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Survival guides, rulebooks, blueprints and more. The best way to prepare yourself as a next generation data leader is this episode of Catalog & Cocktails with Tim Gasper, Juan Sequeda, and special guest Veronika Durgin, VP of Data at Saks 5th Avenue.
Get deep into some preparation-curriculum with topics like:
Do’s and Don’ts of Data Leaders
Are you skeptical enough?
How curious are you?
Joining communities that educate and inspire
Speaker 1: This is Catalog and Cocktails, presented by data.world.
Tim Gasper: Hello, everyone. Welcome, welcome. It's time once again for Catalog and Cocktails, presented by data.world. It's your honest, no BS, non- salesy conversation about enterprise data management with tasty beverages in hand. I'm Tim Gasper, long- time data nerd, product guy, customer guy at data.world, joined by Juan. Hey, Juan.
Juan Sequeda: Hey, everybody. I'm Juan Sequeda, principal scientist at data.world. And as always, it's Wednesday and it's actually 5: 00 where I am right now, because usually it's 4: 00 in Austin. Well, it's still 4:00 in Austin. It's 5: 00 out. I'm in Boston right now. And I am super, super excited, because our guest today, who's sitting here physically next to me, is Veronika Durgin, who's the VP of data at Saks, somebody who's extremely vocal on LinkedIn, who has been already on so many podcasts. We need to have you on the show. And we just met three minutes ago in person. Cheers.
Veronika Durgin: Yeah. And it is 5: 00 here. So, we can officially drink.
Juan Sequeda: So, let's kick it off. What are we drinking? What are we toasting for? Veronika, what are we drinking?
Veronika Durgin: I'm going to be a little silly and funny. Juan is as tall in real life as he is on Zoom. So, I'll drink to that. The world of Zoom virtual, we're always surprised how tall or short people are. So, I'll drink to that.
Juan Sequeda: We're both having the same good- old fashion
Veronika Durgin: Same good- old fashion.
Juan Sequeda: How about you, Tim? We miss you. You're not being here. This would've been awesome inaudible.
Tim Gasper: I miss you, too. I wish I could be there. Unfortunately, I'm not feeling well today, but I'm glad that you all were able to meet up and be in person. I am sure, you get a cocktail that I made up. Because I've got this liquor called Punt e Mes. It's like a vermouth. And I don't know what to use it in. So, I'm inventing cocktails to have it. So, Punt e Mes with Drambuie and soda, I call it a punchy cola.
Veronika Durgin: All right. Hopefully, you're still sitting up straight by the end of this podcast inaudible.
Juan Sequeda: You're ready for the second one afterwards. Let's see how that goes.
Tim Gasper: Yeah, hopefully, hopefully. It's bitter, but it tastes good. Pretty low- proof, which is good. So, cheers.
Juan Sequeda: Cheers, cheers.
Veronika Durgin: Cheers, cheers.
Juan Sequeda: All right.
Tim Gasper: So, we got our question of the day, our warm- up question. So, today is all about guiding the next generation of data leaders. What class do you wish you could have taken in school to guide you in some way?
Veronika Durgin: Psychology of leadership. I am fascinated by psychology. I think we're all focusing on tech. Tech is logical and easy to understand, humans are not. In being an effective leader, you truly have to understand who you are working with and how to best work with them. Really, really, it's on my list to hopefully find some courses to take, and hopefully understand how humans work.
Tim Gasper: That's a great answer. We don't learn enough how people work when we're in school, right?
Veronika Durgin: Mm- hmm.
Tim Gasper: Psychology was on my list. The other one I had on my list was personal finance. I think, that would've been very helpful.
Juan Sequeda: The psychology is one. I think, I'm lucky I'm married to a psychologist. And she's a behavior analyst. So, I've been learning a lot of that.
Veronika Durgin: So, you can marry once.
Juan Sequeda: That's right.
Veronika Durgin: So, you can either take a class or marry a psychologist.
Juan Sequeda: That's what I got, a little bit covered. I've taken statistics, but I really didn't pay that much of attention, statistics. I wish I should have taken advantage of that more. And the other one is history. I mean, I've taken history, but I know, when you're young, I don't pay attention to that. And now, I'm like, " Oh, my gosh, I should have really think of..."
Veronika Durgin: Is it for fun, history? Or do you find practical lessons from it?
Juan Sequeda: So, both. Depending on the type of history, you can learn lessons from it. But I also, I think, it's about the critical thinking about learning what happened from that history.
Veronika Durgin: From that, yeah.
Juan Sequeda: And even just thinking about how you learn from the past. And then, apply that to where you are today. I think, that's the important thing.
Veronika Durgin: Sure, 100%, yeah.
Juan Sequeda: Those again, keep reinventing the wheel. We talk about reinventing the wheels. And if you don't know the past, you're doomed to repeat it. So, I think, it's all about history.
Veronika Durgin: So, so true. I remember I didn't like history, either. It was all about memorizing dates. And it had no meaning.
Juan Sequeda: Exactly.
Veronika Durgin: But now, when you look back, it's not memorizing dates, it is the events of the outcomes.
Juan Sequeda: And I would actually argue that, maybe, the way it was taught, it was not the best. Anyways, that's for a different topic. But actually, we're going to talk a little bit about education and stuff. So, let's kick it off with our discussion today. So, honest, no- BS, what are you excited and what are you annoyed about the next generation of data leaders?
Veronika Durgin: Well, I'm just excited, my oldest son is in college. Honestly, I just want to go back to college now. Everything he's describing, everything he's learning, I'm like, " Oh, my God, can I take a class instead of you?" Super excited, excited of things that are available to kids, to younger generations now. Internet, I'm excited. I'm excited that there's so much content available. I remember, back in the day, I'm going to date myself a little bit. We had a book with the commendation. And then, you had to read through it. And then, if you couldn't figure it out, you had to pick up an actual landline and call support. So, excited that we don't have to do that. That information is readily available to us. Annoyed about information readily available to us, also, because the quality of that information is oftentimes questionable. So, I'm excited and annoyed about the same thing.
Tim Gasper: So, is everybody an expert now because they have the internet at their fingertips?
