Product Updates

Hi everyone,The Cognite Python SDK now supports State time series, from cognite-sdk 8.20.0.State time series track the discrete states of equipment, such as OPEN/CLOSED or RUNNING/STANDBY/ERROR. Each time series links to a state set that defines the valid states. CDF validates datapoints against that set and offers aggregates built for states: time spent in each state, number of transitions into each state, and count per state. If State time series are new to you, start here: https://docs.cognite.com/dev/concepts/resource_types/state_timeseriesGetting startedpip install --upgrade cognite-sdk # or uv add cognite-sdkfrom datetime import datetime, timezone from cognite.client import CogniteClient from cognite.client.data_classes import ( StateDatapointsInsert, StateDatapointWrite,)from cognite.client.data_classes.data_modeling import NodeId, StateSetEntry from cognite.client.data_classes.data_modeling.cdm.v1 import ( CogniteStateSetApply, CogniteTimeSeriesApply, ) client = CogniteClient() # 1. Define the valid states and create a state time series that uses them state_set = CogniteStateSetApply( "my-space", "pump-states", name="Pump states", states=[ StateSetEntry(0, "OFF"), StateSetEntry(1, "RUNNING"), StateSetEntry(2, "STANDBY"), ], ) pump_state = CogniteTimeSeriesApply( "my-space", "pump-101-state", is_step=True, time_series_type="state", state_set=("my-space", "pump-states"), name="Pump 101 state", ) client.data_modeling.instances.apply([state_set, pump_state]) # 2. Insert datapoints by numeric or string value ts_id = NodeId("my-space", "pump-101-state") client.time_series.data.insert_states( StateDatapointsInsert( instance_id=ts_id, datapoints=[ StateDatapointWrite( datetime(2026, 10, 1, 0, tzinfo=timezone.utc), 1 ), StateDatapointWrite( datetime(2026, 10, 1, 6, tzinfo=timezone.utc), string_value="STANDBY" ), StateDatapointWrite( datetime(2026, 10, 1, 9, tzinfo=timezone.utc), string_value="RUNNING" ), ], ) ) # 3. Time spent in each state per day, one column per state df = client.time_series.data.retrieve_dataframe( instance_id=ts_id, start=datetime(2026, 10, 1, tzinfo=timezone.utc), end=datetime(2026, 10, 3, tzinfo=timezone.utc), aggregates="state_duration", granularity="1d", )Reference docs:insert_states: https://cognite-sdk-python.readthedocs-hosted.com/en/stable/generated/cognite.client.AsyncCogniteClient.time_series.data.insert_states.html Datapoints API: https://cognite-sdk-python.readthedocs-hosted.com/en/stable/time_series.html Core data model: https://cognite-sdk-python.readthedocs-hosted.com/en/stable/data_modeling/core_data_model.html

Hi everyone,We’re pleased to share an update to the Unified 3D application in Cognite Data Fusion that makes navigating CAD models faster and more informative.What’s newNew CAD tree display — a structured tree menu on the left side of the screen makes model hierarchy easy to explore at a glance. Node highlight — selecting a node in the tree highlights the corresponding geometry inside the 3D view. Auto-zoom camera — when you pick a node from the tree, the camera automatically zooms to that part for immediate, clear visibility. Node properties via info icon — an info (i) icon at the end of each node name opens a properties panel showing metadata and attributes. The panel opens without changing your current camera view, so you can inspect details while keeping visual context.Why this helpsFaster navigation — jump directly to relevant parts of large models without manual orbiting or searching. Clearer context — highlight + auto-zoom reduces ambiguity about which component you’re inspecting. Immediate metadata access — view node details quickly from the tree without interrupting your inspection or shifting the camera. Improved collaboration — presenters and reviewers can point team members to exact nodes and their properties more reliably during reviews and handovers.We’d love your feedback — what’s working well, what could be improved, and any fields or interactions you’d like added to the properties panel. Please leave your thoughts in the comments below.For a quick look at the zoom, highlight, and info-panel behavior.Thanks,The 3D Product Team

Hi, Unfortunately, we experienced unforeseen challenges with the Preview Panel Redesign in the Search application after the Q3 release September 8. Due to these challenges, we opted for rolling back the update, so that we can assess the issues and handle them appropriately. We deeply apologies for the issues experienced with the release, and we will ensure better testing before a new release. Current estimate on timeline for re-releasing the updates with bug fixes and other improvements, is the Q4/December release. Please let us know if you would be interested in a limited Private Preview spot on the updates, which will open up for a period of time before the December release.  Apologies again, and thank you to all reporting challenges with the release, Sofie, Product Manager 

Hi everyone,We're excited to share one of the standout features from our Q3 release: real-time, multi-user collaboration in Industrial Canvas — now in Private Preview.The problem we're solvingUntil now, Industrial Canvas has been a single-user experience. That meant teams working on the same problem couldn't work in the same space at the same time — creating silos and slowing down team-based decision-making.What's newIndustrial Canvas is becoming a truly collaborative workspace, where multiple users can work together simultaneously — exploring data insights side-by-side and driving faster, unified decisions together. Where this creates value — real use cases (not limited to):Root Cause Analysis — multiple engineers investigate different parts of a failure simultaneously and consolidate findings instantly. Field-to-Office Bridge — field operators and back-office engineers mark up the same P&ID together, in real time. Meeting Facilitation — run operational reviews or shift handovers with Canvas as a live, shared working document. Expert Consultation — SMEs jump into active sessions to advise without disrupting the workflow. Turnaround Planning — teams collaborate concurrently on complex work packages, cutting out linear delays. Isolation Planning — multiple engineers review and verify isolation plans on live P&IDs together.Let's hear from youWhether you're already exploring this in Private Preview or hearing about it for the first time — we want to hear from you.Already using it? Would love to get your feedback and use cases. Not in yet? Let us know and we'll get you set up.   Thank you, Andreea.

