0:10 Hey everyone, thanks for joining us for
0:13 the next session of Fabric Data Days. My
0:15 name is Anna. I'll be your producer for
0:17 this session. I'm an event planner for
0:18 Reactor joining you from Renmond, Washington.
0:20 Washington.
0:22 Before we start, I do have some quick housekeeping.
0:24 housekeeping.
0:25 Please take a moment to read our code of conduct.
0:27 conduct.
0:29 We seek to provide a respectful
0:30 environment for both our audience and
0:33 presenters. While we absolutely
0:35 encourage engagement in the chat, we ask
0:36 that you please be mindful of your
0:38 commentary, remain professional and on topic.
0:40 topic.
0:41 Keep an eye on that chat. We'll be
0:43 dropping helpful links and checking for
0:45 questions for our moderators to answer live.
0:46 live.
0:48 Our session is being recorded. It will
0:50 be available to view on demand right
0:53 here on the Reactor channel.
0:54 With that, I'd love to turn it over to
0:56 our presenters for today. Thank you both
1:02 >> Hello everyone.
1:03 >> Hello everyone.
1:06 >> Welcome. Welcome to So You Want to Be an
1:09 Analytics Engineer. We will jump right
1:11 in. Thank you so much Anna for that
1:13 introduction in the code of conduct. Uh
1:16 and we will start with Shabnam with an introduction.
1:18 introduction.
1:20 >> Thank you Stephanie. Hello everyone. I'm
1:22 Shabnam Watson. I'm a Microsoft data
1:25 platform MVP and I help companies with
1:28 their data and AI projects. I've worked
1:30 with Microsoft data products for 20
1:33 years and for the most recent years I've
1:36 been focused on PowerBI and then most
1:38 most recently on Microsoft Fabric.
1:41 Speaking on uh speaking about Microsoft
1:44 Fabric and PowerBI, I'm also
1:45 co-authoring a book on analytics
1:47 engineering with Microsoft Fabric and
1:51 PowerBI which is a great timing and I'm
1:52 so happy to be here today in this
1:56 session uh talking to you about it with
1:58 Stephanie of course. So Stephanie back
2:00 to you.
2:02 >> Thank you so much Shabnam. I'm so happy
2:04 to be here with you today. Uh my name is
2:06 Stephanie Bruno. I'm also a data
2:09 platform MVP and a Microsoft certified
2:12 trainer. I am an independent consultant
2:14 and trainer for mainly fabric and
2:18 PowerBI. Um like Shabnam, I've got about
2:20 20 years of experience in this field and
2:22 have been mainly focusing on fabric and
2:25 PowerBI in the past few years. Um and I
2:27 am joining you today from Pittsburgh,
2:31 Pennsylvania where it's very very cold.
2:33 Um so next up, let's meet our
2:35 moderators. We are so lucky to have
2:37 three experts today who are going to be
2:39 very happy to answer all of your
2:41 questions about fabric in the chat.
2:44 We've got Anu Pama, Philippa, and Chris.
2:46 So feel free to make this very
2:48 interactive. Ask them all of your
2:50 burning technical questions and they'll
2:53 be happy to answer them for you.
2:56 So with that, let's learn a little bit
2:57 more about what we're all here for.
3:00 We're here for Fabric Data Days, which
3:02 is 50 days of live learning,
3:04 certification prep, vouchers. I know a
3:06 lot of you are probably here for the
3:08 vouchers, uh data viz contests,
3:10 community connection, and more. So,
3:12 there's a lot to be uh part of with this
3:15 fabric data days. So, just make sure you
3:17 you look at this link here, this aka.ms/fabric
3:20 aka.ms/fabric
3:23 data days. The organizers will put lots
3:25 of links in the chat for you throughout
3:28 the hour together. Uh but when in doubt,
3:30 visit aka.msfabricdata
3:33 days for all of the information. Um so
3:35 we'll start with just what we can
3:38 expect. We've got a fabric data days.
3:41 This is a little bit of the schedule. So
3:43 earlier today there was a get certified
3:45 um from Azure data engineer to fabric
3:48 data engineer. And what we've got now is
3:50 so you want to be an analytics engineer.
3:53 Uh, next up tomorrow is going to be
3:55 leveling up your analytic skills with
3:57 the DP600. So that's very relevant to
3:59 what we're talking about today. We'll
4:01 just touch on that a little bit today,
4:02 but you're you can learn a lot more
4:04 about that tomorrow. And you can see the
4:07 rest of the upcoming um events on this
4:09 schedule, but this is just a little bit
4:10 of the schedule. There's even more. So
4:13 make sure you go to these links below um
4:15 to see more detail on what you can experience.
4:17 experience.
4:20 We've also got contests and challenges.
4:23 Um, this is a great way to both get
4:25 practice and get involved with the
4:27 community. It's a good resource for
4:29 learning and for inspiration. So, you
4:31 can see, for example, there's a contest
4:33 on data viz, there's a contest for
4:35 students, there's a contest for
4:38 notebooks. Um, so it's really a great
4:41 way to even if you don't want to enter
4:44 anything into the contests, um, it's
4:46 it's a great source to just at least
4:48 check out, you know, you can learn a lot
4:50 from these contests and challenges.
4:54 >> Did you run the data viz um challenge or
4:57 the world championship in Fabcon? The
4:58 last FabCon.
5:01 >> The last FabCon. Yes. In Vienna. Um,
5:04 right. So there was the uh data viz
5:06 world champs which was really really
5:08 exciting to be part of. It was amazing
5:10 to see what people could build. Um my my
5:12 mind was blown with that one. Um and I
5:14 know there's a lot of that going on too.
5:16 So you can actually see some of the
5:18 interviews with the people that were the
5:20 finalists in that. So highly encourage
5:22 you to see that. Um and also just one
5:24 note, you know, these contests are a
5:25 great way to get involved in the
5:27 community. Um Shabnam and I have known
5:29 each other now I don't even know how
5:31 many years, five years or more. um
5:34 because we bumped into each other at an
5:37 event and you know it's a great way to um
5:37 um
5:40 >> I was gonna say what you're into 20
5:43 maybe [laughter]
5:45 >> it wasn't past summit actually we met
5:47 through the community we met at past
5:52 comm um SQL conference in Seattle a long
5:53 time ago.
5:56 >> Yeah. Yeah. It's great. So, I love being
5:58 part of this community because I get to
6:00 meet and learn from uh people like Shopnam.
6:01 Shopnam.
6:03 >> So, definitely jump into these contests
6:06 and challenges.
6:09 >> We also have um some certification
6:11 challenges and here you go for these
6:14 links for your discount vouchers. Um so,
6:16 there are skills challenges with the
6:19 learn modules. You can also check out if
6:21 you have a local user group because a
6:23 lot of them are hosting study groups for
6:26 the DP600 and the DP700.
