Microsoft Fabric & You!

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Pat Powers
BI Trainer and Consultant
Senturus, Inc.

Pat is one of our most popular presenters, regularly receiving high marks from participants for their subject matter knowledge, clarity of communication and ability to infuse. Pat has over 20 years of experience in data science, business intelligence and data analytics and is fluent across multiple BI platforms. They are a Tableau Certified Associate and well versed in Power BI. An expert in Cognos, their product experience goes back to version 6. Pat has extensive experience in Actuate, Hyperion and Business Objects and certifications in Java, Python, C++, Microsoft SQL.

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0:10
Hello and welcome to another Senturus webinar, this time around, Microsoft Fabric and you.

0:19
First off, I’ll introduce myself.

0:21
That’s the boring part.

0:23
Then we’re going to talk about the foundations of fabric, what it is, what it encompasses, why you care.

0:30
I’m going to do a demo on it.

0:33
We’re going to talk briefly about licensing and then we’ll wrap up with Senturus overview, additional resources and of course any Q&A that might still be open.

0:42
OK, that’s what we’re looking at here.

0:45
I’ve got some additional players on the on the back here in there.

0:48
Let me real quick say this that if at any point you do want to talk to us more about any of this is a pretty hefty topic.

0:57
OK?

0:58
You can get on our calendars, you can get on Kay Knowles’s calendar, and I just put a link in the chat window for you.

1:06
You can set up a 30 minute meeting with Kay to talk more about this, to go in depth and to just get some more information because again, today we’re going to talk about the foundations.

1:18
Let’s start with who is this weirdo talking to you?

1:20
Hey, that’s me, Pat Powers by, A consultant, trainer, data architect, yada.

1:27
Things you need to know about me.

1:28
We are coming up on 2024.

1:31
Wow.

1:32
You know, we’re closer to 20-30 than we are 2020.

1:35
How scary is that?

1:39
But yet.

1:39
But yet Seinfeld is coming back and we got Frasier on the air.

1:45
So I don’t know, I don’t know what decade it is some days, all right.

1:49
But I have been doing this for 27 years, coming up almost 3 decades.

1:55
Data science, data analytics, data warehousing, whatever term du jour you wish to give it.

1:59
This week been looking at data.

2:01
I am certified in Tableau, Cognos work with Power BI and just about every tool under the sun in these last 30 years.

2:10
I’m also certified in a number of programming and database languages, and I’m coming up on my 19th year with Senturus Wow.

2:21
And I know some of the Senturusions who are on this call are going wow.

2:24
2 decades of putting up with that.

2:27
That’s right.

2:29
You’ve put up with me for almost 2 decades.

2:31
How crazy is that?

2:35
Let’s start off this with a poll.

2:37
Let’s get you engaged.

2:39
Let’s see if you’re actually paying attention.

2:41
Are you ready?

2:42
Today’s polls, everybody.

2:44
I know.

2:44
I know.

2:44
You’re just on the edge of your seats here.

2:47
Today’s poll When do you expect to start using Fabric?

2:51
We give you some choices here.

2:52
I’m going to launch this poll as soon as I figure out where my mouse is.

2:55
There it is.

2:57
I’m going to let this run for about a minute, maybe two.

3:01
Half of you.

3:02
Half of you have no plans.

3:05
And again, I would wager that is because you want to know what the heck it is.

3:11
You don’t have any plans because you don’t know what it is you’re supposed to be implementing, right?

3:16
Right.

3:18
Let’s just scroll through his names a little bit.

3:20
Here.

3:20
Oh, look, I see some of my fan club.

3:22
Hello.

3:23
Hello, Penny.

3:24
We’re always there.

3:25
Penny.

3:29
Get all my, all my fans.

3:31
See.

3:32
Here’s Penny.

3:35
I see you all.

3:36
Don’t worry.

3:37
You’re always seen and heard in mind.

3:39
So again, we see a 50% of you have no plans.

3:41
I am surprised at the 15% of you that have actually implemented.

3:46
But then again, we’ll see when we actually get into this, we’ll see why it’s something you should consider implementing, right?

3:58
Therefore, let’s start with this.

4:01
What?

4:01
What is it?

4:03
What is Fabric?

4:05
No, it’s not the bad horror movie that you should be watching because it’s October that don’t.

4:10
It’s really a bad movie.

4:11
But this is a different kind of fabric, more of a suede.

4:16
I don’t know, kind of allure maybe.

4:18
No, let’s go back in time for a minute.

4:25
Let’s go back in time.

4:26
Let’s think back.

4:27
Some of you may not be old enough to remember these days, but there used to be a time when we had pay phones on the street.

4:37
Hey, there used to be a time when you would buy Microsoft Word, Microsoft Excel, Microsoft PowerPoint, those were all individual products.

4:53
Hey, that’s, I’m going way back for that.

