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Data Engineering in the Federal Sector

Posted Apr 05, 2024 | Views 211
# Data Engineering
# Federal Sector
# Devis
Shane Morris
Shane Morris
Shane Morris
Senior Executive Advisor @ Devis

Former music and entertainment data and software person somehow moves into defense and national security, with hilarious and predictable results.

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Former music and entertainment data and software person somehow moves into defense and national security, with hilarious and predictable results.

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Demetrios Brinkmann
Demetrios Brinkmann
Demetrios Brinkmann
Chief Happiness Engineer @ MLOps Community

At the moment Demetrios is immersing himself in Machine Learning by interviewing experts from around the world in the weekly meetups. Demetrios is constantly learning and engaging in new activities to get uncomfortable and learn from his mistakes. He tries to bring creativity into every aspect of his life, whether that be analyzing the best paths forward, overcoming obstacles, or building lego houses with his daughter.

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At the moment Demetrios is immersing himself in Machine Learning by interviewing experts from around the world in the weekly meetups. Demetrios is constantly learning and engaging in new activities to get uncomfortable and learn from his mistakes. He tries to bring creativity into every aspect of his life, whether that be analyzing the best paths forward, overcoming obstacles, or building lego houses with his daughter.

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Let's focus on autonomous systems rather than automation, and then super-narrow it down to smaller, cheaper, and more accessible autonomous systems.

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Join us at our first in-person conference on June 25 all about AI Quality:

Shane Morris 00:00:00: Hey, what's up? I'm Shane Morris. I'm a senior executive advisor for Devis. Along with being on the board at DataX, I'm an advisor for a little ETL company called Mage as well. And I take my coffee with two wooden spoons of sugar, and then I have a milk frother that I use and so I have a very fancy milk frother, so a lot of frothed milk and sweet and a french press. That's my coffee.

Demetrios 00:00:26: What is up, ML ops community? We are back for another podcast today with Mister Shane Morris. I've been really enjoying talking to him because he's one of those guys that I found on TikTok and then I started following on social media and just see him as a multidimensional human being without getting too woo woo. He's got many different lives that we get into here. He was in music manager before he got into data engineering, worked for the government. We went into some of the qualms, some of the challenges of working for the government as a data engineer and getting design documentation and getting new tooling through in these design specs or these projects that you're building out in government operations and how that differs from being in the private sector. And we also talked a whole lot about Data X, the company that he is an advisor for, and this crazy programming language language, which I will not spoil at all and I will let him explain 100%. My biggest takeaway that I thought was the best piece of information is how bullish he is on data engineering lines up 100% with what I have been feeling. And a lot of people have been saying, the more llms we've got out there, the more AI we've got out there, the more important data engineering is going to become.

Demetrios 00:01:59: And you made some really funny analogies, which I will hold off on saying because I do not want to spoil anything. Let's just get into it with Shane. Huge thank you to everyone out there listening. And if you like it, you know what helps a ton is hitting that follow button if you're on Spotify. And of course, if you are listening on YouTube or watching, it would mean the world to me. If you go ahead and comment, let us know what you thought of the episode. Let's get into it with Shane. We are recording this for the second time because the first time the recording and the audio didn't go through.

Demetrios 00:02:45: But since we started recording, or since that first time that we were recording until now, you got a new job. And when we last talked you were like, oh, yeah, I literally just left today. I left the job. Then the next day, I saw on LinkedIn you congratulating yourself, which I thought was hilarious post, but we can get into that later. And so I think, man, one of these things is that you put yourself out there, and the surface area you cover is so large that of course you're going to get a job in two days.

Shane Morris 00:03:18: Yeah, I think the success I had in getting a job was going places where I didn't belong, even though I just kind of showed up randomly. So the reason I was able to get a job quickly is because I met a guy on TikTok because he had heard about me through a Netapp event. Netapp is a storage company for you guys who don't know. And he ended up in my fantasy football league. And so when I was a free agent, you know, we were all data science dudes and machine learning engineers, which, by the way, is an absolutely vicious fantasy football league to have a whole bunch of dudes who study data science and a fantasy football league. And, yeah. So within 24 hours, I had talked to him, and 24 hours later, I had an offer that I liked. And so I know that it could be a tough job market, but I think if you're willing to go places where you don't necessarily think you're going to be doing anything there just to meet other people in adjacent markets, like, I don't do storage stuff.

Shane Morris 00:04:16: I mean, kind of, but not really. But that's. That's the reason I ended up, you know, getting the position I got.

Demetrios 00:04:21: Exactly that, man. It's all about putting yourself out there in that way. Like, getting out there, just hanging out. That's why we do the local, in person mlops community meetups. But also, I think one other thing that you do really well is you talk about your experiences on LinkedIn, you talk about some really awesome experiences on TikTok. And that's actually how I found you back in the day. And so I'll tell this story again because I find it to be incredible. It was the biggest pattern interrupt I think I've ever had on TikTok.

Demetrios 00:04:55: And that's saying a lot, because TikTok is one of those places where you get pattern interrupts. But it went from me scrolling, tick tock, and then landing on one of your videos, and you were talking about how you were a music manager, and I was looking at the video, and I was like, yeah, that tracks. Like, you had the gear on. You have a grill for everybody that is just listening. You've got some gold teeth, you've got tattoos. It was like, yeah, music manager. That's, that's all right. And then at a certain point in the video, you went from talking about how you were managing Travis Scott or panic at the disco, and then you were like, yeah.

Demetrios 00:05:33: So I just spun up some vms. And then I started sending traffic to it and it was like, wait, what did he say? Vms. And then you were like, and this data pipelines and stuff. And I was just like, no. Is he, is he talking about like stuff that I actually talk about on a daily basis? And then I went deeper down the rabbit hole and I got to know who Shane Morris is and it's like, wow, multidimensional here.

Shane Morris 00:05:57: Yeah, it's kind of interesting because most people don't associate music management with data pipelines or data integration. But back in the day, it was really the wild west of data pipelines and being able to spin up vms and game Soundcloud and other services to create fake plugs plays because you could fake until you make it on the Internet. And it was especially easy if you were someone like me who was industrious and willing to spend $30 a month on cloud services to have your vms running. And so as soon as I realized that Soundcloud didn't have anything getting in the way of me getting on mechanical turk and having them create accounts for me and then logging in, I was like, wait a minute, I can have 1000 Soundcloud accounts? This is great. This is all I need. And yeah, I think it's funny now looking back, because people are like, what does music management have to do with data? Well, I mean, music is an inherently digital asset. Your streams, your accounts, your analytics, your e commerce sites, your ticketing, this is all digital management at a very high level. And so the reason I was successful in the music industry is because so few of my peers were as advanced as I was from a software sophistication level.

Shane Morris 00:07:13: We had people who are just getting used to maybe like Google Analytics back in maybe 2012 or 2013, just be like, how many people are coming from North America? And I'm like, how many people am I faking coming from North America? Let's find out. So I had a lot more. I guess I had a lot more fun than some people did. But a few people got angry at me. They were like, hey, it's not okay. You broke the rules. I'm like, there are no rules.

