Forum Discussion
Impactful Individualusing datasets instead of etl
you could do this with Fabric, reading data with DataFlows Gen 2 and storing in a lakehouse/warehouse.
even a F2 could work (300€/month).if you can't use Fabric capacity, there's a more trivial approach that is to mount a custom ADLS Gen 2 to your workspace. if you create a dataflow gen 1 that read the data from your sources it will store it in CDM format in ADLS Gen 2 and you'll be able to read it back with Power Query.
basically you can schedule the dataflow to run every month and it will create the snapshot file in ADLS and you'll be able to compact all the snapshot in a Semantic Model leveragin ADLS connector.
I wrote about it 4 years ago: https://medium.com/riccardo-perico/pbi-dataflows-organizational-adls-put-your-pbi-into-data-pipes-e605df36f52f
official docs: Configuring dataflow storage to use Azure Data Lake Gen 2 - Power BI | Microsoft Learn
Super Useryou could do this with Fabric, reading data with DataFlows Gen 2 and storing in a lakehouse/warehouse.
even a F2 could work (300€/month).
if you can't use Fabric capacity, there's a more trivial approach that is to mount a custom ADLS Gen 2 to your workspace. if you create a dataflow gen 1 that read the data from your sources it will store it in CDM format in ADLS Gen 2 and you'll be able to read it back with Power Query.
basically you can schedule the dataflow to run every month and it will create the snapshot file in ADLS and you'll be able to compact all the snapshot in a Semantic Model leveragin ADLS connector.
I wrote about it 4 years ago: https://medium.com/riccardo-perico/pbi-dataflows-organizational-adls-put-your-pbi-into-data-pipes-e605df36f52f
official docs: Configuring dataflow storage to use Azure Data Lake Gen 2 - Power BI | Microsoft Learn