6:29 Um, and so just again, you know, join
6:30 your community. If there's a local one,
6:32 you're in luck, but there's a lot of
6:35 virtual communities, too. Um, so you can
6:38 definitely participate in that. Um, you
6:40 can get these discount vouchers for the
6:44 DP700 and 600. Those are 100% discount.
6:47 and the PL300 and DP900 have a 50%
6:49 discount. So again, we've got these
6:51 great links here. These should all be in
6:53 the chat for you. So you can This is how
6:55 you can go and you can learn more about
6:57 what you need to do uh to snag yourself
6:59 one of these vouchers.
7:02 And if I went through that too quickly,
7:04 here's this great big QR code that you
7:06 can grab. So you can we'll leave this on
7:08 the screen for just a second. Um, this
7:12 is a great opportunity to be able to get
7:14 yourself certified for free. So, these
7:16 certifications can be really helpful for
7:19 a lot of reasons. Um, such as, you know,
7:21 it just looks good on your resume,
7:22 right? It's a way to show off that you
7:25 know what you're doing. Um, it also for
7:28 me, I like it because it makes me learn
7:30 parts of fabric that maybe I haven't had
7:32 the opportunity to work on in my
7:34 day-to-day job. So, it's also a great
7:37 learning uh experience and especially
7:39 for free, right?
7:41 >> Yeah. Chabnam, did you have any I think
7:43 you had a whole great laundry list of
7:44 good reasons to get certified. I'm going
7:46 to put you on the spot.
7:48 >> The ones that you said, I think mostly
7:51 it's a great uh way to learn the
7:53 product. It gives you some, you know,
7:56 incentives to,
7:57 you know, finish it at a certain time,
7:58 especially if you're going to take
8:01 advantage of the uh free vouchers to
8:03 take the exam. It's always good to give
8:05 yourself a timeline to work on something
8:08 and then the certification itself really
8:11 is really helpful to give you a good
8:13 idea of the product end to end and like
8:16 Stephanie said you may not be using some
8:18 parts of it in your day-to-day life even
8:20 if you're using it. So this helps fill
8:22 those gaps for you. So in that way was
8:25 super helpful for me when I took either
8:28 one of the exams.
8:31 >> I totally agree. Um, and another way to
8:34 learn uh are these free instructor-led
8:37 handson workshops. So there is fabric
8:39 analyst in a day. You can see this link
8:42 here, next FIAD and real time
8:45 intelligence in a day. And here's just a
8:46 quick plug. You'll have a lot of these
8:48 opportunities coming up. Many are
8:52 virtual. And on January 13 specifically,
8:54 Shabnam and I are going to be delivering
8:58 one together. Um, so we do have a link
9:00 uh tinyurl.com fiadjan13.
9:02 fiadjan13.
9:04 So hopefully that's getting in the chat.
9:07 Um, but if you want to join Chabnam and
9:10 myself for an all day free uh fabric
9:13 analyst in a day, you can register there
9:18 or you can do it sooner if you want to.
9:20 >> Okay. So what are we here for? We are
9:22 here because you want to be an analytics
9:24 engineer. So this is the specific
9:27 content for today, right? Um we're going
9:31 to learn a lot about um the different
9:33 roles. You know, you don't have to come
9:35 from a specific background to be able to
9:37 do this, right? So you can come to this
9:38 from many many different career paths.
9:41 No two journeys look alike. So we're
9:43 going to learn all about how you can
9:45 take the skills that you already have
9:47 and use those and propel yourself into
9:50 an analytics engineer. We will also give
9:52 some practical guidance on how you can
9:54 build this experience and get these
9:56 skills. So this, you know, the goal
9:59 isn't to give you this single road map.
10:01 There is no one path to do this. Um
10:03 we're just going to learn about all of
10:05 us and where we've come from and how
10:07 we've landed here today. And we're going
10:09 to show you that it doesn't matter what
10:11 your background is. Um you can gain
10:14 these skills, this knowledge, uh this
10:15 community, and you can be an analytics
10:18 engineer as well.
10:20 So specifically what we're going to
10:22 cover is what even is this? What is an
10:24 analytics engineer? And it's really
10:25 funny because Shopnam and I have had a
10:27 long conversations about this. It's not
10:30 super clear, right? Um it's fuzzy.
10:32 There's overlap with other roles as
10:34 well. So we'll talk about that. We're
10:35 going to talk about the skills that
10:38 matter to become a fab uh an analytics
10:41 engineer specifically working in fabric
10:44 for this. uh how you can get started if
10:46 you're new at this and what are some
10:47 career paths and some next steps that
10:50 you can do uh to get there. So, we're
10:52 going to go through all of this and by
10:53 the end you should have a clear
10:55 understanding of what an analytics
10:58 engineer is and why the role is so
11:00 important in the fabric era and then how
11:03 you can start building toward it.
11:06 So, just a reminder, you know, this for
11:08 those of us who are here right now
11:10 together um live, you can ask your
11:11 questions in the chat and you have a
11:13 bunch of people that are experts in this
11:15 field and they can answer your questions
11:17 for you. Um so, let's just get started
11:19 by getting to know each other a little
11:22 bit. And if you don't mind, let us know
11:24 in the chat what your current role is
11:25 because we're going to see that we're
11:27 coming from this from all different
11:29 paths. Um, so we would love to just hear
11:31 a little bit more about um who you are
11:34 and where you're coming from.
11:35 >> And so we can talk about that a little
11:38 bit in the in the comments. And with
11:40 that then I'm going to turn it over to
11:43 Shabnam to talk about what this
11:46 analytics wonder what is and I'm trying
11:48 to do the same thing that the character
11:51 is doing. What is what is an analytics
11:55 engineer? Um so it's one of the terms
12:00 that has guess become popular in recent
12:03 years. Um I think so many of us may have
12:06 been doing this type of job before it
12:09 was called an analytics engineer. Um so
12:12 what the literature says what it is is
12:15 that it's someone who bridges the gap
12:18 between the super technical folks that
12:22 bring in data from outside resources and
12:26 then uh gets that data models it and
12:29 makes it reusable and understandable by
12:33 business people so that they can create
12:36 reports from it. So by definition, it's
12:38 a role that sits between the technical
12:41 folks at a company and the business
12:44 folks. So that's the um easiest
12:47 definition that I can give. And now if
12:50 that sounds vague, it's because it is
12:52 vague. So let's dig in and talk about it
12:55 and see what that is.
12:59 Um so in recent years or probably now
13:02 more than recent recent years so I would
13:05 say for a decade uh the data teams have
13:07 been evolving but in organizations but
13:10 there's still these silos that needs to
13:11 be broken down. So there's still lots of
13:14 lots of silos in the companies um
13:17 regardless of what everybody wants the
13:20 landscape to be. So business on one side
13:22 needs to have clean data, reliable data
13:25 and then on the other side um you need
13:27 to have somebody who's technical enough
13:30 to be able to work with whoever is
13:32 bringing the data in and then take it
13:36 and makes it make it presentable um in a
13:38 form that includes the business rules
13:40 and it can grow with the company and
13:43 then give that to the report developers
13:46 or the analysts. So the role of the
13:49 analytics engineer itself has been
13:53 evolving over the last decade or so. So
13:56 when I think about an analytics engineer
13:58 for example from myself I've been really
14:01 working in this role in recent years
14:04 because I've been working with projects
14:07 that are really big projects. So they
14:09 have dedicated
14:11 data transformation teams that they
14:13 bring data from their sources and they
14:15 land it somewhere and then they have
14:18 dedicated analysts that work with
14:21 business to create certain reports.
