Product User Community

Hello! Is it possible to do forward filling of CDF timeseries datapoints at second level? Example: I have a timeseries with values at certain timestamp as the tag is coming only on value change. But I want to forward fill the values to be able to analyze the tags better in PowerBI (this operation takes much time in PowerBI). 

I would like to use variables in a SQL transformations, trying the syntax shown in this StackOverflow article.  However, the following SQL fails with the message “Query is not safe for execution. It must not contain commands like CREATE, DROP, INSERT, etc., but found node: SetCommand.”  How can I use SQL variables with this Spark implementation?SET startDate="2023-01-01"SET endDate="2023-12-31"SELECT explode(sequence(to_timestamp(${startDate}), to_timestamp(${endDate}), interval 12 hours)) AS timestamp

When using Entity Match/Quick Match, the user cannot “Select All” matching Events (or Timeseries) when applying an additional filter. The check box is grayed out. Pretty frustrating. Am I missing something? 

Hello,  We have trouble deploying new container via cdc-toolkit after changing an attribute property. We have the following error:  cognite.client.exceptions.CogniteAPIError: Cannot change type for property ││ 'PullDate' in container 'space:container'. | code: 400 Notes;This container contains data before this change We incremented the model version to a new major one We have consumers consuming the current versionWhat is the best practice for handling this kind of changes with the least effect on our current consumers ?  Thanks,

Hello,  We noticed that we get throttled (429 HTTP responses) on our backend whenever we make more than 10 req/sec. Is this a soft limit? Could we increase it? Thanks

Hello,  Does CDF provide CDC connectors/extractors for common SQL databases ?  Thanks

I have a use case where I need to filter events from CDF based on a “string.contains” pattern. Is this possible with the current API? Consider the following metadata field for an event in CDF. "EventData": "[{"version":"2.0.0"},{"type":"opcae"},{"id":"1d21002a-77a2-4531-a9bf-27f28876f2e1"},{"source":"47-PA-9810B-M01"},{"time":"5/29/2024 8:56:41 AM"},{"eventType":"Simple"},{"eventCategory":3439269159},{"eventCategoryName":"Process Simple Event"},{"severity":404},{"message":"Start"},{"eventCounter":187718259},{"Class":127},{"ObjectDescription":"PH PotWtr Pump B"},{"PriorityLevel":5}]"The value for `EventData` is a JSON string that has not been unpacked as proper metadata fields. Several of these fields are relevant for us to filter events down to the relevant subset.It is unclear from the documentation whether the API supports the “string.contains” pattern. The only class that maybe sounds like it is relevant is the 

The PI AF Extractor is missing link to the documentation, is there someone that could add this in.For the PI extractor the links are there and something similar for the PI AF extractor would make it easy to navigate to the documentation. The PI AF looks like this:The documentation does exist and lies under this area, would be great if we could navigate from CDF directly to the documentation.https://docs.cognite.com/cdf/integration/guides/extraction/pi_af Regards,Markus PettersenAker BP - CDF Data Delivery

Hi all, I am encountering an issue when calling the client.iam.groups.list(all=True) method in my Python 3.12 environment within Azure Batch. The code runs without issues in my local setup (also Python 3.12), but it fails in Azure Batch with the following traceback: Traceback (most recent call last): File "GroupMembersCapabilities.py", line 55, in <module> groups = client.iam.groups.list(all=True) File "D:\Users\...\site-packages\cognite\client\_api\iam.py", line 297, in list return GroupList._load(res.json()["items"], cognite_client=self._cognite_client, allow_unknown=True) File "D:\Users\...\site-packages\cognite\client\data_classes\iam.py", line 224, in _load [cls._RESOURCE._load(res, cognite_client, allow_unknown) for res in resource_list] File "D:\Users\...\site-packages\cognite\client\data_classes\iam.py", line 147, in _load capabilities=[Capability.load(c, allow_unknown) for c in resource.get("capabilities", [])] or Non

Hi,I’ve set up notification alerts for the extraction pipeline for two projects (dev and prod). However, the notifications I’m receiving don’t indicate whether they are from the dev or prod environment—they’re quite generic. Could you please advise if there’s a way to enhance the messages to include project-specific details?Thank you!  

I no longer see the Events button on the Chart UI. But I do see events previously configured. Please help. 

Hello:I am using DB Extractor  v 3.4.3 to read columns from an Excel file but it gives me the following error: 2024-08-20 18:53:06.550 UTC [ERROR ] QueryExecutor_0 - Unexpected error in query1: Could not read file C:\Cognite\SAT-SX4 - 2023-03-21.xls: required package 'xlsx2csv' not found.Please install using the command `pip install xlsx2csv`.The following is my config file:  databases: - type: spreadsheet name: "DSN_FT" path: "C:\\Cognite\\SAT-SX4 - 2023-03-21.xls" queries: - name: query1 database: "DSN_FT" sheet: "P_VSD" query: #"SELECT STS PreCOM FROM SAT-SX4 - 2023-03-21.xls" "SELECT * FROM P_VSD" destination: type: raw database: FTV table: AE_MOLYNOR primary-key: "{EventID}

Hi Cognite Community! I'm Henry Martinez, Head of Products, Operations data  in SLB.I’m thrilled to announce that I’ll be speaking at Cognite’s Impact user conference, where I’ll be diving deep into how Cognite Hub has played a pivotal role in advancing the SLB and Cognite partnership. We’ve seen firsthand how the Hub has streamlined internal processes, enhanced user onboarding, and boosted product engagement. With nearly 7000 active members, the Hub has become a go-to place for expert advice, industry discussions, and knowledge sharing.Here are some key highlights I’ll cover at Impact:How the Hub’s collaborative environment fosters innovation and strengthens partnerships Ways to make the most of dedicated feature groups for early adopters, gaining access to beta features and expert support Leveraging event calendars, submitting product ideas, and voting on

