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A use case we encounter at Cognite is writing data back to SAP. This can happen in several contexts where the main goal is to create or update data in SAP (ex: work orders, notifications etc.) based on the analysis of data stored in Cognite Data Fusion. The ability to write back to SAP allows to take decisions without going back and forth between applications, which can save a lot of time. It is also less error prone than manually filling fields in SAP based on what you read in Cognite Data Fusion.  This use case, which is all about automating processes, definitely fits Industry 4.0. In addition to that, SAP is one of the most used ERP systems in the industry. As a side note: we are talking today about SAP, but the same would be possible with other ERPs (as long as they have an API we can send requests to). For example, in a maintenance context: when analyzing data of your industrial machinery, you might notice that one of your machines needs maintenance. Instead of going to SAP, looki

Would like to see if there are examples of use cases that merge subsurface and surface data to solve a specific operations challenge that has been deploy via CDF?

Cognite Hub Community, as a member of the Cognite Partnerships Team, I am thrilled to share that Cognite has been awarded the Microsoft Partner of the Year in Energy & Resources! This is the third year in a row Microsoft has recognized Cognite as a global leader for its ability to deliver meaningful, scalable, and user-friendly industrial data solutions for our customers. We are looking forward to furthering this partnership, and the great work being done with all of our partners and customers, as we continue to make groundbreaking innovations with Generative AI and low-code digitalization solutions together.Full Cognite Press ReleaseMicrosoft Partner Blog Post 

We are proud to announce the publication of our latest research on the application of Topological Data Analysis (TDA) for Condition-Based Monitoring (CBM) of wind turbines. This new study will be presented next week at an international conference focused on equipment health and prognostics in Prague https://phm-europe.org/.Abstract: Our research investigates how TDA, a sophisticated branch of data analysis, can enhance the monitoring and maintenance of wind turbines. By analyzing complex datasets obtained from standard vibration sensors in turbine gearboxes, we identify patterns, anomalies, and trends that are often undetectable using traditional methods.Key Highlights:Data Source: gearbox vibration data, collected from a wind park in Norway, data contextualised in CDF. Methodology: Conversion of time series data into multi-dimensional point clouds through time-delay embedding Analysis Tools: Utilization of topological methods, including persistent homology Indicators: Key health indic

I am facing slow response issue while working with Jupyter Notebooks. Also, when I restart the kernel it takes almost 5-7 minutes to get it started.Is there any standard method to overcome this?   

The camera control commands seem to change over time and I couldn't identify why that happens in our app, though I was able to see a similar behavior in CDF 3D Scenes.The default control  when dragging with mouse's left button is to describe a rotation of the camera, but keeping the focus point in place (Gif 1). Sometimes, however, the same mouse movement causes the camera position to change describing an arc-like movement (Gif 2). While I think that “static” mode is using the “Orbit” control, the “arc” mode does not look like the other option, “Fly” control, because on the Fly control the camera position (shown at the top-right) does not change, but in the “arc” mode it does.When I click the "Home" button, a red focal dot in the center of the screen vanishes and it goes to the “arc” mode. When I click the "Fit View" button, the red dot reappears and camera commands are back to the “static” mode. There is nothing particularly wrong with either “static” or “arc”, but I'd like to keep th

Hello Cognite Community,We are thrilled to invite you to join our Early Adopter Program focused on exploring what CDF usage metrics can help you evaluate the value of Cognite Data Fusion (CDF) for your operations.Why Join?Uncover Key Metrics: Help us identify the most impactful usage metrics that demonstrate CDF's value for your company. Drive Improvement: Your feedback will guide us in refining how we measure and communicate the benefits of CDF. Exclusive Engagement: Be among the first to provide insights and shape the future of CDF usage analytics.How to Participate:Comment Below: Share your experiences and thoughts on which metrics could best capture CDF's value for your operations. Direct Message Us: Prefer a more private discussion? Send us a direct message to discuss your usage metrics in detail. Like This Post: If you find this relevant, please like this post to show your interest, and we’ll send you an invite to join the program.By participating, you’ll play a key role in enhan

I’m working on a prototype for a flexible data model to store time series data in a way that is easy to catalogue, query and filter. Using Pygen both to populate and use the model seems convenient.At its current iteration, I’ve only applied direct relations and (undocumented?) @reverseDirectRelations in the GraphQL schema. I expected to be able do something similar to client.windmill(windfarm="Hornsea 1").blades(limit=-1).sensor_positions(limit=-1).query()as found in the Pygen documentation, but it does not work (my client.windmill analouge has no methods corresponding to its relations). Do I have to use edges instead of relations to query easily and declaratively with Pygen?