14:22 where I've been sitting is I've been
14:25 sitting in the data modeling space
14:27 creating those semantic models and then
14:30 creating the star schemas and
14:32 translating the business requirements
14:36 that the analyst give me into metrics
14:41 that are reusable. Um so with that if
14:44 you want to look at the landscape of
14:47 roles in the data uh in the data
14:50 ecosystem these are some of the key
14:52 roles. Now this doesn't include every
14:55 single role that's out there. So for
14:58 example the architect role is missing
15:00 from here. There's always a data
15:03 architecture that oversees the whole
15:06 process. Hopefully that role isn't here.
15:08 Um but as you can see it's sitting
15:11 between the source data and the business
15:15 people. It's a it's a person who
15:17 collaborates across many different
15:19 teams. So it needs to be a technical
15:21 person and also somewhat businesss savvy.
15:23 savvy.
15:27 So um with that said now what does an
15:29 analytics engineer do if you want to dig
15:30 in a little bit more. So what are the
15:32 core responsibilities?
15:35 Uh it's the modeling the data. So
15:38 building clean, reliable uh tables and
15:40 relationships. Again, this doesn't mean
15:42 it's the person who's doing the data
15:44 ingestion. Uh that would be a different
15:47 role. That would be a data engineer. Um
15:49 but it's the person who starts from the
15:50 point where the data engineer has
15:52 finished their job and then continues
15:55 from there. turning data, messy source
15:58 data that's raw in its form and then
16:01 taking it through maybe the uh medallion
16:03 layers and promoting it and then
16:06 creating reusable models and metrics for
16:09 everybody else to use.
16:12 >> Just to add on to that, you know, I kind
16:13 of feel like it also depends on your
16:15 organization where you are. You know, if
16:17 you're in a smaller organization, you
16:20 might be the the data engineer, the
16:23 analytics engineer and the data analyst
16:25 and the visual design, you know, you
16:27 might be all of those. And I also think
16:29 that in in different organizations,
16:31 there's some overlap, you know, between
16:33 the data engineer and the analytics
16:36 engineer. Um, so it's not this hard and
16:38 fast line. It's really a little bit
16:40 squishier than that. And you'll find
16:43 that different organizations will have
16:46 the different roles um have more or less overlap.
16:49 overlap.
16:51 >> Yes. Um
16:56 so uh I see Shannon saying it's a data
17:02 um
17:05 Yes. So and also that's correct. So
17:07 anybody who's we're and we're going to
17:09 talk about that in a little bit. So for
17:12 those who are working from PowerBI in
17:15 PowerBI space, fabric is the natural
17:17 progression to take their skills and
17:20 then take it to the enterprise level. So
17:22 we'll talk about that in a little bit.
17:24 Um so as far as the skills, so what are
17:28 the skills that some we expect the data
17:33 uh an analytics engineer to have? um the
17:37 the way that Microsoft has created um
17:40 these roles and corresponding
17:42 certifications, it has been by doing a
17:45 lot of research with Microsoft partners,
17:47 with customers
17:51 um and organizations to see how
17:52 Microsoft can create these
17:55 certifications that really apply to
17:58 those roles skills so that they're
18:00 relevant. So you take this
18:01 certification, it's actually going to
18:04 help you become a better data analyst or
18:06 analytics engineer or data engineer. And
18:09 so what you see here is that we have
18:12 three exams that are relevant to this
18:17 space. A PL300 is mostly PowerBI is for
18:19 data analyst and then from there when
18:22 you want to grow into the analytics
18:26 engineer that's when you take DP600 and
18:29 of course there is also DP700 and DP700
18:32 is for the data engineers that there
18:33 those are the ones who are going to do
18:36 the uh some of data ingestion and work
18:38 with pispark that the analytics
18:42 engineers mostly do not. So,
18:42 So,
18:44 >> I think also with the the PowerBI, the PL300,
18:46 PL300,
18:49 um, I was looking at the skills measured
18:51 for that one and I feel like you could
18:54 get away with pretty much spending most
18:57 of your time in PowerBI desktop. Um,
18:59 other than the admin piece of that, you
19:02 know, you could learn what you need to
19:05 do for the PL300 mainly in desktop, not
19:09 fully, but mainly, whereas the DP600,
19:11 you can't. You definitely need to be
19:14 within fabric for that. What do you
19:16 think about that kind of differentiator?
19:18 >> That that's actually exactly what I was
19:20 going to say in the next slide. So you
19:22 you made my life easier. [laughter]
19:24 >> Sorry about that.
19:28 >> No, that's perfect. So when you actually
19:31 look at the DP600, when you look at the
19:33 categories, okay, so one of the first
19:35 categories is maintaining a data
19:37 analytics solution. So what does what
19:40 does that mean? That means um thinking
19:42 about the things that you just said.
19:45 It's about um adding governance and
19:48 monitoring and security and things that
19:51 you do in the service versus in PowerBI
19:54 desktop. So all those workspace rules
19:56 and things [clears throat] like that
19:58 they're they're all in the maintain a
20:00 data analytics solution. So that's the
20:04 natural progression to go to DP600 from PL300.
20:05 PL300.
20:07 Whereas some of the third category which
20:09 is implementing and managing semantic
20:12 models the implementing part of it
20:14 specifically that part is going to have
20:17 an overlap with uh PL300 because those
20:18 are the things that you can do in
20:20 PowerBI desktop as far as creating
20:24 creating a model relationships cardality
20:27 calculations things like that.
20:28 Um so
20:31 >> why do IQL on why do I see KQL on this
20:33 list Jnam?
20:35 >> Um so it's at the bottom. Great
20:36 question. It's at the bottom and the
20:39 list of languages that you need to know.
20:42 Uh so KQL
20:46 um I think in the so for for everybody.
20:49 So KQL um originally was not in this
20:51 certification. So if you took this
20:53 certification a long time ago, you did
20:56 not have KQL. You instead had Pispark.
21:01 Um since then pispark has moved to DP700
21:05 but uh has been replaced with KQL. So in
21:09 here KQL is would be in um I would say
21:12 preparing data.
21:15 Um I think it falls under the preparing
21:18 data. So, um,
21:20 these these are just very high level
21:22 categories. And so, if you want to learn
21:24 more about DP600,
21:27 the exact skills that go into each
21:29 category, the best place to start is
21:30 Microsoft learn. And what you want to do
21:33 is you want to go to Microsoft learn and
21:37 then type in DP600 study guide. That
21:39 study guide is your guide that you need
21:42 for the whole um to prepare for the exam
21:44 and know exactly what to read, what's
21:47 included and it will keep you on track.
