Community Discussions

Yesterday, we hosted our first Digital Services and Sales Forum for our manufacturing customers who are building new applications on top of CDF.  The goal of this forum is to create a space for our customers to learn from each other on key topics relevant to building and launching digital services and software.  The focus for this forum was pricing where we heard from @mortenhuse from Properate and Rune Larsen of Accenture, but in the future we’ll cover other areas such as recruiting, product positioning, onboarding, customer success, etc. Thanks to all who attended!  The presentations we shared are attached. If you weren’t able to attend, but would like to in the future, please contact your Customer Success Director.  Hope to see you at the next event!All attendees of our first Manufacturing Digital Services and Sales ForumMorten Huse and Morten Hveding Juel presenting their Properate solution, built on CDF 

If you’re at CERAWeek this week, we hope you’d like to stop by and say hello us! Look out for some of our leaders @John Markus Lervik, @Paula Doyle, and @Francois Laborie in the CERAWeek Executive Conference and Agora sessions, and come find us at the @Cognite and @Aker Horizons house at Innovation Agora.  

Are Synthetic Time Series able to reference other Synthetic Time Series in their formulas specified?   This is the problem I am trying to solve:Synthetic Time Series is limited to referencing 100 time series Sometimes a unit/facility/system/functional location/site may have more than 100 direct child assets I need to calculate a “Synthetic Time Series” that is the daily average of a time series found on all child assets My thought is that, for 300 assets, I would have:STS A: Sum for assets 1-100 STS B: Sum for assets 101-200 STS C: Sum for assets 201-300 STS D: Sum of STS A, B, and C divided by 300Is there a better way to accomplish something like this in CDF?

Hey! In accordance with how you’re thinking about flexible-datamodels. Has Cognite done any exploration around flexible time-series modelling? Akin to what you’re doing with charts, a fleet of sensors might be viewed as having the same transformational needs as an Asset does.  At Statett, we have “views” of  data (typically some linear combination of time-series). We persist these with synthetic time-series as an intermediary, then upload to a new TimeSeries. Unfortunately, the data is not immune to updates or backfills. So these computed TimeSeries either have to have a pretty severe lag, or we have to re-compute at an alarming rate, wasting compute. These computed time-series are further used in computations that we would like persisted. Do you envision expanding Charts to cover this functionality? I.e. having the configuration as code, with Stream logic for re-computes etc.? Or, do you have no plans for Managed Data Transformations?  Thanks for your response :) 

I tried logging in with these commands: platypus login --cluster greenfield --tenant "schema-test"which didn’t work as I thought the tenant was a CDF tenant and not an Azure AD tenant. Then I tried:platypus login --cluster greenfield --tenant "c08c2afd-4823-482b-9113-ed2746fe6026"Which gave me a “Login successful” webpage, but the CLI said: “failed to authenticate against CDF project: platypus”. Then Soumesh pointed out that platypus is the default project, and I needed to type: platypus login --cluster greenfield --tenant "c08c2afd-4823-482b-9113-ed2746fe6026" schema-testWhich worked I think having default values made it harder to use as the CLI didn’t guide me on missing project name / CDF cluster etc, and maybe a better name than tenant as it can be interpreted as CDF tenant/project.

Feel free to join this session on 9 March at 7-8pm CET by Inna Zrajaeva about ocean data in situated experiences. Get ideas about how complicated ideas and concepts can be explained in a direct environment and situation. Inna is writing her master thesis together with UiT Norges arktiske universitet and HUB Ocean 🐳https://www.eventbrite.com.au/e/ocean-data-and-situated-experiences-tickets-289415408277#oceandataplatform

We are happy to announce a new CLI tool for managing your Template Data Models. This should help you in developing an application, and we hope to also make this tool useful for data science workflows as well.Here’s the link to the public documentation for the CLIhttps://www.npmjs.com/package/@cognite/cdf-cliInstallation instructionsTo install, make sure you have Node (at least v14) installedand then runnpm install --global @cognite/cdf-clioryarn global add @cognite/cdf-cli Key features:This CLI is meant to be an additional tooling that allows you to do everything the UI can.1. Logging in/out through the CLI for users and machines (Users for local workflow, Machines for CI/CD)Logging in as a user - Loom demo, docs Logging in as a machine - Loom demo, docs2. Managing Templates via CLI  - docsCreating, deleting and listing templates3. Development Workflow via CLI - docsInitializing workspace Code generation Please comment and add any request or comments around features below or as a new t

While many manufacturers have successfully fostered lighthouse sites, very few have been able to replicate this success across their other production sites.The purpose of these lighthouse sites are to act as the guiding model for other production sites, providing a wave of innovation to address use cases that will increase productivity, improve quality, reduce energy and water consumption, and much more. The problem is that lighthouse facilities on average only account for 10-15% of production volume, and manufacturers are struggling to replicate this success to the significantly outstanding 85-90% of their production.Read in our latest blog why aren’t lighthouses scaling. And how to ensure that your lighthouse success can be carried forward to other production sites. Did this article cause some reflections? We’d love to hear from you and your experiences.