Demetrios 00:07:32: Yeah, well, show me where it says that. Show me where it says the rules, and I will show you a lie. That is for sure. There's so many good stories that you have from this. I would love to go down the rabbit hole of just like you were working at a website that, again, when it comes to data, you could get really granular with the data, because how mature you were back in the day. So you could know a. Which bands were popular, where they should go and tour. And if I'm remembering this correctly, different places like Coachella or Lollapalooza would reach out to you and be like, who should we get for.

Demetrios 00:08:11: For next year's festival?

Shane Morris 00:08:15: Correct. Yeah. So really early on at Earmilk, you know, this is. You have to remember, Earmilk had a streaming music server before Soundcloud existed. So we would upload our own music. We had our own analytics dashboard, because when you're hosting content on a music blog, you want to post what's popular, because then people will come back and you'll see, you know, where the fans go. Now, by doing this, we also started hosting our own small events in Atlanta and San Francisco. Toronto, or as the Canadians say, Toronto.

Shane Morris 00:08:42: Strangest way to pronounce it. But, you know, we would host our own events, and then talent buyers for, you know, EEG and live Nation and some smaller promotion groups would say, hey, Shane, you know, we're booking this band. We need a good support act. Who you think would be a good draw locally? I'd be like, oh, these guys, you know? Cause according to my analytics, there's a lot of fans of indie band X in Southern California, whatever it is. And so, you know, I. You know, I've got so many strange stories. Like, there was this band called Foster the People. We booked their first ever show.

Shane Morris 00:09:12: I booked G Eazy's first ever show in Atlanta, Georgia. Obviously, I got involved working with Travis Scott, but we were the first ever for a lot of people. I remember Ellie Golding playing beer pong. My sister taught Ellie Golding to play beer pong because we booked her in Isla Vista, outside of Santa Barbara. And so she was kind of a small, little indie electronic act, and we got her. I want to. I want to say we paid her $500 for the first show, and of course she blows up. So, you know, it was cool being on the front end of that analytics because we could see this huge upward growth trajectory.

Shane Morris 00:09:46: But the fans hadn't really figured out how popular some of these artists were yet. And so, yeah, we had talent buyers who were happy to be like, here, please help me. And that's why I made so many connections. Cause I probably could have sold that information, but I was trying to be friendly and helpful, so it helped me later down the road.

Demetrios 00:10:01: Wow, dude. And so you were the epitome of, like, I knew this band before. They were cool.

Shane Morris 00:10:06: Yeah, yeah. I mean, going back, I remember we had this festival called music in the middle in 2013. And looking at that headline flyer, we were the first, I guess, promoter in that sense because we were promoting our own show. But we booked the 1975 right when they were coming out. I remember they had just put out this single called chocolate and we were the first one. And if you go back to their original reissue, they just did a reissue of on the stuff on their website. And you can see that original tour they did in 2013. And it says ear milk festival for the first date.

Shane Morris 00:10:36: It was because we flew in them and landed them. Like, the reason they were able to land here is we paid them an exorbitant amount of money to get here. I remember that. And, you know, Jeezy was there. TV girl, I remember flying out to LA. TV girl is a big band now. I was hanging out. I think it's just a one man band.

Shane Morris 00:10:49: I'm not sure if it's like lead singer situation. But anyway, I remember hanging out with him in his apartment. I want to say it was an echo park or something. And I was like, here's this dude. He was a great artist, but nobody knew who tv girl was. And then now, of course, tv girls, huge. So there's so many of those stories where I would just like randomly run into these artists before they got huge. And today it's kind of funny because I'm still on a first name basis with a lot of them and a lot of their management because they're thankful that I was giving them their start when no one else would.

Shane Morris 00:11:17: Because we had our own events, we could throw earmuk shows in Atlanta. And so I'd be like, I'm gonna book you, you know, our monthly. And they would show up and play shows. It was, it was a really awesome time to be a music blogger, dude.

Demetrios 00:11:29: So music blogging is one of those things that sadly, it feels like it has kind of gone out the window with the Spotify recommender systems. But I feel like, do you have a playlist on Spotify where you've compiled all of the people that you've worked with over the years?

Shane Morris 00:11:45: I have a blog house playlist, yeah. So we called it the blog house era. And it's interesting you bring this up, because I was just talking to my buddy Brendan. Shout out to pizzas on TikTok, Brendan. He's a pizza pizza account, but he's also just an all around cool dude. But he and I were just talking about the blog house era, because it doesn't. It did. I think it had to exist.

Shane Morris 00:12:06: That perfect moment in time where it was decentralized, as we say. We didn't have Spotify and apple music. It was decentralization before the blockchain was cool, bro. And so, you know, we had this fragmented ecosystem that allowed us to be independently successful. And, you know, it was cool because the music, I feel, out of that era, was strangely unaffected by what was algorithmically cool. And I've had discussions with artists about this, where I think sometimes they chase a sound that they think is currently cool rather than defining themselves. And I think because you could still be hyper regional, you know, music from the Bay Area sounded like Bay Area music.

Demetrios 00:12:42: Houston.

Shane Morris 00:12:43: You know, Houston rap sounded like Houston rap. Atlanta had a sound, Memphis out of sound. And now, because you have these, like, top rap playlists, top pop playlists, some of the regional idiosyncrasies of music do get lost to the bigness of it. And I do have very strong opinions about this.

Demetrios 00:12:58: Yeah. And that's so funny. We'll link to the playlist that you have, because I feel like that is a treasure trove of great jams. And it is so true how you can just, like, lose yourself chasing the streams and because basically, it's all gone on to Spotify right now. The other thing that I think is fascinating, and this is not going to be a conversation, 100% about music streaming and how to be a music manager. We're going to get into the data aspect of it, because you went and you worked for companies that related with the government, government companies. So I want to go into that whole thing and the headaches and pros and cons trade offs and all of that. But before we do, I think there was something that stuck with me when you talked about how much money is in technology versus music, and I couldn't get that out of my head.

Demetrios 00:13:56: Like, once I heard it, it was just like, oh, dang, musicians are chasing crumbs compared to tech companies. And so can you reiterate that for us? Yeah.

Shane Morris 00:14:07: So this is the analogy that I always try and draw people so people understand how much money is in music versus how much money is in literal hardware. So last year, Spotify made $17 billion. It was the first time they were actually profitable. Right. Also, last year, Apple made $17 billion selling AirPods. Just the airpods? That's it. That doesn't include their computers, their phones, just their AirPods. And so when you break down what your expenditures are, and I learned this when I was working at Sony Music Nashville, what do people spend money on? Well, you get your streaming revenue, which is pennies.

Shane Morris 00:14:47: It's nothing. You got some ticketing revenue. You got some merch revenue. You'll get your publishing, you know, if you manage to get your song, commercial, movie, whatever. But there's just not that much money that people actually spend. You know, if I look at a high value fan, the Taylor Swift fans, the rabid fans, they'll buy those tickets. But Taylor Swift is the extreme outlier. Most, you know, concert tickets are that maybe $40 to $50 range.

