What's New In Data
A podcast by Striim (pronounced 'Stream') that covers the latest trends and news in data, cloud computing, data streaming, and analytics.
What's New In Data
From the Marines to Data Engineering with Alexander Noonan (Dagster Labs)
Discover how Alex Noonan transitioned from the flight deck of a Marine aircraft to the world of data engineering. His unique journey, enriched by a some time in the finance industry, gives us a firsthand view of the diverse backgrounds shaping the data industry. As Alex recounts his experiences, we explore the vibrant community he found on data Twitter, a realm buzzing with shared insights and collaborative spirit. However, the landscape shifted following Elon Musk's takeover of Twitter, leading to content fragmentation and a migration towards emerging platforms like Blue Sky. Join us as Alex discusses how these changes have impacted the cohesion and knowledge-sharing dynamics within the data community.
Navigate the complex world of professional networking with tips from Alex, as he breaks down the strategic use of platforms like LinkedIn, Reddit, and Hacker News for data professionals. Learn how to creatively tailor your content to fit the quirks of each platform's algorithm, and prepare to engage with varied audiences. The conversation also highlights the transformative potential of AI tools in elevating data processes, reducing mundane tasks, and fostering high-value work. Discover innovations like Dagster and its role as an orchestrator, integrating key business intelligence tools to streamline the data engineer's experience. This episode is a must-listen for anyone intrigued by the evolving interplay of technology, social media, and the power of community.
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What's New In Data is a data thought leadership series hosted by John Kutay who leads data and products at Striim. What's New In Data hosts industry practitioners to discuss latest trends, common patterns for real world data patterns, and analytics success stories.
Hello everybody, Thank you for tuning in to today's episode of what's New in Data. I'm super excited about our guest today, someone who I've been following for a long time and always been in communication with and I'm sure you've seen his tweets go around your feed every now and then and some great YouTube videos as well. We have Alex Noonan, developer advocate at Dagster. Alex, how are you doing today?
Speaker 2:John, it's great to be here. Big fan of the show, long time listener, first time caller, I'm happy to be here. Big fan of the show Long time listener.
Speaker 1:first time caller. I'm happy to be here. Yeah, likewise, this is going to be super fun. Alex, first, it would be great to just hear you explain your story and how you ended up in data.
Speaker 2:Yeah, so my job history is a little bit Forrest Gump-ish, where I hopped from different things and ended up in an interesting place. But so out of high school I joined the Marines. I was an aircraft mechanic for five years. At some point in there, the big short came out and I remember being like super fascinated with the ability that, like people, could forecast the future, so to speak, and I think what was interesting with that is you can use data to really make decisions and make a lot of money. So I thought I would try to be like a Wall Street person. So I ended up going to undergrad when I got out of the military for finance.
Speaker 2:I graduated in 2020, which is a tough job market. Then I got a role as a data data analyst just hacking slash in SQL databases all day, excel sheets for the works. And then I did a job after that. I was like a data engineer, building like cloud resources, using data warehouses, etl, all that good stuff. And then after that I did I was in a pure marketing role at an agency for two years and that aligned with me because I love making content and it was good to learn and to apply your data skills in the domain. So that was my experience doing that. And then I was a Dagster user while I was at that agency as the one data guy. And then when I saw this developer advocate role opened up, I jumped on it and here I am at Daxter.
Speaker 1:Yeah, that's awesome and that's one of the great things about working in data. You have so many smart people who come from different backgrounds, just really good at whatever their domain was, really good at whatever their domain was, and we all sort of align on this idea of using data to make people more productive, work smarter, be more predictive. And we see that in movies now the big short and money ball and some of the ways that data has seeped into pop culture. I think that's just what's so fun about it, whereas I think there's certain other parts, categories of tech, that are more niche for people who only have computer science backgrounds. But data is just awesome because you have all these great smart people coming from different areas. What are some of the main channels that got you into the data and working with people in the data space?
