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The Data Engineering Show is a podcast for data engineering and BI practitioners to go beyond theory. Learn from the biggest influencers in tech about their practical day-to-day data challenges and solutions in a casual and fun setting.
SEASON 1 DATA BROS
Eldad and Boaz Farkash shared the same stuffed toys growing up as well as a big passion for data. After founding Sisense and building it to become a high-growth analytics unicorn, they moved on to their next venture, Firebolt, a leading high-performance cloud data warehouse.
SEASON 2 DATA BROS
In season 2 Eldad adopted a brilliant new little brother, and with their shared love for query processing, the connection was immediate. After excelling in his MS, Computer Science degree, Benjamin Wagner joined Firebolt to lead its query processing team and is a rising star in the data space.
For inquiries contact tamar@firebolt.io
Zach Wilson:
I've been doing big data for a long time, like, uh, since like about 2014, 2015, like I actually started my career in like data science, doing like analytics stuff, doing like Tableau and SQL and stuff like that. But I realized that I like to build more than I like to like build models and query data and stuff like that. And that's where I found this nice intersection with data engineering. And I got this job at Teradata back in 2015. And like, I've mostly been on the data engineering train since then. I've like, kind of fallen off every once in a while where I've been like, I'm done with pipelines, I don't wanna do this anymore. But then I like, I end up coming back around, I end up coming back around. Like it happened, it especially happened near the end of my time at Facebook where I was like, I'm done with data engineering, I'm out. But then I came back around. And so like, it's been great. And then more recently, last month or so, I decided it was time to do content full time and to also try some other things out. So I'm actually doing, Three separate things I'm trying to do right now. One is content. I make content on five platforms, LinkedIn, Twitter, Instagram, TikTok, and YouTube. Those are the five that I'm on right now. And
Benjamin:
Awesome.
Zach Wilson:
it's been fun. Really trying to get those rhythms going and get that pattern going of just video. Because I have a theory that actually all social media is gonna merge into short form one to two minute videos that they're all kind of going that way. It's interesting because I've even seen the short form videos perform really well on LinkedIn as well. Where it's like,
Benjamin:
Okay.
Zach Wilson:
I'm like, wow, like, ooh, this is cool. Like, I'm like, and it makes it easier for me because then I'm like, oh, I can make one video, post it five times. Whereas before it was like, I had to make a LinkedIn post and then convert it to a video. And it was just like, there was a lot more like processing and all these steps to it. Right. So I think that that's like one of the things for me is like, I really do think that video is going to be a big part of the future. But like, so that's one. The second thing is I'm doing a data engineering boot camp. So I have taken on about 50 students, and I'm doing a six week boot camp for them. I call it like, it's kind of like the good to great boot camp. So this is not like a breaking into data engineering boot camp. This is you have a job as a data engineer, and you want to learn the skills that are going to take you to the next level. And they're going to make you a better data engineer. And so that is definitely what I've been working on. The very first session of that is tomorrow. So like we're starting. day one, week one, tomorrow. And it's been a lot of planning and stuff like that. And that's been really fun. And the third thing I have is I have this platform called Tech Creator, which is going to be a platform that makes a lot of managing your job as a tech creator easier. And it's going to have these chat GPT plugins to make content creation easier, especially content creation in your voice. That's the part that's gonna be really, really exciting. That stuff should all be done in the next couple months as well. So be on the lookout for that stuff. So techcreator.io, it's pretty cool. Yeah,
Benjamin:
Nice.
Zach Wilson:
so yeah, that's kind of what I've been doing in the last month or so.
Benjamin:
Awesome. So I'll definitely be on your lookout. And like, that's a, that's a lot of stuff to talk about. So like, let's start right in the beginning, right? Kind of like then a month back, kind of you made the decision, okay, I'll leave kind of my like comfy job at Airbnb kind of become full time, focus on kind of content creation and all of these other things you talked about, uh, like take, take us through that journey. Right? So like at this point, I think you have like more than 250,000 followers on LinkedIn. So that's obviously a kind of huge audience. What, what actually got you started kind of like? creating content,
Zach Wilson:
Yeah,
Benjamin:
sharing your knowledge, all of that stuff.