Shane Morris 00:15:08: You'll spend, I don't know, $12 a month on Spotify. So on a good year, you know, assuming you're, you know, unmarried single person, or maybe you're married, no kids, and you can actually go to concerts, you don't have to arrange for the babysitter. You might go to four 5610 concerts a year if you're really heavy on it, one festival. But even then, that's not even a car payment. That's nowhere close to what you're probably paying for your rent or your mortgage. And so it's just a tiny amount of money that people spend on music. And so. And the music business, you are fighting for people's the smallest amount of revenue and the smallest piece of piece versus the reason.

Shane Morris 00:15:42: The main reason I got out of working at music is I had a friend who was working advance the shoe company, and he was like, hey, can you do the little six month project? And it was literally my entire annual salary at Sony. But in six months of work, I was like, wait a minute. I could double my pay selling shoes. But then I was like, wait a minute. I pay a lot more for shoes than I do for music.

Demetrios 00:16:03: Makes sense. It makes total sense, man. And it's so wild to think about that, especially because the merch, it just makes me think, like, next time I'm at a concert, I definitely got to buy merch just to support those artists.

Shane Morris 00:16:17: I mean, the merch is huge when we look at your markup, because you're buying hoodies or t shirts or whatever in bulk. Yeah, 35 to 40 assured is pretty steep. But you got to remember, they don't have to pay shipping when they're at a concert because the logistics kind of come with them. And they do pay for shipping, but it's landed. And so, yeah, they might be making $30 a shirt by selling merch at their shows, versus if you buy it on the website or Amazon, you still got to pay the fulfillment. You got to pay Amazon their fee. They make a lot less. So I would always say, please buy merch at the shows.

Shane Morris 00:16:47: It definitely is a higher profit margin.

Demetrios 00:16:49: Yeah. And even now, nobody's selling CDs. Right. That was I. So I was making music and going around touring. We could say from, like, 2012 to 2020, when the pandemic hit. And right up until the end there we were still selling the occasional CD. We had them.

Demetrios 00:17:12: It wasn't that big of a draw. But after 2020, man, after the pandemic, nobody's buying CDs.

Shane Morris 00:17:18: Yeah, nobody's buying CDs. I buy vinyl, but I only do it for the reason everybody else does. To be pretentious when I have company. That's it.

Demetrios 00:17:25: Yeah, exactly. So it makes it even harder. It's like, yeah, and just makes it so hard. But that's not why we're here. We'll pivot right now and talk about this idea of, you went and you were working for the government, we could say, and it's really hard doing stuff in the government. And I know we went into so many different ways and challenges that it's hard. I would love to kick off the conversation on just getting a design document okayed when you want to get something through for a project, break down that process.

Shane Morris 00:18:04: Yeah. So about. I spent two years and nine months working for Booz Allen Hamilton, which is a large federal and defense contractor. And Booz Allen is unique because there's Booz Allen and Deloitte and GDIt and guide house, light center federal. They all kind of operate in the same space. And so if I would like to change someone's it architecture, you know, any government entity, it's not like, in the commercial world, where I can just say, we find this more efficient, and we might save 17% on cloud costs. Whatever the reason behind it is, you have to go through, depending upon the agency, there's, like, ato processes, which means you have to get an authority to operate. Is your software fedramped? What level of Fedramp is it? You know, and then there's also stakeholders.

Shane Morris 00:18:48: Fedramp, meaning Fedramp. There's Fedramp low, fedramp moderate, and Fedramp high. And it's whether or not you can put it into classified environments. So it's your level of classification that's allowed. And so there's a lot of classified environments in the government, and some of them are places you wouldn't think, like NASA has classified environments, for instance. There's a public space agency, but they still have skiffs that they have to operate out of sometimes. And so whether it's DoD work or NASA work, health related work, sometimes you have to have Fedramp software, and that's incumbent on the software company to submit their software through a Fedramp process. And so you can find software that you want to use sometimes, but if it's not fedramped, you have to find an alternative.

Shane Morris 00:19:32: So you're a little more limited in your toolset and what you can and can't use, which can be a little annoying because you have to. There's workarounds in that case.

Demetrios 00:19:42: And even just saying, all right, we're going to use the newer version of Python would be something that you would have to get checked out. Yeah.

Shane Morris 00:19:49: So anytime you want to upgrade something, you, you typically get locked in a version because they want to make sure that there's no security holes. Like everything in the government is hyper security oriented. And so even if I want to go from, you know, some Python buster 3.9 to whatever, you know, I guess 4.0 whenever it arrives, uh, I'm not, I can't just go do that. I can't just update the newest version, even if I believe that it's going to be more secure and they're advertising all these bug fixes, et cetera. You still have to go back through an entire security process to say, will it break anything? And there's, you know, a testing process we go through. It's, once again, it's not just as easy as it is in a commercial environment where, you know, you're making the best decision. It's that the government has to say yes, and we agree to this because there won't be any effects that we're not expecting.

Demetrios 00:20:37: And the people that you're having to make the case to are not necessarily deep in the weeds on this, I imagine.

Shane Morris 00:20:44: No, often they're not. And it doesn't mean that they don't have good intentions. It's, uh, it's just that many times what you'll find is the people who are administering government programs, they might have phds in a specific type of earth science, right. Or they're, you know, a colonel or a general, and their degree was in biology. Right. These are people who are educated, just not specifically in it. There aren't that many people who have computer science degrees that go and work as military officers or as you know, GS 15s or seses. And the simple reason is pay.

Shane Morris 00:21:21: If you have an advanced degree in computer science or you're somebody who is a software engineer, it doesn't really make sense to stay in a government position given that the pay is not there. Granted, they have the retirement programs and some federal benefits, but it makes more sense to be in the private sector, which is why I don't bemoan anybody who decides to exit the military after six years and then decide to go in the private sector. There's rather, unfortunately, a real talent gap in the federal government right now where we actually need a ton of talent. So if you feel driven and you're a public servant, you're motivated, that kind of thing, I highly encourage you to go either become a military officer, go work for NASA or NOAA or the Department of Health, any of these other departments that desperately need people with computer science degrees.

Demetrios 00:22:10: So when you were doing stuff, you got security clearances. Did you get to get classified?

Shane Morris 00:22:18: Yeah, so I did get. So as part of the process, I became cleared. Right. And there's an entire process where you fill out an SF 86 and they investigate your entire life and everyone you know and your family and all your neighbors. It's. It's interesting to say the least. And because, you know, on your SF 86, you need to be honest about your past. And, you know, I was like, hey, yeah, back in the day when I was in California, I smoked like a fire, you know, and so I had a long weekend talking to my investigator, going through all the video games that I was playing at any given time.

