Product User Community

Hi. How can I get aggregated values from synthetic timeseries fields on templates? Intuitively, I would expect the “datapointsWithGranularity” operator in the graphQL query to give me this, but I get the same results as for the “datapoints” operator. Should the aggregation type and granularity be part of the definition of the synthetic timeseries field in the template? If so, how? 

Hi! I’m curious what is the best practice to do this basic thing and visualize on Grafana ( ideally without code): With a flow transmitter, summarize the total volume per day or week.  Correct the above against a threshold or a running signal. Ie. I now when the flow should be 0Here’s how I do nr 1, it feels a bit like a hack. Nr2 I am solving with Functions in CDF, however it would be nice if there’s a quick way of doing it without code,  The query looks like this: 24*ts{externalId="arba:one:s=0:PLC_03!Vc_S07_200_FT01_PV", alignment=1653386400000}-Should the alignment be at the middle of the day, 12:00, or at midnight 00:00? When we get back to wintertime, it will be off by one hour..  

Hi, When parsing a large production model some high level concepts we want to filter on are structured as assets. I.e. a fiscal region for power in the Statnett case.  Our model is now hitting limitations of subtree queries. With more than 100k assets per region for some regions. What is your thinking around how to handle such cases in a model? To spike the conversation we’ve considered moving high-concept parts of the tree to labels, or making more of the DB type of operations locally. However, the former demands a pipeline for moving concepts suited for a tree  to a Label “just because”. The latter requires quite a lot of iron present on the local instance processing the query. An instance of the SDK quering we do can be seen in the power-SDK in github.

Hello, Today’s topic is about Cognite functions: Cognite functions enable the user to run and schedule arbitrary code to clean, process and do calculations on your data. They are easy to use and have seamless integration with Cognite Data Fusion (CDF), allowing the Cognite Function to interact with CDF directly. The service automatically scales the computing infrastructure to handle a fluctuating number of function calls.   They can be triggered on schedule, on click in the UI, and even with the experimental Python SDK, which makes them adaptable to a lot of use cases. We also have a full CI/CD flow for them, which allows to deploy and maintain them really easily. Some examples of use cases that can be done with Cognite functions:Change point detection, thanks to machine learning models, that run on schedule Anonymisation of videos, with computer vision models @Gaetan Helness is quite familiar with Cognite Functions. If you have any questions, don't hesitate to reach out to us in t

After confirmation from @Geir Engdahl, is there any plans for developing models where application developers can attach data to users, like preferences or even work orders?

The best way to play around with Flexible Data Modeling in CDF is through Templates.  Please be aware of the following before getting started! Currently, the App Dev Journey team at Cognite is working on a new Schema Service that will replace Templates towards the end of 2022. For more details on the differences between Schema Service and Templates, go here.There will need to be a migration between Templates and Schema Service in the second half of 2022. The migration will be simple for the data modeling features and querying features as they will be both be using a GraphQL interface. However, moving the data could be a more difficult migration.Hence we highly recommend continuing with Templates for POCs and early experimentation. You can also productionalize with Templates, but please reach out further clarification around quota and SLAs on this Cognite Hub group. Click to see the official documentation on Getting Started with Templates

 Hello everyone! Nicklas from InField here 😊 It’s been about a month since we were on the grounds with the crew at Celanese, and the InField team has been hard at work analyzing, prioritizing and planning work going forward based on all the insights gained from the visit. The visit has been invaluable and we want to keep the momentum going full steam!While we’re working on delivering new functionality and fixing bugs, I just wanted to reach out and say a huge thank you to the awesome and helpful team at Acid South, that makes this possible – we learned so much from you and it was a blast to work together on the grounds and I am very hyped about our collaboration going forward! 😊 See y’all again soon, Nicklas on behalf of InField   Cc. @Crystal Connor Richards @Kylie R @ibrahim.alsyed 