As part of the September/Q3 product release, we enabled Smarter Search Results across all clusters except westeurope-1.  For westeurope-1, we’re currently working on stability improvements. To avoid introducing additional changes while that work is ongoing, we’ll enable Smarter Search Results there once those improvements are complete.  Smarter Search Results is improving how data is analyzed to provide more relevant and precise search results. See more information about this improvement in the linked documentation. 

  Q3 2026 makes Cognite Data Fusion faster, cleaner, and more autonomous: 3D scenes load and respond quicker, Records connect two-way with the knowledge graph, and any compliant AI agent can plug into your data in minutes, with investigations that used to take a week now taking minutes. Search gets cleaner, Atlas AI gains an open MCP server and a persistent sandbox, Cognite Flows matures across native and custom apps alike, and an open-sourced AI skill pack plus a one-pass Foundation Deployment Pack get new projects running faster than ever.Review the highlights below to see how these advancements accelerate time to value, cut downtime, and expand what your industrial workforce can do.Data Fusion Time Series Gap Filling State Time SeriesPublic Preview General Improvements in Unified 3D and 3D Management Scalability and Performance Improvements in 3D Smarter Search Results: Precision Search Records: Views & Graph Discoverability Atlas AI for Data Fusion Workspace Zero-Copy Inbound for Microsoft Fabric OneLake Atlas AI Industrial MCP Agent Builder CLI Atlas AI Sandboxes Seamless and Trustworthy Experience Cognite Flows: Native Applications Search Preview Panel Redesign Hierarchy Across Categories InField: Asset Centric Data Model Parity InField: Bulk Update to Done in Checklist Overview InField: Group Checklist by Asset Attribute Industrial Canvas: Real-Time Collaboration Industrial Canvas: Cause Map Improvements Industrial Canvas: Upload Files Cognite Flows: Custom Applications & Adaptive Experiences 3D Widget for Flows, Adaptive Experiences, and Partners Custom Apps: Developer Experience Adaptive Experiences: A Sticky, Personalized Landing Page 1000x Scalability AI Coding Assistant Skills (Marketplace MVP) Foundation Deployment Pack Data Fusion Feature Problem (Challenge) Solution: What You Can Do Now Time Series Gap Filling Public Preview Increase industrial workforce capacity Extractors push data points exactly as stored in the source system. Where little or no compression is configured, that's already the full signal. Where historians compress heavily (10-100x), they rebuild the signal at query time before anyone sees it, and that reconstruction step doesn't come along with the data. Turn on signal reconstruction with fill.limit on aggregate queries, capping the largest gap CDF will fill. Interpolation follows the OPC UA standard (linear, step, or forward-fill past the last point), and it's fully opt-in, so queries without fill are unchanged. Documentation   State Time SeriesPublic Preview Increase industrial workforce capacity CDF stores time series as numeric or string. Both are right for what they cover: measurements you plot and aggregate, and free text you read. Machine states are a closed set of values that needs a code to plot, a name to read, and time spent per state, so state data from historians fits neither type cleanly. New state type on CogniteTimeSeries, linked to a CogniteStateSet that defines the allowed numeric and string value pairs. New per-state aggregates like stateDuration, stateTransitions, stateCount, alongside common aggregates. Documentation   General Improvements in Unified 3D and 3D Management Production Increase industrial workforce capacity Outdated UI styling and controls, plus unintuitive navigation, made it harder for users to find their way around 3D management. Updated styling and icons, a redesigned landing page, page structure, and action bar, a redesigned tree view (now including Points of Interest for easier navigation), and improved icon clustering for 360 images add up to a cleaner look and feel across Unified 3D and 3D management, with better navigation and usability. Documentation   Scalability and Performance Improvements in 3D Production Increase industrial workforce capacity Long loading times for 360 images interrupted the user flow, interactions in heavy 3D scenes felt laggy, and performance on large datasets fell short of expectations. Faster loading for 360 images, improved responsiveness in heavy 3D scenes, and improved performance for large datasets, including contextualization, zoom-to-asset, and queries, make interactions smoother, even in large or heavy 3D scenes. Documentation Smarter Search Results: Precision Search Production Increase industrial workforce capacity Poor “partial” matching of search queries, and search queries matched too loosely, expecting any part of the query to match rather than all of it, which hurt precision. Updated /search API logic now breaks both the search query and the underlying data into smaller searchable terms using dashes, dots, whitespace, and capitalization, so searching "unitCode" finds "unit" and "code," and it recognizes that "0059" and "59" are often the same thing, matching with or without leading zeros. Combined with the AND search operator, this delivers more relevant results every time (the operator has to be specified when using the API). Documentation   Records: Views & Graph Discoverability Production Rapidly scaling use cases across your enterprise A record could already point at the graph instances it relates to, but nothing on the graph side pointed back, leaving records as a separate store you had to know about and query on their own. Views now work over record containers, and a Data Model view can link back with a reverse direct relation: two-way navigation from a record to its node, and from a node to the records that point at it. Documentation   Atlas AI for Data Fusion Workspace Public Preview Increase industrial workforce capacity The Data Fusion workspace has no built-in AI agent to help with day-to-day work, so complex workflows are difficult and time-consuming to understand, monitor, and troubleshoot, and finding the root cause of a