Zach Wilson:
that's good. I think there's kind of like two journeys here that are important, and one of them is like a content creation journey, and one of them is like a confidence journey, but I think they both matter. So content creation, I've actually been doing content for a very long time. So I started posting almost daily in 2010, 2011, when I was like 16, 17 years old, and a lot of that content is really terrible. It's all on Facebook. I only have like 500 people there and I intentionally keep it small because there's so much bad content like I think I feel like I
Benjamin:
Thanks for watching.
Zach Wilson:
need to go back through and delete some of it because I feel like I'm going to get cancelled but but anyways.
Benjamin:
But so that was also technical already, or kind of like
Zach Wilson:
my thoughts and my perspectives. It just kind of shifted over time. For a while, it was just my thoughts about school and math. And then it was more like, especially after that, it was more into politics. Because three or four years, I got way into politics for a while. I was way about that. 2013 to 2016, I was obsessed. And that's all I wrote about was politics. And then I would say after that, especially after I got the job at Facebook, it started to get more technical. And then I realized, one of the things I realized about it was, it was actually in 2020, this is something that happened when I quit my job at Netflix. I remember telling people, I was like, when I quit, I was like, you're gonna know my name. I'm gonna come back around and you're gonna see me, I promise. Even though at that point I had no big following or anything like that, but I had the feeling that I knew the skills that I needed to get engagement and all that stuff. And then I really started making content consistently a couple months before I got in Airbnb. It was like December, 2020. And then that was when, yeah, things have been going pretty well since then. Like I like, it's been consistency is the main thing, right? Where... So now since then, since December 2020, I show up and post almost every day for the last two and a half years. I've missed, I think, 12 times. I've not posted 12 days in the last two and a half years. So consistency is important. And I try to shoot for like 95-ish percent of the days. So I get like one day a month, right, where I'm like, yeah, I don't need to make content today. So, but like that's, that consistency is I think one of the big important factors of like why I've experienced so much growth because that's the part that's hard. That's the part that is challenging. That's the part that like separates me from a lot of people is like, uh, is that. And I know I'd say that that's like been the main thing. And now, now it's been like, now that I have more time, it's been like very interesting because it's like, uh, there's like so many different strategies on how to like grow, like cross platform and all that stuff, but Yeah, I'd say that's my journey for sure.
Benjamin:
Okay, gotcha, super cool. So, but I mean, that's interesting, right? So you actually kind of started out kind of like just posting a random stuff or kind of whatever
Zach Wilson:
Mm-hmm.
Benjamin:
And then kind of, I guess kind of like over time, you found more of your technical audience in a sense. That's super interesting. Like at least on my end, right? I always feel like there's a certain barrier to writing something, right? Do I actually have something to say, which is smart enough?
Zach Wilson:
Yeah
Benjamin:
will people care?
Zach Wilson:
Definitely. And that's what I was saying about these two journeys, right? One of them is a content journey and one of them is a confidence journey. And I feel like for me, that confidence journey was like, especially after I got the staff tech lead role at Airbnb, then I was like, okay, I have the credential now, right? People are gonna care about what I have to say now. right? And that made a big boost for me and just in like my, uh, my own perspective of my own thoughts, I guess, in that like, wow, okay, I can actually do these things. I think that is actually that is tricky.
Benjamin:
woke up one day, looked at your CV and you're like, hell yeah.
Zach Wilson:
Yep.
Benjamin:
Kind of like I have the credibility now. I can start writing about this.