Veronika Durgin: Yes, yes, yes. And I'm sad to say that I pulled it off on my doctor, too. I said I have to do some research about something, and it was very important. But knowledge is power.
Juan Sequeda: So, we catch ourselves doing that, in so many different spaces over that.
Veronika Durgin: Absolutely.
Juan Sequeda: But I think, one of the things that I'd like to go in, and this is what we want to call the BS on things, is, how are you identifying the things that you should be calling BS on? What are the techniques? And actually, what are actual examples that you've been reading? I can't believe somebody wrote that because that is wrong. And whoever's following this is believing this is the right thing to do, that is wrong.
Veronika Durgin: I think, everybody feels like they're an expert now, like, " Oh, I can Google it. I can look it up and I'm an expert." The reality is, well, I am naturally very skeptical. So, for me to trust something, I have to understand it, not truly fully, completely know, but understand it enough where it starts making sense in my head. So, anything new that comes with me, if I read something, I'm very, very skeptical. So, I keep researching on that specific topic. And what I've seen very recently is I absolutely hate, and I hope I don't offend you, the influencer. " Oh, you're an influencer." I'm not. Everybody's an influencer now, because they can put two paragraphs together that are easy to read. No, you're not. I was reading an article recently that was just blatantly wrong, and it was suggesting something that was very dangerous. Luckily, what it was suggesting wasn't possible. So, somebody who wrote it didn't actually test it. So, they were saying, " Do this." You can't actually do it. And then, the fact that they were suggesting to do it, I was quite shocked. But this is what happens, people just write, and it was on a trustworthy website, too. Nobody's validating. Like, research, you have PhD, somebody's reading, re- reading, editing, commenting, ripping you to shreds before you publish something. It doesn't happen in the world of the internet.
Juan Sequeda: This is tough. Because you see people, they have lots of followers, or they get lots of comments, they're posting things that seem very insightful. You want to believe it, you want to trust it. And we live in a time right now where trust is really hard.
Tim Gasper: Or you see somebody else who you trust liking something or commenting, then, " That must be true." So, I think, this is...
Juan Sequeda: A social validation.
Tim Gasper: Exactly. Social validation
Veronika Durgin: Exactly. That's exactly it. And I go back and forth. I want to encourage for newcomers to share their thoughts. Because that's how I learn. I talk about it and you say, " But wait, have you thought about this?" And I'm, like, " Oh, no, I haven't." Because I don't know everything. As a matter of fact, the more I learn, the less I know. But I really want to learn everything. I want to know about everything. So, I want to encourage people to be comfortable speaking and writing, but at the same time, how do we help others understand that this isn't necessarily correct? And I think, my best thing is just always be skeptical, researching.
Juan Sequeda: So, let's dive into some things that may be touchy. So, what are the data topics that you're seeing that is people are talking about in angle A, and they're completely ignoring angle B? Or what are the things that are on top of mind, right now?
Veronika Durgin: So, I would say three things. Whenever a platform says that it'll solve all your problems, just buy a tool, it'll solve your problems. And it's not there's anything wrong with the tool. And I'll give you an example. You buy a car, you give it to your 10- year- old. Do you think a 10- year- old can drive a car? No. So, you can't say, " Oh, 10- year- old can get around by just giving them a car." No, they're not ready for it. So, tools like that. No, you have to know how to use it. Obviously, data modeling, I talk about data modeling a ton. Just, I've gone through data model instead who use data modeling, just dump everything. I don't want it as hard. I don't understand it. " Oh, now, forgive me, I'm going to pick on DBT for a second." DBT model is a data model. No, it's not. There's also data modeling in machine learning, data science. So, that's another area that's just gone crazy back and forth, circles up and down. And then, the third area is AI, mythical magical, " Oh, is going to solve all the problems." It's like the Cloud. We're going to go to the Cloud. Well, the Cloud is a computer somewhere. There's nothing magical about it. But I think, to a lot of people, AI is this next mythical magical thing that'll solve all the problems, when the reality is, whatever you put into it, that's what you get out of it.
Juan Sequeda: And then, one of the things that could go connecting to the influencers is, are the influencers more on the tool sides that you're seeing the people? Or is it on particular AI data modeling? I bring this up because you want people to really... again, how can we make them be skeptical, not make them, but how can we incentivize people to be skeptical? Like, you're seeing this thing connect the dots, that person is probably working here or does work at this. There's an incentive of why that person is actually pushing that message. And I think, that's important to understand.
Veronika Durgin: That's interesting. That's the psychology part of it. What is driving somebody to be out there? A lot of people, just, they feel it's their way to move to get more gigs, to get a new job. To others, they're simply trying to share their experience.
Juan Sequeda: Is there something wrong with it?
Veronika Durgin: No, there's nothing.
Juan Sequeda: This is important. I want to go say, there's nothing wrong with that. It's just understand their motivations because people have biases.
Veronika Durgin: Oh, hundred percent. And then, that's okay. I'm a fan of Snowflake and I'm a fan of data vault. I'll be talking about it, but I'm also, I'm not in it to sell. I'm just simply, I'm here to educate and also learn. I'm like, " Oh, snowflake is amazing, blah." Somebody comes back and say, " Well, you know what? Oracle has actually been doing this same thing that Snowflake just came with up with for the past 20 years." I love those conversations. It's opens my eyes to other things. But you answer your question, I've seen it everywhere. People love tools, people love patterns, people push their ways of doing stuff. Going to go again into psychology, I think, people, inherently, are very competitive and love to be right and win. So, I see a lot of these discussions are fights and it's not necessarily, it's not fundamentally to just collectively be better but to win. And I always find it very fascinating. And when I say, I'm not here to change your opinion, it's okay?" And by the way, they're more than one way to do stuff, and I'm totally fine. Don't change your opinion. That's okay. I'm just sharing my experience and people get shocked.
Juan Sequeda: I think, that's a big challenge that we have these days, is that, there's always such a competitiveness to especially conversations about our right way to do things. And as we all know, being in the data space, there's some religions, there are some factions, and how do we deal with that? Do we all need to learn to be better citizens? That on one hand, we all have to be a little more humble and willing to hear other people's opinions, and on the other hand, be willing to call out somebody and not say, and say, " Hey, I see you saying this." But what about this situation? I don't see how that applies. Is it both of these things?
