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DennesTorres's avatar
DennesTorres
Power Participant
2 years ago
Solved

Eventstream and other architectures

Hi, On previous architectures, we are used to two core objects to build a real time data architecture: event hub and stream analytics. Stream analytics is capable to summarize the data received...
data engineering
  • Anonymous's avatar
    Anonymous
    2 years ago

    Hi  DennesTorres ,

    Eventstream offers same transformations as were available in streaming dataflows. The transformations are available when you configure a destination for Eventstream. Currently, those set of transformations are available for Lakehouse destination. 
    The transformations for KQL can be expected in the upcoming release of MS Fabric, as it is already on roadmap.
    Hope this information is helpful. Please let me know if you have any other questions.

    Please refer this document for more information :

    Process event data with the event processor editor - Microsoft Fabric | Microsoft Learn

Anonymous's avatar
Anonymous
Not applicable
2 years ago

Hi  DennesTorres ,

Eventstream offers same transformations as were available in streaming dataflows. The transformations are available when you configure a destination for Eventstream. Currently, those set of transformations are available for Lakehouse destination. 
The transformations for KQL can be expected in the upcoming release of MS Fabric, as it is already on roadmap.
Hope this information is helpful. Please let me know if you have any other questions.

Please refer this document for more information :

Process event data with the event processor editor - Microsoft Fabric | Microsoft Learn

  • DennesTorres's avatar
    DennesTorres
    Power Participant
    2 years ago

    Hi,

    I can confirm this solution works, but it doesn't seems a good architectural idea. 

    Each destination will need to develop their own transformations, duplicating the development time and creating a problem of parity, when one destination advances on the transformation more than others.

     

    The streaming dataflows could do the transformations as part of the dataflow, independent of the destination. My feedback is that this seems like an architectural throwback and implementing the transformations more like the streaming dataflow could bring advantages.

    We can notice this exactly now, when the transformations were already implemented for the lakehouse and need to be implemented again for the Kusto database, while it could have happened only once.

    Kind Regards,

    Dennes