Zach Wilson:
Yeah, and like, I realize now though that like, that that was actually kind of stupid of me, that like actually the correct way to go is to just put your thoughts out there and let, let the algorithms and let the audience decide if it's valuable, not whether you think you are credible already. Because I mean, I see these, for example, there's a couple people on LinkedIn I follow who are in high school, right, they're 18 ish years old, and they have 25,000 followers on LinkedIn, and they're in high school, right? And it's like, okay, there's no way they have the credibility or the established they're in high school, they're still learning, right. And that's like, but that's the thing that like I learned in my journey here is that like, really, it's more about just putting your thoughts out there, putting your opinions out there and letting the feedback come. And then you can know if you know what you're talking about or not, because, you'll get the feedback from the internet about it. And like, and I think that's actually one of the beautiful things, that for me, I think was actually an unnecessary barrier. And for me, I'm like, I wish I would have started earlier. I wish I would, because I had the, I feel like I had the content writing skills for many years, for many, many years. I think I learned those in like three or four years, kind of the copywriting skills of how to write content that's engaging, as opposed to content that's maybe educational is a separate skill, but engaging content is pretty universal. And so for me, I think that that was actually a mistake and a thinking error. where I'm like, oh, I have to build up all this credibility and resume before I should speak. And that's actually like, definitely one of the things that I want anyone watching this podcast, make content, just like put your thoughts out there. Doesn't matter, doesn't matter where you're at. Like remember, there's people on LinkedIn who have 25,000 followers who are in high school and they're making money from LinkedIn. And like, there's no way they have more credibility than you, like, and so, like, and so definitely just remember that, right?
Benjamin:
Awesome. Super cool. So another thing I find interesting, now you're super focused on data engineering, both in terms of technical content, right? I watched some of your TikToks today about window functions, those types of things. And really digging into how to be a successful data engineer. And then also things around how to build a career in data engineering. How do you make the decision? What's worthwhile content?
Zach Wilson:
Yeah, that's great. I think some of it is there's kind of two sides to it is that like some of it is around I get feedback from people. This is where TikTok is really amazing, where LinkedIn needs to do better on this. But like TikTok, like the people in the comment section, they give me so many ideas. They give me so many ideas where I'm like, yeah, that's totally right. That's totally like the thing that I need to be talking about right now. And like, because for me, that's actually interesting and kind of perplexing about content is that the content that I really like to write about is the content that is like, I would say more like intermediate to advanced data engineering. But if you post that stuff, like a lot of times it kind of bombs because it's just not relevant to most people. And so and that makes it so the content doesn't do well. But like on the flip side, I don't want to do just like 101 content because I just feel that that market is already tabbed. that there's enough creators teaching select and group by and where, and I don't need to be teaching that. Right? So for me, I'm trying to find a way to make these more intermediate advanced concepts more approachable. And that's the tricky part. Right? Because usually my V1, I write it and I'm like, ah, no, this is more for practitioners, not for people learning. And so it's an interesting balancing act. But then there's the other side of it of like, okay, more than just the technical content, like of how to do data engineering, like the actual nuts and bolts of it, but how to grow your career, how to be, because I feel it's how to do data engineering and how to be a data engineer. Those are separate things, right? And so I definitely think that that's... One of the things for me is like, especially on LinkedIn, this is something I learned. There's this lady named Leah Turner on LinkedIn. Definitely follow her, she's amazing. And she has this framework where there's three, there's three types of content, right? You have show content, which is just explaining something, a concept, right? Then you have grow content, which is gonna be more around like inspiring people to take action or like really speaking to their emotions. Right? And then there's a third piece of content which is like get to know, right? And that's how they can learn more about me. They can learn more about my story and my journey. And I found that like if you kind of those three buckets, like for me personally, I think the correct breakdown of those three buckets is show content should be like half, grow content should be like 30% or 40%. and get to know content should be like 10%. It's like the spice that goes in there a little bit much. Because you want to be careful to get to know stuff because if you post it too often, people just unfollow, they boo you. But if you post it, but infrequently though, sometimes those posts can be your most viral pieces though. And it's like, but you just don't want to talk about yourself all the time, otherwise people are like, this guy's full of himself, he only wants to talk about himself. And so you want to be careful with that type of content because it's powerful, but also dangerous. So
Benjamin:
say it's kind of one of those three pillars, like, right. So on my end, like I'm just kind of like C++ database nerd. It kind of, I care about how to build concurrent index structures. I don't know how to build a fast multi-threaded join and so on. And whenever I look at data engineering, right. And I interface with a lot of data engineers and Firebolt kind of, it's always seems really daunting just because like the kind of breath of the field in terms of the, like just amount of different technologies you have, just seems so crazy, right? So how do you decide on what to focus on in terms of actually teaching people skills that are valuable for their career?