Veronika Durgin: Great comments. So, be humble and always learn. Sometimes, and I've seen it happened to me many times. I might be sure that I know, and I'll make a comment. But the reality is, maybe, this was true five years ago, it's no longer true. Things change. So, learn, be humble, always ask questions. I think, oftentimes, somebody comes in, I'm an expert, but the reality, maybe they are in their specific little area. The world is complex. There's a lot of different things. The other side, I often see people who are in consulting, their view of the world is different from people who actually work full- time on teams day in and out. It's a completely different ball game. It's different how you approach things. You're there for long haul.
Juan Sequeda: I'm glad you brought that up because I'm going to call out myself here. I work for a vendor and I have experience, I have past experience, previous companies and stuff. But right now, the last four years, I've been at a vendor. My experience is from a vendor perspective implementing, which is different from somebody who's actually on the buyer side implementing thing, which is different from the consulting side. And I catch myself as like, " Oh, I am pushing. This is how we should go do this." And a lot of it is, " I postulate about things and I'm obscene things." But this is from my perspective, which people should take that from with the grain of salt. Obviously, I have a bias, because I work for a vendor. But I think, this is also, again, goes back to understanding who are the people, who's actually talking, what are their backgrounds? Where do they work for? What experience do they have?
Veronika Durgin: Where are you coming from? You were, as a vendor, you come in, we bought a tool. So, money's there, I'm sitting, I don't have a budget. That's a different perspective, different ways, I'll have to solve the same problem. Then, there's all these different sides. And then, the other thing that I often see, somebody worked for one company, and they successfully implemented something and they come out and say, " This is the way to do it." I've worked for very different companies, I worked for software companies, financial, right now I work for retail e- commerce. Different cultures, different people, skill sets, completely different approaches and implementations. You can't just come and say, " Well, I worked for company APC, we successfully did this, and this is how everybody should be doing it." It's not how it works. Again, world is very complex. We're all different
Juan Sequeda: On this, I think, talking about experiences was something we were chatting before, was, what do we mean by experience? So, their experiences, for consulting, I've helped other people let them go do it, but I haven't really rolled up my sleeves, or I have rolled up my sleeves in the same place for 10 plus years, or I've rolled up my sleeves in five places for two years each. So, I mean, I think, this is one of the things that we've talked about of understand what experience means.
Veronika Durgin: And again, it's completely different. You helped 50 different customers implement something. I've been on the side where I have to support, which you've implemented. Completely different experiences. And as far as years of experience, somebody doing the same thing for a very long time, say for 15 years. Is it the same as somebody also working for 15 years in different company, different team sizes, different maturity of these companies? Different experiences. Again, know where the person's coming from and be skeptical. But also, be humble even though when... And I try to, in my teams, to get that psychological safety with the team, where I, actually, absolutely want to hear what everybody has to say. It doesn't matter that you're inexperienced. Sometimes, somebody will ask a question, then it will lead somebody else to think of something great. So, you might be like, I'll give you an example, which is like anti- pattern, but I'm sometimes I learn from my own mistakes. My very first migration to the Cloud. So, I was a database administrator, and I was specializing in performance tuning. I could tune that SQL server to, it was array. So, when they're, like, " Oh, we're moving to the Cloud, because data center, we can..." I'm like, " No, I can tune that server. It's just fine. I did not want to go to the Cloud." Like, job security. I was worried
Juan Sequeda: It's cool to go. I love going into an explain plan and seeing this, and it's a fun thing, like, " Oh, they're taking this away from me."
Veronika Durgin: Yeah. And I was, like, " Why?" It works just fine. I am comfortable. I can make it. And now, mind you, there's only... how many people do you know who can really fine tune something, not many. So, it was like,-
Juan Sequeda: Was that becoming a long part?
Veronika Durgin: ... "Should I be doing this?" Well, that's beside point. So, we're like, " Okay." Leadership comes in moving to the Cloud, AWS team came in, " This is what you're doing." We're like, " No." We're, like, " Okay, let's test it out." So, we spent quite a few months tested things out, in and out. We, actually wanted to show that this doesn't work. So, the way we went in was, like, " No, let's show that we don't need to do it." But what came out is... what AWS suggests is not how we want to do it. We want to do it in a different way. And it's incredible, it's the options of the Cloud. And we went to much faster servers, cheaper, high availability. We set things up in an incredible way and I was, like, " Wow, I want to learn, I want to know more." So, my mentality go in was, like, " Let's make this fail." But I came out of it, I'm, like, "I want nothing to do with data centers anymore. Please take me to the Cloud." To me, that was learning
Juan Sequeda: That's interesting to hear that story where you were trying to honestly prove a different point, and even the vendor was recommending a certain approach. But then, when you went in with an open mind learning, asking questions, coming up with ideas, you were able to actually figure out a better idea, a better approach that was tuned to what your specific use case was.
Veronika Durgin: Exactly. Precisely. And be humble and be learning. Always be learning. I think, we all know that in data, how things are changing. It's a place where if you stop learning, then you'll be left behind.
Juan Sequeda: I think, another experience thing that we hear a lot about is, well, what if you came from Facebook, or you came from Netflix, or you came from LinkedIn, or you came from... and it's, like, " Oh, we did Kafka at massive webscale or whatever." So, I know, I know, how streaming works, I know how databases work. What do you think about that kind of experience, and how it applies when it applies?
Veronika Durgin: Please, don't judge my Instagram feed. I had a video, Cardi B was on Jimmy Kimmel, and she was saying how she got a Lamborghini truck and he's, like, " But you don't drive." She's, like, " That's okay. I take pictures with it." Kafka streaming, what Facebook does, what Netflix does is that Lamborghini truck, that we're trying to bring to our garages to take pictures with them.
Juan Sequeda: Out of the best analogies I've heard, we need to put this on a t- shirt. Kafka is like the Lamborghini truck that you put in your garage. You can't drive but you take pictures of it.