Hi there,I was wondering how one would go about representing a CDF Client credential as a connection string.The main idea is to have a single string representing the credentials needed to connect to CDF. The connection string itself should be unwrapped by whatever client it is passed to (Python SDK, JS SDK, etc), and should of course not be sent as part of the request to the API.For tenants using API-keys, something like this may make sense:cdf://CLIENT_NAME:API_KEY@api.cognitedata.com/PROJECT_NAMEHowever, using tokens instead of api-keys is a bit trickier:cdf://TOKEN_USERNAME:TOKEN_SECRET@api.cognitedata.com/PROJECT_NAME?token_url=TOKEN_URL&token_scopes=TOKEN_SCOPESNaturally, it is possible to but anything in the URL parameters, but generalizing the username, password and path-properties would be great.Some challenges that come to mind:How to differentiate between different authentication methods (i.e. api-keys and tokens Is it possible the keep a consistent scheme regardless of a

Hi Community,We are excited to announce that we have made some changes to our documentation portal! We have migrated to a new content management system. This will allow for improved search and internationalization.This is the first step to revamping the documentation portal to allow for quick access to relevant content as well as a introducing a dedicated developer portal with guides and an overview of all available libraries.As part of this, you might see some small changes to where information is located. If you have any feedback or feature requests, please let us know. BROmar

Hello, Thank you for all the great docs and tutorials you’ve put together on CDF.  I would like to get an expert opinion on the best way to accomplish a specific use case I have.We have a daily time series that we calculate for predicted availability of an asset, for a time range that is usually between 5 and 40 years (usually between 1,826 and 14,610 data points per time series).  We have a view were an engineer will want to see this time series graphed, but also will want to see a single KPI value that represents the average of the time series from today to end of the range.  For all of our assets, this KPI could be ready several times a day as people view this KPI.  When we were storing this data in Azure SQL, to speed it up, we pre-calculated and stored this KPI average and stored a new time series (throwing away some of the time series points and linear interpolating).  That way, reading a single data point representing the average of from today to the end of the range and display

Hello Charts Early Adopters,On December 7th, 2021, we released a new and improved version of the no-code calculation builder (see image below). You can read more in this release post or watch the webinar recording. Ever since this release, all new calculations have been created using this new implementation of the no-code calculation builder.The “new” no-code calculation builder.We will release new features on Monday, February 28th and, as a result, we will no longer be supporting the old version of the no-code calculation builder. Therefore, if you have a calculation in any chart that was created using the “old” calculation builder (see image below), you must recreate it using the new version to avoid losing your work. The “old” no-code calculation builder; calculations using this old calculation builder will no longer be supported from Monday, February 28.If you have not used Charts before December 7th, 2021, or, you have no need for the calculations you created using the old no-code

During the last 2 years, Cognite has taken a very iterative approach to enable flexible data modeling. The key highlight towards this effort can be outlined through 3 key initiatives - Templates, Schema Service and ITG. In this article we will hope to outline the key difference between the 3. Templates Templates is a data modeling capability built on the concepts of GraphQL. Through building a GraphQL data model, you can define the data model that is intended for your solution. Additionally, you can load data into the data model via referencing existing CDF resources or loading in values directly for primitives like String, Boolean, and Float. Lastly, you get the ability to query the data via a GraphQL endpoint, supporting pagination and complex logical filters (with ANDs and ORs).Here is a list of features doable via TemplatesModeling data modelPrimitives (String, Boolean etc.) Relationships between them (1-1, 1-N, N-M). References to Asset, TS, Sequence, File VersioningQueryingBasic

Thanks to those of you who joined us live for yesterday’s New Features & Feedback webinar series.You will find the session recording, below, and you can view the slides that we presented by clicking here.As always, if you have any comments, questions, or feedback on these new features or anything else Charts-related, please do post about it here on Cognite Hub.