failed run often means navigating multiple tasks and views. A built-in Atlas AI agent now lives in the Data Fusion workspace: it explains complex pipelines, workflows, dependencies, and task sequences, reviews run history, debugs workflow runs and investigates task-level failures, and can modify workflows by adding and configuring tasks, for faster onboarding and more efficient workflow management. Documentation   Zero-Copy Inbound for Microsoft Fabric OneLake Public Preview Accelerated time to value with your use cases Bringing Microsoft Fabric OneLake data into CDF meant duplicating it into RAW first, which added storage cost, pipeline complexity, and delay before it could even be transformed. Fabric OneLake integration graduates to public preview with incremental reads, configurable volume/concurrency limits, and observability on inbound data volume, letting you bring OneLake data straight into the knowledge graph with a single Transformation SQL query, with no extractor or duplicate RAW copy required. Documentation   Atlas AI Feature Problem (Challenge) Solution: What You Can Do Now Industrial MCP Preview Rapidly scaling use cases across your enterprise Connecting an AI agent to industrial operational data has required months of custom work per agent, per system, per use case, and most projects never reached production. Any compliant AI agent, whether Claude, Microsoft Copilot, or a partner-built agent, can connect to Cognite Data Fusion in minutes and query the full industrial knowledge graph under the user's own permissions, with usage metered separately from native Atlas AI usage. Documentation   Agent Builder CLI Preview Reduce the amount and cost of downtime Validating changes to Atlas AI agents meant manual testing in the Fusion UI, with no version history, no automated quality checks, and no reliable way to catch regressions before they reached operators. Develop, execute, and evaluate Atlas AI agents via the Cognite CLI. Supported by insights from the knowledge graph, your coding agent aids in constructing and optimizing Atlas agents. Integrated evaluation metrics automatically score factors like accuracy, completeness, and hallucination reduction within unattended CI pipelines, preventing unverified updates from reaching production.Documentation   Atlas AI Sandboxes Private Preview Increase industrial workforce capacity Atlas AI agents could answer questions and call tools, but couldn't persist intermediate work, run arbitrary code, or resume an investigation after a session gap, so complex analysis had to be pieced together manually across people and prompts. A secure, isolated sandbox gives Atlas AI agents a filesystem and code execution, so an agent can investigate, analyze large datasets, build visualizations, and produce files across a multi-step session that survives interruption, turning investigations that took half a day to a week into minutes. Documentation   Seamless and Trustworthy Experience Production Increase industrial workforce capacity Atlas AI responses need to be grounded in the knowledge graph so customers can trust their accuracy and completeness, and beyond the chat response itself, users need transparency into the agent's thought process and how it reached an answer. Tag CDF data directly in a request with @mentions, watch new thought traces and thinking presentations while the agent works, and preview citations to see the underlying knowledge graph items behind an answer, establishing visible trust that responses come straight from the knowledge graph. Documentation   Cognite Flows: Native Applications  Feature Problem (Challenge) Solution: What You Can Do Now Note: The Search Preview Panel Redesign has been rolled back. For more information, please visit this page Search Preview Panel Redesign Production Increase industrial workforce capacity The Preview Panel loaded every relationship widget up front regardless of whether it had data, with no inline filtering or column selection, and properties for instances modeled across multiple categories were locked behind manual category-switching. A simplified two-tab panel (Overview, Properties). Most relationship widgets are now collapsed by default, and the Properties tab shows properties across all relevant categories in one place, with no more per-category lookups. Documentation   Hierarchy Across Categories Production Increase industrial workforce capacity Search's tree view only rendered instances within the currently selected category, so a hierarchy modeled across categories only ever showed a partial tree. The hierarchy view now renders parents and children spanning categories, giving a complete tree regardless of where each node is modeled, so you can navigate with confidence that nothing's missing. Documentation   InField: Asset Centric Data Model Parity Production Rapidly scaling use cases across your enterprise Field operations data sat in an app-specific model, disconnected from the common foundation used by Atlas AI and Cognite Flows. InField now runs fully on the Core Data Model, including SAP write-back, observations, hierarchy search, and Data Explorer configuration, giving InField the same data foundation as Atlas AI and Flows for faster roll-out, no separate APM model to maintain, and a clear path off Classic. Documentation   InField: Bulk Update to Done in Checklist Overview Production Increase industrial workforce capacity Supervisors had to close finished checklists one at a time, meaning dozens of clicks at the end of a shift. Multi-select checklists in the Overview and set their status to Done in bulk, so close-out now takes seconds instead of minutes. Documentation   InField: Group Checklist by Asset Attribute Private Preview Increase industrial workforce capacity Checklists showed as one flat list unless groups were configured in the template, so operators jumped between areas and equipment, wasting steps and losing overview. Group checklist items by asset properties such as Location, Criticality, or Manufacturer directly from the filter row, bulk-update whole groups of tasks at once, and work one area at a time in logical order, for faster execution with fewer clicks. Works in both Classic and CDM. Documentation   Industrial Canvas: Real-Time Collaboration