Speaker 2:Yeah, I think I don't remember what the exact time that was, but I was on finance Twitter for years and I think I was just like tweeting out about a power BI problem or a SQL server issue, something I was running into and I don't know someone like replied and then, as you do, you work the reply guy game and you make some mutuals there and then you discover like more people in the network graph and then I like found my way into data Twitter and it's been one of my favorite communities because everybody's so like open to share and is always like publishing like a project GitHub repo or like a sub stack, and it's been like super interesting to learn from that community.
Speaker 2:And it was a real damn shame when Twitter imploded and we lost that community because everybody like splintered. Of course everybody's on linkedin but not everybody posts on linkedin. You had a lot of folks on threads, but threads was lame and like blue sky up until like fairly recently. I was just there but it was interesting to see like the network effects through starter packs and now it feels data Twitter is reconstituted on blue sky. What are your thoughts on that?
Speaker 1:First, I want to ask you yeah, a lot of very interesting stuff there. What do you mean by like data? Twitter imploded. Is it just that people stopped posting there? What led to this?
Speaker 2:When Elon took over, I don't know. Know, the safety on the network has gotten like pretty bad and catching a stray is like pretty high odds, where you're like next to some tweet that you don't want to be next to. So like from a brand safety perspective and like people's personal beliefs, they left the platform and it is pretty toxic if you like aren't in your niche group oh, that's a good point.
Speaker 1:One of the things that I observe because I'm on we interact on data twitter as well. It's just yeah, since the elon takeover it just I typically don't follow like politics on twitter. I I do just use twitter to to stay in touch with other folks in the data and engineering community and my feed, just completely. It used to be this rich, organic source of, like you said, just really valuable technical content, things I could learn about from the market, and it suddenly became like this very open attempt to indoctrinate a specific political view.
Speaker 1:I'm not taking a side on politics by any means, but you know just the fact that every time I logged in and my feed was just all about politics instead of data, I was like what happened? There's nothing I did here to provoke this from Twitter. I didn't follow any accounts or anything like that. I didn't follow any accounts or anything like that. I didn't even follow Ilan yeah, it was really, but he was always on my feed. Like every time I popped open Twitter, it was like Ilan commenting on the state of the economy or the country in general. So, yeah, from that perspective, twitter did fall apart, and I guess it's X now too, which confuses everybody, because everyone's oh, follow me on twitter. Wait, should I say twitter or x? And I always say I think if you say either one, people will know what you mean. Most likely, if you say twitter, I think saying oh, follow me on x it still doesn't really roll off the tongue.
Speaker 2:I don't know, I'll never say x, even if there's a fire, it's always going to be Twitter to me.
Speaker 1:Yeah, same, I see people struggling with that for sure. It just sounds odd. Follow me on X. Okay, what is X? Then you learn about Elon Musk, who has done a lot of incredible stuff with SpaceX and Tesla, obviously. So not knocking that by any means, but certainly funny to see what happened to Twitter since he took over. And then you mentioned Blue Sky. So what's Blue Sky? Tell me about that.
Speaker 2:It's like a Twitter clone. As far as I can tell, it feels the exact same. The curation controls are a lot better. I noticed like you can create a custom list of like specific people that you want to see posts from and then you can like switch between those feeds to really like, hone your experience so you don't get that fire hose of stuff you don't want to see. It also doesn't penalize outbound links, which I think is great for allowing like people to share and especially in a technical domain where, like you can't really summarize a data project or like a github repo in 180 characters. So you need to allow that like native sharing of outbound links. And it encourages like creators, especially on a smaller scale, to like, share and create and they don't have to be, like twitter poster dunk champions to get their stuff discoverable yeah, there's definitely a formula on on twitter, especially since the elon takeover, where don't put outbound links.