Hi!Don’t miss the Cognite Data Fusion February release note just published in the Product Updates section here on Hub! We also have a Product Release Spotlight Webinar lined up for you where our product experts will go through the main features in the release. Sign up and find previous Product Release Spotlight Webinar recordings here.Let us know if you have comments or questions, small or big, we’d love to hear your thoughts 

Hi Community, I just wanted to let you know about our new Demo Hub that recently launched.  You should take a look at the great video content here, which walks you through a number of existing use cases and capabilities within CDF. Check this out and let us know what you think.  

Hi Community! We’ve got a new Demo Hub for you with product stories and demos, you might want to check it out! Let us know your thoughts 

This is a friendly reminder that we will be hosting our New Features & Feedback webinar today from 15:00-16:00 CET  → You can join the Microsoft Teams call by clicking here ←If you have any trouble joining, please leave a comment in this thread or send an email to eric.stein@cognite.comWe’ll be giving a live demonstration of the newest features that were released this week.We’ll also have an open discussion about use cases that you – our early adopters – want to solve or have already solved with Charts.This session will also be recorded and the video will be shared here in the Early Adopter Group afterwards.Looking forward to seeing you soon on the call!Eric & the Charts team

Hi! If one or more of the source-timeseries are missing data-points they will also be missing in the synthetic timeseries output..We would like an option to default to zero for missing values so that the API will use the available values to calculate a partial result. Example:TS1: [1,2,3]TS2: [3, undefined, 4] Output now if calculating a sum of TS1 + TS2:[4, undefined, 7] What we would like:[4, 2, 7]  

The team and I are happy to announce that a new version of Charts (charts.cogniteapp.com) has now been released! You can read the specific details about this release below.Please try these new features for yourself and share your feedback with the community here in the Charts EA group.Remember to join our webinar on Wednesday, January 26th from 15:00-16:00 CET, where we'll be walking through the details of this release, discussing real use cases you’re facing, and giving you a sneak peak of some upcoming functionalities. For details and joining info, please visit this post.  Release Details Search for time series related to a specific equipment tag Once you’ve found the equipment tag you’re looking for, click on the equipment tag name to perform a sub-search for time series related to that specific equipment.   Better usability for y-axis zooming and scrolling Adjust the y-axis by simply clicking + dragging or by scrolling while hovering your cursor on the y-axis. No more “Adjust y

In heavy asset industries 3D scans are used for applications such as quality inspection, project progress reporting, project planning, evaluating the state of a site ‘as built’ or even evaluating the vegetation around it. The scans span from simple scans performed with a drone in 15 minutes to complex and detailed scans of a plant, taking several weeks to complete. In this 3 part series about 3D scanning with robots we will take you through how to capture, transform, analyze and visualize 3D scan data in order to improve daily operations. What we’d like to know before going is if someone has any questions or topics that you want to learn more about? All questions are greatly appreciated and can span from how do you set up the robot to capture the data to what robot and data management pipeline would you use for use case X? Point cloud from drone scan with contextualized segmentation 

Hello Charts Early Adopter Community and happy 2022!We’re going to kick off the new year with our second New Features & Feedback invited to a webinar where we in the product team will be sharing the latest and greatest features in Charts.When: Wednesday January 26th from 15:00-16:00 CETWhere: Virtually (Microsoft Teams)→ Click here to join the call; we’ll be re-posting this link on January 26th before the webinar. The features we’ll highlight in this session will include…An in-depth walkthrough of the latest features and functionalities, including... New list component design Better usability for y-axis zooming and scrolling  New InDSL functions for creating lines and synthetic signals + more A sneak peak of the features we're working on nextLike last time, I’ll be giving an in-depth walkthrough of these features and how to use them. We'll also have an open discussion between you, our early adopters, and the members of the Charts team about ideas for future improvements – we’l

You need to provide “source_oidc_credentials” and “destination_oidc_credentials” run your transformation via API/SDKBelow is the code sample how to use OIDC credentials in your transformationstransformations = [ Transformation( external_id="<external_id>", name="<name>", query="<Query>", destination=TransformationDestination.raw("<Database>", "<Table>"), conflict_mode="<upsert>", source_oidc_credentials=OidcCredentials( client_id="<CLIENT_ID>", client_secret="<CLIENT_SECRET>", scopes=f"<scopes>", token_uri=<"TOKEN_URL">, cdf_project_name=<"COGNITE_PROJECT"> ), destination_oidc_credentials=OidcCredentials( client_id="<CLIENT_ID>", client_secret="<CLIENT_SECRET>", scopes=f"<scopes>", token_uri=<"TOKEN_URL">, cdf_project_name=&

A technical introduction to parallel write-optimized and read-optimized Cognite Data Fusion TSDB.  Read the full article here: