Important: This post was written and published in 2019, and the content below may no longer represent the current capabilities of Power BI. Please consider this post to be an historical record and not a technical resource. All content on this site is the personal output of the author and not an official resource from Microsoft.

INTERIOR: pan over cubicles of happy, productive office workers

CLOSE-UP: office worker at desk

NARRATOR: Is that Susie I see, giving Power Platform dataflows a try?

SUSIE: That’s right! With dataflows I can have all of the data I need, right where I need it!!

NARRATOR: Dataflows. They’re not just for Power BI anymore.

OK, you may not remember that orange juice ad campaign from the late 1970s and early 80s^[1], but I’ve had it stuck in my head since I started working on this post and video. I couldn’t figure out how to work it into the video itself, so here it is in written form.

Anyway, with that awkward moment behind us, you probably want to watch the video. Here is it:

As the video discusses, Power Apps now have a dataflows capability that is a natural complement to Power BI dataflows. Power Platform dataflows have been generally available since November 2019, and have been in preview since summer.

Power Platform dataflows use Power Query Online – and the same set of connectors, gateways, and transformation capabilities as Power BI dataflows. But there are a few key differences that are worth emphasizing.

Power Platform dataflows can load data into the Common Data Service, either into the standard Common Data Model entities used by Dynamics 365 apps, or into custom entities used by custom Power Apps. This is important – this makes dataflows more like a traditional ETL tool like SSIS data flows in that at the end of the dataflow creation process you can map the columns in your queries to the columns in these existing tables^[2].

Power Platform dataflows can load data into ADLSg2 for analytical scenarios, but Power Apps doesn’t have the same concept of “built-in storage” that Power BI does. That means if you want to use Power Platform dataflows to create CDM folders, you must configure your Power Apps environment to use an ADLSg2 resource in your Azure subscription.

The “link to data lake” feature in Power Apps feels to me like a better integration experience than what’s currently available in Power BI. In Power Apps you define the link at the environment level, not the tenant level – this provides more flexibility, and enables non-tenant admins^[3] to configure and use the data lake integration.

The first time you create a Power Platform dataflow and select the “analytical entities” option, you’ll be prompted – and required – to link the Power Apps environment to an Azure Data Lake Storage resource. You’ll need to have an Azure subscription to use, but the process itself if pretty straightforward.

I can’t wait to hear what you think of this new capability. Please let me know in the comments or via Twitter.

See you in the new year!

^[1] I just realized that this was 40 years ago. Were you even born yet?

^[2] CDS entities aren’t tables by the strictest definition, but it’s close enough for our purposes today.

^[3] I honestly don’t know enough about Power Apps security to go into too much depth on this point, but I am not a Power Apps admin and I was able to create a trial environment and link it to my own ADLSg2 resource in my own Azure subscription without affecting other users.

Important: This post was written and published in 2019, and the content below may no longer represent the current capabilities of Power BI. Please consider this post to be an historical record and not a technical resource. All content on this site is the personal output of the author and not an official resource from Microsoft.

The enhanced compute engine in Power BI dataflows has been in preview since June. It’s not really new, and I’ve posted about it before. But I still keep hearing questions about it, so I thought it might make sense to record a video^[1].

This video.

I won’t go into too much more depth here – just watch the video, and if you want more details check out one of these existing posts:

• Power BI dataflows and query folding
• Power BI dataflows enhanced compute engine
• Power Query Memory Usage, Dataflow Container Size And Refresh Performance from Chris Webb

Now to get back on schedule with that next video…

^[1] Also, I’m behind on my video schedule – this was a motivating factor as well. November was an unexpectedly busy month^[2], and between work, life, and not really having the video editing skills I need to keep to a schedule… Yeah.

^[2] And I expected it to be very, very busy.

Important: This post was written and published in 2019, and the content below may no longer represent the current capabilities of Power BI. Please consider this post to be an historical record and not a technical resource. All content on this site is the personal output of the author and not an official resource from Microsoft.

At this point I’ve said “Power BI dataflows enable reuse” enough times that I feel like a broken record^[1]. What does this phrase actually mean, and how can you take advantage of dataflows to enable reuse in your Power BI applications?

This Power BIte video is a bit longer than its predecessors, and part of this is because it covers both the problem and the solution.

The problem is that self-service BI applications often start out as one-off efforts, but don’t stay that way. At least in theory, if the problem solved by the application was widespread and well understood, there would be an existing solution already developed and maintained by IT, and business users wouldn’t need to develop their own solutions.

Successful applications have a tendency to grow. For self-service BI, this could mean that more and more functionality gets added to the application, or it could mean that someone copies the relevant portions of the application and uses them as the starting point for a new, different-but-related, application.

Once this happens, there is a natural and gradual process of drift^[2] that occurs, as each branch of the application tree grows in its own direction. A predictable consequence of this drift in Power BI applications is that query definitions that start off as common will gradually become out of sync, meaning that “the same data” in two datasets will actually contain different values.

Moving queries that need to be shared across multiple applications from multiple datasets into a single dataflow is a simple and effective solution to this problem. There’s no dedicated tooling for this solution in Power BI today, but the steps are still simple and straightforward.