21:50 So um I think KQL is in the preparing
21:54 data because you would be uh querying
21:57 quering data with it. Does that make sense?
21:58 sense?
21:59 >> That's not something that's going to be
22:01 on the PL300, right? Because you're not really
22:02 really
22:05 >> correct so much in desktop for example.
22:08 >> Yes. PL300 does not have as far as I
22:11 know it doesn't have SQL doesn't have
22:16 SQL and doesn't have obviously KQL
22:19 um it's purely PowerBI
22:20 >> right and I think that's an important
22:22 distinction because yes as the analytics
22:25 engineer um SQL is really important you
22:28 need to be able to query your data
22:30 wherever it is um yes
22:32 >> not just pull it in with power query right
22:34 right
22:37 >> yep SQL SQL continues to be I call it
22:40 the king of analytics and I think it
22:43 will stay it will stay for a long time I
22:46 hope um although I have to say my second
22:50 favorite language is KQL because of its
22:55 simplicity and its power it's um almost
22:59 for me it rival SQL okay
23:01 okay
23:04 so um so all right so how do you build
23:08 your analytics engineer skills. And like
23:11 Stephanie said earlier, there's no one
23:14 right way to do things or to learn this
23:16 stuff. So, we're curious to know for all
23:19 of you who are live with us right now,
23:22 uh what's your favorite learning method?
23:25 Like, how do you learn? Like Stephanie,
23:27 how what's your favorite learning
23:30 method? Do you like watching training
23:33 videos or do you read? What? How do you learn?
23:34 learn?
23:37 >> No, it's funny cuz I'll read or I'll
23:38 watch a video and I'll think, "Oh, yeah,
23:43 I get it." But if I then try to do it, I
23:45 won't. You know, it's like I have to get
23:47 my hands into it. So, having an actual
23:49 lab to go through and click and
23:52 understand and make mistakes and come
23:54 back, that's really the best way for me
23:57 to learn. How about you?
24:01 Yeah, I think for me uh watching the
24:04 training videos um they help me a lot
24:07 and then I will go and read a little bit
24:10 about it and then obviously trying
24:14 things will solidify the learning in my
24:18 in my head. So it's a combination of um
24:21 it's a combination of them. And also I
24:25 noticed that um the method that you
24:27 learn may also shift throughout your
24:30 career. So just because you were really
24:34 good at learning by reading, it may not
24:36 be the same when you advance in your
24:38 career. So I would say explore all
24:39 different options and see which one
24:44 works for you. Um, and definitely uh
24:46 when it comes for when it comes to
24:48 passing the certifications, you do want
24:51 to have some hands-on experience. Even
24:53 if it's within the, you know, a trial
24:56 capacity or a lab environment, I highly
24:58 recommend you have some hands-on uh
25:01 practice because the purpose of the
25:03 certifications is not just for you to
25:05 pass the test and put it on your resume
25:07 that you pass the certification.
25:09 hopefully it's for you to actually learn
25:13 some of the material.
25:15 >> Yeah. And those hands-on activities that
25:17 are so important for my learning. I
25:19 really appreciate that. Um on Microsoft
25:21 Learn, there are a lot of those where
25:22 you can, you know, you can jump in and
25:24 there are these lab environments. It's
25:26 not everywhere, but it's in a lot of it.
25:29 And that helps me a lot. Um just because
25:31 it's so funny how much I think I know
25:34 something and then if I try it, I'm
25:36 like, "Wait, what was that? Where was I
25:38 supposed to click? What does this mean?
25:39 So, I need to I need to just get in
25:43 there and and break things to learn it.
25:45 >> Yeah, it it always looks easy when
25:50 someone else is doing stuff and um you
25:52 know, you're like, "Oh, that's not that
25:54 hard. I can do it." And then when I try
25:57 it, it's really hard. But yeah, so
26:02 repetition uh makes things easier. >> Okay.
26:02 >> Okay.
26:04 >> If only I would have learned that a long
26:06 time ago.
26:09 Yeah. So, um, all right. So, let's talk
26:13 about working in Microsoft Fabric. Um,
26:15 when it comes to an analytics engineer
26:18 and preparing for DB600.
26:21 Um, so just a quick recap of Microsoft
26:23 Fabric. Microsoft Fabric is an
26:28 end-to-end analytics and AI platform in
26:30 in uh a software as a service format
26:34 meaning that it's um backed by Microsoft
26:36 cloud and you don't install anything you
26:40 don't configure any uh services. Uh so
26:41 for example if you're coming from an
26:43 Azure background you're maybe used to
26:47 Azure data bricks or Azure SQL DB this
26:49 is entirely different so this is very
26:51 close to what PowerBI
26:53 is and actually it has inherited
26:56 PowerBI's foundation as far as the uh
26:58 workspace model the capacity model and
27:01 the security. So you have all of these
27:04 tools in the cloud that you can do uh
27:06 data projects data and AI projects with.
27:09 So you have data factory which is the
27:14 green icon here to do ETL or ELT in the
27:16 cloud. Um and then you have the blue
27:19 icon which is called analytics here.
27:22 That's the uh data warehouse, data
27:24 lakehouse. That's where data engineering
27:26 goes. That's where you have access to
27:29 these massively parallel processing uh
27:32 engines um that you can use for your
27:34 analytics. This is where you get your
27:37 lakehouse and warehouse. And then uh we
27:41 have now we have also the purple icon um
27:44 databases which supports the OLTP
27:47 workflows. So the analytics workloads
27:49 are for what's called OLAP workflows.
27:52 Those are workloads that are for
27:54 analytics. So these are queries like
27:56 they they're really good at answering
27:59 queries like uh give me the sales by
28:02 store for the last 10 years u by region.
28:04 So those are analytics queries compared
28:09 to OLTP workloads where they're read and
28:11 write heavy applications, transactional
28:14 systems like point of sale systems or
28:15 customer relationship management
28:18 systems. So now we have the OOLTP
28:20 support with the arrival of databases
28:22 also in fabric.
28:25 The next workload in fabric is real time
28:27 intelligence. This is for handling
28:30 streaming data. So instead of landing
28:33 the data and then promoting it through
28:36 layers and as it goes through layers you
28:38 apply transformations.
28:39 This is for cases where you're working
28:43 with data in real time in near real time
28:45 uh at high volumes and it's streaming
28:48 and you want to apply transformations on
28:50 the fly as it's arriving. So it's a
28:53 really powerful component of Microsoft
28:56 fabric. And then we have PowerBI which
28:58 is for creating semantic models and
29:02 analytic analytics and reporting. Um I
29:05 think PowerBI just turned 10 years so
29:08 hopefully most of you have used PowerBI.
