Open Source Data Operations with Douwe Maan – Episode 56

Data Leadership Lessons
Data Leadership Lessons
Open Source Data Operations with Douwe Maan - Episode 56
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Douwe Maan was GitLab’s 10th employee and is now the founder and CEO of Meltano, GitLab’s spin-off data operations platform. His story is fascinating, and his passion for an open source approach to data ops is contagious! Enjoy this great conversation, and please share with your network!

Watch this episode on YouTube: https://youtu.be/XhnJAA_6wrc

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About Douwe Maan:

Douwe Maan is the founder and CEO of Meltano, an open source data operations platform. Born and raised in the Netherlands, Douwe has been around computers since a young age. While a college student, he began working at GitLab as their tenth employee. While working for GitLab, Douwe spent six months travelling the world, visiting colleagues in 14 countries on 5 continents.

In late 2019, Douwe joined the internal Meltano project at GitLab and quickly became its General Manager. In early 2021, Douwe led Meltano in spinning out of GitLab to become an independent startup while raising $4.2 million in seed funding, led by Alphabet’s GV. Today, Meltano is on its way to becoming the world’s first open source data operations platform.

Meltano Home: https://meltano.com

Meltano Slack Community: https://meltano.com/slack

Douwe Maan on Twitter: https://twitter.com/DouweM

Episode Transcript:

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douwe_maan: So I joined GitLab as employee number

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douwe_maan: ten. No idea where it could go. And for those who haven’t seen the news,

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douwe_maan: GetLab actually went public on the NASDAQ Stock Exchange in October, So it’s

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douwe_maan: been an incredible journey that I had the privilege of seeing from the front

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douwe_maan: lines from this very early stage.

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anthony_algmin: Welcome to the Data Leadership Lessons podcast. I’m your host, Anthony J.

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anthony_algmin: Algmin. Data is everywhere in our businesses, and it takes leadership to make

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anthony_algmin: the most of it. We bring you the people stories and lessons to help you

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anthony_algmin: become a data leader. Our show is produced by Algmin Business Media, where we

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anthony_algmin: make having your own video podcast as easy as joining a call and sending an

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anthony_algmin: email. The stage is yours. Visit Algmin.com to learn more. Today on

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anthony_algmin: again to day under Italyship to day on data leadership lessons. We welcome

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anthony_algmin: Douwe Maan. Douwe is the founder and C e O of Meltano, an open source data

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anthony_algmin: Douwe Maan. Douwe is the founder and CEO of Meltano, an open source data

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anthony_algmin: Douwe Maan. Douwe is the founder and CEO of Meltano, an open source data

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anthony_algmin: Douwe Maan. Douwe is the founder and CEO of Meltano, an open source data

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anthony_algmin: operations platform.

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anthony_algmin: While a college student he began working at

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anthony_algmin: Gitlab as their tenth employee. In late twenty nineteen, Doa joined the

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anthony_algmin: internal Meltano project at Git Lab and quickly became its general manager

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anthony_algmin: and early twenty twenty one. Dowa led Malano and spinning out of Git Lab to

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anthony_algmin: become an independent Startu while raising four point two million dollars in

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anthony_algmin: seed funding to day. Meltano is on its way to becoming the world’s first

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anthony_algmin: open source data operations platform. Dowa welcome to the show,

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douwe_maan: thanks. Anthony. Uh, very excited. beer.