Private Preview Increase industrial workforce capacity Teams require a unified, real-time workspace to collaborate simultaneously on the same canvas, ensuring faster and more aligned decision-making. Canvas is now a true collaborative workspace where multiple users can work simultaneously, explore data side-by-side, and accelerate unified decision-making for root-cause analysis, planning, and complex workflows. Documentation   Industrial Canvas: Cause Map Improvements Production Increase industrial workforce capacity Cause Map trees in Canvas supported only one layout orientation, and repositioning a very large tree was cumbersome. Choose between horizontal and vertical layouts, and move an entire Cause Map tree instantly by dragging its root box. Documentation   Industrial Canvas: Upload Files Public Preview Accelerated time to value with your use cases Uploading a file into Canvas required manually selecting a data set or data instance first, an extra step that often stalled the import. Drag and drop files directly into Canvas, with no pre-selection required. Documentation   Cognite Flows: Custom Applications & Adaptive Experiences Feature Problem (Challenge) Solution: What You Can Do Now 3D Widget for Flows, Adaptive Experiences, and Partners Production Rapidly scaling use cases across your enterprise The 3D widget carrie internal dependencies that block it from being a free-standing component that AI agents, Flows, Adaptive Experiences, and partners can use. A re-architecture of the 3D widget, plus new skills, makes it usable outside Cognite's native applications, enabling AI agents, Cognite Flows builders, and Adaptive Experiences to build 3D experiences, and opening the widget to partners it couldn't reach before. Documentation   Custom Apps: Developer Experience Production Accelerated time to value with your use cases As the Custom Apps builder community grew, limited full-screen and device-API support and short-lived CLI sessions kept the platform from feeling production-ready. Adds full-screen app mode, camera/microphone/location access, code signing with per-version trust inspection and tamper protection, longer-lived CLI sessions, clearer build-and-deploy errors, higher per-app version limits with instant rollout, and Windows OAuth fixes, all delivering faster, safer iteration for builders. Documentation   Adaptive Experiences: A Sticky, Personalized Landing Page Private Preview Accelerated time to value with your use cases Every user started from a blank Search each session with no memory of past work, and Search and Atlas AI were two separate entry points to choose between. Search, configure, and save views as live widgets on a personalized landing page. A single entry box unifies Search and Atlas AI, a growing widget ecosystem is supported (tables, KPIs, bar charts, Cognite Charts, Custom Apps), users can build widgets by describing them to Atlas AI, and create, switch between, and share multiple dashboards. Documentation   1000x Scalability Feature Problem (Challenge) Solution: What You Can Do Now AI Coding Assistant Skills (Marketplace MVP) Rapidly scaling use cases across your enterprise Partners and customers lacked a fast, reliable way to apply Cognite's best-practice patterns, such as data modeling, naming, transformations, workflows, and DMS queries, so every team was reinventing them from scratch. Cognite's recommended CDF patterns now ship as a Cursor and Claude plugin, open-sourced via the marketplace GitHub repo: the same AI skills that already accelerated Cognite's own internal workflows are now available directly in your coding tools.  Documentation   Foundation Deployment Pack Accelerated time to value with your use cases Every new CDF project repeated the same manual setup, including access groups, data model, extraction pipelines, and CI/CD, work that was slow, easy to get subtly wrong, and inconsistent across deliveries. A Toolkit module with a guided wizard deploys a clean, best-practice project foundation, including access controls, data model, extraction pipeline configs, and CI/CD, in one pass, together with templated data models and contextualization pipelines. Documentation    For a comprehensive list of all technical updates, improvements, and bug fixes, view the full Q3 2026 Release Notes. 

Hi everyone,The Cognite Python SDK now supports Records and Streams, from cognite-sdk 8.12.0.Records give you high volume storage for structured data such as logs, events, tabular data, without overloading your knowledge graph. Streams control retention, mutability and throughput for that data. If Records is new to you, start here: https://docs.cognite.com/cdf/dm/records/concepts/records_and_streamsWhat is includedBoth APIs are fully covered:Streams: create, list, retrieve, delete Records: ingest, upsert, filter, sync, sync_resume, aggregate, deleteThis is an alpha releaseThe feature is not generally available. The SDK raises a FeaturePreviewWarning when you use it, and we may make breaking changes to the SDK without notice while we improve the ergonomics. Use it in development and test projects, not in production pipelines.We will keep it in alpha until early to mid September, before deciding on the next step. That time is there so you can test it and tell us what should change.Getting startedpip install --upgrade cognite-sdk# oruv add cognite-sdkfrom cognite.client import AsyncCogniteClientfrom cognite.client.data_classes.data_modeling.records import TimeRangeclient = AsyncCogniteClient()records = await client.data_modeling.records.filter( stream_id="historic_alarms", last_updated_time=TimeRange(gte="2026-08-01T00:00:00Z"), limit=100,)Reference docs:Streams: https://cognite-sdk-python.readthedocs-hosted.com/en/latest/data_modeling/streams.html Records: https://cognite-sdk-python.readthedocs-hosted.com/en/latest/data_modeling/records.html What we would like feedback onNaming and method signatures. Does anything read wrong or work differently from how you expected? Data classes such as RecordWrite, RecordSource and TimeRange. Are they convenient to build and inspect? Error messages. Did anything fail in a way that did not tell you what to fix? Gaps. Is anything missing for what you are building?Comment on this post to share your feedback.