This document outlines a concept that CDF has been under development for the last 2 years. As mentioned during the disclaimer above, please use this Cognite Hub group and share any feedback you have around flexible data modeling.This document is reflecting some very early thinking from the App Dev Journey team, and is a mental model that will likely change overtime with your feedback!What is a Data Model​A data model enables users to customize the shape, structure their expectation of data. It plays a crucial part in building solutions (like data science models, mobile and web apps). But it is also is the core of an ontology, knowledge graphs, or industry standard.There are some crucial reasons why data models are effective for the industrial space. Data modeling enables explicit language, flexible customization, governed iteration, and enhanced accessibility towards data. Let's dive further into each of these qualities of data modeling. Data Model is Explicit​A data model needs to be

Hi everyone! 👋We are thrilled to announce that the next major release of Cognite Data Fusion is just around the corner, launching on June 4th, 2024. This update is packed with exciting new features across our Industrial Tools and Data Operations capabilities.Head over to Product Updates for a sneak peek of what's coming! Tip: hit the subscribe button on Product Ideas, and you'll be notified instantly when our product leaders share product updates.We look forward to your feedback and appreciate your continued contributions!

Hey Data Workflows users!TL;DR If you’re using the workflow execution cancellation endpoint, with the python SDK or without, you will have to update your workflow execution cancellation calls before 29/05/2024. More information below.In our push towards making the Data Workflows API generally available in the June release, we aim to ensure a consistent and expected experience across the API. For this reason, we’ve decided to make a breaking change to the workflow execution cancellation endpoint. Endpoint changesIn summary, the cancellation endpoint now only allows the cancellation of a single execution at a time instead of allowing multiple executions to be cancelled in one call. The updated API documentation can be found here. The python SDK, starting from version 7.42.0, will now point to the new endpoint. An example call to the new endpoint using the Python SDK can be found here.TimelineThe new endpoint is already available for use. To assist with the transition, the old endpoint wi

Hi!We have decided to move the feature from “Integrate” to “Contextualize”. We believe the process of parsing documents is a contextualization process as this is a process that takes places when the data is in CDF. This change should be reflected by the end of the day.Regards,Redza RosliSoftware Engineer in AI in Data Onboarding

We're thrilled to share that CDF user interface is available in 10 new languages (Deutsch 🇩🇪, Español 🇪🇸, Français 🇫🇷, Italiano 🇮🇹, Nederlands🇳🇱, Português🇵🇹, Svenska 🇸🇪, 한국어 🇰🇷, 中文🇨🇳, 日本語 🇯🇵)  to make your experience even more accessible and user-friendly.We've expanded our language support to bring the power of our platform to a global audience. You can now select your preferred language from the upper right corner of your profile by clicking on <Manage account>:Click the avatar on the top rightWhen on the /profile/ page click on the <Language>  left button pick your preferred language from the list:Button reads <Langue> as user has chosen Français language from beforeAfter one selects the language, the page will reload and present the user with the interface in the chosen language.  Your Opinion Matters 🗣️We're committed to ensuring that our multilingual interface exceeds your expectations & we want to hear from you! To do that, we need to

Hey Data Workflows users!We’ve just released highly-requested feature for Data Workflows, namely, being able to retry failed or timed-out executions. This endpoint resumes a previously failed, timed out, or terminated workflow execution by retrying tasks that did not complete successfully. It aims to resume execution activity from the point(s) of failure.Behaviour of the retry operation:Targeted Task Retry: Only retries tasks that have stopped in a terminal state such as CANCELED, FAILED, FAILED_WITH_TERMINAL_ERROR, and TIMED_OUT. Optional tasks are not retried. Subworkflows and Dynamic Tasks: When a failure occurs within a subworkflow or as part of a dynamic task, only the individual nested tasks that failed are retried. The subworkflow or dynamic task container itself is not retried. Retry Limits: Tasks that have reached or exceeded their designated retry limits will not have their retry counts reset to zero. Instead, each retry request permits these tasks a single additional retry.T

When installing pygen in a CDF notebook you may be met with ValueError: Requested 'typing-extensions>=4.10.0; python_version < "3.13"', but typing-extensions==4.7.1 is already installedThis is currently a known bug, which we are working on solving. For now, the workaround is to manually uninstall `typing-extensions` using micropip. The code to do so is documented in the installation of pygen along with other known issues and solutions.

If you ever wondered what each of the different professions in Cognite do, Generative AI has the answer :)  

The recently released version `7.37.0` of the Python-SDK which pygen depends on broke pygen. This is fixed in `0.99.20`. For versions before 0.99.20 you will be met with `ImportError: cannot import name 'ListablePropertyType' ...` when you try to generate an SDK.