Introduction In this post we continue to share some of our internal material aimed towards solution builders, such as data scientists, who want to develop their ability to develop high quality solutions by creating more reliable, maintainable and readable code. This is the second part of 2.MotivationHigh code quality is easy to recognize but can be very hard to describe concretely. The assumed benefits are easier maintainability, modifiability, and more. While code style, like formatting, can be a matter of different taste, most parties agree that other code practices that fall under the umbrella term “anti-patterns” should be avoided. To stop endless formatting discussions and the like, having (and adhering to the industry) standard makes reading and understanding code across repositories easier.What this guide is notThis guide will not tackle the topic of  “how to set up a Python project” the right way . Please let us know in the comments if you would like us to share more of our ex

The Cognite OPC UA extractor reads data from OPC UA servers and writes data to Cognite RAW, assets, time series, events, and relationships, depending on how it is configured.  Now, we’ve made the GitHub repository public. The extractor serves as a comprehensive example for using the .NET extractor utils, or as a baseline for developing custom applications working with OPC UA and CDF. The repository also contains the Cognite OPC UA Extractor test server, which comes with a CLI and can be used to test OPC UA client applications. Use this test server to simulate common server issues and generate data points, events, and even new nodes and references. With the open-source OPC UA extractor code, you’ll get insight into how Cognite works with OPC UA which will hopefully make it easier to develop your own extractors.

I am doing some ETL jobs in Azure Databricks and have successfully managed to use Cognite’s Spark Data Source to read and write time-series, datapoints etc from and to CDF. I know that databricks itself is a cloud platform. However, it is interesting for me to be able to run some or all of the jobs locally during development phase. I wonder if it is still possible to somehow test-run Spark jobs locally? The configuration does not seem to be trivial.  I played a little bit with PySpark, and I was able to run it on my Mac but I could not create a connection to “cognite.spark.v1” to read or write data. Do you know if it is possible to perform such operation? If not, what would you suggest?  

Hello! I wanted to share a good infographic on how Industrial DataOps enables data product strategies. What is Data Fabric? What is Data Operations? What are Data Products?I was just at a Utility Analytics conference the other week in the US where this was a big topic of discussion- how to embed domain expertise in governed data products through joint ownership between the data product managers and the data product consumers. Several utilities are already formalizing these teams internally into a mesh-based architecture.Power Market Operations is a great example of how a data product strategy can enable smarter pre-trade analytics. Who is already executing a data product strategy?

In this short video you’ll see how you can submit feature requests, so we can build better products together. Share the feature requests you’d like to see in our products, applications, and solutions, and support ideas submitted by others by upvoting them. Watch the video and head over to Product Ideas to get started:)   

Many organizations need to integrate and discover their IT (Information Technology) and OT (Operational Technology) data to explore and resolve operational issues.Cognite Data Fusion (CDF) streams your data into the CDF data model where the data is normalized and enriched by adding connections between data resources of different types and stored in a graph index in the cloud. With your data in the cloud, you can use the CDF services and tools to build solutions and applications to meet your business needs.This course gives you a high-level overview of the CDF architecture and data model and the main steps to fast-track your CDF implementation.Take the course and let us know if you have questions or thoughts!  

Proud to be a Cogniter!  Cognite is a signatory of the World Economic Forum’s Cyber Resilience Pledge.  We are among 18 global oil and gas ecosystem companies championing a unified approach to mitigating growing cyber risks.  This kind of commitment and collaboration will make mission critical industries safer from cyber threats.https://www.weforum.org/press/2022/05/global-ceos-commit-to-collective-action-on-cyber-resilience-ffa0ba5f56/ (edited)  World Economic ForumGlobal CEOs Commit to Collective Action on Cyber ResilienceSahil Raina, Public Engagement, World Economic Forum, +41-795596273, sahil.raina@weforum.org

Through 3D scanning technology, Cognite obtains accurate point clouds of large-scale industrial plants with a high level of detail. We're currently using these point clouds for visualization and measurement purposes to provide value to our customers.However, we're still missing a fundamental understanding of the objects located in a three dimensional scene. This understanding starts with semantic segmentation, the process of assigning each a class to each point in a point cloud. In the D-MVP and 3D team, we are currently working hard on solving this with state-of-the-art Deep Learning technology. Our goal is to automatically connect assets in a point cloud directly to Cognite Data Fusion, enabling rapid development of fully contextualized as-built digital twins.In the attached video you'll see what we are aiming to achieve: A point cloud from an industrial site, where all the points are split into different classes. Let me know what you think eller I'd love to hear your thoughts  