We’ve launched the first phase of the new Cognite Flows learning journey on Academy - designed for developers and technical builders who want to start building production-ready industrial applications with Cognite Flows.Cognite Flows is the new AI-native action layer within the Cognite platform, enabling teams to build contextualized workflows and custom industrial applications significantly faster by combining operational data, AI, and modern development tooling.The newly released Step 1: Cognite Flows Foundation introduces the core concepts, architecture, and development approach behind Flows, including:Understanding the Flows ecosystem and custom app architecture AI-native development workflows and builder skills Local development, deployment, and app lifecycle basics Foundations required for the Flows Builder Certification journeyThe learning path is especially relevant for:Developers building industrial applications in CDF Technical consultants and implementation teams Solution architects exploring AI-native workflows Anyone interested in scalable, production-grade industrial app developmentFlows provides a modern development experience with built-in scaffolding, authentication, deployment workflows, and support for agentic AI coding tools.You can explore the Flows hereStep 1:  Cognite Flows Foundations: Start your journey toward becoming a Cognite Flows Builder with our new foundational learning path. This content is designed to help you build a solid understanding of the Flows ecosystem, core concepts, architecture, and development approach. Step 2 is currently under development and will expand further into advanced implementation and builder workflows.If you’ve already started exploring Flows, share your thoughts, questions, or early experiences below 👇 

The Cognite Data Fusion (CDF) Bootcamp schedule for the second half of 2026 is now available.This 4-day, instructor-led, in-person training is designed for technical professionals who want hands-on experience building solutions with Cognite Data Fusion. Participants will work through key concepts including data ingestion, contextualization, data modeling, orchestration, and application development through practical exercises.Ready to build your CDF skills?Check the calendar and register for a session that fits your schedule.Open bootcamps are offered in:Oslo, Norway Phoenix, ArizonaWe recommend registering early to allow enough time to complete the required pre-work before the session begins.  Event Name Event Date Location Cognite Data Fusion Bootcamp in Phoenix - July 2026 July 20-23, 2026 9:00a.m.  US/Pacific Phoenix, Arizona Cognite Data Fusion Bootcamp in Phoenix - Aug 2026 Aug 17-20, 2026 9:00a.m. US/Pacific Phoenix, Arizona Cognite Data Fusion Bootcamp in Oslo -  Aug 2026 Aug 24-27, 2026 9:00a.m. Europe Oslo, Norway Cognite Data Fusion Bootcamp in Oslo -  Sep 2026 Sep 7-10, 2026 9:00a.m. Europe Oslo, Norway Cognite Data Fusion Bootcamp in Phoenix - Sep 2026 Sep 21-24, 2026 9:00a.m. US/Pacific Phoenix, Arizona Cognite Data Fusion Bootcamp in Oslo -  Oct 2026 Oct 12-15, 2026 9:00a.m. Europe Oslo, Norway Cognite Data Fusion Bootcamp in Phoenix - Oct 2026 Oct 19-22, 2026 9:00a.m. US/Pacific Phoenix, Arizona Cognite Data Fusion Bootcamp in Oslo -  Nov 2026 Nov 9-12, 2026 9:00a.m. Europe Oslo, Norway Cognite Data Fusion Bootcamp in Phoenix - Nov 2026 Nov 16-19, 2026 9:00a.m. US/Pacific Phoenix, Arizona Cognite Data Fusion Bootcamp in Oslo -  Dec 2026 Dec 14-17, 2026 9:00a.m. Europe Oslo, Norway Cognite Data Fusion Bootcamp in Phoenix - Dec 2026 Dec 14-17, 2026 9:00a.m. US/Pacific Phoenix, Arizona  Confirmed for a Bootcamp?Once your participation is confirmed, be sure to join the Cognite Data Fusion Bootcamp Group on Cognite Hub.The group contains:Preparation resources and pre-work Session updates and practical information A place to ask questions before and during the bootcampStay informed, stay prepared, and get the most out of your bootcamp experience. 

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.Cognite Flows Foundation Certificate: We’ve recently introduced the Cognite Flows Foundation Certificate learning journey, designed for developers and technical builders working with industrial applications and Cognite Flows.The learning path provides a strong foundation in the Cognite Flows ecosystem, including core concepts, a human-centered approach to application building, engineering architecture, submission processes, and security guidelines — everything needed before getting started with deployment and development in Flows.Who it’s for: Developers, consultants, and solution architectsValue: Build foundational knowledge for industrial application development and Builder Certification preparationDuration: 120 minutesBy completing the learning journey, you will earn the Cognite Flows Foundation community badge and certificate - the official prerequisites for accessing Part 2: Cognite Flows for Builders, which is coming soon. 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.Diagram Parsing for Data ModelingLearn how static engineering diagrams such as P&IDs, process flow diagrams, and electrical schematics can be transformed into structured, connected, and contextualized data within CDF. The course focuses on extracting engineering elements from diagrams and mapping them into data models to improve operational visibility and contextualization across industrial use cases in oil & gas and manufacturing.Who it’s for: Data engineers and users with basic understanding of industrial diagrams and processesValue: Understand how engineering diagrams become searchable, connected operational dataDuration: 40 minutes Cognite ToolkitThis course focuses on configuring, deploying, and managing CDF projects using YAML-based configurations and DevOps workflows. Participants will learn how to manage transformations, extraction pipelines, RAW tables, groups, and data models across development, staging, and production environments while gaining a practical understanding of Toolkit modules and deployment workflows.Who it’s for: Data engineers and users with coding experience and foundational CDF knowledgeValue: Simplify and standardize deployment workflows in CDF projectsDuration: 40 minutes Deployment Packs: Ready-to-Use Configurations for CDFWe’ve added a new set of deployment packs designed to simplify and standardize CDF setup. These packs provide pre-configured modules, data models, and environments that help you deploy solutions faster and more consistently across projects. Oil and Gas Data Model — CFIHOS & ISO14224 ExtensionA