4:56
But that there used to be a time when that was the norm.

5:01
And then one day Microsoft said, hmm, it’s really difficult.

5:07
If you try to copy and paste something from Microsoft Office into Microsoft Excel, it’s kind of hard if you try to have these things talk to each other.

5:19
Hey, how about we put them all together and make this easy?

5:24
And let’s call it Microsoft Office, right?

5:29
And that, of course, has merged into the O365 that we know today.

5:34
But that didn’t happen overnight and that wasn’t how it was in the beginning.

5:41
So those of us who’ve been using Power BI and Power BI service and Azure this and Azure that, it’s all been individual products.

5:51
It’s all been individual admin screens.

5:54
It’s all been individual trying to copy and paste and move things and have things talk together.

6:00
Oy, welcome to Fabric.

6:04
Fabric puts this all together for us.

6:08
Fabric is our modern day equivalent to that release of Microsoft Office.

6:17
In the same way that Office changed how you did business, Fabric is going to change how you handle data.

6:27
It brings everything together.

6:30
And because everything is together, we’re laying the foundation for modern machine learning.

6:37
That’s where you all go.

6:39
Oh, ah, I know, I know that you can’t chat with me right now, but I know that you’re all sitting there going, oh, ah, I can feel it.

6:47
I can feel the vie.

6:50
Right.

6:52
So here we go.

6:53
Look at this one.

6:57
Stop shopping.

6:58
My gosh, it’s like a Tesco over here.

7:02
We’ve got Azure Data Factory, we’ve got Azure Synapse, we’ve got Power BI.

7:08
We’ve got Co Pilot, We’ve got a Data warehouse.

7:12
Oh my goodness gracious, all of this under one umbrella, under one roof.

7:19
Everything that we need unified together and more important, one access point.

7:29
Look at this, look at this.

7:32
Explore the experience.

7:36
You know, sometimes it’s just so straightforward to say and use Microsoft’s words because look all your data in one location.

7:47
Organize, collaborate, create.

7:50
It doesn’t get any simpler or more straightforward than that.

7:53
Everything works together seamlessly.

7:56
And this is really, it really is going to drive change for your organization, OK, Because we all know the problems with silos.

8:04
We all know the issue when you’ve got multiple IT teams and you’ve got multiple data points and you’ve got multiple tools and Oh my gosh.

8:17
So we can put this all together.

8:19
And because of how this works, we’re going to see performance improvements.

8:26
And I’m going to get to those.

8:27
We’re going to see performance improvements.

8:30
We’re going to see modelling improvements.

8:33
We’re going to see getting to data faster.

8:38
OK, no more multiple tools.

8:41
You want to build a SQL query?

8:43
I got you covered.

8:45
You don’t like to write SQL.

8:46
You want to drag and drop?

8:47
I got you covered.

8:49
You need to take that transactional database that you’ve got and make it into more of a star schema so that it can actually work and be put into a data Mart.

9:00
All of this one place.

9:03
And there’s our underlying theme from right from the beginning, unification.

9:10
We all strive and struggle for one source of truth, don’t we?

9:17
We all want to be able to say we have one source of truth in our organization, whether it comes from Department A or Department Z.

9:28
You want one source of truth.

9:31
With fabric, we can have that, OK.

9:36
And a lot of this is built around this new Lake House experience.

9:41
Lake House has been way around for a while.

9:43
That’s not the new, it’s the way that they’re being implemented and integrated.

9:49
That’s the new and exciting part.

9:51
It is to sound like a salesperson.

9:54
It truly is game changing when it comes to how we can ingest and get our data out there.

10:02
So are you all excited now?

10:04
Have I?

10:04
Have I got your attention?

10:06
Come on, raise those little hands.

10:08
If you’re all paying attention and excited, show me that you’re here.

10:13
We having a good time?

10:14
Look at that.

10:14
There we go.

10:17
Penny, were you nervous?

10:20
When they introduced Microsoft Office, no, you said, Oh my gosh, I can now copy and paste my Excel spreadsheets into a Word doc.

10:31
That’s what you should be thinking here.

10:33
Any nerves you have should be nerves of excitement.

10:37
Hey this, this is first date material.

10:40
This is first date with the one material.

10:45
Tyler, we’re going to talk about licensing.

10:47
Towards the end, Ray Tyler asked a question about whether or not this is PPU, how that translates to fabric.

10:55
There is an entire section on this at the end, towards the end of air where we’re going to talk about licensing.

11:02
OK, Tyler, So just stick around, don’t go anywhere.

11:07
Promise me you won’t go anywhere if you, if you stick around, I’ll talk about that.

11:12
OK.

11:14
All right.

11:16
I know, Penny, that you love Cognos Framework Manager, and I promise you that you’re going to see a better modelling experience under the Fabric umbrella.

