent Document Parsing (LLM Vision) (Beta) Streamlined Annotation Review for Diagrams (Beta) Identify and Optimize Transformation Inefficiency Agent Reliability and Precision (Atlas AI) Data-Driven Agent Confidence Precise and Predictable Agent Queries Platform Foundations Enhanced Japanese Search  Operational Excellence & Field Work ReplacementStreamline field work, enable safe remote operations via 3D, and boost efficiency with improved operational context and real-time troubleshooting.Faster, Cleaner Troubleshooting in Industrial Canvas (Beta)  Problem Solution: What You Can Do Now Cluttered Canvas: Complex diagrams became unreadable due to overlapping connection lines, slowing down troubleshooting. Instant Flow Tracing: The canvas is now cleaner. Hover over any connection path to instantly highlight that specific flow, increasing user efficiency when troubleshooting. Get Immediate Context: Location Prompt (New User Experience)  Problem Solution: What You Can Do Now Overlooked Context: New users often missed the critical Location filter, leading to poor data context and frustration. Contextual Prompting: If a location is not selected, a pop-up prompt appears, directing users to the filter. This ensures a greater set of users set the right context immediately, improving the data quality of their work. Seamless Planning for Frontline Teams (Beta)  Problem Solution: What You Can Do Now Limited Schedule Transparency: Supervisors lacked full visibility into what was planned for the upcoming weeks and control over what was published to frontline teams. Full Schedule Control: The new Schedules tab provides full transparency into scheduled checklists. Supervisors can decide what is published to the frontline teams and when, ensuring priorities are met. Users can also create checklists from the schedule Smarter, Faster 3D Measurements (Replace Field Work)  Problem Solution: What You Can Do Now Manual Measuring: Measuring basic dimensions like pipe diameters was laborious. One-Click Precision: Perform one-click diameter measurements for pipes, vessels, and tanks. This increases the viability of using 3D to replace field work. Customized 3D Experience (Reduced Friction)  Problem Solution: What You Can Do Now Manual Tweaking: Hardcoded default 3D settings forced users to manually adjust them per session. Customizable Settings Per Scene: Define default Model Visibility, Rendering Quality, and Point Cloud settings. This reduces friction for regular users and improves the first-time experience. Full 3D Support for Hybrid Projects  Problem Solution: What You Can Do Now Migration Bottleneck: 3D was not fully supported by the Cognite Data Model (CDM) or hybrid projects, blocking customer migrations Seamless Hybrid 3D: New 3D service API endpoints for CDM enable a seamless 3D user experience in hybrid projects. This enables migration to CDM and allows contextualization using both asset-centric and data modeling assets. Unlocking Scalability and OptimizationHandle billions of records and unlock full-field industrial optimization that was previously impossible, accelerating enterprise value realization.Massive Log and Event Data Storage: Records API  Problem Solution: What You Can Do Now Scaling Limitations: Billions of high-volume log entries and events overburdened the Knowledge Graph node structure, limiting scalability. Records API: Store billions of structured records with seamless integration to Data Modeling. This unlocks the next level of CDF scalability. Full-Field Production Optimization (Beta)  Problem Solution: What You Can Do Now Blocked Use Cases: Current CDF capabilities couldn't handle optimizing entire offshore fields with thousands of wells simultaneously. Large-Scale Workflow Support: Run complex simulations across entire facilities, handling thousands of parameters. This unlocks true field-wide production optimization. Driving Data Quality & Model ConfidenceGuarantee reliable data ingestion, enable better decision-making with high-quality observations, and validate complex workflows before deployment.High-Quality Field Observations  Problem Solution: What You Can Do Now Incorrect Asset Links: Operators struggled to attach the correct asset to field observations, reducing data quality. Correctly Contextualized Observations: Access the asset hierarchy to browse and find the correct asset on both mobile and desktop. This ensures observations are captured with the correct asset, leading to better decision making. Reliable Workflow Development  Problem Solution: What You Can Do Now Trial-and-Error: Complex definitions made subtle errors difficult to find, leading to a frustrating configuration. Actionable Validation: Perform comprehensive on-demand validation and auto-validation on publish. Errors are surfaced, explained, and made actionable directly in the UI. Total Process Visibility (Workflow Triggers)  Problem