Shane Morris 00:22:51: And I was. What'd you do this week? I went to Colorado and went skiing, you know, just, you know, kind of going through it. And so finally I had to go through an adjudication process and write a right on a thing saying, I promise I will never, ever smoke as long as I'm working with the government. And so I can. I can honestly say I haven't smoked since I wrote on that piece of paper because I do take the job seriously, and I still am a contractor now with my new job, so I have to, you know, make sure to play by the rules.

Demetrios 00:23:18: Yeah, yeah, yeah. So this is. This is fascinating. I was going to say, how the fuck did you get the clearance? But I wasn't. I think they did a good job. If they were that thorough, I imagine if there was anything to find, I hope that they would have found it seems like you're clean as well, the.

Shane Morris 00:23:35: Thing is, you can have an interesting background, but you can't. So remember, when you're thinking about clearable jobs or this, for anybody who's maybe listening to this and is thinking, can I get a clearance? Typically, you need to be us born. That's. There's a few people out there that are Canadians that, you know, got married maybe, but for the most part, it's five Eyes nations. So Canada, Australia, New Zealand, England. Um, and maybe you got married to somebody, and at the most, you're going to get a secret clearance there. Sometimes you go to top secret if you're ex military and you're a five Eyes nation. But what they're really looking for, if.

Shane Morris 00:24:07: If this, like, makes sense, is that you can't be compromised. You can't have a ton of, like, outstanding debt, gambling debt. Um, they don't want people who have, like, a background of domestic violence or violence. They're looking for people who are pretty stable, financially secure. Um, no good credit score. I think they do a credit evaluation. So they're just basically trying to make sure that you're responsible with money and in a way that, you know, if somebody was to come to you, you know, with a heavy russian accent and, you know, offered you $40,000, you wouldn't be inclined to take it because you don't need the money. Uh, and I think that's the one of the biggest things, is they look for financial security, but also us background, and.

Shane Morris 00:24:43: And they do look for foreign travel as well. My background is a little interesting because my wife's family is from Baku, Azerbaijan. And they were like, so what's it, you know, what's your wife's family like? And I'm like, um, they speak Russian. Their house is way too hot. I think they're kind of cheap. You know, they should run the air conditioning a little far. What do you want to know? Eat funny food. I have no idea.

Demetrios 00:25:05: That's so cool, man. And again, this just confirms my point of, like, the more that I get to talk to you and get to know you is the more interesting it gets. And so now you. You moved on from Booz Allen Hamilton, which. It was great. What are you doing these days?

Shane Morris 00:25:24: Yeah, so I was like, I was unemployed for 48 hours, and I got an offer from a small business called Devis out of Arlington. And so, you know, in the government contracting world, you're either bigger or small. And I decided to go into an SBA. So I'm doing that, and then I'm doing some consulting on the side, uh, one for a company that I know you're familiar with called Mage, which is a ETL and data pipeline tool. So I befriended Tommy and I've just been kind of helping him navigate some of the startup process a little bit. So he and I text back and forth, you know, pretty much all day, every day now. Um, and then the guy never sleeps. Yeah, dude, the guy never sleeps.

Shane Morris 00:26:01: He's a machine. It is absolutely crazy. Um, and so uh, I've been, I've been talking with mate, just trying to, you know, help out Tommy because I love their product. And then I'm also on a board now with a company called decision zone. They've got a product called Dadax. And I was really thrilled that they invited me part of their board because I wanted to for a long time. I just couldn't get it through the approval process at booz Allen because they felt it might be a little conflict of interest. And I was like, eh, I'm not going to force it, I can always do it down the road.

Shane Morris 00:26:31: Booz allen's a big company, they've got a lot of investments, so if they feel like me being on the board would compete with one of their investments, I understood. They kind of explained it to me that way. One of the, there's upsides and downsides of working for a super huge company. And I, you know, I called up Sarah Eaton, she's this wonderful woman, she's got a PhD from Yale, she's absolutely brilliant. I was like, sarah, I got some great news, I can be on your board. And so she was like, yeah. So I said, send over the paperwork. And like 24 hours later, you know, I did, and I've been having great meetings with them today.

Shane Morris 00:26:58: We've been mostly strategizing. But anyway, Data X is really cool and it's a really nerdy thing and I'll try and distill it the best way I can. So data X is the distributed autonomous decision, Agent X being you, the person, the thing, the entity. So it is built upon this programming language called Repeat. And so Repeat started at Stanford in 1996 and actually met the two professors that started it. And so repeat was then compiled for the first time. I think they compiled it in Java in 2003, and then it sat and did nothing for 20 some odd years because it's meant for super parallelization. And basically if you think about JavaScript being single threaded where you can only do one process at a time, repeat is a programming language that I want to say, inverts the classic model view controller triangle, but it gets rid of your model and your model goes into your controller language.

Shane Morris 00:27:55: You actually program your business logic into your controller. And so when you think about how programming languages typically work, it's a very different way to think about how programming works in general. I mean, me trying to wrap my head around it as somebody who's learned multiple programming languages, and I think I understand syntax across a couple different. This one has been like next level for even me to understand, which is why I think the adoption curve has been pretty steep. But with all that said, I'm hoping that in the next year I can make it more accessible. I won't go to all the secret plans we have, but I do really believe in what they're doing, and I think they happened at the right time, given the need for a lot of autonomous systems that we have right now with like the AGI need across the horizon.

Demetrios 00:28:38: Yeah, talk to me more about that. So then there's this wild programming language, and data X leverages that in their product. What's the product that they have? That's the autonomous. I'm not sure I fully understood.

Shane Morris 00:28:52: So Data X is their patented product that allows it to be deployed on IoT devices and devices. So it's, it's chipset based more than it is language based. And so give you an example, when we talk about, say, in the defense world, I'll use the defense background because I can speak to it for like class two UA's systems. Right now, the war between Russia and Ukraine involves a lot of drones, small class two commercial drones for the most part, too. And so right now they require a human pilot. The ideas in the future, autonomous agents could be piloting these so they can kind of self pilot, self correct. And in a war zone where you have casualties, things getting shot down, they can complete their mission. This could be for surveillance, this can be for understanding sigent if you've got a second need.

Shane Morris 00:29:39: There's a lot of different ways that you can maximize coverage of drones if they're making their own decisions autonomously, based upon the model built into their own controller, rather than you having to have a human pilot making decisions about what happens next. That's the best process. But it also be wonderful in industry where you have to have a lot of different sensors. Anywhere you've got a sensor, it could be useful as you want to make sure things are happening in sequence, or if there's an issue on one side that you can correct.

Demetrios 00:30:04: So that's where the parallelization comes in. That's where it shines is you've got a ton of sensors and you need to be processing all that data super fast. I see.

Shane Morris 00:30:15: So I think it was a language and a product that was in need of the compute and then also the infrastructure. I mean, in 1996, when they were doing this, people were connecting with 56k dial up modems. This is one of the funny things that happens in academia. They think about the, hey, could we do this? Not whether they should, right, the old Jurassic park thing, like could you versus should you? And I think in 1996, you've got a bunch of Stanford professors who are really intelligent people and are like, we're going to make our own programming language that can do distributed autonomous decisions, and it's going to work on IoT devices, which, by the way, in 1996 barely existed.