Veronika Durgin: Well, but that's exactly. Do you really need this massive, expensive, powerful thing for the problem that you don't have, or the tool that you don't know how to use? And I think, we've had this, but because these companies that will look up to, we want to copy them.
Juan Sequeda: Okay, I'm going to take a quick turn here. Kafka streaming, is this something people really need to start paying attention to? Or is this just too much of...
Veronika Durgin: Okay. So, I think, we often joke, and my team, it's like, when you give a mouse cookie. So, I think, micro-batching batching is just fine for majority of the companies. But as people get more educated in data, how to use data, the thirst to get quicker data is there. So, I think, streaming is coming and people are interested in this practically real time analytics. So, I don't think everybody needs to do it still, and we have some time to go before it happens. But I am seeing this, like, " Oh, well, I don't want data every hour. Give it to me more often, give it to me more frequently. I want to be able to make decisions, I want to embed this into applications. I want that decision making quicker."
Juan Sequeda: Okay. This is a perfect clap. I appreciate this. We're going to put that on the t- shirt. We have so many t- shirts that we're going to go to. We're really going to do this one day. So, you bringing up the Facebook and bringing up the experience. I think, I do, I want the audience to take, have some clear takeaways of identifying the motivations or the biases people come from. So, I think, one of experiences, like, " Oh, I come from a Big Tech." So, you're saying all these things, but you realize that their background is Big Tech. Well, where they're coming from? Then, there is one where people have been working at a place for 10, 15 years. They have that experience, they have other experience where they've been working, they have a lot of diversity and smaller amount of time. What other ones would you add there, and how would he characterize it? How do you manage all this?
Veronika Durgin: I honestly want to have one of each on my team, just to bring these different perspectives. Somebody who's been with the same company, same team, know what it takes to build something that's easy to maintain. They've been there, they know things break, and they know, hopefully, they aren't just, like, " I don't care." But they know how to fix it that it doesn't break anymore. And I think, people who come out of these, like, " Oh, I've been here for a year, there for a year." Don't have that. So, I see it a lot. People build as engineers will love to build. We don't think about operational and support. Is this actually going to live? And how easy it's to support people that have these different experiences, different companies, just they see a lot of things. Like, how does this team, this specific business solve this problem? People who come out of big companies? To me, honestly, I think, because there's so many people and a lot of problems have already been solved. They bring... I don't know how to phrase it. I'm going to go with the chronology, again. Would you go from racing, say, I don't know, a Ferrari to driving a tricycle? You don't know how to step back. They've had Facebook, Netflix, they have hard problems to solve. They've been around for a while, they solved it. The engineers who truly built it from scratch are not there anymore. So, people who use the tools and that's cool. They, hopefully, know what a good tool looks like, but they don't actually have experience growing something from the ground up. So, it's like it depends. inaudible.
Juan Sequeda: The Big Tech experience are folks who know how to use the big tools. Are they the 1%?
Veronika Durgin: Probably. And I mean, it's a great for resume, but honestly, I'm not impressed. Sorry, for my inaudible.
Juan Sequeda: This is the honest, no- BS podcast, so, listen.
Veronika Durgin: I don't want to hurt anybody's feelings. But I've worked with some brilliant people from smaller companies, self- employed. And I've seen some not particularly exciting people from Big Tech. Again, my experience, my biased to little side of the world experience.
Juan Sequeda: Yeah. All right. I think, that's an honest conversation. I mean flip it on its head on, like education, like, " Okay, you went to Harvard." Or something like that. Doesn't mean that you're instantly going to be the right fit for my organization, for my use case, that it may not be so. So, there are other factors.
Veronika Durgin: A hundred percent. Yeah. Don't get impressed with big college names. I am appreciative of somebody with education. A hundred percent. You put the work in, and you hear stories. People like PhDs, there's dedication right there. PhD is not, I mean, you could be practical and be like, " I'm done with my master's. I'm going to go work and make money." Or you can be actually determined to accomplish something. So, to me, I couldn't do it. I was too lazy. I'm getting a master's degree, I'm done. So, I really appreciate it. And also, I love when people come with... my undergrad is in biology. I was planning to become a doctor. It didn't happen. People who come with different kind of undergrads churning into something different and going with it, and the continuously learning to learn more about the area that they're in, also love that all, and love different perspective.
Juan Sequeda: So, let's dig a little bit more into... so we talked about experience. Let's talk a little bit more about education. We're starting to go in that direction a little bit. What is the kind of education profile that you look for? Do you value computer science? What do you think about bootcamps? For example, data bootcamps and that sort of thing.
Veronika Durgin: So, to me, and maybe because that's a bachelor's degree is a must. I don't particularly care in what. I think, learning in college and broadening your horizon with different classes makes you just a better person in general. You can take a bootcamp on top of that. When I enter the workforce in Tech, if you won a program, we'll take you, because there was not enough people. Now it's competition. So, there's a lot of strong programs across just about every college. So, I think, without a degree now, it's a lot harder. Sure, I've seen, I know, maybe two people who succeeded without a degree out of thousands of people that I know. So, to give you a perspective, yes, you can succeed without degree, but it's very, very hard.
Juan Sequeda: Sorry, this is an honest take here. Acknowledge that it is in a way, today a little bit more elitist. But not everybody probably can go to get a four- year education.
Veronika Durgin: Let's talk about that.
Juan Sequeda: Well, yeah.
Veronika Durgin: Everybody can go get it for four- year education, and I'll tell you why. So, I don't usually talk about it. So, I immigrated to this country when I was 17. We came, immigrants with just a bag of clothes age. I have a bachelor's degree, I have a master's degree. There's state colleges. I took out loans. I invested in myself, I worked, I went to college. I will talk about language a little bit. I still have PTSD from taking animal biology, because not only did I have to learn names in English, I also, then had to learn them in Latin. So, if I translate from Latin to English, I still had no idea what that was, and then I had to translate it into Russian. That's where I'm from. So, I had a hard time, was double learning, learning two languages at the same time. But everybody can absolutely do it. I think, you just have to persevere.