11:26
OK, so why is this new Lake House experience so exciting?

11:30
What makes it worth your time and effort?

11:33
Part of it is this Delta Lake Parquet open standard, OK, by saving the connectors in this new standard, we’re going to get a huge, huge improvement in performance.

11:47
Now of course, performance is subjective to your environment.

11:52
OK, we can’t say yes, it’s X milliseconds faster this or that because it’s not necessarily about the speed as much as it’s about the size and the speed.

12:03
You know, if you have a 10,000 row data source, come on, you can use that on your phone, right?

12:11
But what happens when you have a 20 million, a 100 million, a billion rows of data, That’s what we’re talking about here.

12:21
That is the type of thing we’re talking about.

12:24
And this holds true whether we’re talking about on Prem or we’re talking about cloud based data because again remember it’s all under one umbrella, it’s all under one interface, it’s all under one household.

12:39
OK, one slide.

12:42
There we go.

12:44
We also can now start doing our ETL work in this one section.

12:48
OK, whether we do it in a notebook and run it in a Spark session, whether we do it in Power Query Online, we’ve got options.

12:57
It’s all accessible from the same spot, it’s all accessible from the same place.

13:03
It moves that data, so we don’t have to worry about dealing with that any compute configure it moves it, it transforms it and when it does it writes it as a Delta Lake table.

13:17
So again we can do large, I’m going to give you a a sort of number here in a minute or so.

13:25
But when I say large, I’m talking large, I mean we’re, we’re talking Biggie Smalls large here.

13:31
OK, we’re going big penny.

13:37
I told you I would take care of you and your modelling issues.

13:41
Look, when we use this new lake house format and we start building these things out, we get a sequel endpoint.

13:51
And with that sequel endpoint, modelling becomes much easier.

13:55
Modelling becomes a lot less of a burden.

14:02
And I know there’s at least one of you out of all of you who are here that you like to write Sequel.

14:10
You know, I don’t care about any fancy pretty pictures.

14:13
I just want to write my queries.

14:16
Guess what?

14:18
Write your queries all from the same place.

14:22
You don’t have to have 73 different windows open.

14:26
You don’t have to try and work anything.

14:28
Come on, which one are you out there?

14:30
Wants to just spend your day writing Sequel queries?

14:34
I know you do.

14:35
Oops, wrong window.

14:36
Sorry, I know you do.

14:39
You want to write your SQL queries.

14:41
You want to copy and paste them all.

14:44
We got you.

14:47
But I’m new to this and I don’t like writing SQL.

14:51
OK, there’s a visual query builder.

14:55
This visual query builder lets you drag and drop.

14:58
So guess what that does?

15:00
That opens this up, That opens this up.

15:06
So now even your, let’s say, folks with less technical writing skills, people who don’t want to try and figure out how to write sequel, you know, for those for those people who don’t find reading sequel, for Smarties, a fun pastime, drag and drop.

15:27
You can drag and drop and you can create joins and unions and merges.

15:33
All of this and again, one interface.

15:39
So now I can do a lake house, I can do my transformations, I can do my modelling, I can do my, my analytics, raw analytics.

15:55
OK, when I build this lake house, guess what else I get?

16:00
I get a relational database and data model already set up that has zero config.

16:10
So think about this flow I’ve been taking you through here.

16:13
I’ve been taking you through a flow from the creation of a lake house to ETL work, to raw analytics with SQL, to now to now having a relational database where I can make relationships, I can write Doc’s and create my own measures.

16:35
And here comes the big one.

16:39
Changes can be viewed real time.

16:42
So again, we’re talking one source of truth, we’re talking one place to do all these things.

16:50
And yes, Vince, you did in fact hear me correctly.

16:56
No configure compute.

17:02
I just made Vince’s day, didn’t I, Vince, Not only that, but we’ve got live, live pools.

17:13
It’s already provisioned.

17:15
So yeah, this this is instant data.

17:19
This is the this is the top ramen of data here.

17:22
Simply add water.

17:24
OK?

17:26
Add water and enjoy a heavy sodium free afternoon.

17:30
Wait, no, no.

17:32
OK, Look, I want you to understand how important this is.

17:40
I can see that some of you are getting it.

17:42
Some of you are realizing that, wait a minute, I don’t have to have 17 different tools.

17:47
I don’t have to have resources I don’t have.

17:50
That’s right.

17:53
It’s all in one place.

17:54
It’s all set up.

17:56
And it grows with you.

18:00
It grows with you.

18:01
Because when you create all this, we’re moving through that flow still.

18:05
Oh look, I automatically get my data sets, and that data set is kept In Sync.

18:13
Automatically kept In Sync.

18:18
Dude, I don’t get excited about the When you’ve been doing this for 27 years it gets hard to get excited about things.

