Welcome to The Dashboard Effect Podcast. I'm Brick Thompson.
Caleb Ochs:And I'm Caleb Ochs.
Brick Thompson:Hey, Caleb. So what are we talking about?
Caleb Ochs:So today, we're gonna do a little episode on Azure and Power BI, using them together. AKA a match made in BI heaven.
Brick Thompson:I love it. You're so literary. (laughs) That's great. Okay, so tell me about it. Why is it a match made in heaven? Bi heaven?
Caleb Ochs:Yeah. Thanks for that clarification. So first of all, Power BI is great. Azure is great, both on their own. But they're especially great when you put them together, and you're trying to do data intelligence on them. So Power BI, there's plenty of content and our other episodes about why Power BI and why we like it so much. From it's $10 per user per month. thing, or its cost and to its flexibility, and its release cadence and all the resources Microsoft's throwing behind it. All those resources that Microsoft's throwing behind Power BI is not limited to just Power BI. They're also throwing a lot at their Azure Data Intelligence stack. So things like Azure SQL Database, or Azure Synapse, those types of tools. Data Factory, stuff like that.
Brick Thompson:Yeah, they've really made it a cornerstone of what they're doing. So much is based on what they're doing in Azure and Synapse. But let's, let's back up. Let's start by defining, what is Azure.
Caleb Ochs:So Azure is Microsoft's public cloud. So you think of it's an AWS competitor, it's a Google Cloud competitor. Essentially, you can put your IT infrastructure up on Azure. That basically covers it.
Brick Thompson:VMs and databases, and all kinds of services. Okay. And so what is Synapse?
Caleb Ochs:So Synapse is Microsoft's latest tool for its
Brick Thompson:the ETLs... all encompassing data intelligence platform. So it's
Caleb Ochs:...yeah, yep, your database pool, which is the going to include a lot of different resources. But, you know, I guess a couple of the resources are things like Data Factory are in there. So it gives you an interface to deal database itself. You put data lakes in there. You can actually with your Data Factory pipelines... put Power BI reports, you know, put them right into your Synapse workspace. So it's this kind of all encompassing data intelligence pane of glass.
Brick Thompson:Okay. And it, obviously resides in Azure, sits on top of Azure.
Caleb Ochs:Yep, it does.
Brick Thompson:Okay. All right. So actually, let's define another term here. What is Power BI desktop versus Power BI service.
Caleb Ochs:So the desktop version is something that you would download from Microsoft for free, put it onto your local machine. It's just an application that runs on your computer, like anything else, like Excel. And that allows you to create reports, create data models, that type of thing. Power BI service is where you would take your Power BI desktop file, and you would publish it to the service. The service is a lot more than just that, also. It has things like Data Flows, which are cloud-based queries that you can create. We could go into more detail on that later in another episode. But essentially, Power BI service is where you're going to distribute your reports for your end users. That's probably its primary use.
Brick Thompson:Okay. Yeah, I mean, the way I think of it, I mean, there's a lot to it. There's a lot of features and a lot goes on behind the scenes. But once you've created a report and the desktop application, in order for other people to view it, you could send them a copy of that .pbix desktop file, but a way better way to do it, is publish to the Power BI cloud service, and then share it with them. And so they can now see your report in a browser, on their phone, that type of thing. And so the Power BI service enables that.
Caleb Ochs:Exactly.
Brick Thompson:Okay, so why and how does Power BI work so well with Azure? Why do you call it a match made in BI heaven?
Caleb Ochs:So we'll start with the how. So the reason why you would even want to pair those two together is that Azure would be your data. That's where your data lives. So if you build, you know, if you engineer yourself a good data infrastructure in Azure, you will have plenty of connection options to pull that data into Power BI, build yourself a data model, and ultimately get some good reporting out of it.
Brick Thompson:even on the desktop, but in the service, it's highly optimized to perform extremely well.
Caleb Ochs:Right? Yeah. I mean, it just speaks to the Microsoft-Microsoft connection. It just works well together. They developed that.
Brick Thompson:Yeah, exactly. Okay, so good performance, really easy to connect to data. What else?
Caleb Ochs:So it's part of it's part of the Microsoft ecosystem. So if you have Office 365, you by default, have what's called an Azure Active Directory. So that's where all of your users live. And that's basically how you do permissioning and stuff on your Office 365 applications. And that is exactly the same security model that Power BI and Azure will sit on top of. It makes it super easy. You don't have to, you know, create new users and some other system. All you do is, you know, all your users are there. All your groups and security groups are already there. It just integrates with Office 365. I mean, it doesn't even integrate. It's just part of it.
