Community Discussions

Hello Everyone 😊, I'm now working on an endeavour that will integrate our organization's multiple old systems with Cognite Data Fusion (CDF). Although there are numerous advantages to CDF's current architecture, there are certain obstacles we must overcome in order to guarantee smooth data transfer between CDF & our more antiquated systems—especially those that weren't created with contemporary data platform in mind.I would appreciate your thoughts on the following specific queries and worries:Data Connectivity: How can CDF and older technologies that use antiquated communication formats or protocols create and sustain reliable connections with one other?🤔 Have anyone of you run across problems with particular kinds of systems?🤔 If so, tell us about it.Data Consistency and Quality: When importing data into CDF from older systems, how can you maintain and guarantee data consistency?🤔 Exist any particular Cognite ecosystem tools or procedures that support preserving data accuracy

Hi There,We would like to confirm if the indexing mechanism in the CDF operates in the same manner as it does in a relational database. Specifically, we need to understand the trade-offs of using indexes carefully in CDF. In relational databases, indexes occupy space on disk and memory when in use, which can be problematic if space or memory is limited. Additionally, maintaining indexes during data insertions, updates, or deletions can slow down these operations and lock tables (or parts of tables), potentially affecting query performance.Given these considerations, do we need to manage indexes in CDF with the same level of caution as in relational databases?Disadvantages of having an index in a relational database:Space: Requires additional disk/memory space. Write speed: Slows down INSERT, UPDATE, and DELETE operations. 

Pygen will now create fields for reverse direct relations in the generated data classes. In addition, with the generated `.list` method you can retrieve the nodes on the other side of the reverse direct relations by calling it with the parameter `retrieve_connections=”full”`. See the documentation for more details an examples https://cognite-pygen.readthedocs-hosted.com/en/latest/usage/listing_filtering_retrieving.html 

Hi,We use CDF workflow to process the data present in data model. Based on the inputs, we trigger multiple workflow instances to take the advantage of scalability. We observed that, the workflow execution time is increasing after we trigger more workflow instances.  Here is the simple diagram to show how we use workflow:   For example:If we run 1 workflow instance to process 20 wellbores (assume some processing logic divided between F1 to F4) with 20 concurrent tasks,  the execution time of that workflow instance is 5 mins.  Now if we want to process more wellbores say 40, we trigger 2 workflow instances , 1st workflow instance to process 20 wellbores and 2nd workflow instance to process other 20 wellbores and we expect the execution time for all workflows to be approximately same but the total time is increasing if we compare with only 1 workflow instance with 20 tasks. Can you please help me to understand if anything is missing? 

Hello, all!This is my first post after just joining this discussion, so please forgive me and provide kind assistance if I have posted to the wrong subsection! I am new here but a real enthusiast and loving this community so far. I have a background in teaching coding and in education and feel I could help with documentation, at least for starters.As a new member in this forum and wish to share and gain some knowledge. I am looking forward to create my own discussion to resolve my query and gain some knowledge though I have taken part in various discussion which is definitely helped me a lot.Also in what category should be taken depends on what factors?🤔Thank you 😊 in advance.

How can I see the default canvas option in my portal?I have seen this visible option in other users.  

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?

The only thing holding back innovation is the ability to integrate data from various sources into a single destination that processes that data agnostically.  The untapped and currently useless data around me is astonishing.  We could be measuring the workloads of LED's in TVs, vibration in Hard drives, the wattage of plugs, the efficiency of lights, on and on. The sensors can be made or integrated without much issue, the problem lies in getting that data to the correct processing application that then handles that data in a manner that produces meaningful insights.  We are on the cusp here of finding an agnostic methodology to handling data and at that point it will be a race to ease -of -use, visual beauty and price.

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