Speaker 1:I think like low quality meme type content is definitely more popular. Yeah, if you post a link to GitHub or YouTube or something like that, you can just tell you get less impressions. So, at least anecdotally from what I saw, saw and you're saying, blue skies is more friendly to creators who post outbound links like their github repos or videos or whatnot yeah, and it's like, and I think it allows more.
Speaker 2:I don't know one of the feeds that popular with friends. You can see stuff that like your mutuals are engaging in and oftentimes you have the kind of like leaders in the community that are always on these networks that like find the interesting, cool projects and like you're gravitated towards it that way, so it allows for more, are back where I think that was something we lost the most when Twitter fell apart was these organic discourses around if analytics engineers are dead or whatever. The topic of the week is yeah.
Speaker 1:And one of the other things that's interesting about it is Martin Kleppman posted about this, sue Martin Kleppman, the famous author of designing data-driven sorry, data-intensive applications, and he was talking about how he worked on the decentralized platform, which is a really cool implementation, and it's not blockchain or any Web3 stuff, it's just it's a distributed system using the at protocol, so a lot of really rich tech goes into blue sky as well, and it seems like it's really just speaking to the, the creators who want to control their feeds in a way that's very blatantly neutral, right, right, just based on, like you said, what your mutuals are interested, based on what you actually want to follow, rather than what the platform has decided is buzzworthy or conversation worthy. So from that perspective, I think it's pretty exciting. So you also mentioned starter packs. What are those?
Speaker 2:They're basically lists that people put out of hey, you should follow these however many accounts, and there's like a follow all button at the top and I'm in a few of these starter packs, so I've gotten a bunch of followers unfairly just by being in the list. But yeah, it's been a great way where it's really made the onboarding experience for new users pretty efficient, because if they want to come in and get a focused group of followers around data, twitter or people with good vibes, they can just go on there, smash that like button and suddenly they are integrated into the community.
Speaker 1:Yeah, that part is pretty fun, and I did notice that now with blue sky, after following some of these data starter packs and distributed system starter packs, it does feel like old twitter. Right, my feed comes in and it's just all this really rich content again about things that I'm interested in and I feel like now it's this really valuable, just source of staying in the know with the latest and greatest technologies and what the smart people are in our industry are working on and advocating and we can all align on best practices again, what's your perspective on LinkedIn? How does LinkedIn play into all this? Well, be careful 'm a linkedin top voice. Yeah, I'm just kidding, but seriously yeah, linkedin is.
Speaker 2:It's like the distribution channel for like professional folks and it runs into those challenges that that twitter has, where outbound links are, pub are punished. So you need to like be creative with your content to make it native on linkedin and a lot of if you're in the software dev tool space, a lot of your like economic buyers are there. So it's like foolish to neglect linkedin. We've seen a lot of success at Daxter with putting some of our newsletters on there through the newsletter feature just for like medium long form content and that's been helping our reach a little bit.
Speaker 1:Yeah, definitely. I would say. Linkedin is certainly a very professional oriented platform. I think it's specifically for those who are trying to go higher up in their organizational ladder. Management and business oriented advice. You get a lot more free engagement versus going hardcore technical, and even when you are doing technical, there's more focus on high level architecture rather than okay, this is the exact code you need to write for how I'm using DuckDB as a offloaded cache or something along those lines. Yeah, it's really interesting as an operator in the data category to just understand the nuances of these social media platforms. And, of course, there's Reddit too. Right, reddit is often overlooked. Do you spend much time there?
Speaker 2:we actually do the our data engineering subreddit. Some of them there are sick of us, but it's pretty effective because, like people treat reddit as I don't know, they append reddit at the end of all their google searches. So if you want what is a informed person's decision or informs person's opinion on a top, you usually go to the subreddit for it. So we hang around the data engineering subreddit a fair amount, and it's funny when you run into competitors, developer advocate on Reddit trying to give helpful advice, it's like, hey, I'm here, I'm supposed to be giving helpful advice. Trying to give helpful advice. It's hey, I'm here, I'm supposed to be giving the helpful advice, but it's a great way to establish social proof and get in front of the power users and the people that are like really, uh, passionate about their craft, because usually those are the people that post on reddit yeah, absolutely.