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Shane Morris 00:31:52: And so they were just doing it because for an academic, academic perspective, it's probably seemed interesting. And now finally, I think that it has a market need, whereas for the first 20 some odd years of existence, it didn't.

Demetrios 00:32:06: That is so wild. Think about the. I wonder if they just had a strong vision or it was just like that whole random thing, like, what am I going to work on? Well, this seems like it could be cool.

Shane Morris 00:32:20: You know, I talked with both Herschel and JJ. So those are the two kind of progenitors, both the Stanford professors. And if they hear this, I want to make sure I'm saying this a nice way. You ever met somebody to, who's like, really smart and you can tell they're thinking super deep things and they think on a level you don't. But at the same time, a lot of academics kind of fall victim to this, is they, they think so much about the theory of how you might do it and what you might do and the getting there that they don't think about the how might someone use this, right? And I think a lot of what, you know, that's the strength of academia with, with commercial sector, right, where the academics figure out some really strange use cases that maybe don't even need to exist yet and then they find a place to be later. And that's really one of the strengths of like, academia is that, you know, there's been plenty of medicines that are developed that way that like, you know, were kind of piggybacked on another different medicines, you know, scientific need. And then for ten years there was no anything behind it and poof. All right, by the way, then now we can regrow your hair.

Shane Morris 00:33:21: Why? Because we did it on mice ten years ago. So not that you have the need, but I do. Thats where Im wearing a hat.

Demetrios 00:33:29: Yeah. That is so funny, man. And that is, it lays dormant until somebody dusts it off the shelf. They grab it and go like, oh, maybe we should try and use this. All right, let's see if that can scratch our itch here.

Shane Morris 00:33:46: No.

Demetrios 00:33:49: So then I want to keep talking a little bit more about the idea of how it is working with the government and getting something pushed through. I think we mentioned beforehand how you, when you do get something pushed through, you like become an advocate to try and help others understand that. All right, now we can use this new tool. It's been like clearance. What was it called? Fed ramp. Yeah, fedramped. That's the word. That's the word of the day right there.

Demetrios 00:34:20: So it's been fed ramped. Now, I want to let everybody else know that it's possible for you to use this in your design documents. Yeah.

Shane Morris 00:34:27: So, well, I'll give you a good example. Like in the past, I want to say two years, databricks was fed ramped up. Right? And so for a long time, databricks in the commercial space, everybody knows what databricks was. And then data, databricks gets fedramped. Right. And so, you know, Databricks has their entire federal sales arm now. Um, and so I was working on a Navy human resources project, which is think about what the human resources is, but just for, you know, the Navy. And, you know, we had the ability to put data bricks into our stack for, you know, the classic medallion architecture.

Shane Morris 00:34:57: And then now that I've got this ability to put it into the stack, right, I have to go to, you know, our stakeholders or GS fifteen s and our ses's and say, guess what? We can now build application level dashboards. You can see recruiting and accession information and understand who the better recruits are and we just pull the salesforce information in and here's how we're going to do it. And they're like what? You know, like so they don't really know what's happening. You're just, you're speaking whatever to them and so you have to really like say, hey, this is how another commercial entity used it. Here's how the government can use it because usually they go, if you tell them a story about how it's used before, they're better. But if I try and explain, you know, application level versus, you know, quote unquote single source of truth, et cetera, they're lost. But because once again they might have a degree in biology or mechanical engineering. Smart people, just not it people.

Shane Morris 00:35:46: And so, you know, databricks was one of those big victories that I looked at on the Navy human resources piece because we had good leadership. Shout out to Admiral Cheeseman, we had good leadership that was able to know that there was a need for it and they were willing to listen to the experts. And then, you know, I, that was what was, you know, one of my, my happier moments because in that case on n 16, Admiral cheeseman, I don't think he had a background in it, but he was very open to new ideas even though he didn't always understand exactly what, you know, was happening and he didn't get into the nuts and bolts of coding it. He knew that, you know, the Navy human resources had a specific need to modernize and he was willing to say, all right, let's use elation. What's data governance? How does this help? Fine, do it. You know, like it seems like the best, best use case, use commercial tools in the Navy space. Get an ato for it, whatever it takes. I want to be modernized and I've worked on other projects where I won't name where maybe their leadership wasn't as ready to change and didn't understand it and felt like they were uncomfortable with it because it was something they didn't understand.

Shane Morris 00:36:49: And so in my experience, the leadership in government that is most capable of change, just trust the professionals and understands that sometimes commercial industry has good solutions that will work in government and they're willing to just, even though they don't understand it, let those products be used in a government space. And those people shout out to Admiral Cheeseman are the people that I think, you know, allow for the best organizational change.

Demetrios 00:37:15: It's very much like you are doing a bit of like Dev rel or evangelizing more developer evangelism or just evangelizing the tool and having to show that or knowing the strategy that works is taking a parallel situation and saying, here's how it was used on this project or with this team. And so if you think about the possibilities we could do here, then we can incorporate it in this way or that way.

Shane Morris 00:37:46: Exactly. Like you take an organization like Walmart, you know, my buddy Ben works over there, so we kind of talk shop a lot. And Walmart, you don't have to sell a product like databricks to their leadership, right? They're going to know and understand it. They've got, you know, an e commerce need. They're selling stuff. And so obviously we want the thing that's going to maximize our profit and the government, because sometimes you don't have that profit motive. You have to say, even though you've never used this product before, maybe databricks can help us solve this recruiting need or whatever it is, help us better understand this. We can get a dashboard out easier, whatever it is, because the government.

Shane Morris 00:38:25: The government's not profit motivated. And I think a lot of the things in the commercial industry are very much. Does it increase our profit margin? Right. There's a very linear, if I use this, this product, then we will make more money or save time or whatever. And the government, it is, or especially in the military, it's, how does it help me complete my mission better? You know, it's all. It's a different type of language. So in the, in the, in the, uh, say, the n 16 space, their mission, and my Navy HR is going to be getting more officers in retaining officers longer, uh, making sure that, uh, it's easier for officers to move their families, making sure, you know, if you're relocating someone, that they're, uh, going with their family in a timely manner, or, you know, just like dealing with the. The people side of things.

Shane Morris 00:39:06: But it's mission oriented. It's not dollar oriented. I know that sometimes hurts people to hear because we're paying, you know, the taxes on it, but it's, they care about the mission direction. And so you have to change the way you frame, um, anything you want to deploy as mission versus profit.

Demetrios 00:39:24: It's so funny you say that, because it is so often that on this podcast, we talk about Roi, and you have to continuously be analyzing what the ROI of anything you do is. And so going into the military and having it be more about, less about Roi and more about, can this help me get to get from a to b or complete my mission, as you're calling it, is totally foreign way of thinking about it.