29:10 Um and then the last two icons that you
29:13 see there for industry solutions and
29:15 partner workloads. So these are uh
29:18 pre-built solutions either by Microsoft
29:20 or third parties that are available in
29:23 Microsoft uh fabrics portal. when you go
29:26 to the portal. Um, so Stephanie, I think
29:29 that was a uh Did I miss anything? Do
29:31 you want to add anything?
29:33 >> Um, just that you know fabric uses the
29:36 open standards like parquet, delta,
29:38 spark, tsql, python. So it's not like
29:40 you're just learning fabric when you're
29:42 learning these skills. Uh these skills
29:45 do transfer to other platforms as well.
29:47 >> That that is absolutely true. So when I
29:50 was actually taking the I was learning
29:54 uh pispark for DP600 before it left it
29:57 before I left that exam the pispark that
30:02 I was uh learning I was also using it at
30:05 work a uh in data bricks in the
30:10 notebooks so um because a lot of the
30:12 along the same lines because the
30:14 foundation of Microsoft fabric which we
30:16 don't have on this slide is one lake and
30:18 Everything in one lake is in an
30:21 open-source data format called Delta
30:24 Paret. That means that all of these
30:26 engines can write to it and all of these
30:29 engines can read from it. But not only
30:32 that, you also are not locked into
30:34 Microsoft. So hopefully you will stay
30:36 with Microsoft, but you're not vendor
30:39 locked. You can now have a lot of other
30:42 tools interact with your data. So your
30:44 data stays in the same place and it's an
30:47 opensource format. So all the other
30:49 engines who can understand delta perk
30:52 can also talk to it which is a huge
30:55 benefit over previous systems where once
30:57 you put your data for example in SQL
31:00 server on prem SQL server onrem was the
31:03 only tool who could understand it.
31:06 So uh great reminder to talk about one
31:11 lake. Um, all right. So now for the DP600,
31:13 DP600,
31:15 you will be tested on certain components
31:17 of Microsoft Fabric. So by the way,
31:19 Microsoft Fabric, as you can see, has
31:22 huge capabilities. Nobody can be expert
31:26 in all of these um areas. Um, and if
31:29 you're just getting started, uh, don't
31:32 let all of the icons overwhelm you. You
31:35 can to do a project you can be choosing
31:37 just PowerBI and data factory for
31:41 example and the databases uh or couple
31:43 other components. You don't have to know
31:45 all of the tools and use all of them. Um
31:48 so for DP600
31:51 you will be tested on data factory with
31:54 the pipelines and data flows. From the
31:57 analytics uh category you will be tested
32:00 with uh SQL. So with the with the
32:02 warehouse you need to know SQL with the
32:04 SQL analytics endpoint of the lakehouse
32:06 you need to know things you can do with
32:09 SQL and then uh with real time
32:11 intelligence is the KQL component of it
32:16 that in DP600 you need to be able to um
32:20 write C uh KQL queries. By the way the
32:22 the queries that you write when I say
32:23 write queries in the exam you don't have
32:25 to write from scratch. are always a drop
32:29 down or a drag and drop kind of um
32:33 question. Uh and then PowerBI. So you
32:36 need to know DAX for DP600. So for those
32:37 of you who are coming from PowerBI
32:41 background, uh you should find this exam
32:43 easier to take because you already know DAX
32:45 DAX
32:47 >> the data factory component because you
32:49 already know Power Query. So
32:51 >> exactly you know you're really just a
32:53 data flow gen 2 is is Power Query. So
32:55 that's part of what you're going to have
32:57 in data factory. So if you are coming
32:59 from that PowerBI background, it it is a
33:02 little less overwhelming.
33:06 >> Yes, that's true. Um so and we when
33:09 you're um this this uh session is not
33:10 about preparing for the exam, but I just
33:12 want to throw this out there that
33:14 because there is there are three
33:16 different categories and three different
33:20 languages. So the in the exam so if you
33:24 know SQL and DAX then maybe you can uh
33:27 study KQL less or rely on KQL less but
33:30 if or if you know SQL and KQL really
33:34 well then you may want to not study DAX
33:35 um like in depth for the purpose of
33:37 passing the exam. So it's really nice
33:39 that there's three different languages
33:42 in there and you can focus on two out of
33:46 three for passing the exam.
33:48 So, uh, Stephanie, what what are your
33:51 favorite languages out of the three?
33:53 >> You have been singing the praises of KQL
33:54 for a long time. I know you're a
33:56 convert. Um, >> yes.
33:56 >> yes.
33:58 >> I don't know a lot of people that would
34:00 say Dax is their favorite, and I am also
34:02 not one of them. [laughter] I have a
34:04 lovehate relationship with Dax.
34:08 >> Yeah, I like for for the I like Kill.
34:10 The reason I say KQL is also
34:12 specifically for the for the exam like
34:15 if you if you are new to DAX and KQL and
34:18 you want to learn one to pass the exam I
34:21 would say 100% KQL because KQL is
34:23 straightforward. So if you look up the
34:25 syntax you can answer the question
34:28 whereas with DAX there's the whole you
34:31 know context and all that stuff that
34:35 makes it complex. So all right
34:36 >> I'm with you.
34:39 >> Yeah. So, all right. So, now I think
34:40 we're going to go back to you,
34:42 Stephanie, and you're going to tell us,
34:44 all right, how do we get started? How do
34:45 we get started to gain some of these skills?
34:47 skills?
34:49 >> I think it's excellent. So, let's talk
34:52 about how we can get started. So, we
34:54 just talked about what is the engineer.
34:56 So, hopefully we all have a rough idea.
34:58 You know, again, it's a little bit
35:00 squishy, uh, but we know what the
35:02 analytics engineer roughly is and we
35:04 know what are some skills that are
35:06 required. So, what can we do? I think
35:08 I've actually seen a lot of questions in
35:10 the chat about okay so I get it but but
35:13 now what can I do um so you know it's
35:16 about taking small practical steps so we
35:18 need to access the core tools we don't
35:20 have to get everything at once but we do
35:22 need the core tools a good idea is to
35:25 maybe pick a small project you know work
35:26 through it end to end even if it's just
35:29 with sample data and then really instead
35:32 of focusing on the specific tools you
35:34 want to focus on understanding the
35:37 workflow so from collecting and cleaning
35:40 data to modeling it. Um, it's that
35:43 mindset that transfers across platforms.
35:44 So, you know, you think about using
35:47 tutorials, sample projects. And again,
35:49 community resources. I'm just going to
35:51 um make a plug for that one more time.
35:53 The community is just the best place to
35:55 learn. So, the key is not mastering
35:58 everything immediately. It's uh
36:00 experimenting, making mistakes, you
36:02 know, figuring things out, problem
36:04 solving, and learning the process. So,
36:07 first up, I would say start with
36:09 Microsoft Learn. Um, I think we, you
36:11 know, we mentioned it a little bit. Uh,
36:13 there are a lot of tutorials and
36:15 learning modules, and some of them even
36:17 have kind of like a lab environment, so
36:20 you can actually get hands-on with it.