18:28
But anything, anything that makes my life easier and makes means less resources and means less time spent.

18:35
Heck yeah, I’m going to get excited, hey.

18:40
Heck yeah.

18:41
And you should be too.

18:44
So I told you a minute ago I was going to give you a number or semi number.

18:50
Hey, Mario.

18:54
Mario just asked a question about what’s the difference between a data warehouse and a data lake.

19:00
That Mario is a bigger question that that I would not adequately answer in the time that I have given to me today.

19:09
That’s one of those questions that I would need a little bit more.

19:13
And Mario, what I would recommend is what I said earlier.

19:18
That’s the kind of question where I would recommend you set up a meeting, get on the calendar because that’s, that’s the kind of thing.

19:26
Yeah, it’s bigger than what I can answer today unfortunately doesn’t mean I can’t.

19:36
This means that right now that would take me too long.

19:40
Give you an adequate answer.

19:42
OK.

19:42
So look, can everybody really focus in for a second and look at that one word in green?

19:49
Look at that one word in green?

19:54
Come on.

19:55
Petabytes of data, Petabytes.

20:01
So I can have the same performance that I get on my 10,000 row data source with a data source that has petabytes.

20:10
Yeah.

20:12
Now, who’s excited?

20:18
Well, yeah.

20:19
Thank you, Sridhar.

20:21
What do I what do I got to do to get you kids excited?

20:23
I’m giving you petabytes of data.

20:28
No complex steps.

20:30
But wait.

20:31
All right, all right.

20:32
You want me to give you something else that makes you excited?

20:34
How many of you have longed for real time data?

20:43
Oh, that perked you up, didn’t it?

20:47
How about real time analytics in this new environment?

20:51
Who’s excited about that?

20:53
Come on, show me those hands.

20:54
Show me.

20:56
There we go.

20:56
Look at that.

20:59
You’re going to love this.

21:02
I can create a KQL database from a dedicated real time analytics pane that lets me hook this up to streaming data.

21:17
So not only do I have my on Prem, not only do I have my cloud, now I’ve got streaming data on one place too.

21:26
How?

21:26
And you One stop shopping.

21:29
Hey one stop shopping.

21:33
Uh oh, thank you Richard.

21:35
Hey, just for those of you sitting on the background, we’ve had a folks, some folks try to grab the presentation and it’s getting the page does not exist.

21:50
So if you can go ahead and take care of that, I’d appreciate it.

21:55
This, Richard, is why I have folks in the background there that can help me out with things like that.

22:02
So again, this should be the link.

22:05
If it’s not the link, somebody, one of my fellow Senturions, please, please correct it.

22:12
And Richard, we will get that taken care of.

22:14
Thank you for bringing that to our attention.

22:17
So I’ve got a lake house, I’ve got a pipeline, I’ve got everything updating automatically.

22:25
I can support petabytes of data.

22:28
Oh, look, I can do ad hoc queries against those petabytes of data.

22:32
I don’t know why I chose a cat icon for that.

22:35
It just seemed appropriate.

22:37
Maybe because, you know, we’re petting it.

22:38
I don’t know.

22:41
I like the cat.

22:42
It’s by the way Happy Black Cat Day tomorrow.

22:46
For all of you who own Black Cats like we do is Black Cat Friday.

22:52
So we can take the streaming data, we can do ad hoc queries against it, we can do it drag and drop.

23:01
We can write sequel.

23:04
We can immediately do our analysis.

23:09
Immediately we take that lake house and we turn that into a data warehouse.

23:22
So here we’re starting to get a little bit into this, Mario, definitely not as deep as you like, but we can use the Synapse data warehouse experience.

23:30
We put that on top, so it goes on top of your lake house.

23:35
So here we’ve got an enterprise data warehouse.

23:39
Don’t have to provision anything, Vince just comes along with the territory, all right.

23:46
It’s all in one, all there.

23:51
When we build that, we can do a recommended artifact, a sample or a tutorial, We name it.

23:58
We don’t have to configure computer storage.

24:01
Boom, it’s accessible.

24:02
Penny.

24:03
You can model against it.

24:05
You’re good to go.

24:07
And how do we get data into it?

24:11
Got a new data pipeline.

24:14
It’s got all the connectors you can want right out-of-the-box.

24:18
So again, we’re going to on Prem, we’re going to cloud.

24:22
It automatically adds the tables.

24:25
They’re in the delta like format.

24:28
We get the performance that we’ve wanted against petabytes worth of data.

24:34
What?

24:35
What more do we need?

24:37
I mean other than $1,000,000 and you know, to retire to the beach, but what else do we need?

24:43
Right.

24:48
So Penny, what we’re talking about is when the tables are added, they’re added in what is known as an open delta format which allows them to have better read performance.

25:01
OK, Vince, I am.

25:06
I would have to look up to see if there’s an IB MDB2 connector.

