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Morten Andreas Strøm / Ben Skal                                                                                   August 22, 2022  Hello digitalization community.  My name is Ben Skal, and this is my first time posting to our community. At Cognite, I am part of our industry team and focus on helping our customers apply Cognite Data Fusion to solving the most difficult challenges within their operations. I’ve spent my career working in industry (11 years and counting). First, for a global steel company, then at a major process automation company, and now at Cognite. I am currently living in Austin, Texas and looking forward to e-meeting and learning from this community. The purpose of this series is to answer the following questions: What makes Cognite unique? Why is partnering with Cognite the best investment of your time and resources? This will be a 3 part series to precisely answer these questions through an in-depth look at how our product, Cognite Data Fusion, can help you use ind

In the 1800s, enterprises organised themselves to use their capital assets effectively. Beginning in the mid-1900s, they organised to take better advantage of their people. Today, “data” are increasingly important to virtually all companies. There are many ways to “put data to work,” each with its own strengths and challenges. One option is to focus on finding and exploiting both value pools for the business and deep, fundamental technical capabilities provided by CDF. This can be done by executing an onsite Use Case Discovery Workshop.  There are three main steps to executing a Use Case Discovery Workshop: Identify qualified use case ideas Prioritize the use case ideas and select the top use cases  Detail out the top use cases  1. Identify qualified use case ideas 2. Prioritize the use case ideas and select the top use cases 3. Detail out the top use cases How do you find the best opportunity to leverage data ?

We are very excited about being officially in General Availability with Cognite Functions! A big thank you to everyone who helped in this journey and your tremendous contribution! Please keep posting feedback and issues, as we are constantly improving the service.As part of GA, there are a few things that you should consider:We have support in the official SDK (cognite-sdk version 3.9.0) and have moved to V1 API endpoint. We recommend you to use only the official Python SDK when creating new functions and migrate the old functions that point to the experimental one. We will remove Functions from the cognite-sdk-experimental starting version 0.94.0. You will still be able to use the experimental SDK with versions < 0.94.0 until we remove the playground API (because the experimental SDK uses the playground URL) by November 1st. Functions in API playground is retired at 1st of November.Check out here more details about the release:  

I have one model.pkl file which is a pre-trained Data Science model. I want to load that file inside handler of CDF function and do some prediction. My first question is where to keep that file in CDF. Second is how to load that file inside handler ?

We’re @Uzair Wali and @kelvin, Senior Data Scientist and Data Science Lead in Cognite’s Manufacturing delivery team. In this post we talk about the increasingly important ability to intuitively and flexibly query data from all steps of a product life cycle and across source systems, with examples we’ve implemented on Cognite Data Fusion together with our users.The need for traceabilityIn many manufacturing industries, the ability to trace a product through its manufacturing life cycle, whether internal or supply chain is extremely important. It entails the collection and management of information regarding what has been done in manufacturing processes, from the raw materials and parts used to the shipment of finished products. An industrial knowledge graph that enables this traceability has the potential to not only let users speed up or automate existing use cases, it also opens up possibilities for considerable value addition.Typical questions A customer complains about the quality o

I am getting error for black while deploying code to AIR. Please find attached screenshot for reference. So in turn code standards are getting failed after pushing the same changes in AIR.

Hello Charts Community,Let me begin by saying thank you for all of your input, feedback, and contributions you’ve provided thus far. On behalf of the entire team, we couldn’t have made Charts into what it is today without your invaluable contributions.   Charts General AvailabilityFor our August 2022 Cognite Data Fusion release, we have announced that Charts is transitioning from early adopter to general availability! We are eager and proud to move this valuable functionality into its next phase of life.In practice, it’s a stamp of approval that Charts is a reliable and stable Cognite Data Fusion feature. As we have done throughout our early adopter phase, we still intend to release new functionalities continuously and as soon as they’re ready to be made available. We will roll-up the communication in our bi-monthly CDF release communications, but will post in this group as soon as anything new is ready for use. What’s next for Charts?For the remainder of the year, our key focus area w

Unable to deploy cognite function. “Upload” button function is not getting enabled.This was working till last week.