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anthony_algmin: So like we do with all our first time guests, Just take a moment. Give us a

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anthony_algmin: little bit more of the context of your career, kind of where you came from

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anthony_algmin: and how what you studied in school and how it led to this amazing

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anthony_algmin: opportunity to work with Meltano work with Giitlab. And you’ kind of be ond

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anthony_algmin: this journey that you on. It’s amazing

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douwe_maan: Yeah, my pleasure. Um, you already hit a couple of the points in your your brief

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douwe_maan: summary, Uh, which I’m I know you were reading from my bio. Uh, but the story

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douwe_maan: really starts pretty at this point eighteen years ago when I was nine years old

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douwe_maan: and I got into programming because a friend of mine showed me that there were

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douwe_maan: some websites that explained how you could write code and would make things

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douwe_maan: appear in your web browser, which of course felt incredibly powerful. I still

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douwe_maan: does today, but especially at nine, as I’m sure you can imagine. Um. So

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douwe_maan: throughout my entire you know high school period from like eleven to eighteen, I

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douwe_maan: was doing freelance Web development. I figured out relatively quickly that this

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douwe_maan: skill that I was teaching myself Uh, was actually marketable and if you didn’t

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douwe_maan: tell anyone how young you were, they were actually willing to pretend that you

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douwe_maan: were an adult. They could go in business with Uh. Even, Um, when I was sixteen,

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douwe_maan: I ended up joining an IOS and Mac at the development studio at

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douwe_maan: Engineering leads, Um, and two years later with one of my founders or one of my

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douwe_maan: bosses. At the time I ended up founding a start up, Uh, when I was eighteen

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douwe_maan: where we were building software for bed and breakfasts to manage their

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douwe_maan: reservations, their guest communications, their website, et cetera, Um, And it

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douwe_maan: is through this company that I, ▁ultimately, ended up at Gitlab, Um. During my

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douwe_maan: time at this, this started I had. then I started studying computer science and

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douwe_maan: the Netherlands. Uh, I quickly realized that I didn’t necessarily need to give

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douwe_maan: that my full time attention because I’d already picked up so much of along the

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douwe_maan: way, so throughout my entire pes, um degree, I tried to go to as many

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douwe_maan: conferences around Ruby on Rails, the programming language that I was working

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douwe_maan: most with Eth time as I could. At some point I was in Ethens at a the European

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douwe_maan: Ruby conference and over lunch I ended up talking to a guy who was also standing

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douwe_maan: by himself at a table and introduced myself and I told him that I worked for

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douwe_maan: well the business I had, and that I was from the Netherlands. Um, and he ended

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douwe_maan: up pointing me towards his boss, who was in the other corner of the room,

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douwe_maan: telling him his boss were Dutch as well, and that we should go chat. So I

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douwe_maan: introduced myself to his boss and I told him about this company I had or rebuilt

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douwe_maan: Soware, Ben and Breakfasts, and as it turned out, this person I was talking to

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douwe_maan: was a s. C brandy. Now the C, e o and co founder of Getlab and his parents had a

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douwe_maan: bed and breakfast in the north of the

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douwe_maan: Netherlands, so just through this kind of chance, Uh meeting at a conference in

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douwe_maan: Athens, Um, he and I stated touch. He got an idea of what I was capable of

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douwe_maan: building even at a relatively young age, and we kept running into each other at

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douwe_maan: different meet ups and conferences around Europe and the netherences over the

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douwe_maan: coming months, to the extent where he asked me one day if I uh, wanted to come

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douwe_maan: join Get Lap, which at the time was a still mostly an open source project with a

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douwe_maan: community of several hundred open source contributors, but there was already a

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douwe_maan: team of about six in full time employees at the time trying to build a company

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douwe_maan: around. This, uh, this software for software development teams. At the time, I

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douwe_maan: was still very happy with my own start up and I, uh, I told Sid I wasn’t

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douwe_maan: interested six months later in January of Uh, twenty sixteen, not doing the

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douwe_maan: mathroon January of twenty fifteen, Um,

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douwe_maan: I reached out to sit again and I told him that Uh, my situation had changed and

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douwe_maan: he basically hired me on the spot And this was right when Getlap was going

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douwe_maan: through the White Combinator, a accelerator program in Mountain

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douwe_maan: View, California, Um, and gitlap was ten per people when I joined and about to

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douwe_maan: View, California, Um, and gitlap was ten per people when I joined and about to

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douwe_maan: ha, raise its first seed round of funding. So I joined GitLab as employee number