Zach Wilson:
Yeah, well, that's great. That's actually a great, great question. So for my boot camp, for example, there's six weeks. Only two of those weeks are actually tech specific. The other four weeks are not tech specific. They're tech agnostic. So where, because I think there's a couple things and a couple philosophies that are really important in data engineering that are actually like. They apply regardless of if you're using Spark or Snowflake or Databricks or Presto or Flink or like whatever, you know, tech you want to use for it. And there's a couple of them, like one is like around like data modeling, how to do, how to do proper data modeling for dimensions and facts and how to really get those things like compacted down. And there's a lot of trade-offs in that space that is very art, very, it's not a science and it's a lot more art and you have to understand like your consumers, and they have to have that empathy. And that part is powerful. For example, for me, when I was working at Airbnb, I would say 80% to 90% of the impact I had was going to be in two buckets. It was in the leadership bucket of inspiring other people and helping them grow. And the other one is data modeling and making robust data models that can then be used by a large number of people downstream. What wasn't as important was like how good I was at Spark. Like, I mean, I go look at Spark as more of like a means of accomplishing something or like as like a, it's just one kind of path forward and that like you just, it solves the problem and maybe it can be a little bit faster. Maybe it can solve those things, but really this is the fundamental thing that I think a lot of data engineers need to remember is your product that you sell is data. It's not a pipeline. A pipeline, it can help in terms of maintenance and pain and suffering. If your pipeline sucks, then the data is going to be annoying. But generally speaking, the value you're providing is in the data sets that you provide. And if those data sets are not modeled properly, then that's where you can have a lot of unnecessary costs. And in big tech companies, these mistakes actually cost them millions and millions and millions of dollars a year. Because if you don't model things the right way, then downstream the compression doesn't work the same way. And then it can blow the data up again. And there's a lot of interesting tricky things that I've noticed with how data modeling works. So that's one. I'd say another kind of tech agnostic thing is around data quality and understanding Again, there's technologies here like Amazon DQ and great expectations, and there's going to be 10 trillion more coming. And but like, it's more again around like how to test data like, of like, is this quality? Is it not quality? How to validate it? Right? That's very like agnostic of like the tech that you're using. And you should definitely be able to do that. And then the last bucket of things that are as tech agnostic is storytelling, right? Can you tell a compelling story? Can you like make some cool charts? Can you persuade people to give you the time to make this data and other things like that? Because there's like the story, there's like the before data story, and then there's also the after you have data story. And both of those stories matter. And being able to construct those narratives in a compelling way, very important persuasion. And then the tech, and then the tech is the last one. And like, in some ways, I think the last one, but also not as important. But it's tricky because, and this is the thing I hate about industry in some regards, is If you go into an interview, right, and you go into the interview, like, 80% of the questions are going to be on like Spark or Flink or like, and be like, Oh, do you know this very specific minor detail about Spark? And it's like, dude, like, this doesn't matter that much, actually, in the end. But like, but that's how it's things are tested, right? And I hope that industry changes in that way.
Benjamin:
You need to be able to tune the number of shuffle partitions and those types of things.
Zach Wilson:
Yeah, exactly.