we are facing issues while using cognite functions. Throwing an error Time out. please find the below screenshot. We are facing this from 1hr. Function ID 2060650160785332.This function was running successfully before this issueAttached is the screenshot image.

 we are facing issue while deploying the functionMessage: Function deployment failed.Trace:('Process-13 terminated unexpectedly with exit code 1 while running job.', 1)Traceback (most recent call last):File "/app/.venv/lib/python3.7/site-packages/cognite/processpool/processpool.py", line 81, in resultret, err = pickle.loads(self.recv_q.get(block=False))File "/usr/local/lib/python3.7/multiprocessing/queues.py", line 107, in getraise Empty_queue.Empty During handling of the above exception, another exception occurred: Traceback (most recent call last):File "/app/.venv/lib/python3.7/site-packages/cognite/processpool/processpool.py", line 163, in _job_manager_threadresult = worker.result()File "/app/.venv/lib/python3.7/site-packages/cognite/processpool/processpool.py", line 91, in resultself.process.exitcode,cognite.processpool.processpool.WorkerDiedException: ('Process-13 terminated unexpectedly with exit code 1 while running job.', 1)

In the spirit of summer reading- here’s a pretty interesting blog post covering 5 emerging challenges in commodity trading. What similarities/differences/additional challenges apply to power trading? Anything missing here?https://www.cognite.com/en/blog/commodity-trading-data-challenges1. Rapid increases in the number and availability of new data sources are accelerating the complexities of managing data and analytics in global markets2. Reliance on legacy systems means participants in high-paced commodity markets struggle to make data relevant and actionable3. Participants in commodity markets need to accelerate their data management and digital development just to keep up with technological improvements4. Existing data solutions in commodity markets are often designed as one-stop single solutions or platforms, with little room to create a proprietary competitive edge5. Continued growth in market complexities will require that IT platforms and data architectures are designed to remain

Browser is none too happy: https://github.com/cognitedata/auth-wrapper/issues/19

We have an equipment with name PI91111 on the P&ID and on the other hand we have this equipment in the asset Hierarchy.PNID Diagarm This equipment is not getting contextualized for this P&ID.I have used standard model and advanced model with min tokens 1 & 2 as well.The ocr output for this is below: 

We are using the Cognite function , to run the Contextualization Jobs. But we are frequently facing this issue.CogniteAPIError: Bad Gateway | code: 502 | X-Request-ID: 001dfc87-9b02-9b69-8144-3d829c126cbc.  could you also assign the ticket to the below emails ,where we can track the status.morten.nesvik@cognitedata.comben.petree@cognitedata.comphilippe.bettler@cognitedata.com

Cognite Data Fusion is a product built to address the challenges of working with industrial data by: Making data available - Liberate their IT, OT, ET and visual data from siloed source systems with our extractor pipelines. This is done reliably and at scale. Making data meaningful - We use AI-powered contextualisation services to create an Industrial Knowledge Graph that delivers trusted, contextualised data Make data valuable - Cognite Data Fusion enables your teams to access this data with the best-of-breed tools of your choice to turn this data into business value Monolith solutions often end up creating vendor lock-in and can even end up creating more data silos within your organisation With trusted, contextualised data available in an industrial knowledge graph, your teams are equipped to scale solutions both in the volume of new solutions and replicating successful solutions across assets, lines, or sites.The videos are based on the “ice cream factory” use case: a use case

Asset ID not getting removed from file  when you delete any annotations/boundaries on a file.when you manually delete any contextualization result on a file, only the annotations are getting deleted , but AssetId’s linked to that file still exists.  

In CDF, when we click on any Asset , there is a file tab, where it shows Appears In and Linked files for that asset.But the Count is not matching with list of files its showing for Appears In.In the below screenshot, the appears in count showing as 20. But only 16 files were listed. 

Hi, In Cognite AIR, I have put in my phone number and e-mail adress. According to the instructions, this should give me notifications on SMS rather than e-mail. However, I still receive (somewhat delayed) notifications on e-mail.Is there a way to test the SMS functionality to confirm that it is actually working?

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.  