Juan Sequeda: Well, this is an interesting topic here. This is actually fascinating that we're talking about education. This is the data podcast but this is something we don't talk about, which I'm really cool that we're glad that we're talking about it. When it comes to education, I think, a lot of people are actually saying that, " Why do I want to invest four years and get loans and all that stuff? Because I could just go take a bootcamp, and then I get enough skills that will then get a job, where I could make enough money where it's the same thing as I would've gotten a four- year degree." Well, I mean, you are a person with so much experience that you have. You lead data teams, you hire people. What is your honest, no- BS assessment here?
Veronika Durgin: I'll be completely honest with you. I mean, I do believe to helping people re- skill. But when I have 200 resumes in front of me, your bootcamp is not going to stand out against a bachelor's degree. I'm just... when I have multiple people, I will try, I actually truly respect professional, somebody who spent X number of years doing something, and now they're trying to reskill. I will try to help you. But again, it's competition at this point. And for me, personally, as you can clearly see, education is very important. I think, it makes us all just better humans.
Juan Sequeda: Yeah. I'm 100% with you. But I will acknowledge again my bias that I have. I come from a very elite. I have a have a bachelor's. I have PhD. My family is a PhD. My wife's a PhD.
Veronika Durgin: It must have been so cool growing up.
Juan Sequeda: I acknowledge that this is where I come from my bias. But I do see a lot of the people are hungry to go learn. But my impression is that, a next generation, in a way, wants it a little bit easier, faster.
Veronika Durgin: It's an easy, yeah.
Juan Sequeda: And you to acknowledge it, it's not that easy, wasn't easy for you. And if it were easy, we would be doing it. And I think, everybody are trying to go do that. But at the end of the day, competition will show you is like, " Well there's people who actually have put more effort into it, and they're going to stand out more."
Veronika Durgin: A hundred percent. And also, like, " Oh, it's expensive loans." There's state schools, you can go to community college. As a matter of fact, I will pick your resume, if you show you went to your community college, you transferred to a state school, you have a degree. To me, it means, you actually did the work because you couldn't afford to go to an Ivy League School. You actually worked hard to get where you are. And this is showing a lot about you. To me, this resume will stand out versus, like, " Oh, I took two- week class..." Bootcamps are great for just adding more things. I am too lazy to go back to college right now. I want to, but I know that I won't. So, to me, that next level- up upskilling I take, maybe a certificate somewhere, or maybe I take some courses somewhere. And then, I hear that a lot. It's education and just loans. I'm old fashioned that way.
Juan Sequeda: Yeah. I thought this episode's going to be a bunch of... with all respect here, bunch of old grumpy people. And that's maybe how we're sounding. I wonder, what people think about that.
Veronika Durgin: I don't know. Listen, that could be, "I am hip. I have party in my Instagram."
Juan Sequeda: What camp are you in, Tim?
Tim Gasper: I'm in the camp of be humble, be learning. And I resonate with a lot of what you're saying, Veronika. And I think, that one of the questions that I have is what's the advice that we should be giving to the new generation? Imagine that I'm an analytics engineer. And I've got two years of experience, and I've worked at one startup or something like that. And I don't know what I don't know, but I know that I love data, and I want to be in the world of data. What should I be doing now? What next for me?
Veronika Durgin: Yeah. So, how I learned when I first started, I literally, I log into forums where people ask questions and it could be anything, pick your own. And I read questions, and I read all the answers. So, you will easily be able to sort through who's attacking whom and which. Don't just read the one that's marked as an answer. Read everything. I learned a ton that way. As a matter of fact, again, be skeptical. I am just naturally skeptical. When you read reviews, say on Amazon, do you read five stars or one star?
Juan Sequeda: That's a great question. I read both. I like to go see everybody who's raving about it, but I'm, like, " Wait, there's people who said one star." I'm curious what they had to go say.
Veronika Durgin: I actually read two, three, four. So, I think, five and one are outliers. Somebody's very, very piss about something and it's very biased or somebody love. When you read people that are, like, " Okay, I dig this but I have a problem." And then, I'm, like, " Okay, does this problem bother me?" And I'll give you an example in shoes. I love shoes. So, slight obsession, you probably see. If I read, they're, like, "Oh, well, I had to spend a month to break them in, but they're amazing now." So, in my mind I'm, like, " Okay, so, they look nice." Do I have enough patient to have blisters for a little bit? But then I have these awesome shoes and sometimes I'm, like, " Nah, I'm too old." I just need comfort right now at this point. Or I'll be, like, " Okay, cool." Because, otherwise, you'll be like, I love the shoes, or I hate them. They're uncomfortable. Remove the outliers, read everything else, and then decide what's important. And you also see patterns, because there are very similar questions being asked, and very similar patterns being answered. So, that's how I learned. I love user groups. Huge fan of user groups, I think.
Juan Sequeda: Can you call it? Some of the ones that you like to recommend?
Veronika Durgin: Well, again, I'm in just Snowflake. So, I love Snowflake user groups. I think, there some of them get pretty detailed. And by the way, if you ever want to know anything about data vault, let me know. There's a few user groups. What else? Which other user groups.
Juan Sequeda: There's so many Slack communities out there.
Veronika Durgin: There's Slack communities, I think.
Tim Gasper: There's a lot of noise and stuff.
Veronika Durgin: There's a lot of noise.
Tim Gasper: Let's keep on the positive side here.
Juan Sequeda: Which are the ones that you recommended to go join?
Veronika Durgin: Okay. So, here's the deal. I am part of all of them, and I read them anyway. I don't necessarily participate. I became a lurker. I love to read. I was actually listening to podcasts on the way here. So, when I have a free moment, like I stop working, make dinner, we eat, and then I transition into family room. And then, I either read something, or I write something, and it's just, it doesn't stop. I mean, I'm a learning junkie. So, be part of communities, read them, take them with a grain of salt. I think, these communities are very biased, kind of my Instagram feed. So, they're clicks of people. But, I think, it's still very interesting. It's still very interesting what people are talking about.
Juan Sequeda: So, even if you see the clicks and they're, like, " Oh, you learn from those." Like, " Oh, here's the same people talk about the same thing." And then, that should generate some curiosity. Why is that?
