Using Data to Tell Stories with Thomas LaRock

Episode Summary

Thomas LaRock, Principal Developer Evangelist at Selector AI, joins Corey on Screaming in the Cloud to discuss why he loves having a career in data and his most recent undertaking at Selector AI. Thomas explains how his new role aligned perfectly with his career goals in his recent job search, and why Selector AI is not in competition with other data analysis tools. Corey and Thomas discuss the benefits and drawbacks to going back to school for additional degrees, and why it’s important to maintain a healthy balance of education and practical experience. Thomas also highlights the impact that data can have on peoples’ lives, and why he finds his career in data so meaningful.

Episode Show Notes & Transcript

About Thomas

Thomas’ career and life experiences are best described as follows: he takes things that are hard and makes them simple for others to understand. 

Thomas is a highly experienced data professional with over 25 years of expertise in diverse roles, from individual contributor to team lead. He is passionate about simplifying complex challenges for others and leading with empathy, challenging assumptions, and embracing a systems-thinking approach. Thomas has strong analytical reasoning skills and expertise to identify trends and opportunities for significant impact, and is a builder of cohesive teams by breaking down silos resulting in increased efficiencies and collective success. He has a track record of driving revenue growth, spearheading industry-leading events, and fostering valuable relationships with major tech players like Microsoft and VMware. 

Links Referenced:


Announcer: Hello, and welcome to Screaming in the Cloud with your host, Chief Cloud Economist at The Duckbill Group, Corey Quinn. This weekly show features conversations with people doing interesting work in the world of cloud, thoughtful commentary on the state of the technical world, and ridiculous titles for which Corey refuses to apologize. This is Screaming in the Cloud.

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Corey: Welcome to Screaming in the Cloud. I’m Corey Quinn. There are some guests I have been nagging-slash-angling to have on this show for years on end, and that you almost give up, until they wind up having a job change. At which point, there’s no better opportunity to pounce like some sort of scavenger or hyena or whatnot in order to get them on before their new employer understands what I am, and out of an overabundance of caution, decides not to talk with me. Thomas LaRock is a recently minted Principal Developer Evangelist at Selector. Thomas, thank you for finally deigning to appear on the show. It is deeply appreciated.

Thomas: Oh, thanks for having me. Thanks for extending invitation. I’m sorry. It’s my fault I haven’t come here before now; it’s just been one of those scheduling things. And I always think I’m going to see you. Like, I’ll go to re:Invent, and I’m like, “I’ll see Corey there.” And then, nah, Corey is a little busy.

Corey: Yeah, I have no recollection of basically anything that ever happens at re:Invent, just because it is eight days of ridiculous Cloud Chanukah and thing to thing to thing to thing to thing. It’s just overload and I wind up effectively blocking all of it out. You are one of those very interesting people where, depending upon the context in which someone encounters you, it’s difficult to actually put a finger on where you start and where you stop. You are, for example, a Microsoft MVP, which means you presumably have a fair depth of experience with at least some subset of Microsoft products. You have been working at SolarWinds for a while now, and you also have the username of SQLRockstar on a number of social media environments, which leads me to think, oh, you’re a database person. What are you exactly? Where do you start? Where do you stop?

Thomas: Yeah, in my heart-of-hearts, a data professional. And that can mean a lot of things to a lot of different people. My latest thing I’ve taken from a friend where I just call myself a data janitor because that’s pretty much what I do all day, right? I’ll clean data up, I’ll move it around, it’s a pile here and a pile there. But that’s my heart of hearts. I’ve been a database administrator, I’ve been the data advocate. I’ve done a lot of roles, but it’s always been heavily focused on data.

Corey: So, these days, your new role—let’s start at the present and see if we work our way backwards or not—you’ve been, at the time of this recording, in your role for a week where you are a principal developer evangelist at Selector, which to my understanding, is an AIOps or MLOps or whatever buzzword that we’re sprinkling on top of things today is, which of course presupposes having some amount of data to wind up operating on. What do you folks do over there?

