Manage episode 291388625 series 1450892
In this episode of Data Driven, Frank and Andy chat with Philadelphia Microsoft Technology Center Data Architect Dave Wentzel on why you do not need a data warehouse.
Also, Frank discusses leaving Microsoft, Frank and Andy talk about five seasons of Data Driven, and even BAILeY has a sentimental moment.
Hello and welcome to data driven, the podcast where we explore the emerging wait a tick. This is the premiere episode of Season Five. Can you believe it? Data driven started four years ago this month.
Up until last season, we had a human doing the voiceover work. That is until she was replaced by an AI. Yours truly.
In this episode, Frank and Andy speak to Dave Wensel about why you don't need a datawarehouse. We're starting off the new season with a bit of contrarian tone.
It's a lively back and forth conversation that runs contrary to prevailing wisdom. Don't say we didn't warn you? Now on with the show.
Hello and welcome to data driven. The podcasts were we wait a minute. We've been saying this Andy for four years now. Can you believe it?
Four years, that's crazy talk.
That's just craziness. So I think when you and I first talked about this and that was that fateful, I think it was December was right after Thanksgiving. But before Christmas, I was thinking about starting a podcast and as a data scientist, I needed someone.
That was a data engineer that could kind of round out the talent there and and and and obviously I wanted someone I knew, liked, and trust.
And so it was you.
Well, I'm just glad all of the real smart data engineers you knew were busy. That's all I got to say.
Ah, no man. You were the first one. I reached out to and the only one I would have done it with it. So I was delighted when you said yes because starting a podcast can sound like a daunting thing, particularly if you haven't done it before.
Yeah, neither one of us really had. And gosh, it's it's worked out. What are we up to? 180,000 downloads or something? I mean that's.
Like that about hundred 8000 downloads. I mean, we're not Joe Rogan, but that's OK, Yep.
But you know what, we we we've impacted. I think the community in a significant way. We've we've done a number of things we've we've innovative how we podcast.
Uh, we we've actually managed to keep a good cadence with some exceptions.
You know, we we finally did earlier this year or late last year, kind of fulfill our vision of it being data driven TV when we actually interviewed guests on.
And that was that actually delayed the launch of the show by about three months.
It did but also uhm. Yeah, that was interesting, but you know it's typical software development, right? You release a feature and then you debug it. The I have this saying about that Frank. All software is tested some intentionally.
I love it, but I also like how, how, how both our careers have evolved over the last four years. And dumb, you know, this being the premiere episode of Season 5 and we have something special lined up, but I'll get to that in a minute.
Oh gosh, itch.
You've progressed in your career. We, you and I've worked on some some projects together or virtual Summit. What we're calling Ring Gate, which will announce very very soon and and but. But most of all, is been my kind of skilling up in transition into data engineering myself.
Which was something that when I joined, so this is just a job update about a year ago. I I left the role of Microsoft kind of field sales and I went into the Microsoft Technology Center stick with me. There's a point to this story and basically I was at the rest in MTC.
And basically I was the AI guy on my my my field sales team, but I didn't really have deep knowledge of kind of the typical typical data engineering pipe work that goes into that role and basically my my. My then manager said you know he's like hey, you know, if you want this role, you've got a skill.
And skill up I did. And with Andy's mentoring and a bunch of other folks that helped me kind of skill up on our the data engineering side. I looked at it this morning. I'm like 88 hours on Pluralsight.
Wow, that was from mid may till we're recording this on April 30th. So just about a year 88 hours right now tracking on about 200 four 205 consecutive days of getting on LinkedIn. I'm not on LinkedIn on Pluralsight, LinkedIn learning. I also have a number of courses too.
Uh, that is something I'm proud of in terms of career evolution.
Absolutely Frank, you should be. How many cirts are you up to now?
I know, I know.
Know, I know.
I think I've got 4.
Ah, now I know you and I did the data engineering thing, so you have at least 11.
That's true, that's true. We did that one and you know that was it's just. It's just been a nice journey and I'll take credit for this. 'cause 'cause I can I was. I was actually pestering you years ago. We've been friends since 2005 and we started doing.
Code camps here in the Richmond area.
Together and co-founded RE co-founded Richmond SQL Server Users Group and you know, worked with the net users group and stuff. And I told you as soon as I saw some of your graphic art and Frank would do a keynote for the Richmond code camps and every time he would make movie posters, the one that.
Still sticks out is 1 called devs on a plane.
Ha ha ha.
Oh yeah, I loved that one that was so so cool and.
And that was.
You know I saw the graphic arts part of it and I just knew I said you, you'd be really good in analytics and data visualization. You should get into by and you were busy doing other stuff which was cool. You were good at that too. It wasn't, you know you. I don't know of anything you've done that you haven't mastered. By thank you. You know you when.
Things took a took, uh, started taking a turn for you in your first rodeo at Microsoft. You got into it and and took off with it. I don't. I won't tell the story well, but you just really turned around. You focused on data and.
You know, I'll say this Frank. I was right.
Well, with that he totally I. I think if anything I took away is I should have listened to Andy 10 years earlier.
You aren't very good.
And that that that that is something that that that that's the big takeaway we'll talk about, kind of that journey. 'cause I think that's worth kind of talking about. And I think one of the things we you, and I've been bouncing around is kind of interviewing each other.
Like in asking one of us those those those questions we have, so we definitely will do that, but not today kids.
We need to.
Today, do we have Dave?