Veronika Durgin: Learn more about it. Exactly. I mean that's how modern data stack happened to me. I joined locally optimistic, and I was, like, " Whoa, okay, let's now sort through the noise, and figure out what is actually important to me that helps me solve the problem for my current company." Like, " This is the problems we're facing. How can I take some of that knowledge and not be a fan girl of some craziness, so I can actually sort through it?" Yeah.
Tim Gasper: Now, that's interesting. And I think, one of my observations here is that you can join a lot of these communities. You can absorb what's happening there, but that also doesn't necessarily mean you have to feed directly into the religion. For example, I know, I'm part of the data mesh learning group, and you learn a ton there, and there's a ton of excitement around data mesh, but you don't necessarily have to prescribe to everything. You can absorb it, you can see what people are talking about, and agree with the things you agree with, and you can disagree with the things you don't agree with or just lurk.
Veronika Durgin: And we talked about use learning from the past. So, these slack groups are all new. They're like nothing, old exists, you're old, you're outdated. I grew up with the old but learn from the history. It's like when you take parts that make sense that still work and put it together, that's when magic happens. It's not one or the other. You really have to mesh together. And the younger hip crowd, they're, like, " Oh, you're old relic, Bill." Still saying the same thing he's been saying for the past 40 years. But it's true. I want to be Bill when I grow up. I'm just going to come out right here on this podcast, and say that I want to be Bill when I grow up.
Juan Sequeda: I think, everybody wants to be Bill when he grows up, when we grow up. He is our hero. I think, in going to the history, one of the things I always caution. Look, just because it's old doesn't mean that's bad. We have to think about the principles, the foundations of things and the names change. But the foundations, the principles behind it are the same. Sometimes, they do, too, because we learn something. I mean, this happens in physics, it happened all there, science. But if we look at computing, we're just 50 years old as an area here. This stuff is super, super new. And go compare accounting, go compare, and all the different areas of engineering, those things been for thousands and thousands of years. Things have changed here. We really can't expect that what is new. What we're doing today the latest, greatest, the most amazing thing. Because guess what? You're doing today, most probably, we've already been thinking about it, talking about it for decades and decades.
Veronika Durgin: Done, tried, failed. On the shoulders of giants, right?
Juan Sequeda: Exactly. We build on the shoulders of giants. And let's not diminish those giants behind us.
Veronika Durgin: Push forward. Don't reinvent the wheel. It's not fun. Why? Waste of time.
Juan Sequeda: People like to reinvent things because it's like...
Veronika Durgin: They think theirs is better.
Tim Gasper: You add new terms to it. The data umbrella or the data inaudible.
Veronika Durgin: Well, data ocean. I was going with the Nemo theme, just so you know, in my post.
Juan Sequeda: As I recall it, the mean DataStreet says Chris, it's an ocean now, over there.
Veronika Durgin: I started laughing randomly. My husband looked at me and, " What's wrong with you?" I'm, like, " I'm going with the Nemo thing." He's, like, " Okay."
Juan Sequeda: So, wait there. There's so much more to talk about here. But one thing that we haven't actually touched, just to wrap up before we go to our lightning round. We talked about the young generation. We actually define what young generation means. What are we defining as a young generation? I want to consider myself young.
Veronika Durgin: I think, people who have never touched anything that's not modern- day stock. I think, that's how I define it. The past 10 years, less than 10 years' experience, less than 10 years in the business.
Juan Sequeda: When can we state that the modern- data stack as a term became that term? Four years ago?
Veronika Durgin: Or maybe Data Lake. Maybe, that's what I'm thinking. Like, 2010 when Data Lake was... I don't know. I don't know.
Juan Sequeda: I don't know. Tim, what do you...
Veronika Durgin: What do you think?
Tim Gasper: This is tough. I want to say, maybe even 10 years of experience is too big. You've really come into your own in the data world in the last five to six years, I would say. There's like, Data Lake Databricks that became very big in 2018 kind of timeframe. So, it's from there, forward, feels like this is the modern- data era.
Juan Sequeda: Let's do a little bit of history here. So, I like to always go back to the beginning of the web. So, Timbers League comes up with the web.
Veronika Durgin: It wasn't born by again.
Juan Sequeda: No, no, the web. Nothing under the web. So, Timbers League comes with the web in 1989. And the web just booms around 1991 with Mosaic, Netscape, all this stuff. And my argument, the way I interpret history here is that, the way the web grows is because of e- commerce. You have things, like eBay and Amazon is trying to go sell, selling things. So, before that, you already had database management systems. There are OLTP, and then the web is a driver to say, " Oh, we need to start analyzing all these things that people are buying." So, that's a big push for Oola and getting into data, pushing more has been doing before, but that was a big push for that. So, I think, there's an era, if we've been working with data warehouses in the mid-'90s and stuff. Then, I would argue in the mid-2000s the web gets even bigger and bigger, and Google pushes their whole... we have so much data, and they do big table, and then map reduce. And then, " Oh, we have so much data that does that we have to go, that we can't have schema and we go to NoSQL. And the first one is Amazon doing it with DynamoDB. So, then you have another era of people who are growing up in the NoSQL in the beginning of NoSQL. I think, this is, like 2009, I would say. And then, Hadoop era, map reduce areas around. Again, sorry, I think, the Google paper and the Dynamo paper is around 2007, I think. So, then you have that other era. When does Cloudera come around? I think, that's that time. So, that's another era.
Tim Gasper: That's almost like another phase of this.
Juan Sequeda: Basically, if you use Cloudera, work that Cloudera stuff, like you're not new generation anymore.
Veronika Durgin: No, I think you're old-timer that point.
Juan Sequeda: Yeah. You're an old timer that point. But that's 15 years ago.
Veronika Durgin: I think, in the past maybe five, six years.
Juan Sequeda: I remember being at the Sigma conference, the database conference, the academic data conference in San Francisco, I think, that was 2015. And Snowflake was presenting an academic paper in the industry track at Snowflake. And I was there. I remember being at that presentation, I'm, like, " What is this thing? Another thing, and then separating Cloud and compute." So, I think, this was 2015, and Snowflake wasn't a thing yet. And then, Databricks haven't come out yet, right?