Solution: What You Can Do Now Hidden Triggers: Managing live pipelines was risky without clear visibility into automated starting points. Workflow Triggers as Nodes: Triggers are now shown as canvas nodes for full workflow visibility. This delivers a true end-to-end visualization and enables safe management of live pipelines Transparent Document Parsing (LLM Vision) (Beta)  Problem Solution: What You Can Do Now Verification Challenge: Users couldn't easily verify LLM parsing results because extracted values weren't visually linked to the source document. Visual Verification: Extracted values are displayed with bounding boxes in the UI, making verification easier. You can also store data in a user-specified space. Streamlined Annotation Review for Diagrams (Beta)  Problem Solution: What You Can Do Now Slow and Cumbersome Review: Users lacked an efficient way to verify and reject annotations, especially in bulk, and faced limitations when dealing with multi-page files. Dedicated Review Workflow: The UI tabs are split into four distinct sections, with the first tab dedicated solely to reviewing annotations. This allows users to verify or reject file and asset annotations individually or in bulk and enables annotation management across multi-page files, significantly improving focus and workflow efficiency. Identify and Optimize Transformation Inefficiency  Problem Solution: What You Can Do Now Wasted Resources: Transformations often re-process and re-write large amounts of identical data, wasting compute resources and masking optimization opportunities. Monitor "No-Op" Performance: The Run History UI now displays a "No-Op" metric, showing how many write operations were skipped because the data was unchanged. This instantly identifies inefficiency and drives smarter, incremental data loads. Agent Reliability and Precision (Atlas AI)Move AI agents from pilot to production by providing tools to monitor performance, guarantee output precision, and build trust at scale.Data-Driven Agent Confidence  Problem Solution: What You Can Do Now Blind Deployment: Lack of visibility into agent performance or failures made it hard to build trust or troubleshoot. Agent Performance Monitoring: Create test sets and run evaluations to view pass/fail results. This enables data-driven confidence in agent reliability and supports smoother UAT. Precise and Predictable Agent Queries  Problem Solution: What You Can Do Now Unpredictable Output: Agent queries could result in slightly different data or format, creating uncertainty in production workflows. Guaranteed Precision: Configure a specific query and lock fields, operations, and output. This guarantees critical data is retrieved correctly and consistently every time.  Platform FoundationsFundamental improvements to core platform elements, including search quality and the initial user experience.Enhanced Japanese Search  Problem Solution: What You Can Do Now Poor Japanese Search: The existing search didn't account for the unique structure of Japanese, leading to poor relevance. Morphological Analysis: Enhanced search capability uses Morphological Analysis to provide relevant results, balancing precision and recall.  We believe these new capabilities will significantly accelerate your industrial data journey. Dive into the detailed release notes to explore all the new features and improvements.We'd love to hear how these features are transforming your operations! 

We've just published The Atlas AI™ Security & Trust Framework , a new white paper detailing how we've engineered our low-code industrial AI agent workbench for the rigorous security demands of industry leaders.We know security and data privacy are critical when adopting generative AI. This framework outlines how Atlas AI is secured by design, with security as an intrinsic part of its architecture, not an afterthought. Key highlights from the framework: Your Data Stays Your Data: We have contractual guarantees that your prompts and responses are not stored by third-party LLM providers and are never used to train or improve their models. Your Access is the Agent's Access: Atlas AI integrates with your corporate Identity Provider (IdP) for SSO. Critically, all API calls an agent makes are mapped directly to your user roles and permissions. The agent can only see and do what you can. Enterprise-Grade Security: The platform is built on a "defense-in-depth" architecture , employs end-to-end encryption (at-rest and in-transit) , and undergoes continuous 24/7 monitoring and third-party penetration testing. Independently Verified: Atlas AI inherits the comprehensive security and compliance of Cognite Data Fusion®, including our ISO 27001, ISO 9001, and SOC 2 Type 2 certifications.  Please find the full white paper here for a deeper dive. We're committed to being your trusted partner in Industrial AI and welcome your questions in the comments.