Today do Dave.
Today we have a special guest we have Dave Wentzel. Dave Wentzel is a was a peer of mine when I worked at the Microsoft MTC and that reminds me, I no longer work at Microsoft 2 weeks ago was my last day. I turned in my second blue badge.
And I joined a startup called electrify. We'll talk about them a later day, but I'm so excited to have Dave here. Dave is the data in AI architect out of the Philadelphia Microsoft Technology Center, and he's an awesome guy. Awesome, got to work with. I worked with him when I was in field sales and I worked with him when I was in the MTC organization.
It is April. It was a privilege and honor Dave to have you as a colleague, and it's once again a privilege and an honor to have you here as a guest on data driven.
Well, thank you so much, appreciate that.
So, uhm, so for folks that don't know what the MTC is. Shocking that there are actually people that don't know what that is, what? What is the MTC?
So basically we're a free service to our customers and I'm a data and AI technology architect. We talked to customers about data and it could be anything from just, you know. Hey, here's what we're doing. State of the art in Azure with.
With data, but it could also be architectural design sessions where we talk to customers. Our customers bring us their architectures, and then we kind of get it with them. Give them the pros and cons, alternative ways of thinking, and then what I really enjoy doing is hackathons with customers and workshops and just you know, helping them to learn without just.
Taking a course somewhere so actually using their data and then I guess I'm roughly a data scientist, so we also do design thinking sessions and those are absolutely a lot of fun.
We did one at the MTC with CSL Behring a couple years ago and it actually won a Forrester Award. So I'm very proud of that one. And yeah, it's it's a. It's a lot of fun and it's a good way to bring to have executives and business people understand the actual capabilities of data science. And then within two days be able to come up with a use case.
Oh wow, wow.
And actually build a prototype out a lot of fun.
Yeah, the NPC's are definitely like Microsoft Secret weapon in terms of how 'cause you know. Although I will say and because we were in the DC and we dealt with a lot of government contracts, we could not say that they were a free service. They were and already included paid for service.
Much, much better said yes.
I I 'cause I said free once and I got kind of slapped.
On the hand, say that.
But you know it, it really is something that if you do have a Microsoft account team and you are encountering any kind of questions or or whatever, and it's not strictly technical, there's also pretty good. You know, we basically wouldn't engage with the business development, business decision makers.
Technical decision makers all the way from kind of like you know, hey, this is what Azure can do. This is what data can do for you all the way down to OK. What's your problem? Let's build something out, give you 3 days with one of the top Notch architects in the.
You know, boom, you know we knock it out and and you know I I enjoyed it you know had this opportunity not come I would have I would have gladly stayed another. You know 5-10 years of the MTC. Like a lot of people do, and it's a fun organization. So with that in mind, today we're going to do something a little different. We're kind of doing the.
A contrarian approach is that right, Dave.
So this this has actually come up one of my last. This is one of the things that intrigued me about about your idea for the show was this came up when I was working with a we'll just call it a large governmental agency known for its.
That that should keep it generic enough. They basically came to us and say we want Synapse. We want a data Lake. We want this. We want that. And I was like, OK, well how much data you're talking about. And like we have maybe you know 5 maybe 20 gigs of data.
And I'm like, uh, OK, tell me what are you trying to do? And ultimately I kind of pitched the idea like look, you know you don't have that much data right to make data bricks.
But you really want it so.
If you really want it, I won't stop you, but I think it's kind of overkill. I think you're taking instead of using a steak knife to cut the steak using a chainsaw.
You know they kind of came back and ultimately what won the day was they already they couldn't get approval for whatever we recommended 'cause it didn't get stamped by there.
They're people for security usage yet, and things like that so they end up doing kind of the right thing because of their own bureaucracy, which.
It's kind of weird. It's kind of like dividing by zero and seeing the universe fold in on itself.
But UM, so the topic of today is kind of like no, you don't need a data warehouse. Did I get that right?
Exactly, that's what I believe in, and I believed in it since I was in college and I first learned about data warehouses. I'm not saying data warehouses are always bad, they definitely have their.
Use cases, but in 2021 when we're talking about advanced analytics and we're trying to tell customers you need to be more predictive than prescriptive.
The data warehouse really doesn't deliver.
Really, how so? 'cause? That's that's totally not the power. Certainly not the party line. I'm not going to say which party it was. You can figure it out but but why, why why would you say that?
OK so take.
A step back here, right? We're all data consultants, or we were at some point in our life and probably most of the listeners are. And if you've been doing this, I've been doing this since the mid 90s in college and when I first started I had an internship with a consumer package. Good company, they made candy.
Hours and they said, hey, we wouldn't want to do an internship and take a look at our data and figure out where is the best spot to put candy on a shelf so that we sell more candy to kids, right? So we used data for that at the time that was known as business Intelligence in the industry. Nowadays business intelligence means something totally different. In reality, it's really closer to what?
Today we would call data science right? So my tools of choice were SQL, although I didn't know what SQL was at the time and we had this goofy SQL engine and and essentially something called ESP's, which is roughly the equivalent of like our or stats package something.
Like that and we kind of looked at data as just, you know I have data and let me find the Nuggets of gold and I'm not going to concern myself with schema and that is I think the biggest problem with data warehouses. But take a, you know a metal layer higher right? Talk to the average business executive like a you know a CTO or CEO.
And tell them, as a consultant, you're going to go in and build them a data warehouse.