Make sure not to miss Cognite Application Developer Session at 4PM CEST/10AM EST May 10th! From simple multiple data source dashboards to cutting-edge hybrid AI solutions, join us for this one hour and learn how Cognite Data Fusion makes industrial application development easier.Watch recording:     

Hi community!I got a query from our customer that they’d like to control the float precision in Chart.I found “Round” function in “Operators” and confirmed applying that indsl to the timeseries data does work.But I’d like to ask if there is any other, easier, way because creating function per every timeseries data is a bit boring.Thanks!

Hi,Is possible to make a sql query to an external SQL server from a Cognite Function? 

Can the default option be that the week start on a Monday, or maybe give the user the ability to say what day is the first day of the week? This probably varies in different parts of the world. Where I work and live and do things; Monday is always the first day of the week :). 

Only a fool believes in different outcomes by doing the same as before.The modern data stack - a more nuanced view of data platforms - is quickly gaining ground, focusing on making data truly useful, not just storing it in the cloud. Modern data stack based platforms are the only means of moving beyond costly, monolithic, closed business applications that maintain business and data silos, preventing real digital transformation.Platforms themselves are equally no longer monolithic products, but equally composed of interoperable platforms services from multiple open platforms. Open platforms with composable business applications are the new technology imperative. Old technology stacks, and “lets only focus on the discrete business solution at hand” approaches don’t work for the 2020s enterprise.OT, IT, and business must work together to prevent tug of wars - and instead - collaborate to secure competitiveness in the new platforms era.Read the full article:  Did this article cause some re

Only a fool believes in different outcomes by doing the same as before.Executive summary: The modern data stack - a more nuanced view of data platforms - is quickly gaining ground, focusing on making data truly useful, not just storing it in the cloud Modern data stack based platforms (simply referred to as 'platforms' from hereon) are the only means of moving beyond costly, monolithic, closed business applications that maintain business and data silos, preventing real digital transformation Platforms themselves are equally no longer monolithic products, but equally composed of interoperable platforms services from multiple open platforms Open platforms with composable business applications are the new technology imperative. Old technology stacks, and “lets only focus on the discrete business solution at hand” approaches don’t work for the 2020s enterprise OT, IT, and business must work together to prevent tug of wars - and instead - collaborate to secure competitiveness in the

Hi, Both a colleague of mine and I keep getting the following message for the first 10-30 seconds when we try to view the list of public charts :  “Could not load chartsFirebasError: {code=permission-denied]: Missing or insufficient permissions.”After some time, the list of public charts appear. This time interval can vary between a few seconds and up to over a minute. Other colleagues of us are not experiencing this issue.  It seems strange that the error only occurs for a short time, but it reappears every time we try to open public charts. We have tried deleting all cookies in the web browser without this affecting the issue. Do you have any clue what could cause this? 

Two of the important goals with a Data Mesh Architecture would be:Ensure that the users of data can easily find and trust the data – through carefully consider distribution of ownership and governance throughout the company/domains Ensure that data is “interoperable” across domains – to understand the meaning of data from one domain in the context of another domain.Question: What are the challenges and advantages of a Data Mesh Architecture to achieve these goals? E.g.:Domain expert knowledge and capacity within the business area/domain vs centralized knowledge Ability to make data interoperable across domains vs all-inclusive master data management Make it easier for end users to make use of the data Change management – move towards a distributed data ownership model where ownership is understood and prioritized day-to-day.Other thoughts? 👀

H! Here’s the latest and a quick read on our compliance: Cognite’s Management System (QMS and ISMS) is ISO 9001 and ISO 27001 certified Cognite and CDF operation and data processing are in compliance with GDPR Cognite has obtained SOC 2® type II certificationRead more 

Hi! Here's an update and some quick reads on our Compliance:Cognite’s Management System (QMS and ISMS) is ISO 9001 and ISO 27001 certified Cognite and CDF operation and data processing are in compliance with GDPR Cognite has obtained SOC 2® type II certificationRead more