36:22 So, you can complete these relevant
36:25 these paths. Again, focus on a workflow
36:26 because this is what you're going to
36:29 need to understand in this role. Um,
36:31 it's not just about the specific, you
36:32 know, button clicks. It's about
36:34 understanding how how am I going to get
36:36 the data? What do I have to do to
36:38 transform it? How should I be modeling
36:41 this for what my end users need to see?
36:44 Um, so start with Microsoft Learn. These
36:47 these learning paths are just fantastic.
36:50 They help a lot. Um, so pick a pick a
36:52 few that match. You know, you don't
36:54 necessarily have to do every everything
36:56 in the order that's presented to you. So
36:58 pick a few that match what you're trying
37:00 to learn. So if you're uh interested in
37:03 the analytics engineering, look for data
37:06 preparation, modeling, PowerBI. So do
37:08 the exercises. You want to learn by
37:12 doing in a guided way.
37:15 Next up, how can you get handson in
37:19 fabric? Um so ideally you're going to
37:21 have an environment. Maybe you can have
37:24 a you can set up a trial where you can
37:27 start building. Um, so you're going to,
37:30 um, set up that trial account. Account's
37:31 the wrong word, but you'll set up a
37:34 fabric trial, jump in there, and just
37:36 start building things. Um, but some
37:38 people may not have that ability, right?
37:40 Maybe your work isn't allowing you to
37:41 set up a trial. Maybe you don't have a
37:44 work account. Um, so again, you can
37:46 attend one of these fabric analyst in a
37:50 day trainings. So it is completely free.
37:52 Um, many, many of them are virtual. the
37:54 one that Shabnam and I are doing again
37:56 on January 13 and you can see the link
37:59 on here I think um this is virtual and
38:01 it does give you an environment so it
38:04 gives you a virtual machine that you can
38:07 just get into and start building so you
38:09 don't actually have to have uh fabric on
38:10 your own. You don't have to have an
38:13 account to be able to to use this. So
38:15 this is a great way if you don't have
38:17 access um to actually get in there and
38:19 get hands-on
38:21 >> and and some people take more than one
38:23 of them. So you can you can take the
38:27 same one a few times. That way you you
38:28 will get access to the environment a few
38:31 times and get some hands-on practice.
38:34 >> Yeah, that's a good point. Um we always
38:36 like having people in there. We always
38:38 uh like helping people learn. Um so
38:40 don't be shy. Feel free to join one. And
38:43 there's also the real time in a day. So
38:46 uh feel free to join one, both, whatever
38:49 works for you. Okay. So now let's say
38:52 you've gotten in, you do have you have
38:53 access, you can work on something,
38:56 right? You've got an environment. Um so
38:57 maybe the first thing is just pick a
39:00 project, right? Um so start small
39:02 because the idea is that you want to
39:05 understand the flow. Um so you'll you'll
39:07 have a data source or multiple data
39:10 sources. Um it's not going to be exactly
39:12 how you need it to consume in PowerBI.
39:14 So this is where your transformation's
39:16 going to come in. Um maybe you're going
39:17 to build some shortcuts. Maybe you're
39:21 going to do some data flows and then you
39:24 might even decide that you want to do
39:26 some notebooks with some PI pispark even
39:28 though that's not necessarily part of
39:32 the DP600 exam. Um I was surprised to
39:33 find out how much I love notebooks and
39:35 pispark you know coming from SQL and
39:38 DAX. Now I love notebooks in Pispark
39:40 just because I started building things
39:41 with it and I thought it was really
39:43 cool. Um so this is what you want to do.
39:44 You want to find what you're interested
39:47 in. Um, if you have a project at work,
39:49 you know, have something build a build a
39:50 project that's similar to what you need
39:52 to do at work or if there's just
39:54 something you want to learn. Um, there
39:56 are so many opportunities. There's so
39:58 much sample data. There are all of these
39:59 challenges and contests that you can
40:03 participate in, for example. Um, so just
40:06 choose something and and get your hands
40:07 in there. you want to have hands-on
40:10 practice because the more you experiment
40:13 and make mistakes um the faster you'll
40:15 internalize this workflow and you'll
40:17 develop these skills that you can use in
40:24 So let's say you've gotten in there and
40:26 you know you've had some frustrations,
40:28 you're trying to figure something out um
40:29 and you just can't figure out the
40:31 answer. So this is where these community
40:34 resources can come in. So there are
40:37 tutorials, blogs, sample files, forums.
40:40 Um you can join the Reddit community. I
40:42 know that's very active for example and
40:45 again uh you know doing these contests.
40:47 Um there's also you know it's more
40:50 PowerBI but there's workout Wednesday
40:51 for example. You can look up workout
40:53 Wednesday and you can do some PowerBI
40:55 challenges. Um, so anything that you're
40:58 interested in doing, there's likely a
41:01 community person who has uh built a
41:03 tutorial or a blog or there might even
41:06 be a challenge out there. So, just to
41:08 highlight a few of these, um, here are
41:11 some names of people that have lots of
41:14 great content specifically on the DP600,
41:17 for example, or on this role. Um, so if
41:19 you want to just look up some of these
41:21 people on here, you'll see that they
41:24 have loads of fantastic content. Um and
41:26 you see Shabnam's name here. She's got
41:29 some great content on um some of these
41:32 some exam prep for example and like she
41:35 mentioned earlier in the intro um she's
41:38 got a book on analytics engineering. So
41:40 I think we can get that link in the chat
41:43 as well. Um what is the what is the
41:46 official title of your book Shabnam?
41:48 >> It's it's uh called analytics
41:50 engineering with uh Microsoft fabric and
41:53 powerbi. Um, so
41:55 >> was that with Nicola?
41:58 >> With Nicola Ilick. Yes. So it's not it's
42:02 not um finished yet. It's in early
42:04 release. So the way it works is that if
42:06 you're um you have if you have access to
42:09 O'Reilly platform, they let you read
42:12 books as the authors are writing it. And
42:14 the idea is that because the technology
42:16 advances so fast, it lets you have
42:20 access to uh the material sooner than later.
42:21 later.
42:24 Um, and you know, fabric evolves with
42:25 such a fast pace. So, it's always a
42:28 challenge with the with the book also,
42:30 um, because we have to make changes as
42:32 we go. But anyways, it's it's an early
42:34 release, so it's not published yet. Um,
42:37 but I appreciate the shout out there.
42:40 Um, I also in the in the community that
42:42 you have um, members that you have
42:45 listed there. I just wanted to um
42:50 mention the Alexi partinan and I was
42:52 trying to put a link to his YouTube
42:55 channel. I think Pam has has I can add
42:57 it in there. U he has a lot of training
43:00 videos for um the certifications as well
43:03 and Microsoft Fabric uh great content there.
43:05 there.