@Ragnhild Byrkjeland, @perolssoen, and @Christopher Tannum After using the new scene functionality in CDF, there are several things that I like about the application but also am experiencing some challenges that I believe need to be addressed before we can get end users to leverage the application:the asset layer seems to take a while to load on the backend. Not sure if there’s a way to set this up to load faster? once assets are loaded, the pop-up window that shows the contextualized information is very glitchy - rendering it almost un-useable In general it doesn’t seem like the asset overlay is very reactive to a click of the mouse, but more of a hover over which seems less intuitive for end users It would be great to have a speed slider - like the one that was present in remote - to fine-tune the navigation. This will be particularly helpful when we bring in our subsea models into the scene It would be great if we could set certain attributes to default to be toggled “off” when firs

Hello Community Members,I am delighted to announce that from today, the Cognite Python SDK now has support for Time Series Data Quality Status codes.Using the SDK, you may retrieve data points including their status codes, and filter on status codes if desired. You may also insert data points with status codes.For a full description of all the methods and functionality, please refer to the Python SDK Documentation and our Developer DocumentationWhilst this functionality remains in Beta maturity, we are targeting the upcoming June release for General Availability. Cognite relies on customer feedback as key part of its product maturation process, and customers are warmly encouraged to begin implementation of support for data quality status codes and to provide the valuable feedback necessary to allow us to make this feature Generally Available.Thank you for your continued support! Glen

December 15th 2022 - SLB Offices @ Pune, IndiaOn the week of Dec 12th to Dec 16th, Maureen Byrne (Software Engineer), Maria Fonseca (Technical Support Engineer) and me (Data Engineer) travelled to Pune, India, to deliver the first Cognite Data Fusion Bootcamp for eighteen participants from SLB and Infosys. This training delivery marks a milestone for customer and partners of Cognite: this is the first training where Cognite instructors guide them through an end-to-end deployment of Cognite Data Fusion, including data integrations and solution-building.The bootcamp is an intensive 5-day training program that focuses on a complete deployment, bringing the different steps together with DevOps best practices from CDF bootstrapping to solution deployment, monitoring, and operations. Congratulations to the participants of the first bootcamp for gaining their Cognite Data Fusion Bootcamp Certificate!The three of us were very impressed by how fast the participants adopted DataOps tools and pra

1. Field ‘name’ is required during creating data model in CDF.  But it is not required if create through SDK:data_models = [DataModelApply(space="mySpace",external_id="myDataModel",version="v1")] c.data_modeling.data_models.apply(data_models) 2.  For data models have values in ‘name’ and ‘description’ fields these fields would be cleared up if these parameters are not specified in DataModelApply. These code will clear name and description values (if ‘myDataModel’ had them):data_models = [DataModelApply(space="mySpace",external_id="myDataModel",version="v1")] c.data_modeling.data_models.apply(data_models)These code won’t clear name and description values (if ‘myDataModel’ had them):data_models = [DataModelApply(space="mySpace",external_id="myDataModel",version="v1", name=”myDataModel”, description=”My Description”)]c.data_modeling.data_models.apply(data_models)To fix make changes in cognite-sdk:Set ‘name’ as required for DataModelApply. Fix clearing values from name and description in D

Hi all!The Cognite Data Modelling product team are looking to make an improvement to how properties from views selected in data modelling graph queries are included in the result data set.The documented and desired behaviour is that only view properties that are explicitly selected in the query will be included in the data output. However, there is a known issue with property selection where in some cases a query would return more properties per view than initially selected.The issue manifests such that properties selected on one view will also be included in the result properties of another view in the query. This behaviour only appears in specific cases: the selection should be in the same table expression, the selection should use two or more views that share some container properties, and one or more shared properties (that is, properties mapped to the same container property) should be selected in one of the views but not in the other.As such, the issue does not cause a data leak

We are pleased to announce that cognite-pygen is available in beta. What does Pygen enable? It enables you to write python-code using the concepts in your data model. The code you can write enables you to query CDF and work with the data in your data model. How? Pygen generates a Python-SDK tailored for your data model. Want to know more? https://cognite-pygen.readthedocs-hosted.com/en/latest/what_is_pygen.htmlDisclaimer! In the beta period, we ask you to be careful when using the pygen generated SDK on a live production project.We would love to get feedback on how to improve the usability and robustness of the pygen. Toward General Availability, we will focus on robustifying the generation process.

Hi everyone,We have great news for you. Our team was working hard to make this happen for this release. We have created a smooth integration and transition between Industrial Canvas and Charts.From now on you can bring your charts to your canvases. You can do it in 2 ways: While in Industrial Canvas, click 'Add data' to open the resource selector and select 'Charts' tab as the fifth option from the tabs. Here you can filter and search your charts, select the ones you want in your canvas like any other data type and shazam! your charts appear in your canvas. While navigating through Charts, select your favorite chart and click the 'Actions' button, then locate Industrial Canvas beneath 'Open In' section. Follow the steps by selecting one of your existing canvases or initiate a new one to add your chart to. With this feature you can fully interact with your Charts from the Industrial Canvas:You can update axis ranges Slide left or right to update time window Use shift to select a speci

Hello everyone! I have several binary timeseries that are displayed correctly when I ask for a short period of time: However, if I ask for a longer timespan, like a year, Charts gives me the aggregates, which I believe would be fine on some cases, but in this particular binary case, it does not make any sense. The final user cannot use this information to perform any analysis; any calculations, thresholds, or just “taking a look to see the trend” will not give an accurate result. Is it possible to choose to disable the use of aggregates for cases like this? Thank you in advance.