Speaker 1:Reddit also very, and it depends on what sub you're on. I think it's similar to Blue Sky and what Twitter used to be, where it was, where developers hang out and trade best practices and like getting into the weeds of things and like comparing vendors. So, yeah, it does totally make sense for data platform companies to try to establish a good relationship. Also, be warned that if anything comes off as advertising, you're just going to get completely beaten up there by the readers. That part can be tough. I do see all these kind of attempts to. I think the Reddit ads work well, honestly, because it's like they're pretty good at curating the ads. But, yeah, you can see that it's a tight rope to walk on when you're going in as a data platform company and trying to talk about your product to Reddit, because you know I think it does skew towards more peer toer information sharing rather than like vendor to consumer. So, yeah, just another another fun detail there, and redditors, since they're anonymous, can be a lot more.
Speaker 2:They have a little bit of a bite to them if they don't like what you're saying. Not as bad as Hacker News folks. I think they're the most extreme. But yeah, to your point, if you come on there with 100% sales pitch, they're going to roast you.
Speaker 1:Yeah, it's funny. You bring up Hacker News post from Dropbox CEO Drew Houston where he introduces Dropbox to Hacker News and this is back in the, I want to say, the late 2000s and the first and most upvoted comment was why do I need Dropbox? I can solve this with some linux hack. And it was. It's pretty funny to read now, I think. Yeah, I believe the comment was actually I can pull it up here drew house and posted about his. It was the title was my yc app, dropbox, quote unquote throw away your USB drive. And the one of the top comments was I have a few qualms with this app For a Linux user. You can already build such a system yourself quite trivially by getting an FTP account, mounting it locally with curl, ftpfs and then using SVN or CVS on the I think yeah, on the mounted file system For Windows or Mac, this FTP account could be accessed through built-in software. See, we didn't even need Dropbox. The sky already had the solution.
Speaker 2:Anyways, that as a Linux user, this is trivial, is like such a good line.
Speaker 1:There's so much in there yeah, there's a lot to unpack on. Yeah, as a linux user, this is trivial.
Speaker 2:Yeah, yeah, if only we were all linux users yeah, if only then it's like that meme where it's like all like the flying cars and stuff, if the world was linux users yeah, absolutely we would.
Speaker 1:There would be no sass if we were all linux users. It just seems like a crazy idea letting someone else run your machines for you. Yeah, the. But it is fun and fun to talk about these social platforms. But alex, I also want to. You have a really cool background I I want to hear more about alex, like before he got into data. You serve this country but you have some really interesting experiences that I never had because I was always in computer science, but would love to hear about that yeah, so in the marines I worked on the f-35, which was at the time and still is.
Speaker 2:it was like the fancy pants new fighter jet. I think the government to date has spent like a trillion dollars on it. But yeah, it was cool to see inside what goes on in these big government procurement projects and it's nothing crazy, corrupt or anything, it's just wow. These bolts cost $20, huh, you can get them at Home Depot for one. But yeah, during my time there I got to see the jet go from testing phase to where it was deployable and it was really interesting to see the amount of work and effort that went into that.
Speaker 2:And it's funny, if you talk to any marine aircraft mechanic after the get-out, they cannot stand airplanes. I remember I talked to this one dude. He was on the flight line, he was covered in grease and he was like, once I get out, man, I'm going to find the point on the map that's farthest away from any airport and I'm going to go there. And yeah, I was like I don't really want to work with all those chemicals all day. Data sounds cool and I liked Excel, so that was my primer into doing, uh, data work. And I'm a gamer too, and I feel like being a gamer gravitates you towards data work, because it's a lot of just min maxing, finding efficiencies and reducing bottlenecks, all that fun stuff. And I've been a poster since high school. You know, on Facebook, when it shows you 10 years ago you posted this, I saw someone from 2009, and it was in my same voice that I use today when I post on a social.