Shane Morris 00:39:56: And it's, it's kind of the weird part is it's sort of the same in many cases, especially, like from an HR perspective, right. Because when we think about HR tools, retaining talent costs money, right. When you're, if you're going to advise somebody to adopt a work day, for instance, uh, retaining people cost, you know, is, is retention is something they think about a lot institutionally. And so the Navy thinks the same way. But for recruiting mission, right. They're thinking broadly about every Navy recruit. How do we get more officers in? How are we getting more people who just graduated with a degree in, you know, biology or really tough to fill positions, you know, for Navy doctors, Navy nurses, chaplains, Navy surface warfare, some of these positions are inherently harder to fill. They're called billets.

Shane Morris 00:40:41: And so it's, it's tougher to fill some of these billets. And so when I'm going in, I'm not saying, hey, we're helping you save money and retention for recruiting and ad budget, right. I'm saying it helps you complete your mission because you're going to have more officers that stay in for 20 years versus leaving after six. And so when you're saying, I would like to deploy databricks because it's going to do x, Y and Z, the real thing is I'm deploying databricks because it's going to help you complete your mission by helping retention, accession, recruiting the things that they actually care about because that's the way that they're graded as officers. And so it just, you just have to change the way you talk about tech. Cause it's not money for them that the military is not a money making organization.

Demetrios 00:41:24: Yeah, that is fascinating. There are places where it aligns and then other places where potentially it doesn't align. So you have to be very clear on what the strategy is going into it and if the juice is worth the squeeze. I think is the other piece like, is it going to be worth it for me to try and champion this? And we have to go through a very rigorous process to actually get it through? Or are we good enough as it is? Like how much of a bonus is it going to get us to completing that mission?

Shane Morris 00:42:02: It's true. I've had so many peers that I worked with, especially on the aforementioned N 16 Navy human resources project, where we were doing things that we knew would be difficult to explain to the clients. Like, I'm sure you're familiar with elation, the data governance tool, it's fairly widely used. We had to get a lot of our clients up to speed on what data governance was and managing metadata and understanding data lineage to people who don't live in that world. Sometimes it's difficult to explain why I need, why do we need to pay the license for this? We've never had it before. These are new tools. And so when you have an organization that's existed without any kind of, you know, data governance tools for forever, right? And then you say, now we've got this new thing that you need to pay money for, you gotta sometimes bring it down to this very like third grade level. Like this is how data governance works.

Shane Morris 00:42:53: And you sit in the room with, once again, smart people, but in, they don't live in the nuts and bolts of it. I think even for people like you and me, when I try and explain, you know, data governance to people and metadata management and data lineage, sometimes it can seem like a little overkill even in my sense. Right. But I know that I need to do it because if I don't do it, my life is hell. So like, I have to explain it that way. Just trust that I'm doing this because if I don't, I'm gonna hate my life in three to five years. And that's kind of the way I look at it.

Demetrios 00:43:23: That is another piece that I think you called out is how you don't get to have that feedback loop right away. Right. You have the feedback loop in years as opposed to days or hours, which is really weird when it comes to software.

Shane Morris 00:43:39: Yeah, yeah. I mean, most of these contract vehicles are going to be, you know, anywhere between three and five years. They'll have option periods and, you know, and so, you know, you get these, these contracts and you've got to implement a lot of, because usually the contracts will have a lot of performance, you know, objectives. And so when you're dealing with five year cycles, it's here, here's the front end development we're going to do, here's going to do Salesforce and servicenow and you've got everything you're doing. And these are, you know, sometimes 100 and 5200, 300, 400 person contracts are fulfilling. I mean, so this, there's a significant contracting force on this organization that's designed to do modernization. And it happens in really every part of our government. It's, it's, it's a really good time to be in big data in the government because there are so many pieces of legacy software.

Shane Morris 00:44:24: I mean, once again, you have to be incredibly patient because some of this stuff is old. Uh, and it, you know that the refactoring can be tedious and annoying, but if you're a patient person and you're willing to undertake it, it's, it's good work. Um, and it's, it's good teamwork. Like it teaches you a lot because like I said before, you're working with a set of uh, constraints that aren't usually something you would see in uh, in a commercial area.

Demetrios 00:44:51: So you probably got really good at putting things into metaphors.

Shane Morris 00:44:56: Yeah, yeah, yeah. The metaphor game is strong here.

Demetrios 00:45:01: I was curious to know like how did you explain data governance to me? Like explain it to me like I'm five, right? What does that actually sound like to somebody who is outside of the world?

Shane Morris 00:45:12: So, yeah, so when I'm trying to explain data governance, I use like a hospital as like a metaphor, right. Um, and at different parts of a hospital you have different departments, you'll have maternity, you're going to have care for senior citizens, you might have an emergency department, right. At all. Parts of data in that hospital are going to need some consistency to them, right. Because let's say you're born in one department of the hospital in maternity, but assuming you're going to go to the same hospital your entire life, there's certain things that you're going to need to carry with you. And the reason you want things like data lineage and data governance is so I know that if I had a certain allergy and I go from being born into pediatrics, that all my data is going to move with me, that all the correct tags are going to be there, that I'm going to know what the doctor was. That's the lineage of the doctor that treated me. I'm going to know when I was there, I need to know when that data was changed and my weight changed.

Shane Morris 00:46:02: Okay, that's data lineage, right. And so then I can draw a picture of me, the person going through a healthcare system. But you know, that data lineage could exist differently for a lot of different organizations. For the manufacturer of say an f 35, thats going to be supplier lineage. So you can look for defects, right? If theres a part defect, where did it come from? What supplier? When, when was it made? So now if ive got an issue where an f 35 is having a mechanical error and then another f 35 has a mechanical error, I can say, hey, wait a minute, according to our parts suppliers, we can trace the data lineage back to know that they had the same supplier, they made the same date at the same time. Thats a commonality, right. You know, it's, it's understanding how every, every organization's gonna have an impact to it, but understanding how data changes, when it changes, who changed it, what authorized users are allowed to make changes? Like hospitals, you know, doctors versus nurses. Some people can change things, some people can't.

Shane Morris 00:46:57: Right. So there's levels of users. And so I think if people think of data metadata, managed data governance and the way that you might design the most efficient hospital data infrastructure, that's the way that I try and explain data governance to people.

Demetrios 00:47:12: Damn, that is awesome. That is a great way of looking at it. And it paints the picture for someone who is like, yeah, I've been to a hospital. I understand that. I would want, when I go to the hospital, I want my doctor to know about my peanut allergy.

Shane Morris 00:47:30: Yeah.

Demetrios 00:47:30: And I even want my doctor to know if I go to a different hospital or a different doctor, that stuff should go with me wherever I go. And so in that case, you want that data to live with the different item. Have you asked chat GPT for like, give me a metaphor about how I can explain this?