tag-centric model that combines data from systems such as AVEVA, SAP, OPC UA, and PI into a simplified and queryable structure. Built on CFIHOS 2.0 and ISO14224 standards, the model is optimized for easier querying, dashboards, AI tools, and search experiences through a denormalized approach to industrial data. Configuring the Infield QS Deployment Pack with the ToolkitThis deployment pack simplifies setup of the InField application in CDF by providing predefined environments, permissions, data models, and Toolkit deployment workflows aligned with InField 2.0 best practices. Once deployed, users can access contextualized operational data including assets, activities, notifications, and maintenance information directly within InField. Quickstart Deployment PackA bundled deployment package that includes Entity Matching, File Annotation, the Quickstart Enterprise Data Model, foundational components, and synthetic data for end-to-end testing. It is designed to help teams quickly deploy a complete working environment with all required capabilities included out of the box.  Coming This Quarter Hands on part for builder CertificationTake the next step in your Cognite Flows learning journey by applying your knowledge in a hands-on certification experience. This practical component will allow learners to build a live simulation application based on a real-world industrial use case, demonstrating their ability to design and implement operational applications using Cognite Flows.What you'll earnCognite Flows Builder Certificate  Builder Community BadgeStatus: Enrollment opening soonThis hands-on certification experience is designed for learners who have completed the foundational Builder learning path and are ready to validate their skills through practical application. 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. 

The Q2 2026 release accelerates the democratization of Industrial AI, empowering teams with production-grade custom app infrastructure and declarative data workflows. We have significantly increased industrial workforce capacity through precision search migration, zero-code CSV exports, and immersive 360° spatial walkthroughs. To rapidly scale use cases across the enterprise, builders can now leverage lifecycle-managed Atlas AI Skills and native agent execution to deliver a unified, intelligent experience across all Cognite Data Fusion (CDF) applications. Review the core release highlights below to discover how these advancements are accelerating time to value by making Industrial AI more robust, scalable, and accessible for the entire industrial workforce.Cognite Flows - Native and Custom apps Cognite Flows -  Custom Apps Cognite Flows - Adaptive Experiences Cognite Flows - Application Certification Cognite Flows - New Developer Experience Industrial Canvas - Symbol Library Update Industrial Canvas- Unified Project Settings for Canvas and Search  InField - Field-Ready Observations Mobile Lists Search UI - Precision Results Search UI - Download Individual and List of Instances (CSV)  Data Fusion - Integrations, Data Management, Knowledge Graph, and Data Processing & Orchestration Data Workflows - Native JSON Mapping Tasks Function- Access Scoping on Execution Level Function Apps Integrated in Workflows (UI)  Hosted Extractors: Email Alerting API  Integrations - API & UI  Cognite Connector - Simulation Run Load Balancer Functions -  Support for Private Link on GCP Clusters  Data Workflows- References in Workflows  Python SDK - Asynchronous Concurrency 3D - Industrial Street-View Spatial Inspections 3D - Forced CAD Node Rendering Atlas AI Enhancements Atlas AI: Data Analysis (Code Execution)  Atlas AI - Reusable Domain Skills Packages Atlas AI - Query Tool Atlas AI - Unified Agent Experience Across Apps  Agent as Task Type in Workflows  Cognite Flows - Native and Custom apps Feature Problem (Challenge) Solution: What You Can Do Now Cognite Flows -  Custom Apps Production Rapidly scaling use cases across your enterprise Industrial builders previously struggled to rapidly deploy and scale tailored enterprise applications inside CDF without creating security flaws or running up massive operational debt. Subject Matter Experts can leverage a robust, secure, and production-grade environment to deploy custom applications directly inside CDF. Features include an upgraded command-line interface tool and a strict two-step validation safety procedure (deploy and activate) to ensure flawless code-signing. Documentation  Cognite Flows - Adaptive Experiences Private Preview Accelerated time to value with your use cases  Every single system user used to inherit the exact same uninspiring home landing page, leaving critical site-specific knowledge siloed in workers' heads. Access an intelligent landing page that builds a personalized dashboard experience via conversational AI within under 5 minutes from first login. Teams can pin custom widgets to hit an aggressive 50% Daily Active User (DAU) organizational adoption target.Documentation     Cognite Flows - Application Certification Production Rapidly scaling use cases across your enterprise   There was no formal certification process for custom applications. Teams could deploy apps without any structured validation, creating risk around data model integrity, security, and production readiness, with no consistent bar across the builder ecosystem.  Applications undergo a verification process before deployment. Automated validation covers data model integrity, app robustness, security, and performance at scale, while selective human review handles higher-risk scenarios. Certified builders follow a verified path, ensuring every app is safe to operate in industrial environments. Documentation Cognite Flows - New Developer Experience Production Accelerated time to value with your use cases Fragmented tooling and manual UI assembly slowed industrial development. Existing tools were not designed for AI-assisted workflows, making it hard to scale innovation efficiently. A standardized Developer Toolbox with SDKs, CLI, UI components, and machine-readable documentation lets developers and AI agents build consistent, production-grade applications for both Cognite Flows and self-hosted environments. Documentation Industrial Canvas - Symbol Library Update Production Increase industrial workforce capacity  Field operators editing Piping and Instrumentation Diagrams (P&IDs) lacked native tools, bottlenecking the creation of clean digital work packages. Access more than 300 new standard industrial symbols organized cleanly by category for rapid discovery