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I was looking at trends in the Remote App web version, using the cursor to touch tags. The detail pane shows the tag name(s) and current value where the cursor x axis crosses. The issue is the tag names are cutoff and mostly unreadable, the tag value is showing almost 10 or more decimal places. I would like to see the whole tag name and 1 or 2 decimal places, or use the signficant digits from the PI Server in the first place.

Is there anyway, one can generate a personal access token using Databricks community Edition? I am working through the training and the only environment I have is to use the community edition. The training requires “Generate a personal access token” which is not possible. Is there any workaround? 

We are seeing inconsistencies between the number of files that an asset “Appears In” vs to the actual number of files that are in CDF:In the screenshot, the count is (5) however there are only 3 files. Upon checking further we realized that the count is coming from the number of Cognite Annotation events that were created during contextualization of the P&ID files.Even if the original file was deleted, the Cognite Annotation events for those file remain.This causes confusion to the user.Is it possible to have this fixed? i.e deleting a file would also then delete all Cognite Annotation events that was created for that file? Or if the Cognite Annotation events are to be kept, but the logic in how the number of files an asset appears in is updated so that it reflects the correct number of files that are still in the CDF project?

What makes Cognite unique? Why is partnering with Cognite the best investment of your time and resources? This is a 3 part series where @bskal  and I answer these questions through an in-depth look at how our product, Cognite Data Fusion, can help you use industrial data to ignite your digital roadmaps. These posts are for those of you who are new to using Cognite Data Fusion and want to understand how we approach the challenges of working with industrial data without losing focus on delivering business impact. The topics we discussing are: What is Cognite Data Fusion and why did we build it? (Last post) Data modeling grounded in business impact (This post) The opportunity cost of custom building your industrial data platform (DIY)  In the first Why Cognite post, Ben and I shared why we built Cognite Data Fusion. The short answer, industrial companies need simple access to complex industrial data. The reason, most operations teams have many business opportunities, but are strugglin

Hi, I'm Damjan, and I work with research in the Cognite Data Onboarding group. We’re on a mission to improve and streamline the data onboarding experience for existing and future Cognite Data Fusion users.Our current focus is connecting data sources, building extraction pipelines and the needs around data onboarding. What's your challenges, needs and expectations with regards to the core Cognite Data Fusion onboarding experience? Shout out, share your thoughts and comments below. Thanks for helping us improve CDF! 

As Requested Creating this new bug on cognite function.Please assign this to below ids: ben.petree@cognite.com & philippe.bettler@cognite.com@Ben Petree @Philippe Bettler 

Request you to share on how to add environment specific URL(dev/prod/test) in handler.py in AIR. Also need to understand on how to get msal_token (bearer) authorization in AIR functions.Example:msal_token = request.headers.get("authorization")requests.get(sequence_generator_api, headers={"authorization": msal_token})where sequence_generator_api is environment specific API

I need to add a dashboard query that is simple to write with SQL but seems to be complex to write with the CDF API.Input parameters:start and end timestamp range List of time series to queryQuery steps:For each time series in the list, sum all values that are in the user provided range Order time series by the sum Return metadata from the top 10 of this ordered listMaybe something equivalent to this:-- Likely SLOW in a SQL DB with many datapoints-- This is a conceptual example, so simplifying by combining Time Series and -- Data Points to single entity.Select TOP 10 TimeSeries.Name, SUM(TimeSeries.Value)FROM TimeSeriesWHERE TimeSeries.Id IN (@ListOfTimeSeries)AND TimeSeries.Timestamp >= @StartTimestampAND TimeSeries.Timestamp < @EndTimestampGROUP BY TimeSeries.Id, TimeSeries.NameORDER BY SUM(TimeSeries.Value)What’s the simplest way to get to this data with the tools provided to a software developer interacting with CDF?

Hello!I am querying for DataPoints using the PostgreSQL Gateway, which I assume will have the same behavior as the DirectQuery feature built on the PostgreSQL Gateway.  What I am noticing is that I am not able to retrieve any DataPoints for future dates.  Is this intended or a bug?  Most of the data we produce and manage is in the future, so this would be an important feature for us if it could be supported.  Thanks! Query results returned in Azure Data Studio from PostgreSQL gateway:In CDF these time series have datapoints representing predictions far out into the future: 

403 ERRORThe request could not be satisfied.Request blocked. We can't connect to the server for this app or website at this time. There might be too much traffic or a configuration error. Try again later, or contact the app or website owner.If you provide content to customers through CloudFront, you can find steps to troubleshoot and help prevent this error by reviewing the CloudFront documentation.Generated by cloudfront (CloudFront)Request ID: 5cVGYuZaPs0Tnwt0Mm7an00fCMQ14bwLFy-SDA27JszqfsOdnRdbtg==

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

Cognite Function throwing Bad Gateway Error:Function ID :4525036003390684Here are the details:error: Bad Gateway | code: 502 | X-Request-ID: d84d076d-6e59-9362-b58e-931d5a63644f 

while we are trying to contextualize , the documents with Equipments’s list. The detections of false matches are more compared to Correct Matches for Non PNID’s documents.Its just the standard model we are using. Please find the below screenshots.Attached is the document , where every detect is a false match in it.Please could you help us in eradicating the false matches.

Cognite Function throwing Time out error.Function ID : 4525036003390684 This was working fine couple of hours back.

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

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)