25:10
If anybody who’s on knows the answer to that already.

25:13
But you know what?

25:14
I can look that up for you.

25:16
I’m happy to look that up.

25:17
I don’t know off the top of my head if it has ADB 2 connector.

25:22
Do you know what?

25:26
I’m going to make a note of that and find out for you.

25:32
Yes, you can.

25:39
Here you go, Vince.

25:45
Boom, there you go.

25:51
There’s a link to bringing your DB2 data into Microsoft fabric.

25:56
I was going to be shocked if there wasn’t one, be quite honest.

26:02
But I wanted to be able to give you an actual answer and that is from just a few, just a couple months back, just from June.

26:11
There you.

26:11
There you go, Vince.

26:15
Now obviously I didn’t have time to read that article.

26:17
I’m trusting because it’s coming from the Microsoft community.

26:22
So let’s see what this creates.

26:25
Let’s take a look at this.

26:29
So here I am.

26:32
I’m in my fabric.

26:34
Notice my fabric.

26:35
Workspace Fabric is enabled in this workspace.

26:38
Fabric is enabled here, which means that when I go to create Richard, just an update on that, there are folks in the background who are looking into it.

26:56
And just for everybody’s knowledge, we will make sure that when this is over that we will, the presentation will absolutely be available to you.

27:03
Usually it’s within an hour or two.

27:05
OK, so worst case, you might have to wait an hour or two, but it’ll be there.

27:14
I promise you.

27:16
People on the background telling me they promised me.

27:20
All right, so I’m going to go into my workspace, and what I would do is I would want to create a new look at all this stuff.

27:29
Look at all this stuff I have access to here.

27:31
All right.

27:33
Got a lake house, a notebook, a spark job, a data flow Gen.

27:37
2.

27:38
I’ve got my data science.

27:41
Again.

27:41
One, stop shopping.

27:43
There’s my KQL.

27:45
There’s a KQL query set.

27:47
Here’s an event stream.

27:48
It’s all in one place.

27:53
This this is cool.

27:54
So I would go ahead and I would create a lake house.

27:56
I would name my lake house and then I would want to tell it how to get data into it.

28:04
OK.

28:05
And what I would do there is I would create data flow.

28:12
Now what I did for mine is I imported.

28:14
You know what I got the time I’m going to do just because I can’t.

28:20
You’re welcome.

28:25
Yes, I contain space.

28:29
I want you to see this next screen.

28:39
This next screen is pretty good.

28:43
So how do I get data in?

28:46
I can create a Gen.

28:48
I can create a pipeline.

28:49
I can have a shortcut.

28:50
I can open a notebook.

28:52
Here’s the tables.

28:53
It’s going to create a Sequel endpoint for me.

28:56
Hey, I’m going to go with Data Flow Gen.

29:01
Now here’s something really cool.

29:05
I can import from a Power Query template.

29:08
I can also import from Excel, SQL Server.

29:12
Here’s all the different connectors I can take it from for my data hub.

29:17
I can write a blank query.

29:19
I can upload a file.

29:21
Look at this when I said out of box connectors.

29:29
OK, raise your hand if you’re impressed.

29:35
Yeah, look at that.

29:40
So if we do, if we look at database, there you go, there’s your IBM DB2 right there.

29:51
But I can bring it in for any of these.

29:53
OK, now I have a Power Query template that I used, OK, and that creates my that created mine.

30:08
I’m going to go into it, please.

30:16
Sales Lake house.

30:17
So there’s my data set, There’s my sequel endpoint.

30:23
Here’s my lake house itself.

30:27
So I brought these tables in.

30:40
Oh, got all my different tables.

30:49
I can go ahead and I can edit these from the endpoints.

30:52
So this is my lake house.

30:53
This is my lake house itself.

30:55
This is where I brought everything in.

30:57
This came from a Power Query template.

31:01
So I’ve got my lake house and if I want I can switch over to my Sequel endpoint.

31:07
When I switch over to my Sequel endpoint that was created when I built that lake house and I move the files in from a data flow here, here’s where I can start doing things like modelling and I can create my joins.

31:25
So Penny, I’m trying to make sure that you see that this is pretty straightforward.

31:29
I can do my modelling here, OK, But the cool part is here where I can go ahead and I can write my queries out, and I can set up my queries and I can go ahead and I can just very simply.

31:47
Let’s just say I wanted to do something simple like this and copy and paste this.

31:51
So not writing a report.

31:53
Not writing a report, gang.

31:55
OK, literally copy and pasting sequel.

32:07
A raw sequel and I’m just running this against this data now.

32:21
Notice what I can do with this.

32:23
I can download it to Excel or I could visualize the results.

32:32
All right, I can visualize this.

32:37
And when it says it, you have to select what you actually want to visualize.