during work package creation. Key workspace updates introduce proportional resizing and persistent color memory to automatically save the last-used shape properties. Documentation   Industrial Canvas- Unified Project Settings for Canvas and Search  Production Accelerated time to value  When visual presentations drift across different tools, users lose confidence that they are viewing the same modeled reality across the platform. Maintain a single, consistent story for your core data model (CDM) content. Cards in Canvas now reflect the exact same properties as Search, matching your Project Settings configuration perfectly. Furthermore, file connections and annotations now display user-friendly names and descriptions instead of confusing system IDs. Documentation InField - Field-Ready Observations Mobile Lists Production Increase industrial workforce capacity Workers in the field could generate new observations on their mobile devices but had no way to track existing entries on the go, resulting in massive duplicate work reports. View, filter, and track nearby location-contextual observations directly on mobile devices. This rollout transitions core field workflows to a unified industrial data model. Documentation       Search UI - Precision Results Production Increase industrial workforce capacity Broad keyword searches historically evaluated entries using a loose "OR" script, flooding engineers with irrelevant records when querying precise tag strings. All core search surfaces across CDF front-ends (Search, Canvas, Charts, 3D, and InField) have migrated behind the scenes to an implicit AND operator. Searching strings like "21-PT-10-19" now demands that all tokens match, improving query precision. Documentation  🔍  23 tv Before:  After:        Search UI - Download Individual and List of Instances (CSV)  Production Accelerated time to value  Domain specialists lacked a simple, zero-code mechanism to extract curated, filtered data models or associated relationships out of the platform for localized evaluation.  Download data lists from Search screens directly into a localized CSV file layout. Extracted tables fully capture user-configured column orders, filtered constraints, sorting layouts, and explicit download timestamps. Documentation     Data Fusion - Integrations, Data Management, Knowledge Graph, and Data Processing & Orchestration Feature Problem (Challenge) Solution: What You Can Do Now Data Workflows - Native JSON Mapping Tasks Private Preview Accelerated time to value Normalizing data structures or reshaping payloads between individual steps in a workflow traditionally demanded heavy, custom-coded external scripts. Evaluate declarative expressions directly against JSON inputs inside the core Data Workflows canvas. Users can apply quick transformations, string filters, and custom aliases natively without leaving the workflow interface. Documentation   Function- Access Scoping on Execution Level Production Reduce the amount and cost of downtime  Execution privileges were historically hard-tied to broad data write permissions, forcing teams to grant overly permissive access roles just to run pre-approved tasks. Grant explicit execution-only privileges through a standalone access level control. This cleanly separates operational execution from data modification roles, protecting your underlying data pipelines.     Function Apps Integrated in Workflows (UI)  Private Preview Accelerated time to value Teams could not utilize versioned Function Apps as a first-class, selectable task type within the Workflows UI canvas, limiting onboarding automation velocity.  Execute Function Apps as native tasks directly within the drag-and-drop Workflows UI editor. Built on top of CDF Functions infrastructure, this packaging model exposes strongly typed parameters, specific path methods, and versioned compute configurations.  Hosted Extractors: Email Alerting API  Public Preview Reduce the amount and cost of downtime  Hosted extractors lacked an integrated notification layer to match on-premises setups, forcing data engineering teams to manually poll pipeline statuses to detect job failures.  Proactively configure automated notification sinks and alert subscriptions for specific extraction jobs via the Signals API. Teams automatically receive email notifications when a hosted REST extractor enters a failure or error state. Documentation  Integrations - API & UI  Private Preview Increase industrial workforce capacity  Traditional extraction pipelines required data engineers to manually access physical VMs to restart service tasks whenever remote configuration files were updated.  Manage remote connectors using a single, unified Integrations surface that automatically pulls configuration adjustments and pushes live statuses without manual restarts. Provides task-level visibility into run history, errors, and system health. Documentation  Cognite Connector - Simulation Run Load Balancer Public Preview Increase industrial workforce capacity  Simulator routines were hard-bound to specific connector pipelines, triggering massive backlog timeouts during peak industrial usage periods. Routes are dynamically queued and picked up by any healthy, available connector. Scale your simulation throughput horizontally by inserting new backend connectors without touching original routine layouts. Documentation Functions -  Support for Private Link on GCP Clusters  Production Reduce the amount and cost of downtime  Enterprise customers utilizing Private Link on Google Cloud Platform (GCP) clusters for strict network isolation were unable to route traffic from Cognite Functions across the hyperscaler backbone.  Configure Cognite Functions traffic routing to natively utilize Private Link architectures on GCP setups. This ensures that all automated computing traffic remains on protected network paths, eliminating public internet interception risks. Data Workflows- References in Workflows  Production Accelerated time to value  Defining dynamic variables inside the data workflows editor was prone to errors, forcing users to type out long, highly specific syntax links manually from memory.  