Speaker 1:I was like wow, I've really been at this for a while now that's what's so great about this industry and folks like yourself who are highly skilled and really talented. Certainly, if you can service an F-35 and understand some of its workings, you can probably build a data pipeline, maybe equally high pressure yeah, I'm joking. And even when you talk about yeah, you've been posting for a long time and, yeah, that honestly does give you good intuition about how communities work online right, and that's extremely valuable. And then, because the same people, the same way they post about, like their personal lives or the concert they went to last week here's a blurry video of a concert I went to and a family gathering what have you? People similarly just go online and post about the data pipelines they're working on and either look for help or look for recognition, look for tips, look for best practices. So these online communities are always going to be really valuable here. Now, do you see AI and gen AI disrupting any of this?
Speaker 2:I don't know. You can go for the inflammatory take and be like we're six months away from the end of the world, or you can be like, oh, ai's a dud. But I think it's like any new technology it's going to reduce the drudgery and allow you to focus on more higher value work. Going to reduce the drudgery and allow you to focus on more higher value work. And I think, as the tools to make more custom applications with AI become easier to work with, we're going to see a lot more interesting novel applications. And because right now I feel like we're constrained by the chat interface, we, how most people interact with ai agents and I don't know there may be some software interface that we haven't thought of yet that works better for communicating with these ai agents, and who knows what that's going to be.
Speaker 2:But it feels like in my life, one of the biggest, biggest technological leaps that I've ever felt. But then you talk to a family member or something that isn't involved, or tech forward, and you show them ChatGBT and have them interact with it and they're like oh, cool, whatever. And it's like don't you see what we're working with here? So I imagine the adoption with AI is probably going to be how cloud was, where it happens in the background and folks that are really passionate about it move the tech forward and then end users, if done correctly, probably won't even know that it's happening. We may just see a lot of these human administrative processes that bog us down either disappear or become pretty minor in our day-to-day lives. That's the hope, anyway.
Speaker 1:Yeah, definitely, automation certainly isn't a new concept.
Speaker 1:So, automation certainly isn't a new concept, and you can look at the way that radiologists automation to detect certain things in images, but it still goes to a radiologist.
Speaker 1:Or autopilot in an aircraft, you can say, yeah, autopilot can be better than the pilot, but at the same time, you still want people in the cockpit. And I think, yes, taking off some of the cognitive load from the human is always going to be valuable, but it's never really replaced anything, especially when these automated systems in the past have been more deterministic, whereas this generative AI is extremely probabilistic, right, so super valuable, for for sure, but always requires a human in the loop. And I always feel like this is going to make the human interactions almost more valuable, because you're going to see so much ai generated content out there now that people be aware that what's being fed to them and is heavily AI generated to seem credible, so they're going to seek out more of these like human interactions, more of these. Okay, what's the expert saying on blue sky and what is the consensus on Reddit from these? Like real people in the community? So it'll definitely be interesting.
Speaker 2:Yeah for sure. I feel like one area that AI is making it really difficult right now is recruiting, Because the ability to create a tailored resume is like the cost is basically down at zero and if you put like open job posting on LinkedIn, in like 24 hours you'll get like 1000 applicants and it's like how is it possible to like sift through these and find, and just find, candidates? And I think it's going to be interesting to see what are the tools that develop to parse out the AI slop and so like the truth can come through yeah, definitely, and yeah, it is all making human interaction and human relationships more valuable than ever in in some ways.
Speaker 1:For instance, what you mentioned recruiting super hard now. Okay, the recruiters are going to look for referrals from a trusted source now because they're getting a thousand applicants and they're going to use their own AI software to filter and sit through that. I want to go back to data and some of the things going on in the industry. There. A lot of people are talking about this so-called unified control plane and I see a lot of this discourse on on blue sky and Reddit and places like that amongst data engineers and software engineers who work on data. So tell me about that.