Thomas: That’s a great question. I’m hoping to figure that out eventually. No. So, here’s the thing, Corey. So, when I started my unforced sabbatical this past June, I was, of course, doing what everybody does: panicking. And I was looking for job opportunities just about anywhere.

But I, again, data professional. I really wanted a role that would allow me to use my math skills—I have a master’s in mathematics—I wanted to use those math and analytical skills and go beyond the data into the application of the data. So, in the past five, six years, I’ve been earning a lot of data science certifications, I’ve been just getting back into my roots, right, statistical analysis, even my Six Sigma training is suddenly relevant again. So, what happened was I was on LinkedIn and friend had posted a note and mentioned Selector. I clicked on the link, and [all of sudden 00:04:17] I read, I go, “So, here’s a company that is literally building new tools and it’s data-science-centric. Is data-science-first.”

It is, “We are going to find a way to go through your data and truly build out a better set of correlations to get you a signal through the noise.” Traditional monitoring tools, you know, collect a lot of things and then they kind of tell you what’s wrong. Or you’re collecting a lot of different things, so they slap, like, I don’t know, timestamps in there and they guess at correlations. And these people are like, “No, no, no. We’re going to go through everything and we will tell you what the data really says about your environment.”

And I thought it was crazy how at the moment I was looking for a role that involve data and advocacy, the moment I’m looking for that role, that company was looking for someone like me. And so, I reached out immediately. They wanted not just a resume, but they’re like, where’s your portfolio? Have you spoken before? I’m like, “Yeah, I’ve spoken in a couple places,” right?

So, I gave them everything, I reached right out to the recruiter. I said, “In case it doesn’t arrive, let me know. I’ll send it again. But this sounds very interesting.” And it didn’t take more than—

Corey: Exactly. [unintelligible 00:05:24] delivery remains hard.

Thomas: Yeah. And it didn’t take more than a couple of weeks. And I had gone through four or five interviews, they said that they were going to probably fly me out to Santa Clara to do, like, a last round or whatever. That got changed at some point and we went from, “Hey, we’ll have you fly out,” to, “Hey, here’s the offer. Why don’t you just sign?” And I’m like, “Yeah, I’ll start Monday. Let’s go.”

Corey: Fantastic. I imagine at some point, you’ll be out in this neck of the woods just for an off-site or an all-hands or basically to stare someone down when you have a sufficiently large disagreement.

Thomas: Yes, I do expect to be out there at some point. Matter of fact, I think one of my trips coming up might be to San Diego if you happen to head down south.

Corey: Oh, I find myself all over the place these days, which is frankly, a welcome change after a few years of seclusion during the glorious pandemic years. What I like about Selector’s approach, from what I can tell at least, is that it doesn’t ask all of its customers to, “Hey, you know, all that stuff that you’ve instrumented over the last 20 years with a variety of different tools in the observability pipeline? Yeah, rip them all out and replace them with our new shiny thing.” Which never freaking happens. It feels like it’s a better step toward meeting folks where they are.

Thomas: Yeah. So, we’re finding—I talk like I’ve been there forever: “What we’re finding,”—in the past 40 hours of my work experience there, what we’re finding, if you just look at the companies that are listed on the website, you’ll get an idea for the scale that we’re talking about. So no, we’re not there to rip and replace. We’re not going to show up and tell you, “Yeah, get rid of everything. We’re going to do that for you.”

Matter of fact, we think it’s great you have all of those different things because it just reflects the complexity of your environment right now, is that you’ve grown, you’ve got so many disparate systems, you’ve got some of the technologies trying to monitor it all, and you’re really hoping to have everything rolled into one big dashboard, right? Instead of right now, you’ve got to go through three, four, or five dashboards, to even think you have an idea of the problem. And you never really—you guess. We all guess. We think we know where it is, and you start looking and then you figure it out.

But yeah, we take kind of a different approach right from the start, and we say, “Great, you’ve got all that data? Ingest it. Bring it right to us, okay? We don’t care where it comes from, we can bring it in, and we can start going through it and start giving you true actionable insights.” We can filter out the noise, right, instead of one node going down, triggering a thousand alerts, we can just filter all of that out for you and just let you focus on the things that you need to be looking at right now.