Assemble complex dynamic parameter strings error-free using a visual Reference Picker directly inside the workflow canvas. The UI automatically populates eligible input and output references for Transformation, Function, and Agent tasks. Documentation  Python SDK - Asynchronous Concurrency Production Increase industrial workforce capacity Handling massive batch operations asynchronously inside local workspaces required developers to manually script complex concurrency patterns. Upgrade seamlessly to Python SDK version 8, unlocking fully integrated async client features out of the box. The update optimizes concurrent data downloads and delivers significantly faster file uploads on Windows machines. Documentation 3D - Industrial Street-View Spatial Inspections Production Increase industrial workforce capacity Verticals like discrete manufacturing and pharma often have detailed indoor environments lacking heavy 3D CAD files, making traditional 3D engines feel flat and unnatural.  Walk through plants using an advanced 360° image walkthrough system. The engine introduces depth cursoring over underlying point clouds, ground-level markers to prevent view blockage, and automatic snapping to the closest capture view. Documentation    3D - Forced CAD Node Rendering Private Preview Reduce the amount and cost of downtime  Dynamic viewport engines stripped down fine-grain geometric detailing during wide facility loads, removing structural visibility for field design reviews. Explicitly target up to 100,000 critical structural nodes to remain fully rendered at high detail across all viewport zoom distances. This prevents visual rendering errors during sensitive site construction evaluations.  Documentation   Atlas AI Enhancements Feature Problem (Challenge) Solution: What You Can Do Now Atlas AI: Data Analysis (Code Execution)  Public Preview Rapidly scaling use cases across your enterprise  Context caps limited assistant queries to small, 100-row batches, causing agents to drop analytical history threads mid-conversation. Atlas AI can write, test, and run internal scripts against massive datasets up to 5,000 objects simultaneously. The system remembers the conversational state across iterative questions to build unified statistical trends. Documentation  Atlas AI - Reusable Domain Skills Packages Production Accelerated time to value  Domain knowledge and specific root-cause analysis logic had to be configured by hand inside separate agent prompts, making configurations bloated and patterns un-reusable.  Package specific domain expertise into reusable, lifecycle-managed assets called Atlas Skills. Builders can rapid-assemble virtual assistants by composing existing skills instead of starting from scratch.  Documentation Atlas AI - Query Tool Production Accelerated time to value  Agents searching through extensive graph spaces required deep upfront schema maps, slowing responses and trapping agents on pre-defined paths. Deploy an intelligent query tool that completely auto-discovers graph data models, inspects properties, and reads available views independently without manual pre-configuration. Requires updating agent runtime to V3. Documentation    Atlas AI - Unified Agent Experience Across Apps  Production Increase industrial workforce capacity AI assistants behaved differently across individual apps, forcing users to constantly re-learn chat commands depending on their screen view. Execute configured AI agents defined through Atlas AI as native steps within your data processing pipelines. This allows data teams to feed the direct output of a transformation task into an agent prompt for advanced processing. Documentation  Agent as Task Type in Workflows  Private Preview Rapidly scaling use cases across your enterprise  Currently there is no provision to execute agents from workflow which limits users to operationalize and scale their use cases.  Execute configured AI agents defined through Atlas AI as native steps within your data processing pipelines. This allows data teams to feed the direct output of a transformation task into an agent prompt for advanced processing.   For a comprehensive list of all technical updates, improvements, and bug fixes, view the full Q2 Release Notes. 

We've just released v4.5.0 of the Cognite Data Source for Grafana, with a focus on making Core Data Model (CDM) the default path for new dashboards and bringing operational context (Activities) into your time series charts. This release builds on v4.4.0 and consolidates several capabilities that make Grafana a stronger fit for teams already working in CDM, or planning to migrate.What's new in v4.5.0Activities tab - a new query tab that lets you query CogniteActivity records directly in the editor, with filtering by Asset, Equipment, or TimeSeries. No GraphQL required. Activity annotations - overlay activities as shaded time regions directly on CogniteTimeSeries charts. Useful for visualising maintenance windows, planned downtime, or operational events alongside sensor data. Numeric time series auto-detection in the Data Models (GraphQL) tab - results with type "numeric" automatically fetch datapoints from CDF, removing a manual step in dashboard setup. Config editor overhaul - settings are now reorganised into Connection and Features tabs, CDM features are enabled by default for new datasources, and CDM/legacy toggles are now mutually exclusive (no more conflicting configs).Recap from v4.4.0 (still relevant if you haven't upgraded)CogniteTimeSeries tab - search and select time series directly from CDM with a single View dropdown, including unit conversion support. GraphQL variables - use Data Model GraphQL queries to drive Grafana template variables, enabling dynamic dashboard filtering and selection patterns.How to get startedIf you're on a new datasource, CDM features are on by default. If you're upgrading an existing datasource, head to your Cognite data source configuration, open the Features tab, and enable Core Data Model features.Existing dashboards built on legacy query tabs will continue to run.Documentation: https://docs.cognite.com/cdf/dashboards/guides/grafana/getting_startedWho this is forThis release is especially useful if you're:Migrating an existing project to CDM and need your Grafana dashboards to come along Building new dashboards and want to start from CDM rather than legacy asset-centric tabs Looking to overlay operational context (maintenance, downtime, events) on your sensor charts without leaving Grafana Using GraphQL and modeled context to drive dashboard variables and filteringWhat we'd like to hear from youWe're shipping CDM support iteratively and learning as we go. If you try v4.5.0, we'd value feedback on:How the Activities tab and annotations fit into your existing dashboards Anything that feels missing compared to your legacy workflows Friction in the new config editor or the CDM/legacy switch Use cases where GraphQL variables would unlock something you can't do today

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