If you use dataflows with Power BI Premium, you probably use linked and computed entities. There’s an overview post here, and an example of how to use these tools for data profiling here, but in case you don’t want to click through^[1], here’s a quick summary:

• When adding entities to a dataflow, you use another dataflow as a data source
• This adds linked entities to your new dataflow, which are basically pointers to the entities in the source dataflow
• You then use these linked entities as building blocks for new entities, using union or merge or similar approaches

This approach is simple and powerful, but^[2] it may not always give you exactly what you want. For example, what if you don’t want the users who have access to your new computed entities to also have access to the linked entities your new dataflow references?

Let’s take a look at what this looks like. I’m using the dataflow I build for that older post on data profiling as the starting point^[3], so if you’re a regular reader this may look familiar.

This is a simple dataflow that contains three linked entities and three computed entities. The computed entities use Table.Profile to generate profiles for the data in the linked entities. When you connect to the dataflow using Power BI Desktop, it looks like this:

As you can see, all six entities are available to load into Power BI Desktop.

What if you only wanted users to be able to read the profiles, without also granting them access to the entities being profiled? Why do dataflows give access to both?

The answer is equally simple, and obvious once you see it:

As with other dataflow entities^[4], the linked entities are enabled for load by default. Removing these entities from the dataflow is as simple as clearing this setting.

Once this option is cleared for the linked entities, the dataflow will look like this, with only the three computed entities being included:

And as desired, only these entities are accessible to users in Power BI Desktop:

Hopefully this quick tip is helpful. If this is something that has been making you wonder, please realize you’re in excellent company – you’re not the only one. And if you have other questions about using dataflows in Power BI, please don’t hesitate to ask!

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^[1] Don’t feel bad – I didn’t want to click through either, and wrote this summary mainly so I didn’t need to read through those older posts to see what I said last year.

^[2] As I’ve recently learned by having multiple people ask me about this behavior.

^[3] Because I’m lazy.

^[4] And Power Query queries in general.

Last month Microsoft announced on the Power BI blog an exciting new capability:

AUTOMATION & LIFE-CYCLE MANAGEMENT

‘Refresh Now’ API provides unlimited data refresh for Power BI Embedded and Power BI Premium

Using the ‘Refresh now’ API, the limitation  on the number of refreshes you can schedule per day is removed and instead  an unlimited number of refreshes can be triggered for each dataset. Combining the refresh now API with incremental refresh, you can build a near real-time dataset that performs small updates of fresh data very often.

Note: The time of existing refresh is not expected to be shorter, so a new refresh of a dataset cannot start before the previous one finishes. Remember that your resource limitations do not change with the introduction of this API, so use these unlimited refreshes with caution and be careful not to overload your resources with unnecessary refreshes.

Although the blog post only explicitly mentions datasets, the same “as many refreshes as you want” capability applies to Power BI dataflows in workspaces assigned to dedicated (Power BI Embedded or Power BI Premium) capacity.

It’s important to note that this is an API-only feature^[1]. If you’re setting up a refresh schedule via the UI, you’ll still see the same daily limits, but using the dataflows API you will now be able to have full control over the refresh schedule for your dataflows.

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^[1] This is by design, and is unlikely to change. A high-frequency refresh schedule can place a significant load on the capacity resources, and is a configuration that should only be made after careful consideration of the implications.

Power BI dataflows have been available in public preview since November 2018. For almost five months, customers around the world have been kicking the tires, testing and providing feedback, and building production capabilities using dataflows.

When Microsoft published the latest Business Applications Release Notes, the “new and planned features” list included dataflows general availability with a target date of April 2019, which could typically mean anything before May 1st.

But… April has just arrived, and so has the dataflows GA!

The full details are on the official Power BI blog, so be sure to check it out. Also keep in mind that although dataflows are now generally available, some specific capabilities are still in preview.