Product User Community

Do Cognite OData APIs also offer have cursor implementation like cursor capabilities on REST endpoints?

Hi! I would like to check if a time series stays within a region for more than a certain amount of time. Is this possible? This would be the equivalent of inverting the “Upper lower threshold” function. Also I would need a time limit for that. Perhaps it would be a simple yet powerful addition to the existing functionality: To be able to invert the monitoring task? Some of the monitoring tasks have a time aspect, and some do not.. perhaps all should have it?  Regards  

When working with CDF or Azure, sometimes I got some errors that were hard to figure out. Experience no doubt will help me figure them out faster, nonetheless this is an important part in enabling usability.

Is it possible to configure in Cognite tools (like CDF & Cognite Infield) to send an email with the information on the user (to designate admin(s)), when a user trying to access the tools is restricted access to those tools, for not being part of the right security groups? Question posted on behalf of Celanese user access administrator.

We are fetching quite a lot of data from CDF, both raw and aggregated, and we have experienced error messages from Postman and Java requests when the aggregated number of datapoints are exceeding the limit (10.000), but we have yet to experience this for the Cognite Python SDK for raw data (limit 100.000). Is there an error message implemented for this intention? From the code in the SDK in GitHub it appears that if the data is exceeding the limit then it would return an empty list, which is a very dangerous return setting. Best regardsOliver

Hey community we are trying to be more active in this cognite hub page and add content consistently from a month to month basis. This will include a series of articles on alerting and monitoring as well as plans regarding where AIR is going into the future.Why Alerting?Automated alerting is an essential part of monitoring. They allow you to spot issues with equipment groups, time series, data quality, pipelines etc.But alerts aren’t always as effective as they could be. In particular, real problems are often lost in a sea of noisy alarms. In short:Alert liberally meaning its ok to spam users rather than not alert at all Make sure that the user has the complete control of how and when they want to be alertedInherent challenges in AlertingSensitivity: Overly sensitive systems cause excessive false positive alerts, while less sensitive systems can miss issues and have false negatives. Determining the correct alerting threshold requires ongoing tuning and refinement. Fatigue: The common ap

I am attempting to run the python file associated with the Data Modeling hands-on lab (https://colab.research.google.com/drive/11NdrGUzFhbwsOlKqXVfYRqu9s1DzAw4b?usp=sharing). I am receiving the same error every time the ‘plot’ function is called: “TypeError: Cannot interpret '<attribute 'dtype' of 'numpy.generic' objects>' as a data type”. After some debugging, the data seems to be in a dataframe data type. I suspect there is some version discrepancy between numpy and pandas, has anyone else seen this error? Thank you, Adam

 I faced some isuees regarding connection with Azure when working with CDF. I believe there is an area of opportunity there! 

Hi, In my endeavor to make a percentwise threshold in Charts: I.e. how many percent of the last week was a time series above a certain value, I stumbled on what I think is a bug.I need to divide the time the timeseries is above a threshold by the total duration of the time window. I therefore tried the “constant value”-function, and set it to one. The default granularity is then at 7 days. I can change the granularity to 1 day as in the screenshot below:  However, when I change the granularity below this. Eg. 1 hour, I get the following warning: Ideally I want it to 1m or something like that, so that I have sufficient granularity to measure percentage above threshold over  the course of a day.Any suggestions as to how I can fix this, or an alternative way to reach my goal?Thank you

@Savieth Aceros and @Gina Esperanza Jimenez Morales - hope you find the learning paths helpful! Are you taking part in a bootcamp as well?

Hello @Kristoffer Knudsen @ibrahim.alsyed  would like to know if Cognite InField would work ok for demo purposes on a giant smartphone (android  55’’)Celanese is planning to demo this to 100 people using something like this https://www.giantitab.com/ cc: @MortenNesvik @Kriti Dhaubhadel @Philippe Bettler @piya dey @Ajo 

We may have many clients with small userbase. What is the suggested deployment model for these clients from below options:As all the CDF projects are isolated, should we as a single Cognite tenant create different projects for different clients. We should provision separate Cognite tenant for each client keeping future creation of multiple CDF projects over time. 

As a Cognite tenant we can have multiple CDF projects. Some CDF projects may have large user base in comparison to others. Do we need to worry about Cognite WebAPI request handling capacity.

Can we get usage cost per CDF project.