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douwe_maan: ha, raise its first seed round of funding. So I joined GitLab as employee number

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douwe_maan: ten. No idea where it could go And for those who haven’t seen the news,

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douwe_maan: Getitlab actually went public on the Naste Stock Exchange in October, So it’s

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douwe_maan: been an incredible journey that I had the privilege of seeing Uh from the front

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douwe_maan: lines from this very early stage. Um. During my time at Get Lab really quickly,

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douwe_maan: lines from this very early stage. Um. During my time at Get Lab really quickly,

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douwe_maan: I became the first engineering lead and manager at Getlab Uh, I ran the at the

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douwe_maan: time fourteen people engineering team for the first uh year or so. Um. And as

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douwe_maan: the company kept growing, I, I stayed in engineering leadership, and Um,

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douwe_maan: learning what it took for my team to be productive and learning also how the

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douwe_maan: tool that we were building get like Arself. was enabling teams to adopt these

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douwe_maan: devops === best practices in a way, Uh, that was never possible, or s as accessible

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douwe_maan: before. At some point though, when gitlap had reached fourteen hundred people, I

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douwe_maan: before. At some point though, when gitlap had reached fourteen hundred people, I

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douwe_maan: was starting to feel that itch of of wanting to work at a smaller company again,

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douwe_maan: a a more um, nascent project where you can have a really massive impact with

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douwe_maan: Um, Yeah, I, I wanted to join a smaller project again, Uh, and I thought that

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douwe_maan: that would require me leaving Get lab. Which I was hard pressed to do because of

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douwe_maan: getlabs kind of unique approach to all remote work, and a lot of flexibility

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douwe_maan: around how people combine their work

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douwe_maan: and their uh life and the freedom to travel, which is something that I can talk

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douwe_maan: about a little bit more, which has been really important. Uh in in my life so

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douwe_maan: far, Um, I live in Mexico City, for what it’s worth, not in the netherences

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douwe_maan: anymore. Uh, so there’s a story there, too, So I was at this point where I

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douwe_maan: thought I would need to leave Getlab to find what I was looking for, and this

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douwe_maan: kind of unique opportunity came up to join the Melano project, which was an

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douwe_maan: internal project in Getlab that was founded into Thousand Eighteen Um by the Get

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douwe_maan: Up Data team, who realized that a lot of these software development best

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douwe_maan: practices that they saw day and day out in the internal team with Ki Lab, as

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douwe_maan: well as at Getlap’s customers, could similarly benefit data teams. Uh, and make

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douwe_maan: them more collaborative, give them higher confidence to experiment and try stuff

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douwe_maan: out without fear of breaking things, and in the end increased their confidence

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douwe_maan: in the results of of the work that they do, and uh the data that their business

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douwe_maan: Uh, uses to make better decisions and and and improve their products. So I

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douwe_maan: joined Notano, Um,

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douwe_maan: kind of to bring that software engineering experience into the The data tool

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douwe_maan: space. Um. At the time, Montana had been around S for about a year and a half,

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douwe_maan: and even though we had this broad vision of what it could be, we had not really

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douwe_maan: been able to find detraction and the enthusiasm in the community that we were

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douwe_maan: looking for, so the decision was made at the time to um take the six person team

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douwe_maan: that existed and basically reduced the head countt on the project from six

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douwe_maan: people down to one. To give ourselves a little bit more runway, to figure out if

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douwe_maan: we could piv it and and find something that would really resonate with these

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douwe_maan: data, people that we were trying to uh to empower Um. So in early twenty twenty,

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douwe_maan: I was left on the Montana project by myself. Uh, and I had to figure out

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douwe_maan: relatively quickly how to turn it Ro. uh. Fortunately I did. We can talk about

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douwe_maan: that more at length, the in duration of the call, Um, to the extent that by the

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douwe_maan: end of twenty twenty, Gitlab had enough confidence in the project that I could

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douwe_maan: bring some more people onto the team, So I hired Um. one of the original people