43:08 >> Yes. Fantastic. So again, just a real
43:09 shout out to all of these community
43:12 members who have created so much content
43:15 to help us all um both learn and prepare
43:18 for these certification exams. So all
43:20 right, so you've built some things.
43:22 You've uh solved some problems. You've
43:24 had some challenges. You got your
43:26 community resources. So now that you've
43:29 done a few projects and you've leveraged
43:31 your community resources, it's time to
43:33 iterate and reflect because this is
43:35 where real growth happens. Um it's not
43:38 linear, right? It's definitely cyclical.
43:40 So, you'll start simple. You'll get some
43:42 feedback and you know what happens to me
43:44 a lot. I'll be walking my dog and I'll
43:46 realize a different way I could solve
43:47 the problem, you know, and I'll be so
43:49 excited to get back to my computer and
43:52 jump back in. Um, so, you know, document
43:54 what works and what doesn't. You will
43:56 have some lessons learned and this is
43:59 the reflection that's going to help you
44:01 replicate your successes and avoid
44:03 repeated mistakes and it will help you
44:05 build your confidence. So the goal is
44:07 not to master everything at once. You're
44:10 just trying to have continuous learning
44:12 through practice. And over time, this
44:14 will allow you to build this solid
44:16 foundation that will transfer across
44:19 tools and will prepare you for the real
44:23 work um of being an analytics engineer.
44:26 So let's talk about some career paths
44:28 and some next steps. Um again, there's
44:30 no single path. Everyone's journey looks
44:33 different. So, um I think I saw someone
44:35 in the chat saying they work in a dental
44:37 practice, for example. You know, we come
44:39 from all kinds of backgrounds. I spent
44:42 many, many years in nonprofit. Um so, we
44:44 don't all have the same start, right?
44:46 So, all you really need is, you know,
44:48 curiosity and you want to solve
44:52 problems. And if you find this work at
44:54 all fun, then I think you're going to be
44:57 successful. Um, like for me, I just love
44:59 it when I can solve a problem and make
45:02 something work and make it faster. Um,
45:04 that's that's what, you know, keeps me
45:07 doing this all the time. So, it doesn't
45:09 matter what your background is. Um, if
45:11 you just get involved and you do these
45:13 challenges and you learn and you get the
45:16 skills, um, you're you're going to be
45:18 fine on this path as well. So, let's
45:21 talk about some different paths. None of
45:24 none of our careers look the same. um we
45:26 can start from analysis, engineering,
45:28 business roles, you know, it doesn't
45:31 really matter. And our early experiences
45:32 don't lock us in. They just
45:34 [clears throat] give us more experience
45:37 and knowledge um and just a different
45:39 background that we can provide for other
45:42 people. So try these different projects,
45:45 these different rules uh roles and tools
45:47 and you know see what resonates with you
45:49 and what fits your interests, your
45:54 strengths and where you're hoping to go.
45:57 and focus on building skills, not the
45:59 title that you hold. I mean, I'm sure if
46:01 we we could probably all have some
46:03 laughs in the chat about, you know, what
46:05 our titles are versus what our jobs
46:09 really are, right? So, focus on what you
46:11 can do and the skills that you want to
46:14 learn because employers care more about
46:17 what you can do than what the title is.
46:19 So if you have skills like data
46:22 modeling, visualization, analytics
46:25 workflows, these are transferable across
46:27 roles. So you'll want to build your
46:30 portfolio through projects, contests,
46:32 contributions again because these are
46:35 concrete experiences that speak louder
46:43 So finally, what are some different
46:44 things you can do? you can seek
46:46 mentorship, learn from others experiences,
46:47 experiences,
46:50 um or even just more community members.
46:52 Just get involved, you know, attend some
46:54 events if you can, if they're in your
46:57 area, and get yourself out there and
46:59 talk to people. Um if you have a local
47:02 user group, definitely go to that.
47:04 Everybody is there on generally a
47:06 volunteer basis. They're there because
47:08 they want to help people. So, it's a
47:10 great way to meet people and learn from
47:12 people that really do want to help you.
47:14 Um, if you don't have a local user
47:15 group, that's okay, too, because there
47:17 are all of these online communities um
47:20 where people also want to help you. So,
47:23 just remember, take small steps. Um,
47:25 just try to build a project, learn new
47:28 skills, um, allow yourself to make
47:32 mistakes and learn from them and um, and
47:35 tackle some challenges.
47:37 >> It's always um, I love the local user
47:40 groups because, um, they just give me
47:43 energy. I just get a lot of energy from
47:45 going to these local user group events.
47:48 Sometimes it's really hard to drag
47:51 yourself to a user group meeting in the
47:54 evening or you know on on weekends like
47:56 on a Saturday to go join some of these
47:59 free one-day events that they have. But
48:02 you but it's always been worth it for me
48:05 because I'm always like, "Oh, why am I
48:07 why am I doing this to myself? I'm so
48:09 tired. Do I really, you know, sometimes
48:11 I'm like, can I even learn anything when
48:13 it's this late in the day and I'm tired,
48:15 but then I go and I'm like, oh my gosh,
48:17 I actually learned something. Even if
48:18 it's just a little bit of something,
48:21 they it adds up over time. And then
48:23 plus, you get to meet other people who
48:26 are doing um jobs that are very similar
48:28 to you and then you talk to them, you
48:31 hear their challenges, you learn that
48:33 there is dirty data everywhere, so
48:35 you're not just alone, you know. So,
48:38 it's always good to hear um from other
48:40 people their their challenges and how
48:42 they're solving them and just to
48:47 socialize um with them. So, I love our
48:48 user groups. >> Yes.
48:49 >> Yes.
48:51 >> Yep. Absolutely.
48:53 All right. And so, I think Shabnam, it's
48:56 it's you now. Tell us more about
48:58 >> Yeah. So, I think um
49:00 we're just I'm just going to uh go
49:02 through a couple of closing slides and
49:04 then if you have a few minutes, we can
49:07 answer any questions that are out there
49:11 um in the in the question section. Um
49:15 so, all right. So, um
49:17 let's see what's what other things there
49:20 are in the fabric data day. So, we we
49:23 saw the schedule earlier. Um by the way
49:26 this one was the first of the so you
49:30 want to um something right? So this was
49:32 the first one. So hopefully you enjoyed
49:34 this one. There is bunch of others
49:36 coming in the same format that's going
49:39 to introduce you to the other roles the
49:40 other data roles. So so you want to be a
49:42 data analyst, you want to be a data
49:46 engineer. Um and as we mentioned um
49:49 there's always going to be an overlap
49:51 between these roles. Usually there's not
49:54 a clean cut there. Uh especially if
49:57 you're working on the smaller projects,
49:59 smaller organizations.
50:02 Um so make sure that you attend as many
50:04 as these as you can, especially if
50:07 you're new to the product or new to the
50:12 certification path. And then um
50:14 if you take the challenges again the
50:17 challenges are a way to motivate you to
50:20 give yourself a time to finish a task.
