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smpa01's avatar
smpa01
Community Champion
1 year ago
Solved

Datflow gen2 incremental refresh parameters

  I am trying to understand this conceptually and came up with this. Is it correct understanding? I am simply trying to decipher what fields to utilize for filter, change data and correct sl...
  • v-hashadapu's avatar
    1 year ago

    Hi smpa01 , Thank you for reaching out to the Microsoft Community Forum.

     

    Yes, your understanding of incremental refresh is correct and aligns with how it works in Microsoft Fabric and Power BI. When dealing with mutable data, using a filter column like OrderDate defines a sliding window, for example, the last 14 or 50 days. Only data within this window is considered during each refresh. Any changes outside it will be ignored unless you increase the window.

     

    Within that window, the data is logically split into buckets based on your chosen granularity, typically daily in your case. For each bucket, the system checks whether the maximum value of ModifiedDate has changed compared to the last refresh. If it has, the bucket is refreshed; if not, it’s skipped. This is how Fabric efficiently refreshes only the changed data.

     

    You’re also right that monthly buckets behave differently, if anything in the month changes, the entire month’s data is refreshed. It doesn’t track day-level changes within the month, so daily buckets are better for precise, recent updates, especially when you're working within the 50-bucket system limit. You are correct, if ModifiedDate isn’t updated when a record changes, the system will miss that change entirely. Incremental refresh relies fully on the accuracy of this column to detect updates.

     

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    If this helped solve the issue, please consider marking it “Accept as Solution” so others with similar queries may find it more easily. If not, please share the details, always happy to help.
    Thank you.