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douwe_maan: that had been involved with the Moltana project. Internally, it get Lap, Tayor,

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douwe_maan: Murphy was our head of product and data, as well as one of the most active

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douwe_maan: contributors from the open source community. A ▁j steers, who was now our head

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douwe_maan: of engineering, and around this time I was talking to the Gillap C Eo, about

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douwe_maan: where G. Notana could go, and I, um, we realized we came to the conclusion that

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douwe_maan: in order for Moltana to really reach its full potential, spitting it out as an

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douwe_maan: independent Startu, raising some external funding and really being able to go

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douwe_maan: all in on this vision we had, without being in some sense limited in our

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douwe_maan: aspirations, because of the bigger machine that get up had become. I was the

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douwe_maan: best path forward, so in May we spun out of GitLab, Uh, which we announced in

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douwe_maan: June, and the team has now grown to ten people, and we have a lot of plans for

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douwe_maan: the rest of the year.

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anthony_algmin: thats. it’s such a cool story already, and I feel like we’re just at the

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anthony_algmin: very beginning of where this is going to hand. And there are so many things

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anthony_algmin: that idlicateated a candy store right now now, because this is the world I

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anthony_algmin: live in and I’ve seen in different perspectives. Um, and it doesn’t really

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anthony_algmin: surprise me that Meltao as part of of Git lab, and and with G it it’s all

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anthony_algmin: that is a different side of the house. Traditionally, which is the side of

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anthony_algmin: the house I come from? Is the data spacease right? And like we couldn’t get

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anthony_algmin: people to, do you know, code management and checkens, let alone a more

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anthony_algmin: comprehensive collaborative uh of way of working. So there’s some things

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anthony_algmin: that I want to dig into on that as well, but I can definitely see where it

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anthony_algmin: once. Given a little bit of distance from that application development side

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anthony_algmin: of the house, the code development side of the house, where if you can

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anthony_algmin: evolve what you’re offering in Meltono to really fit the data community,

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anthony_algmin: I could see it being tremendously valuable, and I’m seeing in in a number of

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anthony_algmin: different capacities. Uh, the the notion and and the practices around Data

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anthony_algmin: Os really starting to hit stride now, so I think from a market perspective,

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anthony_algmin: Uh, the the world is your oyster. You have so much potential here, so can

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anthony_algmin: for the people who are not as detailed and familiar with what even uh Gitlab

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anthony_algmin: does. Can you talk a little bit about what the origin of Meltao was? And and

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anthony_algmin: kind of where how its spun out of that? What? What get liberally does? What

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anthony_algmin: Meltao kind of was in the very beginning, and kind of where you see it going

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anthony_algmin: from here. And and what your kind of unique market, uh position is going to

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anthony_algmin: be with this company

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douwe_maan: Yeah, absolutely, and I agree with you that the the timing for day ups tooling

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douwe_maan: has has never been better. So we are a really nice infction point here and how

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douwe_maan: we think the data space will look, and the unique background in Get Lab that we

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douwe_maan: have, I think will bring significance, um, gap value to the table for us to

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douwe_maan: actually make this happen, So to provide that context that can be useful for the

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douwe_maan: audience, Get lab, Um is a an endtentth platform for the entire software

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douwe_maan: development life cycle, So if you have a company that builds software and this

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douwe_maan: can be a dedicated tech company, or just a the software division at any large

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douwe_maan: company. These

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douwe_maan: days that maintains some amount of internal toling you are talking about

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douwe_maan: software engineers. you probably have product managers. You have Um, user

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douwe_maan: interface designers and a whole bunch of different disciplines that come

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douwe_maan: together to build high quality software. And there’s a life cycle to the way

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douwe_maan: that this process works. Where you usually start with some kind of idea, or you

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douwe_maan: get user feedback on something you want to build. Then the next step is to spck

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douwe_maan: what this can look like in terms of additional functionality in the product that

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douwe_maan: you’re building. Then there is usually some stage where Um, other stakeholders