Hello,I am trying to set up a database extractor for SQL Server database. I would like to pass parameters to the query so that the extraction logic can be controlled via the env file. Here is what I have done.Created an environment variable called “DEPT_ID”, In the YAML file, I am using ${DEPT_ID} to use the value of the env variable.  SELECT *FROM [Employee] WHERE [Dept_id] = ${DEPT_ID} While running the DB-Extractor, I am encountering the following error.KeyError: DEPT_ID I am wondering if the product even supports the use of parameterized queries in the DB-Extractor.Any help will be greatly appreciated.  Thanks,Dinesh

I am just playing with the analysis of open industrial data (mostly time series IoT sensor data). It is my understanding that encoders play a role in detecting anomalies in the data. Can decoder only models such as Lag-Llama, TimesFM, Moirai detect anomalies or they can only predict? thank you.

Hello team, I am bulding an application using the methods in cognite python sdk.I want to create dropdowns for metadata and the values for the same. This similar to the filters on the data explorer screen in the fusion UI:I could not find a direct SDK method to do so.Could you please guide me on how can I achieve that?

Hello All! As this is my first time writing, I will do a quick introduction. My name is Patrick Mishima, and I am one of the Cognite Product Specialists. My main specialization is data integration and governance. I've worked with data since 2011 with different roles, projects, and job titles all related to data management. I also have experience with various ETL and BI tools, mainly from Microsoft and SAP and a few others.I lead the Product Specialists team, and we'll soon have more articles to share our knowledge and experience with you.Today and in my following articles, I will focus on the different options that Microsoft Azure and Cognite offer on data integration between our platforms and tools. But first, let's talk about a few essential and fundamental points when working with cloud providers. Using a cloud provider, most of the time, we think about saving on infrastructure costs. That is indeed true, but it's not the only truth. When you decide to move to a clo

I want to visualize some data from the API directly in Power BI. For instance, I want to display data about extraction pipelines https://api-docs.cognite.com/20230101/tag/Extraction-Pipelines/operation/listExtPipes. How can I use the CDF API as a data source in Power BI and input my credentials there in the correct way? Thank you! 

Hello,I am trying to understand the difference between Sequences and Events to see what is pertinent for me in a specific use case. For me, both of them allow saving a native CDF object along with a timestamp, enriched with other data. I feel like we can also achieve the same goal by making a data model with timestamps in one column and all the properties we are interested in, in other columns;  though this approach would be harder because it would necessitate using pygen for input output. In my company, we work on Industrial processing lines. At any given moment, a line is operating under a set of conditions, defined by a fixed set of set points, like ‘conveyor speed’ and various temperatures. Let us call the set points a, b, c etc. For set points a, b and c, we have measured values i, j and k.To assess whether the machine is operating normally, we want to compare the aggregated measurements (i-j-k) taken during a period when the set points are a-b-c with

Hello Cognite experts, I trying to find our some reference material to explore regarding integrating data from RTSP in Cognite. Are there any workflows/POC/documentation/examples available for this data source? 

Hello,Is it possible to host a pickle file (which contains a ML model) in CDF and use it to make predictions for the available data in the platform?If yes, what are the steps ? I browsed the documentation but I haven’t found anything relevant

In some AI demos built on top of Cognite, our approach to real-time inference involves retrieving the latest time series values for the equipment. Since our machine learning models are deployed and running on a cluster outside Cognite, we leverage the SDK to handle real-time inference by retrieving the most recent data as follows:real_time_data_bomb_hfx = cdf_client.time_series.retrieve_latest(id="bomt_hfx_time_series_id")Is anyone working with a different approach to send new data for inference to their machine learning models? I'd be interested in hearing how others are managing this process.We are also exploring more resilient approaches, such as: Streaming Data for Real-Time Inference (Event-Driven Approach): We plan to test Cognite's Kafka extractor as soon as possible to enable more seamless streaming. On-Demand Inference via API

I am making comparisons between time series data in CDF and PI. The reason is that in our tenants the CDF data is not 100% accurate compared to PI.However, from my testing I think that PI performs its aggregations with the timestamps “centered” at the aggregated time periods, while CDF puts the timestamps at the start of each aggregated period. Is it possible to specify how this is done with the Python API? From my study of the docs it appears not to be the case. The same applies to the PI Web API as well: I cannot specify how the timestamps are placed. The agreement with PI becomes significantly better if I place the CDF timestamps at the center of the aggregated time periods.My current workaround is the following:Fetch RAW data from CDF Shift the timestamps by 0.5x of the granularity Resample to the desired granularity Compute mean Interplate any missing valuesThe issue is that fetching raw data is a lot mo

Hi,I am running parallel tasks(Cognite functions) in Cognite workflows. One of the task is creating table in Raw db, where as other one is deleting the table. Whatever table is create, its name is passed as input to other function deleting it.First attempt I ran 150 parallel tasks in a 10 workflows(each 15 tasks). One table was not deleted from raw db  In second attempt I ran 200 parallel task in 10 workflows(each 20 tasks). In this case 8 tables were not deleted from rawI was not able to debug why those table not get deleted. As the delete function status was showing ‘Completed’ on Cognite UI.Can you please help why this tables are not getting deleted? If there is a limit of parallel task execution then how to exceed this limit? And how to debug this scenario where there is no function execution failure?Attaching the test workflow for reference. Run workflow-deploy-job.py file to deploy workflow on cognite project. Edit functio