 Hi everyone,I'm happy to share some great news! We've just shipped a product update that hopefully makes working with Annotations more fluid and intuitive.We heard your feedback that it was sometimes hard to pinpoint which annotation you were working with, especially on a busy file. To solve this, we've made two key improvements:See what you've selected: When you click on an annotation—either in the side panel or on the file itself—it will now turn green so you can easily see what you've selected. Instantly zoom in: Clicking an annotation in the side panel will automatically zoom in to its location in your file.Check out the screen recording showing the new behavior (it is available both from the search tool, and the “add data” functionality in Canvas and Charts):We hope these improvements help you work more efficiently and accurately with annotations. As always, your feedback is invaluable—please keep sharing your thoughts to help us get better. Best, Sofie, Product Manager

This quarter’s release features new training on building industrial AI agents, enhancing 3D environments, improving data workflows and an all new and improved CDF fundamentals course to help you make better decisions and get more value from your data. Courses: Build In-Depth Skills, Step-by-StepCognite Data Fundamentals with Data ModelingA new and improved CDF Fundamentals course is now available. This introductory learning path is perfect for anyone new to Cognite Data Fusion. The updated content now covers the Core Data Model, hands-on exercises with Industrial Canvas, and an overview of Atlas AI. Complete the course to earn a shareable badge and certificate.3D Configuration and ManagementThis course is designed to give you the skills to set up and configure 3D environments and integrate them into your daily work. You will learn to use 3D solutions to improve visualization, enhance decision-making, and boost operational efficiency.This course is for anyone interested in learning about 3D technology and applying it in their professional field.Microlearning videos: Bite-Sized LearningsAI Agents in Industrial WorkflowsDiscover how Cognite's industrial AI agents, powered by Atlas AI, can help your teams act faster and with greater confidence. Learn how to leverage contextualized industrial data to streamline troubleshooting, automate routine tasks, and enhance operational efficiency.Building Agents in Atlas AIMaster the process of building and customizing industrial AI agents using Cognite Atlas AI. This low-code workbench enables you to automate complex tasks by connecting to contextualized data in Cognite Data Fusion. See how to create, configure, and deploy agents to analyze time series, retrieve documents, and support decision-making—all without writing a single line of code.Contextualizing Data for AIUnderstand how to transform raw industrial data into a scalable, connected foundation for advanced workflows. Learn to build and enrich a Knowledge Graph using tools like Data Workflows, diagram parsing, and document parsing, enabling seamless reuse of data and powering robust AI agents and applications. How-To Guide articles: Practical Tips from the ExpertsChoosing the Right Extractor for Your CDF IntegrationCognite Data Fusion offers a wide range of extractors to integrate data from industrial and IT systems into a unified platform. These are divided into two main types: Prebuilt Extractors, installed within customer infrastructure to connect with systems like OSI PI, OPC UA, databases, SAP, and document libraries; and CDF-Hosted Scalable Extractors, managed in the cloud for high-volume, real-time data ingestion from sources such as Azure Event Hub, MQTT, Kafka, and RESTful APIs.Understanding Function Memory Quotas in CDF Learn best practices to prevent memory errors in CDF functions by designing workloads with memory limits in mind. This guide explains why breaking tasks into smaller, manageable chunks is the most effective approach.How-To: Simple Streamlit App for CRUD Operations on CDF Data Model viewThis guide shows you how to create a simple Streamlit web app to manage data model instances in Cognite Data Fusion (CDF) using the Python SDK. Learn to create, read, update, and delete (CRUD) instances in real time—perfect for demos, quick data manipulation, or onboarding users to structured data modeling.Getting started with CDF performance testing This guide explains the importance of performance testing in Cognite Data Fusion (CDF) and how to assess key metrics like data ingestion rates, query performance, and API response times. It includes example notebooks to help you test efficiently, optimize costs, and ensure production readiness.Changing Your Identity Provider (IdP): Playbook and Key Information This guide explains the relationship between projects, organizations, and Identity Providers (IdPs) in Cognite Data Fusion. It covers the implications of changing an organization’s IdP, including session invalidation, user ID reassignment, and potential data access issues, and provides guidance on planning and requesting IdP changes safely.Unsubscribing from Cognite Charts AlertsThis guide explains how users can unsubscribe from Cognite Charts alerts and notifications, ensuring they no longer receive emails when time series thresholds are breached.Fixing Video Playback Failure – Error Code: 101102This guide helps troubleshoot Error Code 101102 when a training video fails to load. It covers common causes—like network restrictions, browser extensions, VPNs, or outdated browsers—and provides step-by-step solutions to restore smooth video playback.Optimizing Data Ingestion PerformanceLearn best practices to improve data ingestion performance in Cognite Data Fusion, including batching datasets, using progress monitoring, efficiently handling asset hierarchies, leveraging delta loads with the is_new function, and scheduling transformations for faster, more reliable workflows. Product Updates: What's New to Enhance Your WorkflowImproving Cognite