Benjamin:
Gotcha. Looking at this bootcamp, because you're framing it in that context, you said in the beginning, the goal is from good to great. So usually those will be people who already know Spark, know how to maybe write Scala code for their Spark stuff, those types of things. On the other end, and if there's hard to say from good to great, is you have someone, maybe that... Okay, not the influencer with 25,000 followers talking about data engineering, but just someone wrapping up high school who wants to get into data engineering. Kind of, you have to pick up some technology, right? And like there, it just seems kind of like daunting to in a sense, like make that choice, right? Kind of what horses do you bet on? Kind of with what do you get started to actually start with that career?
Zach Wilson:
I mean, I totally agree. I think it's similar to, so I'm a pretty athletic, sporty guy, right? And one of the things that I remember as a kid growing up, my parents were always like, you gotta do sports, right? And then I was like, okay. And then I tried a bunch of them. I tried soccer, I tried basketball, I tried baseball. I tried all these different sports that were all interesting and different and like. But one of the things that I learned about it was, especially going through that process, was like, yeah, you just got to pick one and be like, I'm going to get good at this one. And for me, that was basketball. I mean, I got lucky. I'm tall. I'm 6'2". So basketball was the easy, obvious choice. And that's one of the things that's tricky about tech sometimes is that the choices aren't so obvious, right? They're not like, oh, yeah, this one is 6'7", and this one's 4'2". So we should go with the taller one or whatever, right? It's not that obvious a lot of the time. And so I think there's kind of a couple pieces there, like on how to pick technologies is, one is gonna be like, okay, what do you see on social media? I'm not gonna say all social media because I still don't trust TikTok here. But on LinkedIn, if you have enough of a network on LinkedIn, do a poll. Polls are broken on LinkedIn. Like if you do a poll on LinkedIn, like even if you have no followers, It's going to be seen by like 10,000 people because polls are broken and they're very good and the reach they get is too good. And so you can learn, you can ask, right? And I found that there's like two or three really high fidelity sources of like where to get like good information. You have LinkedIn. The thing about LinkedIn though is it doesn't give you very, it's not as good about negative feedback. So if you're like asking someone to be like, hey, say why my video sucks. Most people aren't going to do that in the comments section on LinkedIn because they're like, I don't want to look like an asshole. And so that's one. Reddit, Reddit's better for that. Reddit's almost too good for that. If you want people to tear you down, go to Reddit. Reddit or blinds even better if you really want to get... But those places, you can also get the more guidance on like, okay, should I learn these texts or these texts? And I think really the big things are going to be just like... learning the languages first, SQL Python, right? If you really want to break into the SQL Python, learn the languages first, and then you can learn the tech after that actually. Cause you can do, like, if you just do like Postgres to learn SQL, just like a very basic database, then you can build into the more complicated ones like Snowflake or Firebolt or Spark or whatever you want to use, right? And then like, you can kind of go into the cloud that way and like... I found that kind of building more locally first and kind of learning languages that way. I've had more success with some students kind of teaching them that way, as opposed to being like, okay, here's how you set up an EC2 instance on AWS and now you have a computer in the cloud and it's gonna crunch the data for you. And a lot of that feels like a lot more complexity that they don't really need yet until they have more confidence in their own skills.
Benjamin:
Right. Yeah, that makes perfect sense. I mean, like looking at SQL databases, I guess kind of one of the good things is looking at the space right now is like so many systems are actually kind of converging around the postgres dialect. There's not like you need to kind of learn like seven different kind of flavors of like SQL or like window function syntaxes, whatever. Actually, like a lot of the system tend to behave at least more similar to data than maybe they used to some time ago. So... Um, that's, that's super cool. So thanks, thanks for the insights there. I mean, I'm sure a lot of listeners will appreciate that. So zooming out a bit, right from the kind of specific technology, like, do you see any kind of big kind of trends at the moment? Like when I look at LinkedIn, for example, like one thing that keeps coming up is like data observability, kind of data monitoring, data quality, like those seem to be what some of the things kind of generating a lot of, a lot of buzz. What else is out there?