50:22 Um if you can take advantage of the
50:25 challenges and the discounts it's it
50:28 makes it even uh easier to go ahead and
50:31 take the certification. Um because a lot
50:34 of people sometimes uh they're afraid to
50:36 take the exam because they're afraid of
50:40 failing the exam. But at least having a
50:44 100% discount voucher hopefully should
50:46 make it easier and take some of that
50:48 fear away so that you can go ahead and
50:51 uh take the exam.
50:54 All right. And here is the QR code one
50:56 more time. And remember when you scan
50:58 this QR code there's some requirements.
51:00 So you have to mention that you watched
51:02 one of these sessions which hopefully
51:04 all of you have done this one or there's
51:06 some other requirements. I believe you
51:09 have to register for one of the um the
51:12 challenges to be able to take this uh
51:15 voucher and there is a timeline to it.
51:17 So you must commit to taking the exam by
51:20 the end of this year.
51:25 Um we also talked about the in a day um
51:26 events. I'm not going to talk about them
51:27 anymore. I think we talked about this
51:30 several times. Uh Stephanie, you did a
51:32 great job with that. And then uh finally
51:36 if all the resources for fabric in a day
51:38 event uh not fabric in a day my mind's
51:42 still there u but the 50 days of fabric
51:44 uh fabric data days um is here in this
51:48 link and I think um at this point we can
51:51 uh just stop and then um for the few
51:53 minutes that are left answer any
51:57 questions from our audience.
52:00 Um, I see a question. How can I train
52:02 slashinform non-technical stakeholders
52:05 about the benefits of PowerBI and
52:07 Microsoft Fabric? Some are change
52:09 resistant, but other departments have
52:12 seen measurable benefits. What do you
52:15 think, Shabnam? What's a good way to
52:17 show the benefits? Yeah, that's oh my
52:19 gosh, that's a really that's a really
52:24 hard one because you do want to have
52:27 sponsorship in order for the project to
52:30 be successful. So, you definitely need
52:33 to have the non-technical stakeholders
52:39 back up uh your project. Um you you um
52:41 you mentioned that other departments
52:45 have seen measurable benefits. So maybe
52:48 get those departments to demo the
52:50 benefits that they've got to your
52:54 department or you know their
52:56 their head the head of that department
52:58 maybe at a higher level they can talk to
53:00 your department and showcase what
53:02 they've done. I mean that internally
53:04 should be a really good example for your
53:07 department to follow. Uh, the other
53:09 thing you can do is maybe you can do a
53:13 PC and then show them what it's like and
53:15 things they can get. Um, maybe you can
53:18 even get them to try one of these fabric
53:21 in a day events or some of the trainings
53:23 to get them excited about it. Those
53:24 those are the ways I think about. I
53:27 mean, Stephanie, what would you do?
53:30 >> Um, I think also find some kind of
53:33 lowhanging fruit that maybe you could
53:36 build fairly quickly. a a good candidate
53:37 I've always noticed is if you can find
53:40 some manual process that some tedious
53:43 process that somebody has to do um and
53:46 maybe that can be if not fully automated
53:49 maybe semi-automated in fabric um so
53:52 maybe just a quick P that's kind of a
53:54 lowhanging fruit and then maybe include
53:57 a little wow factor too you know so like
53:59 I don't know something with co-pilot or
54:03 or the new translitical task flows that
54:05 allow you to do right back into power
54:07 into the database from PowerBI or you
54:09 know just some kind of like something to
54:11 make them gasp a little bit. [laughter]
54:19 >> Yeah, I agree. I agree.
54:22 >> Uh so let me see
54:25 our moderators if you see any questions
54:27 that we haven't answered or you have not
54:31 answered um feel free to post them
54:34 again. Um one for those who already got
54:38 a certification, what resources exist to
54:40 continue learning? I would say this is
54:42 where these challenges and contests come
54:45 in. Um you know, because they give you a
54:47 starting point and you now that you've
54:49 got the certification, you know how to
54:50 do some things.
54:52 >> I I think it'll like spark some
54:56 creativity for you. Um and then you can
54:58 share what you build through these
55:00 challenges and contests. I think that's
55:02 a great resource for continued learning
55:06 as well as learning from other people's
55:07 >> into these contests.
55:10 >> Yes. Also, if you want to learn, one of
55:13 the best ways to learn is to present. So,
55:13 So, >> true.
55:14 >> true.
55:17 >> Yes. So, prepare now that you've learned um
55:19 um
55:22 internally to your company or to your
55:25 community user group. Uh try to teach
55:27 one of those topics to someone else or a
55:28 new topic that you want to learn. That's
55:30 one of the best ways for yourself to
55:32 also grow.
55:35 >> Yeah, that's a great point. I love that.
55:38 Um, that definitely is what I did. You
55:40 know, I started speaking at local events
55:43 and it terrified me. Um, and I was
55:46 really afraid of looking stupid, right?
55:48 So, I wanted to I wanted to make sure I
55:51 knew the content inside and out. Um, so
55:52 that was a great way for [laughter] me
55:54 to learn.
55:56 >> Yes. the the although for those who are
55:59 who are thinking about starting your
56:01 local user group is the friendliest
56:03 place to start. The local user groups
56:06 support new speakers and their their
56:09 whole purpose is to for the community.
56:13 So um that that's a good place and even
56:15 if you're if you're a new speaker no
56:17 one's going to expect you to be perfect
56:19 and not make mistakes. So don't worry about
56:21 about
56:24 sounding um you know not so perfect. Yes.
56:24 Yes.
56:27 >> Yep. 100%. Yep. That those are some
56:30 great resources, good ideas.
56:33 >> Um and I don't see any others. So, I
56:35 would just say, you know, make sure you
56:38 continue to uh follow along with these
56:40 data days, come to some more events. Um
56:43 check out all of the opportunities that
56:46 are available and um we'll see you in
56:48 the next one.
56:50 >> We have one one last question. I just
56:51 want to squeeze the answer to that one
56:54 in. So someone asks about the user
56:57 group. So a good place to find your user
57:01 group is on the meetup. So um you can
57:04 search for whatever city is close to
57:09 you. Type the city and then uh PowerBI
57:13 user group or fabric user group and that
57:17 will bring up the meetups or the yes
57:22 shout out.
57:23 >> That's it. So hopefully everybody
57:24 enjoyed this session and hopefully
57:26 you're going to join the rest of the
57:29 soul you want to be and hopefully it's
57:31 clear for everyone what an analytics
57:37 >> Thank you.
57:49 Thank you all for joining and thanks
57:52 again to our speakers.
57:54 This session is part of a series. To
57:57 register for future shows and watch past
57:59 episodes on demand, you can follow the
58:03 link on the screen or in the chat.
58:04 We're always looking to improve our
58:07 sessions and your experience. If you
58:09 have any feedback for us, we would love
58:11 to hear what you have to say. You can
58:13 find that link on the screen or in the