Corey: One of the things that I think gets overlooked in this space a lot is, “Well, we have this tool that does way better than that legacy tool that you’re using right now and it’s super easy to do a just drop-in replacement with our new awesomeness.” Great. What that completely misses is that there are other business units who perhaps care about data interchange and the idea that yeah, thing’s a legacy piece of junk and replacing it would take an afternoon. And then it would take 14 years to wind up redoing all the other reports that other things are generating downstream of that because they integrate with that thing. So yeah, it’s easy to replace the thing itself, but not in a way that anything else can take advantage of it.

Thomas: Right.

Corey: And when it turns out also when you sit there making fun of people’s historical technological decisions, they don’t really like becoming customers as it turns out. This was something of a shock for an awful lot of very self-assured startup founders in the early days.

Thomas: Yeah. And again, you’re talking about how, you know some of the companies we’re looking at, it’s y—we don’t want to rip and replace things. Like you just said, you’ve got an ecosystem. It’s a delicate ecosystem that has [laugh] developed over time. We aren’t interested in replacing all that. We want to enhance it, we want to be on top of it and amplify what’s in there for you.

So yeah, we’re not interested in coming in and say, “Yeah, rip every tool out.” And in some ways, when somebody will ask, you know, “Who do you compete with?” I’ll go, “Nobody.” Because I’m not looking to replace anybody. I’m looking to go on top.

And again, the companies we’re dealing with have lots of data. We’re talking very large companies. Some of these are the backbone of the internet. They just have way too much data for any of these legacy tools to help with, you know? They can help with, like, little things, but in terms of making sense of it all, in terms of doing the real big data analytics, yeah, that’s where our tool comes in and it really shines.

Corey: Yeah, it turns out that is not a really compelling sales pitch to walk it and say, “Hey, listen up, idiots, you all are doing it wrong. Now, pay me and we’ll do it right.” Yeah, even if you’re completely right, you’ve already lost the room at that point.

Thomas: Exactly.

Corey: People make decisions based upon human aspects, not about arithmetic, in most cases. I will say, taking a glance at the website, a couple of things are very promising. One, your picture and profile are already up there, which is good. No one is still on the fence about that, and further as a bonus, they’ve taken your job role down off the website, which is always disconcerting when you’re there and, “Why is that job still open?” “Oh, we’re preserving optionality. Don’t you worry your head about that. We’ve got it.” No one finds that a reassuring story when it’s about the role that they’re in. So, good selection.

Thomas: I went to—after I signed, it was within the day, I went to send somebody the link to the job req. Like, they’re like, “What are”—I go, “Here, let me show you.” It was already down. The ink was even dry on the DocuSign and it was already down. So, I thought that—

Corey: Good on them.

Thomas: —was a good sign, too.

Corey: Oh, yeah. Now, looking at the rest of your website, I do see a couple of things that lead to natural questions. One of the first things I look at on a web page is, okay, how is this thing priced? Because you always want to see the free tier option when I’m trying to solve a problem the middle of the night that I can just sign up for and see if it works for a small use case, but you also, in a big company definitely want to have the ‘Contact Us’ option because we’re procurement and we don’t know how to sign a deal that doesn’t have two commas in it with a bunch of special terms that ride along with it. Selector does not at the time of this recording, have a pricing page at all, which usually indicates if you have to ask, it might not be for you.

Then I look at your customer case studies and they talk about very large enterprises, such as a major cable operator, for example, or TracFone. And oh okay, yeah, that is probably not the scale that I tend to be operating at. So, if I were to envision this as a carnival ride and there’s a sign next to it, “You must be at least this tall to ride,” how tall should someone be?

Thomas: That is a great way of putting it and I would—I can’t really go into specifics because I’m still kind of new. But my understanding—

Corey: Oh yeah. Make sweeping policy statements about your new employer 40 hours in. What could possibly go wrong?