Functions stability with new rate limitsWe're implementing new rate limits for Cognite Function calls to improve system performance and stability. A new limit of 250 concurrent running calls per CDF project will apply immediately to new projects and will go into effect on November 1, 2025, for existing projects. This change will affect a small number of projects with very high usage, ensuring a more equitable distribution of resources.Power BI REST Connector now Generally AvailableThe Power BI REST API connector is now Generally Available (GA), offering better performance and broader capabilities than the legacy OData connector, which will be retired on August 18, 2026. The new REST API connector provides broader authentication support, enhanced data access, and a significant performance boost of up to 10x, and should be used for all new reportsNew features for Cognite's GAP ConnectorWe've added two new capabilities to the GAP connector that address common challenges Petroleum Engineers face when working with GAP simulation models in production environments.Information Model Extraction The GAP connector can now automatically extract detailed flowsheet information models from simulator model revisions. This makes flowsheet data, including equipment, properties, and connections, accessible for analysis directly in CDF, eliminating the need for separate simulator access. External Dependencies Support This new feature allows you to manage simulation models by linking separate CDF files for components like well models or VLP tables, instead of repackaging the entire model. This simplifies updates, reduces storage overhead, and enables different team members to work on components independently. Explore new trainings on Cognite Academy. For guides, community, and to share your ideas, visit Cognite Hub. 

We've added two new capabilities to the GAP connector that address common challenges Petroleum Engineers face when working with GAP simulation models in production environments.Information Model ExtractionThe GAP connector now can automatically extract detailed information models from simulator model revisions, making flowsheet data accessible for analysis without requiring direct simulator access. When you upload a GAP model revision, the connector can parse the complete flowsheet hierarchy, capturing all functional blocks such as wells, pumps, valves, pipes, and tanks along with their operating parameters, physical properties, and configuration settings. The system also extracts connection relationships between equipment and graphical positioning information for visualization processes.This extraction is configurable through a modelparsing.config.yml file where you define which properties to extract from different equipment types. You can specify common properties that apply to all equipment (like oil rates, water rates, and gas rates from solver results) as well as equipment-specific properties (such as well model types, pipeline correlations, or valve characteristics). Each property definition includes the data type, units, and the GAP address suffix used to retrieve the value from the model.The parsing process activates automatically when a new model revision is created or when you reparse an existing revision, provided a valid configuration file exists. The connector loads the GAP model, discovers all equipment instances, extracts the configured properties along with unit information, analyzes equipment connections to generate material flow relationships, and stores the complete information model in CDF alongside the simulator model revision.The parsing adds some processing time during model validation, with the duration depending on model complexity. If property extraction fails for individual items (due to invalid addresses, incorrect unit quantities, or wrong value types), the connector logs the errors and continues processing rather than failing the entire operation.For configuration instructions and examples, see the public documentation.External Dependencies SupportThe GAP connector now supports a new external dependencies mode that changes how you manage simulation model components. Traditional GAP workflows require packaging everything into single .gar archive files, which can get quite large for big networks. Any change to individual components like well models or VLP tables necessitates repackaging and re-uploading the entire bundle, creating storage overhead and making frequent updates impractical.The external dependencies mode allows you to upload .zip files containing only the GAP network files (production and injection networks) while mapping individual node dependencies like .OUT, .VLP as separate CDF files. When creating a model revision, the user provides a mapping structure that links each external dependency file to its proper location in the GAP model using the simulator's address notation. For example, you can map a PROSPER .OUT file to GAP.MOD[{PROD}].WELL[{A1}].File and a .VLP file to GAP.MOD[{PROD}].WELL[{A1}].VLPFile.This approach enables to update individual well models, VLP tables, or other components without rebuilding entire model bundles. Different team members can work on different components independently, and you avoid duplicating unchanged files across model revisions, significantly reducing storage costs. The connector handles the complexity by downloading the network files, processing the external dependencies mapping, downloading each referenced CDF file separately, resolving file paths using the provided arguments, and assembling everything into a complete simulation model.For complete setup instructions covering both the new external dependencies mode and the single bundle mode, refer to the the public documentation.These features are currently in beta and subject to change based on user feedback. Access the parsed information through the existing simulator model revision endpoints, and configure external dependencies through the standard model revision creation process.