Shane Morris 00:47:53: Yeah, I'd say what's kind of interesting is I experiment a lot with different llms. I started messing around with anthropic and Claude, Claude four, I think, or maybe it's Claude three, I forget. And I like, try and see, I do the same prompt to see what response differences I get. One thing that I do believe that large language models fail at is applying analogies and metaphors to people's lives because there's a certain type of lived experience that a large language model can't tokenize effectively because once again, they're just trying to predict the next thing that sounds correct. Whereas if you've lived, you know, it hit your life and you've been to a doctor and been to a hospital, you can say, hey, this is how a medical record has changed. You're like, oh, I get that. That makes sense. You know, I've, I understand data governance now.

Shane Morris 00:48:39: Like sort of, you know, you've, don't get me wrong, elation is still a bear to learn, but like, if you wanted to, you could, you could understand the concepts. And I think that llms still fall dramatically short when I'm trying to say, create an analogy. I encourage you because, you know, chat GPT loves hallucinating. Try and get chat GPT to come up with an interesting analogy. And it won't, it does terribly analogies.

Demetrios 00:49:05: Yeah, that is a huge insight because of the non learned experience or non lived experience and how it's almost like the only way that it's going to know about that is if there are enough training data samples on the Internet of analogies being created for certain things. And analogies are inherently so abstract that it's hard to make them hold up and not, and you're toeing that line between like, hallucination and quality.

Shane Morris 00:49:38: Analogy abstraction is inherently artistic in nature. Like, you have to think in a way that isn't directly about what you're talking about. And I do this a lot. You know, when I'm talking to my students about code, right, when I'm trying to like, explain what theyre doing in Python or SQL, I have to give them another example that theyve used in the real world to say, oh, think about this way. Because to me, and I always come back to this, coding is just giving a computer direction step by step directions. The people who do the best at coding, in my opinion, were the most efficient. Just understand how to give people directions in the dumbest possible way. You remember youre giving a logic board, really what a computer is.

Shane Morris 00:50:18: You're giving a logic board directions, and it's got to be step by step, or maybe not step by step or, you know, putting directions in groups and then executing and hoping that you're really, really good at explaining directions, which is why marriage has always been challenging for me when I'm navigating in the town with my wife. I digress.

Demetrios 00:50:35: So let's talk a little bit about your course, because I think it's super cool that you decided to create a course on data engineering that you said, you know what, instead of making this a paid course, I'm just going to make it donation basis. I want people to learn. I want to help basically bring everyone into the field that wants to be in the field and not have any type of cost be a hindrance. Yeah.

Shane Morris 00:51:00: So one of the big things that I tried to do was not create a barrier to entry because I just, I don't see it being useful. And I understand that there's people out there who sell their courses. Uh, but for me, that wasn't my interest. Uh, my interest was getting people through, um, and not to be like purely altruistic about it, because I wanted to get people through and get them to a point where they're junior level data engineer capable. You know, I can get things through an ETL pipeline and then maybe deploy a tableau dashboard or something on streamlit, something basic, right, but employable was the most important part there. Uh, the basics of git, the basics of Docker, SQl and Python. Just getting there. And then I think there's a second piece of that is having the confidence to then take the next step to take those intermediate level courses.

Shane Morris 00:51:47: And so in my course, I say, hey, this is the way I would learn step by step. I supply a lot of other people courses on Udemy and then I also even tell the students, and I actually learned this after the fact and had to update my course, that a lot of local libraries with your library card, you get one of the premium udemy subscriptions. And so you don't even have to pay for Udemy if you have a library card in many cities and towns, and so you don't pay the $30 a month. It is literally as free as I can make it. And so I say, here's Brett Fisher's docker course. He's huge in the Docker community here in Virginia. Here's what you're going to need to know. And it's a little bit of coding, a little bit of DevOps so you can containerize things because that happens a lot and it gets you functional.

Shane Morris 00:52:30: And, and about six months end was 180 days. My goal is to put people through an interview, like a tech interview, which is a little different. And I want them to walk me through a capstone project and tell the, you know, tell me why you made the decisions you made. Show me your application, you know, show me something that you're an expert in. And I even make it open ended. Like if you're into sports, make it sports related. If you're in a Pokemon, make it Pokemon related. Find yourself something that you, you know a lot about.

Shane Morris 00:52:57: You're a subject matter in, but whether it be Pokemon cards or wine, and build me a dashboard, build me a tool, show me something that's interesting that you've made using your skills. And if you can do that, at least my belief, if you can do that, you can go work in a corporate environment because you can learn their specific need, whether it's shoes or makeup or ring lights, whatever it is.

Demetrios 00:53:22: Yeah. And then you also have some stuff. You did mention that you aren't doing this purely out of altruism, right. You get to do fun stuff and engage with people that go through that, that have the persistence. Yeah.

Shane Morris 00:53:36: So if you get through, you know, I know some recruiters out there that are looking for folks and they'll pay me a recruiting fee to find them. Talent. And I think, you know, the other thing is most recruiters are not highly technical people. They're not going to be able to evaluate somebody from a technical perspective. And so a lot of it is me saying, no, I'll go on my reputation if I'm willing to co sign you and say, hey, uh, you walk through a coding interview with me, and I don't think you're going to embarrass yourself if you end up, you know, in a technical interview with somebody at a capital one or a Nissan, wherever it may be, right? I think if you wanted to do a junior level position and those kind of like, you know, 80 to 90 grand a year kind of salary rolls, that's where I want you to get. Now, it's beyond that. It's up to you to learn more. Right? You're still going to have to refine your skills, but everybody's got to get a start somewhere.

Shane Morris 00:54:23: And I know that right now, as much as everybody talks about AI and machine learning and the need for these next generation high performance computing models, you're going to have to have immaculate data to put in these models, and that means that you're going to need a lot of quality data engineers. And I think it's one of the pieces that rather, unfortunately, no shade to Nvidia. Please, Nvidia, be nice to me. But the folks in Nvidia, they're selling high performance computing. Nobody wants to talk about the nuts and bolts of clean data. And so I'm here saying, I don't, I'm not going to be the dude who develops those advanced mathematical models and algorithms. That's not me. I'm not interested.

Shane Morris 00:54:58: But I'm going to help you make data pipelines. That's what I'm good at. And so I'll let somebody else who's much better at math do the algorithm side.

Demetrios 00:55:07: It's so not sexy to think about the ways that you can do the data pipelines versus the next new thing in the large language models or that high performance computing.

Shane Morris 00:55:21: Yeah, I'll give you the analogy. Right. It's the difference between the people who build the high performance car engines, right? Oh, I just built a thousand horsepower, five liter supercharged Mustang engine. That's Nvidia. I'm the guy at the Jiffy loop. I'm doing the oil change every 5000 miles. You got to come and see me no matter what, right? I'm not, I won't build the high performance computing for you. But, you know, if you need your wiper blades, replace I can get the oil done for you.

Shane Morris 00:55:44: Might upsell you on the air filter for $75 because I'm greedy, you know, like, but that's me, that's. That's my business. But the thing is, you're going to get a lot more oil changes than you do. Thousand horsepower, you know, drag. Drag strip Freddy engines. And so I'm betting on oil changes, not high performance engines.