P.S. This is the first Power BIte video recorded in my home office. After struggling unsuccessfully to get decent audio quality in my office at work, I’m trying out a new environment and some new tools. I know there’s still work to be done, but hopefully this is a step in the right direction. As always, I’d love to know what you think…

^[1] For my younger readers, this phrase is a reference to when Spotify used to be called “records” and the most common service outage symptom was a repeat of the audio buffer until the user performed a hard reset of the client application. True story.

^[2] Is there a better term for this? I feel like there should be an existing body of knowledge that I could reference, but my searching did not yield any promising results. The fact that “Logical Drift” is the name of a band probably isn’t helping.

Important: This post was written and published in 2019, and the content below may no longer represent the current capabilities of Power BI. Please consider this post to be an historical record and not a technical resource. All content on this site is the personal output of the author and not an official resource from Microsoft.

This week’s Power BIte is the first in a series of videos^[1] that present different ways to create new Power BI dataflows, and the results of each approach.

When creating a dataflow by defining new entities in Power Query Online, the final dataflow will have the following characteristics:

Let’s look at the dataflow’s model.json metadata to see some of the details.

At the top of the file we can see the mashup definition, including the query names and load settings on lines 11 through 19 and the Power Query code for all of the entities on line 22.

At the bottom of the file we can see information about the refresh and storage.^[2] Line 26 identifies the entity as a LocalEntity, which means that the entity’s data is physically stored in the current CDM folder.

Line 30 shows that the entity is fully refreshed rather than incrementally refreshed, and line 31 shows the file name where the entity data is stored. Lines 97 through 99 identify the single partition where the data for the current version of the entity is stored, including the full URI for the data file. If this entity used incremental refresh, there would be multiple partitions to match the incremental refresh policy.

If this information doesn’t all make sense just yet, please hold on. We’ll have different values for the same attributes for other dataflow creation methods, and then we can compare and contrast them.

I guarantee^[3] it will make as much sense as anything on this blog.

^[1] New videos every Monday morning!

^[2] The same information is also included starting on line 103 for the Promotions entity,  but is not pictured here.

^[3] Or your money back.

Important: This post was written and published in 2019, and the content below may no longer represent the current capabilities of Power BI. Please consider this post to be an historical record and not a technical resource. All content on this site is the personal output of the author and not an official resource from Microsoft.

This is still one of the most common dataflows questions: what’s the difference between Power BI dataflows and Power BI datasets?

For the last year I have resisted tackling this question head-on. This isn’t because it’s a bad or “dumb” question. Just the opposite – this is a very simple question, and the simpler a question is, the more complex and nuanced the answer is likely to be.

If you’re a regular reader of this blog, you probably already know the answer, because I’ve answered it already. Sort of. The existing answer is distributed across dozens of posts, and if you’ve read all of them and picked up the answer along the way. But I keep hearing this question, and I keep thinking that there must be a more direct answer I could share.

Here it is, in a single, simple table^[1].

Update: I’ve added a few rows to the table after the post was originally published, to incorporate feedback from readers on differences I had missed. Thank you!

Each of the rows in this table could easily be an in-depth topic in and of itself, so if you’re looking at any of them and thinking “that’s not quite right” I might very well agree with you. There’s a lot of context and a lot of nuance here, and we’re trying to sum things up in a word or two… which is kind of the whole point.

Oh yeah, there’s a video too.^[12]

I can’t wait to hear what you think!

^[1] A simple table with ten footnotes.

^[2] The storage aspect of dataflows and datasets is one of the most significant differences between the two. Datasets use the Vertipaq column store to load data into an optimized and highly compressed in-memory representation that is optimized for analysis. Dataflows use text files in folders, which are optimized for interoperability.

^[3] The Analysis Services Tabular engine uses the BI Semantic Model (BISM) to represent its metadata. This is a metadata model originally included in SQL Server 2012 Analysis Services, and used by the Tabular engine ever since.

^[4] Saying “this is the primary purpose” of any complex tool is fraught with risk, because no matter what you say, there are other valid things that remain unsaid. With this said… the big gap that dataflows close is that of self-service data preparation for the purpose of data sharing and reuse. Power BI has always had self-service data preparation through Power Query, but before dataflows the data that was prepared was “locked” in a dataset, for analysis, and not for sharing or reuse.

^[5] Once you have loaded data into dataflows, authorized users can reuse entities from multiple dataflows, and use them as the building blocks for new dataflows or new datasets. Once you have loaded data into a dataset (and published it to the Power BI service) you can enable users to connect to it.

^[6] With dataflows, users can pick and choose the entities they want, but a dataset can only be reused as-is.

^[7] Dataflow entities can be used as data sources in the same Power BI Desktop file as other data sources, and can serve as part of a mashup or composite model, but a dataset can only be reused as-is.

^[8] Although you can obviously use dataflows for reporting, you do so by first importing the data from the dataflow into a dataset.

^[9] It’s interesting to point out that using your own organizational ADLSg2 account does not require Power BI Premium, but using the XMLA endpoint to connect to Power BI datasets from non-Power BI clients does.

^[10] You can only import data into your dataflow entities, but tables in your dataset can import data or use DirectQuery, and a dataset can use a combination of the two.

^[11] You can only import data from a dataflow into a dataset. When connecting to a shared dataset you can only use Live Connections.

^[12] I’ve been thinking of making videos to supplement this blog for almost as long as I’ve been hearing the question that inspired this post. Please take a moment to share your thoughts on the video. This is something of a “soft launch” and although I have plans for a few dozen more videos already, your feedback will be a main factor in how the video series evolves.