32:45
So it’s going to.

32:46
Oh, all right.

32:51
And now we have a technical issue.

32:53
Of course we do.

32:54
But this would open it up and it would give me a visualization of my data right from here, right from this one spot.

33:04
I’m still, I’m not building reports, I’m not using desktop.

33:07
I’m doing this all from one place.

33:10
I’m setting everything up.

33:11
And then again, what if I want to do a visual query?

33:15
OK, we can do a visual query.

33:20
So if I just want to drag things over, look at that.

33:28
So any of you who’ve used tools like SSIS, if you’ve used Informatica, if you used any of these tools, even Tableau Prep, just dragging things over and then I can use this to transform my data.

33:44
So I’m doing Power Query online, right?

33:48
So I can go ahead and say, hey, reduce rows.

33:50
You know what?

33:51
Just give me the top 500 rows.

33:55
Look at that.

33:58
So any skill level here, Penny.

34:03
So everybody, Penny just said that part of the issue is that this is changing rapidly.

34:08
You’re absolutely right.

34:10
It is.

34:11
It is changing rapidly and that’s why you have us, right, Penny, we’re here to help you get through these rapidly changing things.

34:20
So we stay on top of this.

34:23
That’s our job.

34:24
Your job is data.

34:29
So now if I want I can bring over another one.

34:34
I can take both of these.

34:36
I can merge them together.

34:37
Again, look at look at how straightforward and simple this is.

34:41
OK, this is Power Query Editor.

34:43
It’s Power Query Editor right here.

34:46
All I got to do is tell it what my join is, tell it what kind of join I want.

34:50
Matter of fact, I’m going to do an inner.

34:52
I match all 500 rows.

34:56
OK, life is good.

35:01
I expand it out, tell it what data I want to keep, and boom, look at that.

35:13
Now here’s the nice thing.

35:16
I can actually save this if I wanted to.

35:20
I can save this as a view.

35:22
I can save these so that I can come back to them later.

35:25
Here’s one that’s basically what I just did, but I’ll finish and again, I could visualize the results.

35:32
Now let’s see if it lets me this time or if it yells at me again, might yell at me again because why wouldn’t it?

35:47
Here we go.

35:51
Look at how exciting that is.

35:52
All right, This is why you don’t do things now.

35:57
But as you can see, at least I didn’t get an error that time, right?

36:11
Right.

36:12
So I can.

36:15
Tyler, to your question.

36:17
I can see the properties, I can add my own DAX.

36:21
I would not necessarily see the underlying M from here.

36:26
I would see that back up the process as I was creating the pipeline and as I was creating the load for the tables.

36:35
OK, I can’t see any stored procs or functions or anything that I put in here.

36:41
This is more the transformation of the data.

36:44
But if so from here no.

36:46
I would go back up a step to see that underlying code and to see where it came from and what was changed there.

36:55
Again, I can save these, I can share them, I can add security if I need to, so I can have any database roles.

37:04
All this I haven’t left.

37:06
I’m in one place.

37:09
So again, Tyler, if I wanted to see what was the underlying for here, I would go back to the lake house itself.

37:15
OK, here, I’m using this for analytics now.

37:21
Now that I’ve got all this right and I’ve done what I want to do, let’s talk about what most of you want to do this.

37:36
Back here at my workspace, I’ve also got my pipeline.

37:44
OK, got my pipeline.

37:47
My pipeline lets me make sure that this stuff gets loaded and that it gets that it gets set up correctly.

37:53
And check this out in my pipeline I’ve added a mail on failure option.

37:59
OK, so if this does fail, I get emailed for it right here in my pipeline.

38:05
So here’s my data flow.

38:08
My data flow also does a mail on failure.

38:12
So I’ve got this set up coming out, and what’s pretty cool is in here.

38:21
Check this out.

38:24
I can add in code in here so I know exactly what pipeline failed against which workspace and when it failed.

38:33
OK, so I can add in parameters, I can add in functions, I can add in any variables.

38:40
All this right in my pipeline.

38:41
Again, I could do that in a notebook if I wanted to.

38:49
All that, all that’s put together, what does that get me?

38:53
Guess what?

38:54
That gets me A data set.

38:59
And the data set was created, known as data set default.

39:03
My data set was created.

39:05
So now I can use this to build reports.

39:09
I can even use it to build paginated reports.

39:16
Look at that.

39:19
One set of data.

39:20
One lake house, one data warehouse, one place.

39:25
I’ve got a sale regional paginated report.

39:30
I can create a report.

39:36
I can let it create a report for my data.

39:42
Well, that’s doing this.

39:46
I’m going to go ahead and I’m going to open up Power BI Desktop.

39:50
There’s the auto created report.

39:52
Look at that auto generated gang.

39:57
Look at that.

40:00
I know you’re all impressed because you’re all still here 45 minutes into it.