Is there any difference between Cognite WebAPI vs Cognite Python SDK in terms of response time and data transfer limit.

In bootcamp documentation there were some guiding principles:https://docs-bootcamp.app.cogniteapp.com/content/chapter_0/#the-ice-cream-factory-dataOne of them was for Cognite functions:Cognite Functions for running and scheduling simple and short workloads in Python (< 10 min. execution time)Can we use Cognite functions for long running and scheduling workloads typically of duration 10-12 hours.  

Do we have option available to deploy CDF on private cloud.

ContextThis write up describes a basic set up of writing data points to Cognite Data Fusion using Apache NiFi. The source data is power consumption readings from the HAN interface of a power meter in a residential fuse box:The starting point here is an existing MQTT broker that receives data from an existing MQTT client device. We will use Apache NiFi to consume the MQTT messages, extract the readings and write continuously to CDF.The end goal is a live updated time series within CDF with power readings every 2.5 seconds that can be used for analysis or automation within CDF or simply visualization: NiFi flow overviewThe picture above shows the complete NiFi flow. From left to right, consuming MQTT messages, transforming and writing to Cognite Data Fusion. Approximately 120 data points per five minutes, which corresponds to the power meter outputting one reading every 2.5 second. The NiFi flow makes use of the following Processors: Processor  Purpose ConsumeMQTT

@Eric Stein-Beldring - is there a way to lock the y-axis when you have a chart in stacked mode? We have a good example that our team has been using where we’ve created a calculated column that we need to zoom in on to see the short term trends, but the moment we move the mouse over the chart the y-axis automatically re-adjusts not allowing us to use the stacked feature. If we could toggle y-axis auto adjustment, that would be very valuable!Not zoomed burgundy line vsZoomed burgundy line 

Would like to know in detail about the fact extraction from drilling reports. Is there any capability in CDF to do such fact extraction and relevant methods to process drilling reports?

We are interested in retrieving data from cdf through the odata service. We would do this not from Power BI, but from another Power Platform component, namely Dataverse. (the purpose would be to create "virtual tables" there which we could query, and in that way we could control access to the data more granularly then we are able to thorough the Cognite API) The documentation we find only mentions the OData connector in relation to Power BI (e.g. Cognite Power BI connector | Cognite documentation (cogniteapp.com) and Tips and best practices | Cognite Documentation). Is Cognite's OData service specifically tailored for Power BI, or is it a full implementation according to the OData standard? Is anyone able to recommend for or against attempting the proposed approach?In case the service implements the standard, does anyone know which version of the standard is it?

I have ingested RAW data into CDF for a bunch of equipment (16) and also applied transformations on those to setup Assets, Timeseries, Datapoints and Events. I have a P&ID diagram (PDF) which has 4 Equipments shown in the Diagram with P&ID#. I would like to know the process of ingesting this file as a resource type for this equipment setup and where do I upload the file?  When the user selects any of the above Equipment in CDF, the system should display the enclosed P&ID diagram.

While trying to setup the Google - colab for CDF environment and authentication, i am getting an error. Unable to trace the rootcause of this error.  This code is a part of Notebook setup given in the Hands-On course Link to notebook - Data processing and analysis for IDA course.ipynb - Colaboratory (google.com) TypeError Traceback (most recent call last)Cell In [2], line 57 55 def get_token(): 56 return authenticate_device_code(app)['access_token']---> 57 client = CogniteClient( 58 ## token_url=f'{AUTHORITY_URI}/v2.0', 59 token=get_token, 60 token_client_id=CLIENT_ID, 61 project=COGNITE_PROJECT, 62 base_url=f'https://{CDF_CLUSTER}.cognitedata.com', 63 client_name='cognite-python-dev', 64 ) 65 print(client.iam.token.inspect())TypeError: __init__() got an unexpected keyword argument 'token'

  @Kristoffer Knudsen In Cognite Infield, It appears that once the checklists are archived, there is a default time period of 3 weeks before they are permanently deleted based on what we see after archiving a checklist (release notes from Jan 2022 seem to show 4 week keep period). Some operating units have longer or shorter retention policy for their checklists, after archival and it would help to have this retention time period configurable. Based on a quick scan of current Infield configuration parameters, it didn’t appear to be a configurable parameter for now, under the Infield configuration items in CDF.  Need clarification on this.