Veronika Durgin: Right.
Juan Sequeda: Anyways, I'm just trying to come up to this. The Snowflake is definitely after 2015. So, 2018...
Veronika Durgin: Can we do like- ish? Let's do '17,'18. Okay. Let's roll with it.
Juan Sequeda: Anyways. So, if you are in data after 2017, 2018, then you're the new generation.
Veronika Durgin: Can we also say, if you haven't touched an old timer, like Cloudera, Hadoop, Oracle, SQL server, then you're also younger generation.
Juan Sequeda: Oh, have you actually installed a SQL server on your laptop?
Veronika Durgin: Yeah. Have you actually touched a real database? Have you worked with a real database? I'm just kidding. I probably just offended both sides of the newer and the old generation.
Juan Sequeda: What is a real database?
Veronika Durgin: What is a real database?
Tim Gasper: It's funny how there's a whole generation of people who don't really know some of these more traditional technologies. I mean, a lot of them are still alive and well. I mean, Microsoft, think, like SSIS, SSAS, SSRS. And sometimes people hear that, and they say, " What did you just say?"
Juan Sequeda: Databases we've been working on.
Veronika Durgin: Yeah. How many access database does it take to...
Juan Sequeda: Okay, we can keep going. And actually, we're going to keep going after this, because we're here in Boston at an honest, no-BS interview we're setting up. Next week, we're going to be in Atlanta. If you're listening, I know we've already had some listeners who've reached out who are going to come to our dinner next week, or in two weeks in Atlanta. All right, lighting round presented by data.world. Let's go. I go first. Here we go. We just came up with these questions. All right. So, here we go. First, a person with 10 years of career experience, would you rather have a person on your data team with two years of experience at five different companies, or 10 years of experience at one company?
Veronika Durgin: Definitely, 10 years at one company, two years with five companies. That person's either contractor, which is then it's probably okay. Or, if that person is just jumping jobs, not being there long enough, I would probably be a little bit concerned.
Juan Sequeda: Love it.
Tim Gasper: All right, second question, is a college bachelor's degree overkill for working in data?
Veronika Durgin: I already answered that, but no, not to me. I think, college bachelor's degree tells me a lot about you as a person.
Juan Sequeda: All right. You took a shot at influencers. Is there a way to be an influencer while also representing your experience properly, humbly?
Veronika Durgin: Well, it depends on your definition of an influencer.
Juan Sequeda: Okay, define it. Go ahead and define it.
Veronika Durgin: I feel like anybody who posts anything anywhere is an influencer now. I think, to me, I respect you and I love to read your stuff when you have content that's useful, not just random hand- wavy stuff. And the more I learn about the subject and I see that, yeah, you truly know what you're talking about, I have a very high pedestal for people that I follow and listen to. But in general, I also love reading random, crazy stuff.
Tim Gasper: inaudible random stuff, too.
Juan Sequeda: I had lunch with a friend today. And then, we had a little comment, mind- melding. And I posted a video on LinkedIn. So, now, I'm like, I wonder what are you going to call me out on something? We'll see. We'll talk about that later.
Veronika Durgin: I hate influencer. To me, it's just such like a, " I'm posting on social media. I'm an influencer." It doesn't speak about experience. People who talk about stuff that's important that I'm learning from, that give me interesting ideas, those are the people that I follow.
Juan Sequeda: All right. Tim, take us away with the final question.
Tim Gasper: My final lightning round question here, actually, I'm going to break the yes- no mold and I'm going to say, what is the most under- the- radar skill in data?
Veronika Durgin: Under the radar... Explaining hard subjects in a way that, I would say, normal person can understand, because I think of us as aliens a little bit. But trying to distill complex problems in a way that your grandma can understand. So, I think that's such an underrated skill, and we don't help people get better at it. We're like, again, super sharp, very smart, but we can only talk to people that are like us. We have hard time talking to people who aren't like us.
Tim Gasper: Don't just speak alien language. You have to speak human language, too.
Veronika Durgin: Sometimes, I talk, and people look at me like, " No idea."
Tim Gasper: Love it. This is great.
Juan Sequeda: All right. Tim, let's take away time. Take us away.
Tim Gasper: Take it away. And then, you all can have your delicious dinner. All right, so much good content today. Thank you so much, Veronika, for joining us today. So, we really started off by saying that there's so much online that you can learn, that you can absorb. The information is at people's fingertips. Young people, the new generation, we'll try to define that later. They have all this access to all this stuff. It's so exciting. But at the same time, also, it creates an environment where it can be hard to know what to trust. There's so much out there. There's so many people out there saying so many things. And you talked about your perspective to really have a good understanding of what is true, what is right, build your perspective. And that was a position of skepticism, taking a scientist's approach to looking at the knowledge that's out there, the people that are out there. Keep researching. Don't just over- index on the influencer. Some suggestions may be right, but some are not right. And some of them are even impossible. And you need to look past just what the words are that out there, just the things that get the most likes, and really think about, contextually, what is the right information to solve the right problem? And really, you were encouraging that folks who are newcomers that are trying to learn, share your thoughts, be comfortable. But also, this importance of engaging in debate and being skeptical and letting that be out there. You mentioned a couple of things that are overhyped or don't have enough skepticism around them. And one of them was the tool that will solve all your problems. I loved your example, where you said, well, let's say you have to get around, you need to get around. Well, a car. Give that person a car. But what if the person you were talking about was a child? Are you going to give the child the car? No, that doesn't fit. It doesn't make sense, right? But you said give them a car, that's the solution that solves everything. Oh, well, maybe it doesn't. Maybe, that tool doesn't solve everything. The second thing you mentioned was the idea that data modeling is dead. It's not dead. Maybe, it's hard, but dbt, for example, is modeling. And the third one was around AI, and the idea that it's some kind of magic bullet. And we know that that's going to accelerate now with all the excitement around GPT and things like that. There's a lot of good that can come from that, but also a lot of misunderstanding, certainly, that will be perpetuated. And there's a competition that happens in the social sphere. And so, you have to understand people's motivation. What are their biases? This is where psychology comes in. And even as vendors, right? So, Juan and I, we work over at data.world. We have to be aware of our vendor biases as well. Juan, what about you?