40:08
All of this is here.

40:09
I can export this.

40:10
I can analyse in Excel, I can send it off to PowerPoint.

40:14
I can embed it, I can save a copy, I can see my data tables and I can edit it.

40:20
But I want to show you one last thing.

40:23
One last fun thing.

40:25
Part of Fabric is also within Power BI itself.

40:30
OK, I’ve got a blank workbook.

40:34
It’s basically just a standard set of data.

40:37
You can see it’s a lot of same data that we just had.

40:40
OK, but I can use what’s known now as Copilot.

40:45
Look at this suggestions with Copilot.

40:49
One of the one of the most challenging things when working in Power BI Desktop is writing DAX.

40:57
You know it, I know it.

40:59
OK, so instead of having to write out Dax, instead of having to try and figure these things out, how about I just ask Power BI and say, hey, can you show me the total sales for mountain bikes for 2000, calendar year 2019?

41:25
Could you do that for me?

41:28
You bet I can.

41:35
Look at that and oh look, there’s all the Dax.

41:42
Tell me that’s nifty.

41:48
That’s pretty nifty.

41:49
Say, hey there, I knew I’d get you on board.

41:52
Penny.

41:52
You know what?

41:53
Here’s an even more fun one that’s very straightforward.

41:57
Hey, you know what?

41:58
Can you calculate me out the DAX for year over year profit?

42:05
You bet I can.

42:12
Here’s my year over year profit and then I can add this right here that it becomes part of my part of my code.

42:32
Here’s my new measure.

42:33
Obviously I would want to rename that something better.

42:43
Got my mountain bike ones, total sales for mountain bikes, calendar year 2000, teen add, rename and Bing bang boom I’m done.

43:22
I just built a report.

43:27
Give me an oh and an ah, come on, that was neat.

43:32
And add my year over year profit to that.

43:39
Look at that.

43:42
All I had to do was type things in.

43:43
Now this, this is here in Power BI Desktop and again it’s called Copilot.

43:49
But this just shows you the future of where we’re headed.

43:52
This shows you what is going to be built in.

43:55
This shows you the excitement.

43:57
This shows us what’s going on.

44:01
This is pretty cool, OK?

44:03
And all of this came from here.

44:08
All this came from this data set.

44:11
So again, I’m staying basically in one place.

44:14
This is all coming from one place.

44:17
It’s all coming into one area, OK, Tom, I don’t, I don’t have a good answer to that one.

44:36
Tom, I just drive the truck, OK.

44:39
Contents may settle during shipping.

44:45
So what do we think?

44:49
We all like what we’re seeing here.

44:54
We’re all happy and excited about that.

44:56
That’s pretty nifty stuff, isn’t it, Kay?

45:01
So let’s get.

45:07
Oh, look, we are.

45:08
Hey, I’ve just been told that the link is fixed.

45:13
All right?

45:14
It should be in the chat window.

45:15
Thank you, Kay, for staying on top of that.

45:20
I want to wrap up, Tyler, I hope you’re still here.

45:22
Tyler, are you still here?

45:25
Yes, You’re still with me.

45:26
I told you, I promised you, Tyler, if you stuck with me, I’d get to it.

45:30
Licensing and Fabric.

45:33
To understand licensing, we need to understand the different components of Fabric.

45:36
We have to understand the fact that that within this world, we’re talking about a tenant, OK?

45:45
We’re talking about capacities and we’re talking about workspaces at the foundation as a tenant.

45:51
Within that is our capacity.

45:54
Within that capacity is our workspace.

45:58
Now there are two types of licenses.

46:01
It’s very unlikely that you’re going to be this lonely person sitting at the food court contemplating our life.

46:07
No, you’re one of these happy people over here holding hands and doing all the fun stuff.

46:13
So, so take a look at this Tyler.

46:16
Here’s where you’re going to be able when you download this presentation, you’re going to see that yes, there are certain capacities that are required in order to leverage fabric PPU to be able to do this.

46:32
You want to be able to create workspaces and share the content, Yes.

46:38
It also depends on your on your SKU and F SKUs.

46:44
A skews do not support fabric.

46:48
You have to have an F SKUs.

46:52
There are individual licenses, but come on, who’s going to use an individual license?

46:56
It’s more here.

47:00
Now I want to pause for a second.

47:02
We got about 8 minutes left.

47:03
I want to pause for a second.

47:05
Look, I’ve only spent 48 minutes with you.

47:08
Really this is much more.

47:15
I’m going to in a few minutes show you here a workshop.

47:19
Our workshop is going to spend a lot more time on this.

47:23
Tyler, I would encourage you to come to the workshop, get a copy of the workshop because we spend a lot more time talking about this.

47:31
There’s a lot more charts that show the different capacities and different SKU types and all that.