Brick Thompson:Yeah. And that, that integration with the Active Directory, the AD, is so nice for being able to publish and share reports. But also, when you start doing Row Level Security, where you have a single report that different populations of users are looking at, and you want the different populations of users to only see their own data, or data that they should see, that AD integration makes that super easy.
Caleb Ochs:Yeah, yeah, anybody could do it.
Brick Thompson:You know, there's so many other advantages of being in Azure, doing your Power BI work there, like, you know, easy access to machine learning and all sorts of other stuff. But I think the the scope of this podcast is really just talking about Power BI and Azure. Are there any other sort of obvious direct advantages you want to talk about there?
Caleb Ochs:You mentioning, machine learning actually just made me think of this, I want to say before I forget it. If you build a machine learning model in Azure, there's a built in Power BI feature where you can call your machine learning API's from within your dataset and pull in machine learning data. It is unbelievably simple and slick. I mean, that's an amazing advantage if you're into machine learning.
Brick Thompson:Yeah. And that's only going to become more and more common, actually.
Caleb Ochs:Yeah. Right. I mean, they even have machine learning and AI out of the box stuff in their visuals in Power BI that goes out and uses Azure back-end resources to generate results. I mean, it's pretty amazing.
Brick Thompson:Alright, you're making me want to go play with some machine learning now. All right. What are the weaknesses?
Caleb Ochs:Well, there's no weaknesses.
Brick Thompson:So when we sat down to record this, I said to Caleb, come on, there's got to be one, at least for credibility, give me one, and we both racked our brains and, the tight integration, I mean, if you're using Power BI, there isn't really one.
Caleb Ochs:No. I mean, you're not going to find a better source to connect to with Power BI. You're also not going to find a better front end than Power BI if you're using Azure. So it really is that match made in BI heaven.
Brick Thompson:Someone's going to call us on this. They're gonna have something, but we couldn't think of it in five minutes of racking our brains.
Caleb Ochs:Oh, I'd love to see it. I would.
Brick Thompson:All right. So before we wrap up, what are... we just talked about machine learning. That's a pretty cool feature. But what are another one or two cool features or capabilities.
Caleb Ochs:So there's a capability that's out there, and it's been out for a while, where you can, I briefly mentioned data flows earlier, but you can take a data flow, which essentially emits a flat file, and it's this nice, easy Excel like interface to do some transformations on your data. You run that data flow, and it'll store that file in a data lake. You can configure where that file gets stored into your own Azure Data Lake, and then you can take that file and do other things with it. We actually had a use-case where we did this for a client, where they had their analysts do some light cleansing of some data in this data flow, dumped the file into the data lake. And then we sucked it up with a script and dumped it into a database. So they had all of their Data Flow data inside of a database, an Azure SQL database, for really easy consumption. It was a really slick, cool way to do that.
Brick Thompson:Oh, yeah, that sounds great. What else. Anything else?
Caleb Ochs:So, just came out is Data Marts. I mentioned this in a couple episodes earlier. And I actually got a chance to play with it a little bit over the last week or so. And Data Marts are pretty cool. You use that PowerQuery data flow feature in Data Marts and populate it. But once you've got it populated, it gives you a SQL Server endpoint to connect to. So you could use SQL Server Management Studio, which is kind of your interface to interacting with SQL databases, to connect to that data mart that's in the cloud, that's been provisioned automatically by the Power BI service. And you can query that data with SQL. It's pretty awesome.
Brick Thompson:And how are you putting the data in the Data Mart?
Caleb Ochs:So you could use all sorts of different sources, but the way that I did it, is I called an API, dumped some data into this data mart. And now technically I could build a Data Factory off of this data mart and go stick it into a data warehouse somewhere, if I want to
Brick Thompson:You just stole my last question about that. Yeah, that's really cool. I'm realizing as you're talking about these things, that these types of features, and cool things are coming out constantly. I mean, Microsoft is doing so much development here, that even in three or six months, this list is going to seem dated. There's going to be something better. So we'll come back to it if some of those pop up. Yeah. And there's a ton of stuff in the past that we kind of take for granted now. It's just part of the fabric that we're not mentioning.
Caleb Ochs:Right. Exactly.
Brick Thompson:All right. I think that about covers it. Any other comments?
Caleb Ochs:Yeah, I mean, we could talk for a long time about this. But I think we've covered the high level and everything I've wanted to wanted to say.
Brick Thompson:Okay, that's great. Thank you, Caleb. Thank you.