Juan Sequeda: Well, a couple things. But I do want to look at our notes. I think you said dbt is not model.
Veronika Durgin: Is not a model.
Juan Sequeda: Clarifying that.
Tim Gasper: Not modeling, okay.
Veronika Durgin: And then, also, dbt is not model. dbt is really transformation. Juan, I'm sorry I'm interrupting you. Tim, who needs ChatGPT when you have Tim to summarize? Can I borrow you and use you to summarize my thoughts?
Tim Gasper: We are working on Juan and Tim bot, coming soon to a chat plug- in near you.
Veronika Durgin: Oh, I love it.
Juan Sequeda: The Tim- Juan bot. So, keep going, because we talk about experience, what do we mean by experience? So hey, I've implemented 50 customers is one thing versus I've supported the 50 customers that you've implemented. So, we really need to know where people are coming from. And that's how we can be humble around that, right? Experience is really contextual. And I really love your story about how you started out saying, " Hey, I work in SQL Server and I can really optimize, make these super- fast queries." But we don't need to go to the cloud. But then, you actually realize and learn, it's like, this is actually valuable. And actually, the way the vendors plan is the wrong way. We actually come up with a better way. This is the context that we have. Be skeptical, honest, no- BS. Be skeptical about those big tech fan company experiences because they have a Lamborghini truck that you can stick in your garage and take pictures with, because guess what? Is that what you really need? Probably, not. Quick turnaround into the streaming, streaming versus no streaming. The thirst for quick data, fast data, it's coming. People really want real- time analytics. They want it embedded into the real-time applications. However, not everybody needs it right now. But it's coming. I'd really like the mix of the teams. Even in the lightning round question, you said that the team mix that you would like, that you want people who actually know about the operations, who know how to go build a tool and how to maintain that. And sadly, honest, no- BS here, if you come from the big tech, you know how to use a tool that was created to go solve a big problem, but you actually didn't build it, because the person who built this is probably not there anymore. Spent a lot of time talking about education. And again, very honest, no- BS precision right now. Bachelor's? Yes, that's needed. It doesn't really matter in what? I mean you can put a bootcamp on top of that. And I think, with people who argue that a four- year education, it's hard to get like you, your position is like, no, actually it's not hard. You have to go work for it, but it's actually not hard. And it's actually completely worth it because, why? If you got two resumes, you got a pile of 200 resumes, guess who's going to stand out? If you just have a bootcamp, you're not going to stand out. I'm sorry, but that's the honest, no- BS right there. Advice, as Tim posed a question, is, you're hungry, you like this data thing. You're in here for two years. What should he go do? Yeah, get into the forms. Get into the user groups, online communities. Go read all the comments. See the ones you agree with, and also the ones you don't agree with. You talked about the Snowflake user vault. You're a big data vault practitioner, and there's data vault user communities. So many Slack communities. It's fine, just go lurk, consume. And actually, my side comment is a lot of the conversations happen privately on the DMs, right? I love your analogy on the five- star ratings. I said I read the five- star and the one- star, and you're just like, " No, you should be reading the two and the three four- star, that's where the nuances are." Find those patterns right there. And then, what do we define as the young generation? You've only interacted with the modern data stack, and then per our little history, like that we're going through, that means that you've been around for the last six years, since 2017, 2018. If you've used Hadoop, Big Data, NoSQL, Cloudera, you're not young anymore over there. And I think there's a lot of awesome coach that we have, we put on t- shirts. One of my favorite ones is, " I want to be Bill Inmon when I grow up," a bit one. The overall takeaway, the takeaway of takeaways here is be humble, learn, ask questions, be skeptical.
Veronika Durgin: Great summary.
Juan Sequeda: How did we do? What did we miss?
Veronika Durgin: Amazing. I want the Tim-Juan bot.
Tim Gasper: All right.
Juan Sequeda: Back to you, three final questions. What's your advice about data life? Second, who should we invite next? And third, what are the resources you follow? You've already said many, but...
Veronika Durgin: Yeah, so don't go into data just for the money. It's a tough field. You always have to learn. Make sure you actually like it. If you like it, you'll have a very long and fulfilling career. So, I talk about a lot of people, like, "Oh, it's money." Don't just go into it for money. It's not easy. Don't think it's easy. What was the second question?
Juan Sequeda: Who should we invite next?
Veronika Durgin: So, I was listening to a podcast with Brene Brown. She had Tsedal Neeley on it. And they were talking about digital mindset. So, I am a huge fan right now of Tsedal Neeley. She's a professor at Harvard Business School, incredible, incredible spark woman. If you can get her, I'd be listening to it somewhere cooking or something. So, there's a lot of things that she said that we can totally resonate and connect with. And follow me on LinkedIn. Actually, I don't just like everything. I curate what I share. Again, my perspective, my bias, but at least I try not to be just on the hype of liking everybody. I'm sorry, friends.
Juan Sequeda: And you're also a big Snowflake...
Veronika Durgin: I am a Snowflake data superhero, for real. It's a true thing. I have a cape to prove it. So, if you have any Snowflake questions, data modeling is a big thing. I think it's very important. So, yeah, follow me on LinkedIn. I blog sometimes, but yeah.
Juan Sequeda: All right. Veronika, thank you so much. Just quick, next week we have Tony Seale, who's a knowledge graph engineer. We'll be talking about knowledge graphs and large language models. We have to go get into the AI topic, but this is a really fascinating topic that I'm really excited about. And with that, thank you so much.
Veronika Durgin: Thank you so much for having me in real life.
Juan Sequeda: Yes, in real life.
Tim Gasper: Yes. Cheer, Tim, and it's dinnertime.
Speaker 1: This is Catalog and Cocktails. A special thanks to data.world for supporting this show, Karli Burghoff for producing, John Moins and Bryon Jacob for the show music. And thank you to the entire Catalog and Cocktails fan base. Don't forget to subscribe, rate, review, wherever you listen to your podcasts.