Background: A production facility had numerous valves that were being opened and closed at varying rates and amounts. The subject matter expert at hand wanted to be alerted when the pressure values of these valves exceeded or dropped below a certain value as set by the subject matter expert.Problem: Today, the majority of valve maintenance is being done according to a fixed schedule. Without AIR and CDF the pressure sensors on these valves would have to be manually read and then converted into an excel spreadsheet. Once the data has been extracted into excel the subject matter expert had to analyze this data and figure out if the thresholds were being breached. If they were then the maintenance is done in order to prevent accidents. As it can be inferred this process was very manual and not easily scalable. The subject matter expert  also wastes their time on tedious tasks as compared to actual important tasks.Solution: Using AIR a data scientist is able to easily define and deploy a t

Cognite Data Fusion is a product built to address the challenges of working with industrial data by: Making data available - Liberate their IT, OT, ET and visual data from siloed source systems with our extractor pipelines. This is done reliably and at scale. Making data meaningful - We use AI-powered contextualisation services to create an Industrial Knowledge Graph that delivers trusted, contextualised data Make data valuable - Cognite Data Fusion enables your teams to access this data with the best-of-breed tools of your choice to turn this data into business value Monolith solutions often end up creating vendor lock-in and can even end up creating more data silos within your organisation With trusted, contextualised data available in an industrial knowledge graph, your teams are equipped to scale solutions both in the volume of new solutions and replicating successful solutions across assets, lines, or sites.The videos are based on the “ice cream factory” use case: a use case

 Hello Charts Early Adopter Community,There have been several new features and functionalities released lately, with some being released just today. I’ve recorded a video (below) to explain several of these core features in detail, namely – calculations running on individual data points, not aggregates.You can also scroll down to read the written details.Please do leave comments below with questions and feedback!DetailsCharts UI/UX Calculations are now running on individual data points, not aggregates In the past, calculations have been running on time series aggregates. This meant that, although calculations were approximately calculations were often not correct his is a very important new feature that greatly increases the accuracy and trustworthiness of calculation results in Charts. Watch the above video or see the slides, below, for more info. If you receive a warning that looks like this (see image, below), then it means that the results of the data has been downsampled to perfo

WhyWhen starting out in Cognite Data Fusion (CDF) project, it's natural to start by creating data governance elements like groups, datasets, and Raw databases from the CDF user interface. But as the solution begins to scale, you'll quickly realize that it is demanding to set up a detailed configuration handling multiple solutions, sources, roles, and other dimensions. For scaling, precise control is needed for access management and data governance and to enforce the guidelines and rules across the solution. The problem is not trivial, and a good way to solve it is by replacing the manual approach with a configuration-driven system, where the configuration language supports higher-level concepts for data lineage and access control. With configuration files as the foundation, you can set up an automated DevOps process and review and approve any changes to the structure before they are deployed. This approach also dramatically simplifies sharing the same configuration across multiple envi

Just stumbled upon an article from McKinsey in 2019 which articulates well what I believe is a crucial role at many of the customers I’ve worked with: the Analytics Translator. The technology in industry is continuing to move forward quickly opening up many new opportunities, but the deep domain expertise in production processes, maintenance, and more is still as important as ever. To me, the Analytics Translator role seems a perfect bridge between these two worlds. I’ve seen a number of manufacturers successfully “upskill” prior machine operators, process engineers, and more to fulfill this role. It’s always very impactful, making for significantly more effective digital initiatives. From my personal experience, I see them doing things like:Running use case workshops and prioritizations Internal training of users Adoption tracking Interim project manager during new technology initiatives Liaison with external technology partners and SIs Curious how others see this role, and whether yo

Cognite Data Fusion is a product built to address the challenges of working with industrial data by: Making data available - Liberate their IT, OT, ET and visual data from siloed source systems with our extractor pipelines. This is done reliably and at scale. Making data meaningful - We use AI-powered contextualisation services to create an Industrial Knowledge Graph that delivers trusted, contextualised data Make data valuable - Congite Data Fusion enables your teams to access this data with the best-of-breed tools of your choice to turn this data into business value Monolith solutions often end up creating vendor lock-in and can even end up creating more data silos within your organisation With trusted, contextualized data available in an industrial knowledge graph, your teams are equipped to scale solutions both in the volume of new solutions and replicating successful solutions across assets, lines, or sites.The videos are based on the “ice cream factory” use case: a use case