Community Discussions

Found a bug? Have a question about how something works? Want something new with Flexible Data Modelling? We want to hear about it!You can choose to either create a dedicated post (topic) in the Flexible Data Modelling group by clicking the Create topic button OR simply post a reply below in this thread.Be sure to tag with “bugs” or “feature request” Features requests You can choose to either create a dedicated post (topic) in the Charts group by clicking the Create topic button OR simply post a reply below in this thread. Remember to say whether your feature request is Nice to have, Important, or Critical to you and why. Screenshots, sketches, or explanatory videos are also encouraged. We will follow up and share progress on features periodically as well! Bugs Remember to include a screenshot or video to help the product team best understand what exactly you’re talking about or referring to.

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

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 

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.

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:     

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

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 

Since Cognite's start, we have prioritized security and take our security role very seriously as a partner to mission-critical industries. Today, I am happy to announce that Cognite has obtained our SOC 2 Type II certification. The American Institute of Certified Public Accountants (AICPA) bases SOC 2 engagements on its Trust Service Criteria. These criteria ensure we have rigorous security guidelines and stick with them. Type II certifications, in particular, are comprehensive and involve significant work to prepare, so a huge thank you to our team and our partners for their contribution to this achievement. At Cognite, we are investing in more automated ways to support continuous compliance monitoring.  We focus on empowering our end users with industrial data operations without managing the related infrastructure. We believe it's our mission to create frictionless security, and our SOC 2 Type II certification is another significant step toward this mission!

Cognite Summit 28.04.22: Industrial data operations: Data as an asset. Presentation by Cognite.

The below is reflecting current thinking from the App Dev Journey team in Cognite and is a mental model that will likely develop over time as this topic mature.A data model enables users to customize the shape and structure their expectation of data. It plays a crucial part in building solutions (like data science models, mobile and web apps). It is also 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 explicit because it provides a clear contract/interface between data providers and consumers. Hence, those loading the data and using the data can both understand the underlying data and use it correctly. By having explicit data modeling, a shared cont

This presentation gives a quick introduction to Cognite as a company and our core product, Cognite Data Fusion.  Get introduced to how our products turn industrial data into customer value by liberating it, contextualizing it, and making it actionable for users.

I was thinking around the idea of adding the bindings and mappings into the Solution Data Model as directives to have more simpler syntax and UX. The idea is to “hide” the data model storage syntax and bindings syntax so everything can be merged into Solution Data Model GraphQL SDL.For example if we have following Solution data model type Person @view { firstName: string lastName: string}The following DMS should be automatically created Data model storage-----PersonTablefirstName: stringlastName: string However, if I want to make some overrides, I can use the additional directives. For example: type Activity @view @bind(filter: { hasData: { "in": "Risk" } }) { position: string @bind("dms.Table2.position") action: string line: Asset @bind("dms.Asset") equipment: Asset}In this case, in DMS, the Activity data model can be created automatically as well as the bindings and mappings to the related tables.Thoughts?

Hey, Currently, it’s hard to maintain a coherent versioned datamodel in CDF. With Templates, and further developments there, this becomes easier.Even with a versioned datamodel it seems to me that there still isn’t a good way to track changes in the model that are not version-breaking.In our datamodel we have two types of changes that we would like to track in a structured way: Field value updates, as simple as a metadata field and as complex as the unit-multiplier on a TimeSeries changing. Edge field value updates. Our model is  an adption of a RDF representation of  a Common Information Model in the Common Grid Model Exchange Specification for Norway. A field on an object can imply an edge in the datamodel, and this field can change. Currently this is solvable with  Labels and Start/End time on Relationships today, though it isn’t obvious that we would want to introduce an Edge type to Templates?This question is a part of the larger struggle of maintaining a sane structure on a time-

 Hi! I’m Dilini Fernando from Cognite! This is my first post, and I am super excited to share a how to guide on Cognite’s DB extractor. This post is targeted to folks who have not used the DB extractor before and wish to extract data from popular tabular databases like MySQL, Oracle, and PostgreSQL databases and ingest it to CDF.The post contains three major steps. The first two focus on setting up a database and ODBC driver, and the third step focuses on running the Cognite DB extractor. You could skip steps that are not required for you. For example, if you already have a database setup, you could skip the first step. Step 1: Setting up a databaseDownload Micorosoft SQL Server Express Edition 2019Install the server Note: Remember the password.  You need to enter the password when you are login to SQL Server Install SQL Server Management Studio. Connect SQL Server Management with SQL server 2019.    Open SQL Server Management and select the Authentication as SQL Server Authentication.

Hello everyone,I was curious if anyone has leveraged Cognite AIR (Automated Identification and Reporting) in a way where alerts could be triggered off of other alerts? If so, how has this been done?I’d like for this to be a core feature of AIR where you can build a workflow from a visual canvas (similar to the charts function builder) and say “If this alert is triggered, check if this other alert has been triggered also” and so on. This would allow for setting up “offline conditions” so we don’t see all anomaly alerts or threshold alerts unless we can confirm the equipment is running, or even automate root cause investigations. Currently if I build an alert, I’m getting too many “false positives” of sorts that I’d like to filter out. Any input is welcome. Thank you,Richard Maidla (Hess Corporation)

Hello!We have a new version of the new documentation around Templates out live on our Documentation portal!https://docs.cognite.com/dev/concepts/resource_types/templates/Here we provide a full guide on how to go from modeling the data to querying the data. As well, we provide much more detailed specifications on what is doable via the UI and also the SDKs.Please provide feedback on what else you would like to see documented and if you run into any issues with the code samples / guides!