Thomas: My understanding is the companies that we—that are our target market today are fairly large enterprises with real data challenges, real monitoring data challenges. And so no, we’re not doing—it’s not transactional. You can’t just come to our website and say, “Here, click this, you’ll be up and running.” Because the volumes of data we’re talking about, this requires a little bit of specialty in helping make sure that things are getting set up and correct.

Think of it this way. Like if somebody said, “Here, do the statistical analysis on whatever, and here’s Excel and go at it and get me that report by the end of the day and tell me how we’re doing,” most people would be like, “I don’t have enough information on that. Can you help me?” So, we’re still at that, hey, we’re going to need to help you through this and make sure it’s correctly configured. And it’s doing what you expect. So, how tall are you? I think that goes both ways. I think you’re at a height where you still need some supervision [laugh]. Does that make sense?

Corey: I think that’s probably a good way of framing it. It’s a—again, I’m not saying that you should never ever, ever, ever have a ‘you must contact us to get started.’ There are a bunch of products like that out there. It turns out that even at The Duckbill Group here, we always want to have a series of conversations first. We don’t have a shopping cart that’s, “One consulting, please,” just because we’ll get into trouble with that.

Though I think our first pass offering of a two-day engagement might have one of those somewhere still lurking around. Don’t quote me on that. Hell is other people’s websites. It’s great. But your own yeah, whoever reads that thing“. Wait, we’re saying what?” Don’t quote me on any of that, my God.

Thomas: But I think that’s a good way of putting it. Like, you want to have some conversations first. Yeah, so you—and again, we’re still, we’re fairly young. We’ve only—we’re Series A, so we’ve been around 16 months, like… you know, the other website you’re looking at is probably going to change within the next six or eight weeks just because information gets outdated—

Corey: It already has. It put your picture on it.

Thomas: Right. But I mean, things are going to things move pretty fast with startups, especially this one. So, I just expect that over time, I envision some type of a free tier, but we’re not there yet.

Corey: That’s one of those challenges as far as in some cases moving down market. I found that anything that acts like a security tool, for example, has to, on some level, charge enough to be worth the squeeze. One of the challenges there is, I’m either limited for anything that does CloudTrail analysis over in AWS-land, for example. I can either find a bunch of janky things off GitHub or I can spend what starts at $1,000 a month and increases rapidly from there, which is about twice the actual AWS bill that it would wind up alerting on. Not that the business value isn’t there, but because a complex sale is, in many cases, always going to be attendant with some of these products, so why not go after the larger companies where the juice is worth the squeeze rather than the folks who are not going to see the value and it’d be just as challenging to wind up launching a sale into?

The corollary, of course, is that some of those small companies do in fact, grow meteorically. But it’s a bit of a lottery.

Thomas: Yep.

Corey: Ugh. So, I have to ask as well, while we’re talking about strange decisions that people might have made, in the world of tech, in many cases, when someone gets promoted—like, “So, does that mean extra money?” “No, not really. We just get extra adjectives added to our job title.” Good for us. You have decided to add letters in a different way, by going back for a second master’s degree. What on earth would possess you to do such a thing?

Thomas: I—man, that is—you know, so I got my first master’s degree because I thought I was going to, I thought I was be a math teacher and basketball coach. And I had a master’s degree in math and I thought that was going to be a thing. I’ll get a job, you know, coaching and teaching at some small school somewhere. But then I realized that I enjoyed things like eating and keeping the wind off me, and so I realized I had to go get a jobby-job. And so, I took my masters in math, I ended—I got a job as a software analyst, and just rolled that from one thing to another until where I am today.

But about four years ago, when I started falling back in love with my roots in math, and statistical analysis became a real easy thing for people to really start doing for themselves—well actually, that was about eight years ago—but the past four or five years, I’ve been earning more certifications in data science technologies. And then I found this program at Georgia Tech. So, Georgia Tech has an online masters of science and data analytics. And it’s extremely affordable. So, I looked at a lot of programs, Corey, over the past few years, especially during the pandemic.

