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More details here: https://github.com/cognitedata/react-auth-wrapper/issues/15Perhaps it could be removed all together?

One thing that would make the finding of the correct data is to label the CDF resources better with labels. https://docs.cognite.com/dev/concepts/resource_types/labels In the contextualization pipeline could things be automatically labeled such as:Document type: P&ID, PDF(Process Flow Diagram), loop diagrams, datasheets, single line. that theses documents are either labeled from the metadata. document ID schema (regex will do it) or from classification algorithm the file, and then gives it a label to the file. that we can see from the label on the resource what it is.The same is for Asset: Is it a Junction box, Transmitter, Skid, Pump, Motor, valve etc etc. Time series: is it a physical measurement, is an inference / calculation / “soft-tag”, is a parameter (like the gain or integration time on PID controller) this could be determent by simple analyzing of the data-point behavior of the time series. cause now when we search for a time series often a time series of a parameter that

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Morten Andreas Strøm / Ben Skal                                                                              September 12, 2022 What makes Cognite unique? Why is partnering with Cognite the best investment of your time and resources?This is a 3 part series where @Morten and I answer these questions through an indepth look at how our product, Cognite Data Fusion, can help you use industrial data to ignite your digital roadmaps. The topics we discussing are: What is Cognite Data Fusion and why did we build it? (First post) Data modeling grounded in business impact (Previous post) The opportunity cost of custom building your industrial data platform (This post)  In the first Why Cognite post, we discussed the data problem Cognite Data Fusion is built to address. The short answer, industrial companies need simple access to complex industrial data. The reason, most operations teams have many business opportunities, but are struggling to effectively use data to improve production. In the

I just finished the cognite academy examples on contextualization. I did notice on the PID contextualization example that there were quite a few errors in what seems potentially character recognition pipeline to identify tags in the PDF.The tutorial stated that “these were all good” and “we can accept all”. I suspect such a process would create missing or strange links in the contextualized dataset.Are these known issues?I lack a bit the understanding of the context for the importance of these mispredictions,but I thought to report them anyway just in case.Happy to support you on improving these if they are something that needs improvement.

When I try to access cdf tenant URL (https://slb.fusion.cognite.com/), I get this error.I have been trying to figure this out but nothing so far, kindly suggest where to get these permissions from. 

<<URGENT>>Hi ,We are Unable to deploy Cognite Function. Below is the snippet for the same.Function ID : 269293733668660    @Philippe Bettler  could you please prioritize this issue.  

It would be really nice if we could control these log-messages in a more granular way:  

Please add better documentation around the behavior of status code 422 for Create Time Series.  Specifically, I’d like these questions answered:When multiple external ids are given and some are duplicate and some are not, will the time series that are not duplicates be inserted and will these created ids be returned in the response? Does this function similar to datapoints where create is actually create or update and the duplicates are updated with the data provided?  If not, what is the recommended pattern for create or update of time series entities through the API?I am aware of advice here around “EAFP vs LBYL”, but I am in a case where 98% of the time the time series will not exist and I’d like to optimize for this case.  Thanks!

This post is a hands-on introduction to the features supported in the Transformations Python SDK.Prerequisites Use Case 1: Triggering Transformations Step 1 - Create RAW Tables Step 2 - Uploading data to RAW using Postgres Gateway Step 3 - Create new SQL Transformations Step 4 - Trigger the transformation from Azure Data Factory Use Case 2: Orchestrating Transformations Step 1 - Create RAW Tables Step 2 - Create new SQL Transformations Step 3 - Orchestrate Transformations in sequence PrerequisitesKnowledge: Basic knowledge of Azure Functions and Azure Data Factory Basic knowledge of Cognite Data Fusion RAW and SQL Transformations Prior experience with Python, Postgres and SQL Required Datasets:Download and Unzip the attached hub.zip file, you should find the below structure Use Case 1 : asset-hierarchy.csv UseCase 2: OID-Asset-hirerachy.csv OID-Timeseries.csv OID-Datapoints.csv Use Case 1: Triggering TransformationsData is extracted from source systems and

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