Forum Discussion
Incremental Data Load Options in Microsoft Fabric
- Anonymous2 years ago
Hi jashfabric
Thanks for using Fabric Community.
You can use a Dataflow gen2 for incremental load. Incrementally amassing data in a data destination requires a technique to load only new or updated data into your data destination. This technique can be done by using a query to filter the data based on the data destination.
For more information please refer to this link:
https://learn.microsoft.com/en-us/fabric/data-factory/tutorial-setup-incremental-refresh-with-dataflows-gen2
Hope this helps. Please let me know if you have any further queries. - 2 years ago
Hi jashfabric from a Spark/Notebook/Delta perspective, you could look into using Change Data Feed:
Delta Lake Change Data Feed in Fabric Lakehouses (serverlesssql.com)
Hi jashfabric
Thanks for using Fabric Community.
You can use a Dataflow gen2 for incremental load. Incrementally amassing data in a data destination requires a technique to load only new or updated data into your data destination. This technique can be done by using a query to filter the data based on the data destination.
For more information please refer to this link:
https://learn.microsoft.com/en-us/fabric/data-factory/tutorial-setup-incremental-refresh-with-dataflows-gen2
Hope this helps. Please let me know if you have any further queries.
- jashfabric2 years agoHelper II
Thanks for your help Anonymous AndyDDC
That really helps.- Anonymous2 years agoNot applicable
Hi jashfabric
Glad that your query got resolved. Please continue using Fabric Community for any help regarding your queries.
- jovianaditya2 years agoFrequent Visitor
Hello, i want to ask some question. Is this method (from the link that you provided) is only works for append condition? If i have 100 rows from day one, and for the next day there are 2 rows that have been modified will that method also automatically update my 2 rows?
- NotebookEnjoyer9 months agoAdvocate II
I think there is no "merge/update" write option in Dataflows. You can only overwrite the whole table or append. It is also not uncommon to keep the old versions of rows as well, for logging purposes.