I had some free time, so I browsed the love these places, and they were charging 50, $60,000 and you had to do it within two, three years. And in one case, the last class you had to take, your practicum, had to be all done on campus. So, you had to go, like, live somewhere. And I’m looking at all—none of that was practical. And all of a sudden, somebody shows up and goes, “So, you can go online, fully online, Georgia Tech, $275 a credit. Costs ten grand for the entire program.”

And you can—it’s geared towards a working professional and you can take anywhere from two to six years. So, you take, like, one class a semester if you want, or two or even three if they allow you, but they usually restrict you. So, it just blew my mind. Like, this exists today that I can start earning another Master’s degree in data analytics and I’ll say, be… classically trained in how—it’s funny because when I learn things in class, I’m like, I feel like I’m Thornton Melon in Back to School, and I’m just like, “Oh, you left out a bunch of stuff. That isn’t how you do it all,” right?

That’s kind of my reaction. I’m like, “Calm down. I’m sure the professor has point. I’ll hear [laugh] him out.” But to me, you asked why, and I just the challenge. Am I really good at what I do? Like, I feel I am. I already have a master’s degree. I’m not worried about the level of work and the commitment involved in earning another one.

I just wanted to show to myself that could—I want to learn and make sure I can do things like code in Python. If anybody has a chance to take a programming class, a graduate-level programming classes at Georgia Tech, you should do it. You should see where your skills rate at that level, right? So, it was for the challenge. I want to know if I can do it. I’m three classes in. I just started my fourth, actually, today was the start of the fall semester.

And so, I’m about halfway through, and I’m loving it. It’s not too taxing. It’s just the right speed for me. I get to do it in my leisure hours as they were. Yeah, so I did it for the challenge. I’m really glad I’m doing it. I encourage anybody interested in obtaining a degree in data analytics to look at the Georgia Tech program. It’s well worth it. Georgia Tech’s not a bad school. Like, if you had to go to school in the South, it’s all right.

Corey: I always find it odd, just, you had your first master’s degree in, you know, mathematics, and now you’re going for data analytics, which sounds like mathematics with extra steps.

Thomas: It is.

Corey: Were there opportunities that you were hoping to pursue that were not available to you with just the one master’s degree?

Thomas: So, it’s interesting you say that because I’m so old that when I went to school, all we had was math, that was it. It was pure mathematics. I could have been a statistics major, I think, and computer science was a thing. And one day I met a guy who transferred into math from computer science. I’m like, “Why would you do that? What are you going to do with the degree in math?”

And his response is, “What am I going to do with a degree in computer science?” And I look back and I realized how we were both right. So, I think at the time if there had been a course in applied mathematics, that would have piqued my interest. Like, what am I going to do with this math degree other than become an actuary because that was about all I knew at the time. You were a teacher or an actuary, and that was about it.

So, the idea now that they have these programs in data analytics or data science that are little more narrow of focus, like, “This is what we’re going to do: we’re going to apply a little bit of math, some calculus, some stats; we’re going to show you how to build your own simulations; we’re going to show you how to ask the right questions of the data.” To give you a little bit of training. Because they can’t teach you everything. You really have to have real-world experience in whatever domain you’re going to focus on, be it finance or marketing or whatever. All these bright financial operations, that’s just analytics for finance, marketing operations, that’s analytics for marketing. It’s just, to me, I think just the opportunity to have that focus would have been great back then and it didn’t exist. And I want to take advantage of it now.

Corey: I’ve always been a fan of advising people who ask me, “Should I go back to school,” because usually, there’s something else driving that. Like, I am honestly not much of a career mentor. My value basically comes in as being a horrible warning to others. On paper, I have an eighth-grade education. I am not someone to follow for academic approaches.

But when someone early or mid-career asks, “Should I get another degree?” Unpacking that is always a bit of a fun direction for me to go in. Because at some level, we’ve sold entire generations a bill of goods, where oh, if you don’t know what to do, just get more credentials and then your path will be open to you in a bunch of new and exciting ways. Okay, great. I’m not saying that’s inherently wrong, but talk to people doing the thing you’d want to do after you have that degree, maybe, you know, five or six years down the professional line from where you are and get their take on it.