Demetrios 00:56:01: Oh, I see what you're saying. So because of this high performance car, you're going to need to change the oil a lot more. Yeah. And that's the same as when you've got these high performance chips or you're doing this high performance computing. You really need to take care that your data is on point.

Shane Morris 00:56:18: Yeah, I mean, every single person who's got. I mean, I got this. This theory that we're all going to have, you know, GPU's on the edge at some point and, you know, a lot of, you know, I mean, obviously, you know, Apple's got their, what do they call the.

Demetrios 00:56:28: The.

Shane Morris 00:56:29: They've got, what are they called? The chips, the integrated chips they have now. Anyway, point is, they have, you know, machine learning capability on your phone. Right? And so the bigger piece here is that if we're all going to have this capability in our pocket to some point in the very near future, where they won't always be requiring these $8,000 Nvidia GPU's, I'm sure that they'll get less expensive when they do. We're all going to have this capability, and we're all going to require immaculate data for all of our interactions, all the devices that are going to be distributed. I think this five to ten year window we're looking at, it's going to change many things in our lives. And when that happens, you're still going to require a metric ton of data engineers, and I cannot wait to meet that market need.

Demetrios 00:57:11: Yes, that is well said. So the. So apple. Well, I wanted to mention too, I can't remember for the life of me the name of the computing language that you were talking about.

Shane Morris 00:57:31: Repeat.

Demetrios 00:57:34: That's. It's gonna be the future right there. If we're gonna have all these different million sensors, people are gonna be, uh, writing in. What is it? Ramped.

Shane Morris 00:57:42: Rapide. R a p I D e. Rapid with an e. Oh, there you go.

Demetrios 00:57:48: Rapide. It's like French. Rapid.

Shane Morris 00:57:50: Yeah, it's French.

Demetrios 00:57:53: Yeah. Yes. That is so good. So before we go, talk to me about buff ranch. Man, I gotta get into this because I can't see one of your videos of you hawking buffer ranch and not mention it on the podcast.

Shane Morris 00:58:10: So here's the interesting thing. I work with a couple different agencies on TikTok and one of the agencies sent me like a gift box of Heinz Buffer ranch. And it was supposed to be, I believe, for some of their food influencers because there's a lot of people who do food talk out there. And so I was stoked because I thought I was going to get sponsored by Kraft Heinz. And so I got this three pack of Heinz Buffer Ranch and I sent an email back to my agent and I was like, hey, can't wait to do the thing. How do you want me to do this? She was like, ah, I think you may have gotten that an error. We don't really actually want you to be sponsored by Kraft Heinz Buffer Ranch. I'm like, no, no, I'm going to get this sponsorship no matter what.

Shane Morris 00:58:50: I tell you what, I'm going to give them one on the house. And so I just made a stupid, like aside and I made some fried chicken and said this Hines buffer it to pairs well with this wine or whatever I said. Then it became like a recurring bit because I sent them an email and they're like, actually, we're fine. We don't really want to sponsor you. And so I probably sent their marketing department like a dozen emails saying, hey, I made another video for Heinz Buffer engine. Here it is. And it's always something wildly inappropriate. Like, it's not the kind of video that Kraft Heinz would ever want to be associated with.

Demetrios 00:59:21: They're going to send you cease and desist letters.

Shane Morris 00:59:23: They're probably going to end with a c and D. Yeah. And so now my audience and including, like, people make inside jokes. If you look at any one of the comments on my TikTok post, there's never a few comments with people saying, I thought this was gonna be a Heinz buffer ranch joke because somehow at the end of a lot of my videos, I bring up this bottle of Heinz Buffer ranch. And actually, by the way, it's, it's actually pretty good. Like, I've been trying to look plug Heinz buffer inch, but like, I do eat it now at my house. Cause I bought so many damn bottles of this stuff, you know? Cause like, there was a one skit where I had like several bottles behind me on the bookshelf. And so I started having to eat it because I had like six bottles and I started liking it cause I started eating it.

Shane Morris 01:00:01: So it is kind of good.

Demetrios 01:00:04: We gotta see, we gotta reach out to him, see if they'll sponsor this episode.

Shane Morris 01:00:07: Let's see if they'll get mlops. The Mlops podcast brought to you by Heinz Buffer.

Demetrios 01:00:14: It's like, wait a minute. Again, it goes back to this whole idea of pattern interrupts. Man, that is the best thing that I think is the most enjoyable part about talking to you and hanging out with you is just. It's a constant stream of pattern interrupts that I do not normally get on a daily basis.

Shane Morris 01:00:32: Yeah, I'm really good at jeopardy, for what it's worth. Like.

Demetrios 01:00:38: I believe that random knowledge bad tracks 100%. So, Heinz Buff Ranch. It reminds me of back in the day how when we were first starting the community, we had one of our virtual meetups during COVID It was like two, three weeks into COVID. And it was talking about the differences between Kubeflow and ML flow. And the presenter who came to talk about it, he was saying, like, okay, this may be a little bit cheesy. I'm going to use a cheese analogy. Like, you know, Kubeflow is your Parmesan and ML Flow is your. I think he was just like, cheddar and cheddar you can throw on anything.

Demetrios 01:01:16: And then Parmesan is a little bit more refined, and it takes a little bit more work to get that going. And so at the beginning of the, I knew that he was going to talk about that in his abstract because he sent it over beforehand. So I was like, this episode is sponsored by cheese. And it was just like, threw up a generic slab of cheese there. And it was like, we're about to go into cheese very deep on this episode, except you kind of, like, took the buffer ranch and you ran with it, man. I stopped with the cheese after the first one.

Shane Morris 01:01:49: Yeah, I. I don't think I'm ever going to stop it at this point. Like, now, I actually. They'll probably have to. I'm supposed to be making. I'm going to be making a video. I'm probably in the next 20 or 30 minutes, but I need to make another tick tock. And so now that I've talked about it, I do think I need to somehow throw in the buffer inch.

Shane Morris 01:02:06: I'm probably going to have to.

Demetrios 01:02:07: Yeah, yeah. I see a world where you are creating diagrams, data flow diagrams, and you're using the buffer ranch as the data flow, and it's going through, like, okay, now it goes into the little container and boom, here we go. And then it goes from the container, it gets transformed.

Shane Morris 01:02:30: So, yeah, exploiting dbts so your data build tools fusion of creamy ranch and tangy buffalo. Wait, what is happening here?

Demetrios 01:02:41: All right, we could go on all day. This has been great, man. I really appreciate this, Shane, and especially because you did this for a second time. It is very kind of you to come on here and chat with me again. I encourage anyone that is out there that is looking to get into the data engineering space. Or if you have someone afraid because maybe you're not actually looking to take 101 type courses on data engineering, but you have a friend that has been pestering you about how to code, send them over Shane's course.

Shane Morris 01:03:13: I appreciate, Dimitrios. Always wonderful talking to you, man.

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