Forum Discussion
Should dataflows live in the same workspace as semantic models?
my personal recommendation is that things should be grouped logically to do the the refreshing efficiently but also only giving access to what is required, so that it can be managed and governed easily. Each situation could be different.
It just depends on how far upstream the data is and if your workspace is operating like a company wide shared data store, then yes probably best to separate out the layers logically.
But if you have a smaller data project it might make sense to keep them all grouped together.
Thank you. Yes, our workspace will essentially be operating like a company wide shared data store. When you say grouped logically, do you mean, for example, grouping the dataflows that reference one another in one workspace, grouping semantic models that fall under domain A in a workspace A, and grouping semantic models that domain B in workspace B? Something like that?
I was also thinking we could use the OneLake data hub to act as the "data store" as a way to have everything in one place and promote certain dataflows and semantic models.