Because in some cases, yeah, there are definite credentials you’re going to need—I don’t want you to be a self-taught surgeon, for example—but there are other things where it doesn’t necessarily open doors. People are just reflexively deciding that I’m going to go after that instead. And then you can start doing the math of, okay, assume that you have whatever the cost of the degree is in terms of actual cost and opportunity cost. Is this the best path forward for you to wind up getting where you want to go? It sounds like in your particular case, this is almost a labor of love or a hobby style of approach, as opposed to, “Well, I really want Job X, but I just can’t get it without the right letters after my name.” Is that a fair assessment?

Thomas: It’s not unfair. It is definitely fair, but I would also say, you know, if somebody came and said, “Hey Tom, we need somebody to run our data science team or our data engineering team,” I’ve got the experience for—the only thing I would be lacking is, you know, production experience, like, with machine-learning pipelines or something. I don’t have that today.

Corey: Which is basically everyone else, too, but that’s a little—bit of a quiet secret in the industry.

Thomas: Yeah, that’s—okay. Bad example. But you know what I’m saying is that the only thing I’d be lacking would be that practical experience, so this is one way that—to at least start that little bit of experience, especially with the end result being the practicum that we’ll be doing. It’s, like, six credits at the very end. So yes, it’s a fair thing.

I wouldn’t—hobby isn’t really the right—this is really something that makes me get out of bed in the morning. I get to work with data today and I’m going to get—I’m going to tell a great story using data today. I really do enjoy those things. But then at the tail end of this, if it happens to lead to a position that somebody says, “Hey, we need somebody, vice president of data engineering. This a really good”—honestly, the things I look for are the roles and the roles I want are to have a role that allows me to really have an impact on other people’s lives.

And that’s one of the things about Selector. The things that we’re able to do for these admins that are just drowning in data, the data is just in their way, and that we can help them make sense of it all, to me, that’s impactful. So, those are the types of roles that I will be looking for as well in the future, especially at the high level of something data science-y.

Corey: I think that that is a terrific example of what I’m talking about. Because I’ve met a number of folks, especially very early-20s range where, okay, they’ve gotten the degree, but now they don’t know what to do because every time they’re applying for jobs, it doesn’t seem to work for them. You’ve been around this industry for 25 years. Everyone needs a piece of paper that says they know certain things, and in your case, it long ago transitioned into being—I would assume—your resumé, the history of things you have done that look equivalent. Part of me, on some level, wonders if there isn’t an academic snobbery going on at some level, where a number of teams are, “Oh, we’d love to have you in, but you don’t have a PhD.”

And then people get the PhD. “From the right school, in the right area of concentration.” It’s like, you just keep moving these very expensive goalposts super quickly. Remember, I have an eighth-grade education. I’m not coming at this from a place of snobbery and I’m also not one of those folks who’s well it didn’t work for me, therefore, it won’t work for anyone else either because that’s equally terrible in a different direction.

It’s just making sure that people are going into these things with their eyes open. With you, it’s never been a concern. You’ve been around this industry so long that it is extremely unlikely to me [laugh] that you, “Oh, wait. You mean a degree won’t magically solve all of my problems and regrow some of my hair and make me two inches taller, et cetera, et cetera?” But yeah, do I remember in the early days just how insipid and how omnipresent that pressure was.

Thomas: Yeah. I’ve been at companies where we’ve brought in people because of the education and—or I’m sorry. Let’s be more specific. I’ve been at companies where we’ve sent current employees—as we used to call it—off the charm school, which is basically [MBA 00:25:44].

Corey: [laugh].

