Power BI is turning 10! Tune in for a special live episode on July 24 with behind-the-scenes stories, product evolution highlights, and a sneak peek at what’s in store for the future.
Save the dateEnhance your career with this limited time 50% discount on Fabric and Power BI exams. Ends August 31st. Request your voucher.
Hi all!
I am currently working on a project and am struggling with multiple issues. Could you help me?
Situation: we manage around 500 databases that contain data about ERP systems of clients. Each database/ERP system is related to one client of ours. The databases are spread over multiple on-premise SQL Servers.
Goal: to share dashboards and reports with these clients. Also, these dashboards and reports should be refreshed once or twice a day, in order for our clients to have access to up-to-date visualizations.
Thoughts: since it is not necessary to have real-time dashboards, I consider creating a streaming dataset as out of scope. I already installed a gateway to be able to get data from our on-premise SQL Servers.
Questions: - What is, in terms of storage and duplicating data, the difference between a dataflow and a dataset?
- Since scheduled refresh is possible in both cases: if ETL is performed before importing the data into Power BI, is there still a reason to create a dataflow? Or is importing a dataset sufficient?
- Is it secure to have all 500 client datasets/data flows in one app workspace? Or should there be 500 workspaces to maximize security?
- Which Azure services are necessary/beneficial to create this? I am considering Analysis Services, Datawarehouse, Event Hubs and Stream Analytics.
- Would it be better to merge all 500 databases into one data warehouse and then load all dataflows from this warehouse OR to keep all databases separate?
- How much of this can be automated/standardized? Because all 500 cases will go through the same process (ETL, creating dataflows, importing datasets, visualizations, sharing,...).
Check out the July 2025 Power BI update to learn about new features.
This is your chance to engage directly with the engineering team behind Fabric and Power BI. Share your experiences and shape the future.
User | Count |
---|---|
30 | |
20 | |
19 | |
13 | |
13 |