Speaker 2:Yeah, so the unified control plane. Right now, as a recording, it's Data Platform Week, but for guys like us, every week is Data Platform Week and the unified control plane is and, like the Daxter approach is, we believe the orchestrator is the perfect spot in your data platform to bring everything in so you can have that unified view of visibility, command and control of your data assets and pipelines and discoverability as well, and so, like operationally, as a data engineer, you can go into Dagster and have everything right there Because, like one of the frustrations I for folks with the modern data stack tools is, they're all built with the Unix philosophy of the perfect tool for the job. That's narrow in scope, but this leaves for kind of a disjointed experience when you have 10 perfect tools for the job that don't interface well with each other. 10 perfect tools for the job that don't interface well with each other and a lot of the features that we shipped in our latest release 1.9 Spooky, because it came out on Halloween bring that vision of expanding what you can have in your asset graph and we launched integrations for business intelligence tools what you can have in your asset graph and, like we have, we launched integrations for business intelligence tools. You can now view and materialize assets with Tableau, power BI, sigma and Looker, which is pretty cool because I don't know.
Speaker 2:I remember when I was a data analyst and power bi, woes were like the bane of my existence. And if, like I could have known that, oh, the upstream dbt model that feeds into my power bi report broke for like this error, and I got a slack alert instead of finding out, like from the cfo 10 minutes ago, that, hey, this dashboard's all messed up and now you've got to fix it. That alone would have saved me, I don't know, months of my life. And we have a few other features we released was Airlift, which is our way to peer and migrate airflow projects. So now, within Daxter, you can have an airflow project and tasks visible within Daxter's asset graph, and I feel like this is a lot of problem for folks in complex enterprises where they have multiple airflow instances and it's really tough to have that view operationally of what's going on.
Speaker 2:So if you have that all in one place, that allows you to have that single point, single pane of glass where you can see all your data pipelines running, which I'm really excited about and I know whenever we show it to a community member or something they're like oh great, this solves an exact problem I'm working with right now, so that's exciting.
Speaker 2:And then we also have pipes, which is our way of passing context back and forth between external compute environments and different different programming languages. So, for example, one of our community members, georg heiler, over at magenta, he used pipes to implement all these different spark runtimes and, depending on the job that was being done, they were able to reduce costs by, I think, like 40%, by having specified EMR jobs or whatever, that they pass contacts back and forth using this pipes protocol, and it was a way that they increased visibility into the workloads, reduce costs and not messing with the integrations. So that standardized integration approach is what is allowing Daxter to be that unified view where you can see all your data assets in one place and operationally manage it to make your life as a data engineer easier.
Speaker 1:So you're doing less reactive work and more high-value proactive work yeah, and I love how daxter has so many roots in software engineering. Just naming the feature pipes I I think of unix pipes and how it, you know, passes outputs from commands to each other, so tell me more about that. So here you're talking about sharing context between different processes, right?
Speaker 2:Yeah. So, for example, if you had, if you have a like modal is one of our integrations that we have pipes with and you can pass context from a Dagster asset into the modal compute. So either like a partition or a previous upstream asset, something like that, and then with the standard protocol, any of the context you want emitted back up to Dagster as asset metadata. It's all standardized so you can switch that between different compute environments or a machine learning external infrastructure. We have seen someone use pipes with R, which is an interesting use case.
Speaker 2:Yeah, Our users are, they're quite prickly and they like their tools and I think, like the benefit of something like pipes is the tool that the engineer wants to use. You can incorporate it into your visibility layer and I think that addresses a lot of the problems you have when someone leaves an organization and it's oh, there was a load-bearing Jupyter notebook over on their computer that only they know how to use. If you use something like Pypes, then you don't have to worry about that because you have visibility into everything involved in your data platform.
