In the Data Foundation-project; there is an understanding that times series data will be represented in accordance with OPC-UA data model using the FDM (DM) functionality in CDF. Is there any issues here in relation to the limitations in FDM stating that there is a limit of 10 million instances? Or will it be designed so that this will not be an issue?
Hi all!I am trying to obtain weather data from Miros for a given asset ID. I am experienced in Python, but new to CDF, and don’t have access to a lot of practical examples of common tasks and patterns. So I thought I’d ask here to speed up my work :) What would be best practice for the given scenario (given in detail to try to avoid the XY problem)?I am going to work on some root cause analyses, and the starting point is likely to be an asset that failed multiple times for the same reason in the past. I want to fetch different types of data for this asset, such as maintenance history, relevant metadata, and weather data at its location during the relevant time window. This will eventually make its way into a simple graphical tool that I can use at the start of new root cause analyses.I have been playing around with retrieving an asset object based on its id or external_id. This seems to be fine. Then I notice that weather time series from Miros are connected to assets. But how would I
I am trying to stream data from CDF to Azure Event Hub with the Python SDK and cannot find anything related to streaming datasets. Only option so far (as I know) is dps = c.datapoints.retrieve_latest(id=184691546499795)which would need a trigger of some sort to keep running. Data from CDF are so called timeseries from different types of sensors. Is there any documentation on Streaming Data for CDF that I could look at or is Streaming really supported?
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