Dear community,We are excited to share highlights from the Q3 2025 release of Cognite Data Fusion. This post will walk you through: Atlas AI becoming smoother, smarter, and more governed New tools for operators in the field to plan and act on checklists seamlessly Richer context in Charts, 3D, and simulator integrations to connect data with real-world operations And there’s much more to explore. Dive into the latest release notes and uncover all the features and improvements designed to accelerate your industrial data journey. These brand new additions will be available starting September 2nd. We’d love to hear your feedback here on Cognite Hub, and we thank all our community members for your contributions so far. Keep the ideas coming — your input helps us shape the future of Cognite Data Fusion.Atlas AI Smoother Agent Interactions Smarter Knowledge Graph Queries Secure & Governed Agent Operations Accelerating Time-to-Value with Templates Document Parser Enhancements Industrial Tools  Checklist Scheduling Ad-Hoc Checklists from Mobile Templates Activities Visualization in CDM Projects Smarter, Faster Ways to Explore Data Support for CDM and Hybrid Projects Automatic Tag Detection for 360° Images New Measurement Tools Power BI App 3D Viewer Extract Information Model from Simulators External Dependencies in Model Revisions Data Management Configurable Tag Detection UI Annotation Box Adjustments Core Data Model Support for DB Extractor Admin App-Scoped Access  Atlas AISmoother Agent InteractionsWe’ve evolved the Atlas AI experience to feel like a natural extension of CDF. Cross-agent chat now enables users to carry conversations fluidly across agents, reducing friction and keeping work moving. A refreshed UI aligns visually with CDF for smoother, more consistent interactions. Together, these updates strengthen trust and make agents an integral part of daily workflows, driving higher engagement.Documentation Smarter Knowledge Graph QueriesThe new Agentic QKG v2 unlocks richer, engineer-like insights. By supporting multi-step queries, diagram annotations, and edge properties, agents can now handle nuanced industrial questions that previously ended in dead-ends. This makes it easier to retrieve deep context and uncover relationships across complex datasets.Documentation Secure & Governed Agent OperationsEnterprise customers now have stronger governance. New access controls (agents:read, agents:write, agents:run) define who can view, create, or execute agents. High-impact tools like Python execution and REST API calls require explicit confirmation before running. This prevents accidental execution and strengthens compliance and security. Documentation Accelerating Time-to-Value with TemplatesBetaWe’re launching ready-to-use agent templates like Data Scout (for instant industrial data retrieval in Canvas) and Time Series Trender (for trend and root-cause analysis). These reduce time-to-insight and empower engineers to self-serve without waiting on data science teams.Documentation Document Parser EnhancementsBetaParsing is smarter and more flexible. Users can leverage LLM vision capabilities, set custom parsing instructions in UI or API, select page ranges, and extract multiple instances. This unlocks more data trapped in files while giving customers more control and performance.Documentation Industrial Tools Checklist SchedulingBetaA new Schedules tab allows supervisors to plan upcoming checklists by shift, day, or week. This ensures the most critical work gets prioritized, giving supervisors flexibility to optimize operations and resource allocation.Documentation Ad-Hoc Checklists from Mobile TemplatesNot all fieldwork follows a schedule. With mobile checklist templates, operators can now generate unique, ad-hoc checklists directly in the field. Filtering options make it easy to find the right template, and mobile support means no dependency on desktop systems. This empowers operators to