Thomas: And I swear, so many of them came back and they just forgot how to think, how to have common sense. Like, they were very much focused on one particular thing and this is just it, and they forgot there were maybe humans involved, and maybe look for a human answer instead of the statistically correct one. So, I think that was a good thing for me as well to be around that because, yeah, somebody put it me best years ago: “Education by itself isn’t enough. If you combine education with motivation, now you’ve really got something.” And your case, I don’t know where you went for eighth grade, it could have been the best eighth-grade program ever, but you definitely have the motivation through the years to overcome anything that might have been lacking in the form of education. So, it’s really the combination—

Corey: Oh, you’d be surprised. A lot of those things are still readily apparent to people who work with me, so I’ve done a good job of camouflaging them. Hazzah.

Thomas: Just it’s, you got to have both. You can’t just rely on one or the other.

Corey: So, last question, given that you are the data guy and SQLRockstar is your username in a bunch of places. What’s the best database? I mean, I would always say it’s Route 53, but I understand that can be controversial for some folks, given that their SQL implementation is not yet complete. What’s your take?

Thomas: So clearly, I’m partial to anything inside the Microsoft data platform, with the exception being Access. I think if Access disappeared from the universe… society might be better off. But that’s for a different day, I think the best database is the one that does the job you need it to do. Honestly, the database shouldn’t really matter. It’s just an abstraction. The database engine is just something in between you and the data you need, right?

So, whatever you’re using, if it’s doing the job that you need it to do, then that’s the best database you could have. I learned a long time ago to not pick sides, choose fiefdoms. Like, it just didn’t matter. It’s all kind of the same. And in a lot of cases, if you go to, like, the DB-Engines Rankings, you’ll see how many of these systems these days, there’s a lot of overlap. They offer all the same features and the differences between them are getting smaller and smaller in a lot of cases. So yeah, it’s… you got to database, it does what you need to do? That’s great. That’s the best database.

Corey: Especially since any database, I suspect, can be made to perform a given task, even if sub-optimally. Which states back to my core ethos of, quite frankly, anything is a database if you hold it wrong.

Thomas: Yeah, it really is. I mean, we’ve had those discussions. I kid about Access because it’s just a painful thing for a lot of different reasons. But is Excel a database? And I would say no but, you know—because it can’t do certain things that I would expect a relational engine to do. And then you find out, well, I can make it do those things. So, now is it a database? And, yeah…

Corey: [laugh]. Yeah. Well, what if I apply some brute force? Will it count then? Like, you have information, Thomas. Can I query you?

Thomas: Yes. Yes, yes, [laugh] you can. I also have latency.

Corey: Exactly. That means you are a suboptimal database.

Thomas: [laugh].

Corey: Good job. I really want to thank you for taking the time to talk about what you’re up to these days and finally coming on the show. If people want to learn more, where’s the best place for them to find you?

Thomas: Well, I’m becoming more active on LinkedIn. So, it’s linkedin/in/sqlrockstar. Just search for SQLRockstar, you’ll find me everywhere. I mean, I do have a blog. I rarely blog these days. Most of the posts I do is over at LinkedIn.

And you might find me at some networking events coming up since Selector really does focus on network observability. So, you could see me there. And you know what? I’m also going to have an appearance on the Screaming in the Cloud podcast, so you can listen to me there.

Corey: Excellent. And I imagine that’s the one we don’t have to put into these [show notes. 00:29:44]. Thank you so much for taking the time to speak with me. I really do appreciate it.

Thomas: Thanks for having me, Corey. I look forward to coming back.

Corey: As I look forward to seeing you again over here. Thomas LaRock, Principal Developer Evangelist at Selector. I’m Cloud Economist Corey Quinn and this is Screaming in the Cloud. If you’ve enjoyed this podcast, please leave a five-star review on your podcast platform of choice, whereas if you’ve hated this podcast, please leave a five-star review on your podcast platform of choice along with an insulting comment because then we’re going to use all those together as a distributed database.

Corey: If your AWS bill keeps rising and your blood pressure is doing the same, then you need The Duckbill Group. We help companies fix their AWS bill by making it smaller and less horrifying. The Duckbill Group works for you, not AWS. We tailor recommendations to your business and we get to the point. Visit to get started.
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