47:38
Or or if you really want to do this the easy way, get on Kay’s calendar.

47:45
Kay can help you out.

47:46
Kay can go ahead and get you set up and figure out what your licensing needs are going to be.

47:52
Here’s that link once again.

47:55
OK.

47:57
And Tyler there.

47:58
That’s the exact kind of thing that I would say, get on Kay’s calendar, spend 30 minutes with Kay because then you can get an answer that’s tailored specifically for you.

48:09
OK.

48:10
I’m talking at the highest level here today, right?

48:15
But that’s what we’re here for, right?

48:21
Let’s wrap this up.

48:22
We got 6 minutes.

48:23
Let’s wrap this up gang.

48:26
The name of the game.

48:28
Unification.

48:30
One place, Real time on demand, getting it into the right hands and doing it from one place.

48:39
Vince is happy because there’s no configure compute.

48:42
We’ve got an integrated, simplified experience and I don’t care if you’re using 10,000 rows or if you’ve got a petabyte of data, it’s going to handle it.

48:53
It’s going to work across your entire organization.

48:57
As I just mentioned, we’re going to have a workshop, we’re going to have it in person, and I believe we’re else.

49:03
So I don’t know why I don’t have dates on this.

49:05
Nobody tells me anything.

49:07
We’ll have a recorded version of it, but if you want to sign up for an in person workshop, here’s the link right here.

49:17
It’s in the materials.

49:18
You can download the materials.

49:20
We also have, as we’re getting towards the end of the year, a 2023 BI Tools guide that helps compare all of these different tools.

49:28
It has been recently updated to include fabric, so you’ve got a nice update.

49:35
You’ve got everything you need right there.

49:39
OK, so if you want it, get it and notice where you have to go.

49:46
Why?

49:46
It’s like we want you to go to our website or something.

49:49
How crazy is that?

49:51
Because you know what else you’ll find there Hundreds of free resources on our website and our knowledge centre.

49:57
More than just comparisons, if you really enjoy listening to me, you can find recordings I’ve done out there.

50:04
There’s blogs that we’ve all written.

50:06
There’s upcoming events.

50:08
My goodness gracious, it’s all at Senturus.com/resource is Speaking of upcoming events next week.

50:19
Next week I will be doing AQ and a session.

50:22
They were known as chat with Pat’s.

50:23
Now we’re calling them Q&A because that sounds better.

50:26
Wednesday, October 18th, I’ll be talking about the Power BI service.

50:31
Spend as much time needed on November 15th.

50:36
Penny, just for you, we’re going to have a cognitive 12/1.

50:41
I’m going to talk all about Cognos 12 just in time for the end of the year.

50:45
And then again we will be having an in person workshop.

50:50
We need to know when and where you need to sign up for it.

50:53
OK.

50:54
Thank you Kay for that link.

50:56
Kay has just put in there the link to the to that comparison chart.

51:03
And so we got things coming up.

51:05
I’m busy towards the end of the year.

51:07
Look at that.

51:08
OK.

51:08
Those of you who’ve been around for a while, you know that about mid-december, I’m done.

51:13
Get in, get on these things now, get on my calendar before the end of the year.

51:19
Hey, here at Senturus, we provide a full spectrum of analytic services no matter what it is.

51:26
We can accelerate your bimodal.

51:28
BI.

51:29
Can you tell that I’m in the marketing stuff now?

51:31
OK, look at all these cool things we do, people look at it.

51:37
We are here not just for your technical stuff, but we’re here for your strategy.

51:41
We’re here to help you with governance.

51:43
We’re here to help you change your corporate culture.

51:48
And you know what?

51:49
We’ve been doing it for 22 years, 1400 different clients, 3000 projects.

51:59
Look at some of these folks.

52:00
You might be some of these folks.

52:03
Hey, we’re here to help.

52:08
If there’s something I missed, please e-mail us [email protected], Use that Calendly link that’s right there in the chat window.

52:17
Talk to us.

52:18
Come to my sessions next week.

52:21
Come talk to us.

52:22
We like to talk.

52:25
And with that, at 258 with two minutes to spare.

52:29
Thank you all for attending.

52:31
You’ve all been wonderful.

52:33
Get the heck out of here and go enjoy the rest of your Thursday.

52:35
Have a wonderful weekend.

52:37
If you’re off to Spain, have a wonderful trip and we’ll see you when you get back.

52:42
OK.

52:43
Have a great time.

52:46
You’re all wonderful.

52:47
Thank you very much.

52:48
Again, this presentation will be available on the website within an hour or two You can get the presentation and soon you’ll have the recording.

52:57
Thank you all for coming.

53:00
You’ve all been wonderful.

53:02
Thank you all for those asked questions.

53:04
Thank you for sticking around till the end.

53:07
Have a great rest of your day everybody, and we’ll talk to you soon.

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