respond to changing conditions on the spot. Activities Visualization in CDM ProjectsBetaCharts now allows engineers to overlay activities (like maintenance or process events) directly on top of time series. By clicking an activity, users can view full details instantly. This bridges the gap between “what happened” (data anomalies) and “why it happened” (operational context), speeding up troubleshooting and decision-making. Smarter, Faster Ways to Explore DataSearch and browse just got a major upgrade. Users can:View denser tables with frozen headers. Resize preview panels for quick file inspection. Apply alphanumeric sorting, Enum filters, and “select all” options. See clearer tooltips and error handling.The result is faster, more comfortable navigation, letting users spend less time finding data and more time analyzing it.Documentation:Introduced Space and External Id across filters, columns and propertiesFilters/columns: “Select all” and filtering on enum propertiesImprovements to the search configuration UI Support for CDM and Hybrid ProjectsWe’ve closed a key migration gap: hybrid CAD projects and CDM point cloud contextualization are now fully supported via API. This unblocks major customer migrations and accelerates the transition from classic to CDM.Documentation Automatic Tag Detection for 360° ImagesUsers can now run text detection on 360° images to automatically generate annotation suggestions with bounding boxes. This reduces manual contextualization time and encourages more frequent updates to this rich data format.Documentation New Measurement Tools3D becomes more practical with enhanced measurement tools: horizontal cylinder measurement (ideal for exchangers and flanges), coordinate-based measurement, and improved UX for precision. These improvements reduce the need for on-site field work.Documentation Power BI App 3D ViewerBetaRequested by customers like Aker, this Power BI app allows direct viewing, rotating, and measuring of CAD models inside dashboards. Users can now correlate Power BI data with 3D spatial context, providing a richer lens for analysis.Documentation Extract Information Model from SimulatorsBetaA long-standing challenge has been the opacity of simulator files. This feature automatically extracts flowsheet hierarchies — equipment, properties, connections — into CDF. With initial support for GAP, engineers and AI systems can finally see and use this data without opening proprietary software. External Dependencies in Model RevisionsBetaInstead of uploading massive monolithic archives, users can now upload smaller ZIPs and manage dependencies as separate CDF files. This saves storage, avoids duplication, and makes updates much faster — particularly critical for large production networks.Documentation:Integration DocumentationSetting up a GAP Connector Data ManagementConfigurable Tag Detection UIBetaThe diagram parsing UI now offers a configuration interface. Users can fine-tune parameters (like minTokens and searchField) and scope which assets or files are searched. This delivers more accurate and relevant results without custom scripts. Annotation Box AdjustmentsBetaWhen annotation boxes shift after file version changes, users can now move and scale them directly in the UI — even in bulk. This saves manual rework and keeps annotations aligned with minimal effort. Core Data Model Support for DB ExtractorThe DB Extractor now writes directly into CDM instance spaces, whether for Time Series, File concepts, or any other data model nodes and edges. This simplifies onboarding of database sources, shortens time-to-value, and makes CDM a first-class destination for structured and unstructured data.Documentation AdminApp-Scoped AccessAdmins can now scope access capabilities to specific applications. By limiting which groups apply for each app, queries in large Data Models are faster and more efficient. This change strengthens performance without reducing flexibility.  