Zach Wilson:
Yeah, like data monitoring, ML ops, data versioning, there's all sorts of interesting things. And then there's a couple of them that come back around sometimes, like data mesh. I hear data mesh once every three months or something like that. And I'm like, hey, it's there. It's a thing. Right? But I think a couple of things that I really am seeing is definitely data observability of like, yo, how is this data changing over time? And it's very closely linked with data quality. And honestly, they should be closely linked because if you aren't aware of how your data, like the shape of your data, what it looks like over time, then you really don't have good data quality checks because you haven't done your due diligence on looking at what is normal and what is abnormal. I mean, there's data quality checks out there that are very easy to know, or what is normal and not normal. Like, is there any data? No data is abnormal, right? Or like this column’s null when it should never be null, that's abnormal, very easy check. But then things, it could get like what you define as normal versus abnormal, it gets more and more complicated as like you look at more and more different data points in together. And that's where, you know, if you look like week over week row counts, that's going to be one that what is abnormal versus normal, it depends. Because a lot of times those week over week row counts, like on Christmas day, they fail because there's not as much data or there's too much data. And like, and it's just, it's actually not like you're looking at the wrong period instead of like week over week. You really should be looking year over year and looking at it kind of on like zooming out to find the actual pattern that matters the most. And I mean, that's why people do week over week, right, instead of day over day, because of like the Sunday, Monday phenomena. And Sunday, Monday is super annoying as well. That one's very common to like trip people up. That's why week over week is better, but it still misses the holiday patterns, right? So I think that those kinds of observability things are super important because it's linked to quality and that is linked to trust. And because it's without quality, you don't have trust, right? And definitely, I think that that I would say is the big thing that I definitely been seeing. I've also been seeing a little bit more of a push towards like streaming and trying to get more people involved with like, I've been hearing about ClickHouse like so much recently, like everyone's like, you got to try ClickHouse, you got to try ClickHouse. I have not tried ClickHouse yet, but I need to just because it's been I've seen it in like every single comment section of all my posts. So yeah, for sure.
Benjamin:
Nice. Okay. Super, super cool. Super interesting. So on, on that kind of up observability space slash kind of data quality, like that as well, kind of looking in from the outside, like that seems extremely kind of fragmented, like there's a lot of companies around this kind of open source frameworks, etc. Do you see that converging in any way, or is it actually getting worse and kind of there's a new thing popping up every day.
Zach Wilson:
Yeah, I think it's going to be similar to a distributed compute environment. Especially when a competitor gets tested, then you see this proliferation happen. And this is also happening with orchestration, because you have Airflow is the giant incumbent. And then there's all these other orchestration layers that are competing. You can see it proliferating a little bit. And then, generally, what happens... In distributed compute, this happened with... You started with Java MapReduce. MapReduce was the king. And then you had all of these things. You had Hive and Pig and Drill. There were seven other ones. And then Spark came along. And then everyone just realized that Spark was... the spark was just so much better than all of those other ones that like these companies made these big pushes of like, okay, we need to everyone get on Spark. And that's great when you have that consistency, right? Where it's like, hey, everyone's doing the same thing. And we're all talking about the same stuff. And I think that like, one of the companies that I find that is probably going to be leading there is definitely dbt. I think dbt is one of the ones that really has a good like, like foothold in that kind data quality environment. It's so good, it's crazy. So I live by this place called Death by Taco, and it's dbt, right? And I'm always, every time I walk by, it's like, I'm just like, it's so funny. And I get that, I get a laugh about that almost every day now. So, and like, but yeah, I think that that's what's gonna happen though, is that there's gonna be this kind of, same thing, proliferation, people learn from each other, and then there's gonna probably be more of a consolidation sort of thing that happens. But maybe not because the thing is, is like the consolidation stuff really only ever happens when you have a technology that is like in order of magnitude better, right? If it's not in order of magnitude better, then there's not enough of a motivation to get people to switch. And that's, and I think that's one of the things that's tricky with Airflow is that Airflow is like, is kind of annoying to work with, but the replacements aren't good enough. to make the switch, to pay the price to make the switch. And I think that's one of the things that these other orchestration companies are realizing is that like that is the hard, that's the hard part for sure.
