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i want to pratice data modelling pratically ,concept based understood so now i want to pratice data modelling to gain more knowledge so is any chance to get the acces to created data model in cognite learning platform.https://cognite-learn.fusion.cognite.com/cdf-fundamentals/data-models?cluster=api.cognitedata.com&workspace=data-management&search=&page=1&sort=updatedTime&order=desc&tab=dmThankyou 

Hi fellow hub members,  I joined Cognite a couple of months ago and now that I have gone through the initial training and onboarded myself, I am ready to share and engage in this community.As the title shows I care about operationalizing data in the cloud through agile practices - so while figuring out how to master Cognite Data Fusion I will also dig into how agile engineering practice can be leveraged when working with CDF. And through that experience I will share what I discover - it may be useful :-)

Hello,  Today if we reference a node in a data model instance to create a relationship between two nodes, the referenced node existance is not validated at insertion level. In order to enforce data quality, do you envision to add a “strict mode” at the DMS level that checks for referenced node existence before creating the reference?   i.e:Running this query results in a successful transformation runselect 'test' as externalId, 'test' as space, node_reference('space', 'external_id_that_does_not_exist') as Child, from staging_tableThanks!

Description / Problem StatementWe are using the Cognite 3D File Extractor to migrate 3D model files from an on-premises Windows VM to Cognite Data Fusion (CDF).The majority of 3D files are ingested successfully with multiple sizes mostly > 10 GB. However, four specific files consistently fail ingestion, even after multiple retries.Key observations:All failing files are close to 5 GB in size Example sizes (as shown in Windows Explorer): 4.99 GB 4.94 GB 4.91 GB When checked via terminal/command line, these same files report a larger actual size (~5.3 GB) It appears that based on the Windows VM file size, the 3D extractor determines it should use a single-part upload. However, when it attempts to upload the actual file, the process fails because the size exceeds 5 GB      

When doing the exercise “Valve mileage use case” of the Domain Expert training in CDF, this error appears (in Portuguese):It says:Competências necessáriasEntre em contato com seu administrador do CDF para solicitar as seguintes competências: timeSeriesAcl:WRITEIn English:Required SkillsContact your CDF administrator to request the following skills: timeSeriesAcl:WRITE Basically, someone should give me the timeSeriesAcl:WRITE permission. But who?   

SummaryThis How-To outlines the critical role of performance testing in optimizing Cognite Data Fusion (CDF) projects, emphasizing its necessity for ensuring efficiency and effectiveness. It details key measurable factors like data ingestion rates, query performance, and API response times, which are crucial for assessing CDF's operational health. Cognite provides a set of notebooks with example code to facilitate testing across various operations, including data ingestion, query performance, and data modeling, with a strong recommendation to use test/development projects and monitor CDF quota usage. Ultimately, performance testing is presented as essential for achieving project delivery success, ensuring production readiness, and boosting development efficiency, leading to tangible benefits such as faster dashboards, reliable ingestion, and significant cost optimization. Monitoring Cognite Data Fusion (CDF) performance involves tracking several key factors that ensure the platform ope

How can we get access to work hands-on with the learning path athttps://cognite-learn.fusion.cognite.com/cdf-fundamentals/data-models?cluster=api.cognitedata.com&workspace=data-management?If not, how can we practice hands-on with Cognite Learn?

Hello everyone,I observe a difference in behavior that I cannot explain.My input data, few datapoints, and before the value is 0, timeseries.is_step = TrueInput datapointsWith client.time_series.data.retrieve, behavior is as expected :And with every granularity results are okay as expected.Nevertheless, with client.time_series.data.synthetic.query it’s… different.that’s okthat’s okNot ok, why the result is not 2 groups : from 46 to 49 then 49 to 52 ? another wrong exampleWhy the start timestamp is 48 and not at 46 ? Again, the timeseries endoint is good : I have lost hope.If anyone has anything to try or an explanation, i’ll be grateful.Thanks in advance,Pierre 

Hello,Please reset my mfa account.nbhatewara@slb.com Appreciate.

Hi All,I have a large production records data set which i am utilizing to create reports using GraphQL queries.I want to perform DESC sort to fetch the latest records for which i am using default ‘StartTime’ property(This property is derived from Schedulable Container). But system is generating this warning : "The sort is not backed by a cursorable index, which means query time sorting is necessary, which in turn means a lot more data must be read. Consider whether a cursorable index is a good fit."What’s the best way to approach this so that i can fetch large data sets with improved performance?Should i make a custom DateTime field as cursorable ? What are the impact of this ?

Hi Everyone,We're currently managing multiple CDF Toolkit solutions in a mono-repo and evaluating whether to maintain this approach or migrate to a multi-repo structure. We'd love to learn from teams who've navigated through this decision.Current Setup (Mono-repo):1. Single repository housing multiple solutions with shared infrastructure. 2. Shared auth-governance across all solutions.3. Teams need independence but also need to prevent cross-team overwrites.We're Looking For: 1. Real-world examples: How did you decide between mono/multi-repo? What factors mattered most?2. Tooling patterns: How do you handle shared resources (auth governance) in multi-repo? How do you manage CDF Toolkit versioning across repos?3. At how many solutions/teams did you reach the limit of mono-repo and decide to switch to multi-repo?4. How do teams with multi-repos handle toolkit version synchronization? Do you enforce a minimum/maximum version across repos?5. Documentation (runbooks or migration guidelines)

OverviewCognite Data Fusion (CDF) evolves continuously, and some capabilities are first exposed through experimental (alpha) API subversions before becoming part of the stable v1 API. These alpha endpoints are primarily intended for: Testing new or unreleased CDF features Investigating Data Modeling Service (DMS) behavior Debugging query execution plans and backend behavior Reproducing issues together with Cognite Support or Engineering To support these workflows, the Cognite Python SDK provides: A debug flag to enable detailed HTTP-level logging However, not all configuration parameters behave the same way.Some can be safely modified after client initialization, while others must be defined at initialization time to guarantee correct and consistent behavior—especially across different execution environments such as CDF-hosted Jupyter notebooks and local development (VS Code, scripts, CI/CD).This document explains what can be changed, when, where, and why. Environment Differences

1. Why x-request-id matters Every CDF API request has a unique x-request-id returned in the response headers. Including this id in your logs allows Cognite (or internal platform teams) to trace the exact backend call in observability tools and Kubernetes logs when investigating issues.For CDF Functions written with the Python SDK, there are two main ways to surface the x-request-id in function logs: On failed SDK calls (via CogniteAPIError) => Exceptions — cognite-sdk 7.91.1 documentation For all calls (temporarily, via SDK debug logging)  2. Log x-request-id on failed SDK calls When a Cognite API call fails, the Python SDK raises CogniteAPIError. This exception includes the request id as e.x_request_id.[Exceptions]Use this pattern in CDF Functions:from cognite.client.data_classes.data_modeling import ViewIdfrom cognite.client.exceptions import CogniteAPIErrorfrom datetime import datetime as dtdef handle(client, data): try: view = ViewId(space="APM_Config", external_id="

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I have got a new phone and am not able to restore microsoft authenticator from the backup on it.Please reset the MFA Access so that I can set up the authenticator again.