The CDF Fundamentals course path, our most popular in the Academy, has a fresh update!This is an introductory-level learning path for anyone new to CDF. It will guide you through Cognite Data Fusion - what it is, how to use it, its building blocks, and the value it brings to industrial organizations. Here’s what’s new in the updated version:Core Data Model - We’ve switched from the asset-centric model to the Core Data Model.   Industrial Canvas - Now part of the hands-on exercises.   Atlas AI - Included in the theory section to broaden your understanding of AI in CDF.  The path at a glance:2 courses + 1 assessment   Around 6 hours to complete   Earn a badge and certificate to showcase on Cognite Hub and your LinkedIn profile.   Enroll in the updated CDF Fundamentals course and strengthen your foundation of working with CDF. 

Hi everyone,We're excited to announce that the Power BI REST API connector is now Generally Available (GA), offering significant performance improvements and broader capabilities. At the same time, we're deprecating the legacy OData connector, which will be retired on August 18, 2026.What's changing:REST API connector promoted to GA:- Broader authentication support: Works with all identity providers supported by CDF (not just Azure Entra ID)- Enhanced data access: OData services, GraphQL for data models, and direct REST API endpoints- Up to 10x performance improvement when using direct REST endpoints- Full API coverage: Access data from any GA CDF API endpointOData connector deprecated:- Deprecation date: August 18, 2025- Retirement date: August 18, 2026- Existing reports will continue to work until retirement- All new reports must use the REST API connectorWhat this means for you:For new reports:- Use the REST API connector exclusively- Choose from three data access methods based on your needs:  - OData services for quick exploration (no coding required)  - GraphQL for data models (minimal coding)  - Direct REST for maximum performance (needs some Power Query coding experience)For existing OData-based reports:- Continue working normally until retirement date- Plan migration to the REST API connector before retirement- We've created a migration guide with multiple migration options ranging from minimal code changes to performance-optimized approachesResources:- REST API connector setup guide: https://docs.cognite.com/cdf/dashboards/guides/powerbi/set_up_rest_connector- Migration guide from OData: https://docs.cognite.com/cdf/dashboards/guides/powerbi/set_up_odata_connector#migration-guide- Deprecation timeline: https://docs.cognite.com/cdf/deprecated- Training (coming soon): While our documentation is ready for you to get started immediately, we're also developing dedicated Academy training courses covering the new REST connector features and migration best practices. These hands-on courses will be available soon to help you make the most of the enhanced capabilities.The REST API connector represents a significant advancement in how Power BI integrates with CDF, providing better performance, broader compatibility, and access to all CDF features. We encourage starting your migration planning early to ensure a smooth transition.For questions or support during migration, please reach out to your Cognite representative or post in the forum.

Hi there,We're introducing new rate limits for Cognite Function Calls, affecting less than 5% of CDF projects based on current usage patterns.Cognite Functions has seen significant growth, and we've identified usage patterns with very high running call counts that are impacting system capacity and performance for all users. Our new rate limits will ensure more equitable resource distribution and maintain reliable service quality across all CDF projects.What's changing:We're introducing limits on concurrent function calls with a phased rollout: Current: Undocumented concurrent running call limits (based on underlying cloud provider) New: Maximum of 250 concurrent running calls per CDF project (independent of cloud provider) New projects: Limits apply immediately starting with this announcement Existing projects: Limits take effect November 1, 2025 Additionally, we're updating the HTTP response status code for the case where the limit of 100 concurrent running function calls per function is exceeded. The code will change from 400 to 429, aligning with HTTP standards. What this means for you: Most CDF projects won't notice any difference. The 250 concurrent running call limit supports typical usage patterns across most workloads. CDF projects currently exceeding this limit will receive direct outreach from our team with optimization guidance and transition support. We recommend reviewing your function concurrency patterns and consider optimizations like batching or queue management for high-volume scenarios. Upon reaching the limit, new function call requests will return HTTP 429 (Too Many Requests), signaling clients to back off and retry later. Some SDKs like the official Python SDK already handle automatic retries with exponential backoff for 429 responses, minimizing the need for code changes. We take these decisions seriously. We're committed to supporting high-concurrency use cases through architectural improvements and will continue to monitor usage patterns to ensure these limits serve our community effectively.We also recognize the importance of maintaining performance during this transition. Our team will be closely monitoring system behavior and is ready to provide direct support for projects that need assistance with optimization strategies.