Speaker 2:So you reduce risk at that end.
Speaker 1:The load bearing notebook that only one person knows about. Every organization probably has their version of that. Every organization probably has their version of that. And yeah, I do like how DAGster definitely gives you this nice layer of abstraction to map out and basically connect your data assets in a DAG directed acyclic graph, and it totally makes sense. And one of the things that's also really cool is the business intelligence tool integration. So tell me a bit about how that works.
Speaker 2:Yeah, so for Tableau, power BI, looker and Sigma I believe they're the ones we currently support the workbooks or dashboards or reports will show up within the Dagster UI as assets that are materialized through that view and you can also within Dagster schedule. You can schedule them to run at specific intervals and have them be dependencies and automations and jobs. So it makes it a lot easier to gain that visibility and kind of push a lot of the transformation logic where it belongs, into your transformation layer, as opposed to like within the BI tools. And similarly it allows that metadata observability, like if a workbook failed, you'll have a job detailing that it'll failed and why. And if you need to refresh a dashboard real quick, you can do it, like within Daxter. So that makes it a lot easier for data teams to manage that whole pipeline. And then also you have visibility from like ingestion transformation down to the service layer, so you have everything all in one place.
Speaker 1:Yeah, that's incredible and it's especially because business intelligence tools they're the business source of truth for the data. Right, that's where you want your go-to-market teams and other operational teams who are interacting with the data, but they can's where you want your go-to-market teams and other operational teams sort of interacting with the data, but they can lack that sort of upstream asset context. So, being able to map that out and from your upstream sources to the transformations and your data modeling, jobs and things along those lines, and all the way to refreshing a specific view in your BI tool, great for data engineers to have that control. Yeah for sure. Yeah, what's coming up next with Daxter?
Speaker 2:So, like I said, we're continuing to push forward and expand the different tools and integrations that you can bring into your asset graph and we want to make Daxter more intuitive and with that involves we're currently in the midst of revamping our docs to be more intuitive and like easier to read and understand and also the developer experience. So we want to have more project scaffolds that make it easy for folks that are maybe like new to data engineering or like the DAX abstractions to get started and get going with some of our like common use cases that we see most people run into, so they can just plug and play with their keys and then like iterate from there.
Speaker 1:Yeah, very cool, very cool. One of the other thing great things about this industry is all the in-person events and networking that can happen. Are you going to be at any of the upcoming industry conferences?
Speaker 2:Yeah, so Daxter will be at reInvent in Vegas and we'll be posting about it on all our channels leading up to it, so make sure you follow us there and I'll be there. So if you want to meet me, I'll be at the booth and we can chat about Daxter and data engineering. I'd love it.
Speaker 1:I'll be there to catch up with you, specifically about F-35s and all that cool stuff, because we talk mostly about data on this podcast and the data communities, and that's just one of the things I love about this industry. Again, I'll say again, it just has this extremely eclectic mix of very smart, talented people like yourself who bring some of their unique backgrounds with them, and we all agree and align on this idea that we can use data to make people's lives better and do business better and, overall, operate more efficiently. Alex, it was great having you on this episode today. Where can people follow along with you?
Speaker 2:Yeah, so I'm on LinkedIn as Alexander Noonan, I'm on Twitter as Alex Noonan six. I'm on blue sky at I think it's like Alex Noonan dot, blue sky dot social. And yeah, if you search Alex Noonan, I'll probably show up.
Speaker 1:Excellent. We'll have those links down in the show notes as well. For those who want to keep following Alex, I totally recommend it. He's always posting some great, insightful, actionable, useful stuff, and also his YouTube videos. I'm a big fan of those as well for people who want some higher level intros into data engineering. Alex, thank you again for joining us today on this episode of what's New in Data, and thank you to the listeners for tuning in. Thanks, john.
Speaker 2:It's great to be here. See you next week.