Community Discussions

If you are curious on how to get the most out of your Cognite Data Fusion subscription, you have come to the right place. This is part of a series of posts where we share some of our experience from working with customers in their journey towards an Industrial DataOps organization. We want to share lessons learned, mistakes made, good practices observed, and observations of pitfalls and risks. This is not the absolute truth, but hopefully a way to spark good discussions around an inherently complex topic! To quickly introduce ourselves, we are @Arjo Oosten, Digital Transformation Leader, wintersport addict and passionate about driving hands-on digital growth strategies and value based decision making, and @Karolina Luna, Solution Architect, cat lover, and passionate about the lifecycle perspective of everything (like solutions and data products). To learn more about Cognite Data Fusion, we recommend this post. DevOps and Industrial DataOpsBoth DevOps and DataOps have many different def

If you are curious on how to get the most out of your Cognite Data Fusion subscription, you have come to the right place. This is part of a series of posts where we share some of our experience from working with customers in their journey towards an Industrial DataOps organization. We want to share lessons learned, mistakes made, good practices observed, and observations of pitfalls and risks. This is not the absolute truth, but hopefully a way to spark good discussions around an inherently complex topic!To quickly introduce ourselves, we are @Arjo Oosten, Digital Transformation Leader, winter sport addict and passionate about driving hands-on digital growth strategies and value based decision making, and @Karolina Luna, Solution Architect, cat lover, and passionate about the lifecycle perspective of everything (like solutions and data products). To learn more about Cognite Data Fusion, we recommend this post. Outsourcing FTW?As many companies have found, it can be hard to attract the

If you are curious on how to get the most out of your Cognite Data Fusion subscription, you have come to the right place. This is part of a series of posts where we share some of our experience from working with customers in their journey towards an Industrial DataOps organization. We want to share lessons learned, mistakes made, good practices observed, and observations of pitfalls and risks. This is not the absolute truth, but hopefully a way to spark good discussions around an inherently complex topic!To quickly introduce ourselves, we are @Arjo Oosten , Digital Transformation Leader, wintersport addict and passionate about driving hands-on digital growth strategies and value based decision making, and @Karolina Luna , Solution Architect, cat lover, and passionate about the lifecycle perspective of everything (like solutions and data products). To learn more about Cognite Data Fusion, we recommend this post. Knowing which roles you need for your Industrial DataOps organization, as c

If you are curious on how to get the most out of your Cognite Data Fusion subscription, you have come to the right place. This is part of a series of posts where we share some of our experience from working with customers in their journey towards an Industrial DataOps organization. We want to share lessons learned, mistakes made, good practices observed, and observations of pitfalls and risks. This is not the absolute truth, but hopefully a way to spark good discussions around an inherently complex topic!To quickly introduce ourselves, we are @Arjo Oosten, Digital Transformation Leader, winter sport addict and passionate about driving hands-on digital growth strategies and value based decision making, and @Karolina Luna, Solution Architect, cat lover, and passionate about the lifecycle perspective of everything (like solutions and data products). To learn more about Cognite Data Fusion, we recommend this post. Different types of cross-functionalIndustrial DataOps, like all other Ops mo

Schedule a personalized demo to learn how Cognite Data Fusion™ generates fast, scalable value from your data, enabling better decision making about maintenance, production and safety.In the demo, we will:Understand your priorities, initiatives and challenges that you are looking to solve Introduce Cognite and share examples of use cases we deliver for our customers today Identify areas where Cognite Data Fusion can help your organizationRequest demo with one of our product experts 

If you are curious on how to get the most out of your Cognite Data Fusion subscription, you have come to the right place. This is part of a series of posts where we share some of our experience from working with customers in their journey towards an Industrial DataOps organization. We want to share lessons learned, mistakes made, good practices observed, and observations of pitfalls and risks. This is not the absolute truth, but hopefully a way to spark good discussions around an inherently complex topic!To quickly introduce ourselves, we are @Arjo Oosten, Digital Transformation Leader, winter sport addict and passionate about driving hands-on digital growth strategies and value based decision making, and @Karolina Luna, Solution Architect, cat lover, and passionate about the lifecycle perspective of everything (like solutions and data products). To learn more about Cognite Data Fusion, we recommend this post.Planning your solutions and Industrial DataOps with Cognite Data FusionTo be

INTRODUCTION A digital twin can be one of the most useful, insightful tools to drive industrial innovation. While the digital twin concept is no longer new, the capacity of the term continues to expand based on technological advancement, particularly in the realm of the Industrial Internet of Things (IIoT). Over time, digital twins have morphed to meet the practical needs of users. In oil and gas, for example, the possibilities of condition-based monitoring and predictive maintenance have amplified the need for a digital representation of both the past and present condition of an object or system. Gartner predicts that “by 2023, 33% of owner-operators of homogeneous composite assets will create their own digital twins, up from less than 5% in 2018” while “at least 50% of OEMs’ mass-produced industrial and commercial assets will directly integrate supplier product sensor data into their own composite digital twins, up from less than 10% today.” In the same report, Gartner indicates that

Hi All, I’m Stig Harald Gustavsen. For the past 15 years, I have been spending a third of my time on the Valhall oil platform. I work there as a metering technician/engineer. My daily tasks are production allocation, operating, calibrating, and maintaining the fiscal and analytical measurement systems.I’m incredibly passionate about Open Industrial Data (OID) and the concept of data sharing in general. I feel there is too little sharing of the data and information within heavy asset industrial sectors, mainly due to risk aversion. Humanity as a collective needs solutions on the industrial scale. When we withhold information, we hinder creativity and miss out on opportunities that we could leverage to create a better future together. A great example is how the internet has democratized information sharing in the commercial IT world. The more heavy asset-oriented industrial world has yet to go through this reformation. Still, I hope and wholeheartedly believe the OID is the start of this

Below we have outlined several frequently asked questions and their corresponding answers.Don’t see the answer to your question? Post as a reply in this thread and we’ll be sure to answer and/or add it to the FAQ list below!Protip: Use [cmd+f] or [cntrl+f] to search for keywords related to your question. FAQs How do i use monitoring in CDF and access the documentation?You can access the documentation for the new monitoring solution in CDF here: https://docs.cognite.com/cdf/charts/#monitoring.  

Hello Cognite Community! Izabela Hawrylko here, Cognite’s Partner Development Director working with our Application and Industry partners. In my day-to-day work, I’m joining forces with other digital disruptors to build “Partner+Cognite” technical and go-to-market strategies for end-to-end industry solutions solving most pressing industrial challenges.I can imagine you might be wondering who Open Industrial Data (OID) is for. Anyone who is an industrial data enthusiast. Curious mind interested in better understanding the industrial reality. Are you an Independent Software Vendor building solution and applications for heavy-asset industries? Market Incumbent looking for data to experiment and drive innovation for your products and services? Researcher? Student? We have created this project for you to explore real-life industrial data streams and experiment with analytics, building new algorithms and creating visualizations.Join hundreds of other industrial data enthusiasts and dive into

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

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

Introduction In this post we 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 first 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 experience in t

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.