Benjamin:
Right, gotcha. So looking back, right, when you started in 2014, you were also working on data pipelines to Tetra. It's not like data quality didn't matter back then, right? It was equally important in a sense. So how did you actually tackle these problems back then? Why do you only have these platforms, in a sense, emerging now?
Zach Wilson:
Yeah, I mean, also back then, like pipeline development was a lot slower. Like that was, I guess that was one of the things, you couldn't like, uh, yet at when I worked at Teradata, right. Uh, the pipelines we worked on, they didn't even have Hive yet adopted yet at Teradata, even though Hive existed, they didn't have it yet. So we just had to use Java MapReduce for everything. And so, uh, that's like where you essentially have to write your own. query engine parser thing. It feels like almost one layer down, kind of the work that you do. But you do it for every single job, right? And that's how it used to be. Right. And so, um, like, at least from the perspective of the pipeline development, that was a part of it, but the testing part, you're totally right in the fact that like after the big data part is crunched, that part has been pretty similar, right? Where it's like, then you have this pattern, right? You have a thing called, and I've seen this pattern like for my whole career, essentially. It's crazy, I've also seen it at companies that don't use this. Like Facebook actually was an exception to this pattern. So this pattern called write audit publish, where you have your big data pipeline right to a staging table, then you run your audits to test it. And then if they pass, you move the data from staging to production. And that's the contract, right? And the audits, are your guarantees. And that's where you can run some really lightweight kind of SQL queries or sample. Back then, we didn't even do it on the full data set. We just did sampling because the SQL, we didn't have Presto. We didn't have the nice distributed SQL queries. And we weren't going to write another MapReduce job to test the data that we already wrote with the other MapReduce job, which was so painful. So like what we did instead is like we just did sampling and we're like, okay, that's good enough. That's one of the things I like about this new world though is that like with these new technologies, you do get guarantees. Like you actually do get full guarantees of like this data is high quality. And so that's like where you do have this interesting, that's why I like this new world, even though there are so many tools and like I hate the proliferation of things because it's like, why can't we all just agree on something? ut I also think that I really like the up level and quality for sure.
Benjamin:
Right, so in a sense, the maturation of the space and some things becoming easier or less work lifts you up to not at the point where you can actually focus more on those quality aspects. Kind of.
Zach Wilson:
Mm-hmm.
Benjamin:
OK, interesting. Cool. All right. Nice. So yeah. Look, any kind of closing words on your end, right? Anything you wanted to talk about
Zach Wilson:
Yeah.
Benjamin:
that we didn't get to that.
Zach Wilson:
Yeah. Yeah, for sure. So, I mean, I think that there's a lot in this data engineering world that is important to remember. And, this is a couple of closing things that I think here are like, one is that getting into this field is not like data science. It's easier. And if you are someone who's considering getting into it, definitely, try it out. It's something that is maybe a six month to a one year road. Like you don't even really need a computer science degree. There's a lot of people who I know who are getting into data engineering with no degree or like an unrelated degree. And so you can really get pretty far into this field. If you could just get the right learnings and get the right teachings from the right people. And that is something that can really change your life because data engineering pays pretty well and it's going to be a big part of the future. There's a lot of job growth in this area. My opinion, it's actually the job that is going to see way more job growth than data science because people are realizing that a lot of the data science roles that they hired for were actually data engineering roles. And that's a big kind of shift that's happening is making data engineering grow really quickly. So yeah, that's my main closing thing is like, if you have any thoughts about breaking in and you want to try it, definitely try it. Yeah, for sure.
Benjamin:
Awesome, perfect. Then thank you so much for joining in, Zach. It was a pleasure having you.
Zach Wilson:
Awesome. Yeah, it was great being here.