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douwe_maan: like these interfacees, designers come in to flash out this this idea, and then

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douwe_maan: there’s the development state where the actual software engineers gets started

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douwe_maan: and software engineers don’t just start writing code and then finish by the end

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douwe_maan: of the day, and everything’s perfect. It is a usually iterative process

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douwe_maan: where it’s starting from fundamentals, making many, many, many changes, and

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douwe_maan: trying to get incrementally closer to this this goal and in order to have a lot

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douwe_maan: of confidence in what you’re building. Uh, both it meeting the requirements and

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douwe_maan: you’re not accidentally breaking things. Um, you

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douwe_maan: need tooling that helps with change management. that helps collaborate in teams

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douwe_maan: of multiple engineers. It helps with automatic validation of Um, the Code of

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douwe_maan: Britain, and, and making

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douwe_maan: sure that functionality rorote last year doesn’t accidentally break Because you

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douwe_maan: made a change somewhere that has some in in unattended impact. And then there

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douwe_maan: are further stages where through the process of writing this code and getting it

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douwe_maan: to that level where it passes some checks, you can automatically deploy it onto

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douwe_maan: Um, users devices, or the the The website where users will get to interact with

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douwe_maan: it, And this is a life cycle because then you usually go back to the first step

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douwe_maan: again. You start working on your next idea or an improvement to the original

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douwe_maan: idea, and Glab is a platform that brings all of this functionality that Soware

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douwe_maan: development teams need to collborate into one massive platform that then becomes

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douwe_maan: kind of like an operating system or the one tool a software Hadp team lives in

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douwe_maan: Um. That enables them to build really high quality of software together, Um, and

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douwe_maan: do so more quickly than Uh. Without you know the help the platform provides. So

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douwe_maan: Gitlab came from an open source project called Gitlab, built around an open

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douwe_maan: source technological gift, which is also the open source ofology, that Getis Hub

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douwe_maan: is built around which you might be familiar with. They were acquired by

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douwe_maan: Microsoft a number of years ago and they do something very similar, but there is

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douwe_maan: a significant difference in that Github, itself, Um, was a proprietary product

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douwe_maan: that you buy and you pay for and new use and you can use it within your own

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douwe_maan: organization. While Gitlab from the one has been open source, so all of the

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douwe_maan: source code has always been available for free to the whole world, And it was

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douwe_maan: very much a collaborate effort, collaborative effort of a really large software

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douwe_maan: development community around the entire world that came together to work on this

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douwe_maan: tool together because they wanted it themselves. they knew it would benefit

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douwe_maan: others, and they saw an opportunity to bring all of their own ideas and

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douwe_maan: perspectives and and mistakes of the past, Uh together to build their ideal

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douwe_maan: platform to make themselves more productive, Um. And very much, building on this

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douwe_maan: philosophy of building tools together with the users of those tools, and the

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douwe_maan: philosophy of Um, enabling more effective collaboration by having automated

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douwe_maan: tests, by having some amount of of change management and code reviews that

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douwe_maan: people can make changes with confidence that they won’t break anything, Because

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douwe_maan: someone else in the team can come in and bring in feedback. The data team at

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douwe_maan: Moano, rather, the data team at Kidlap, realize that these were qualities that

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douwe_maan: data teams would similarly benefit from, and Um. They decided to start building

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douwe_maan: open source data tools that would try to encompass the entire data life cycle

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douwe_maan: and Um, do so while bringing in some of these Deth Os development operations

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douwe_maan: best practices that I’ve talked about a second ago like Version Control Code

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douwe_maan: review and continuous integration and employment, which is fancy words. that

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douwe_maan: basically mean automated testing and nothing goes life until all the check boxes

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douwe_maan: are checked, Um, and wrapping this all up in in in tolling, that would

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douwe_maan: revolutionize the data spacease in the same sense, Deaf Ose software

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douwe_maan: development.

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anthony_algmin: that’s awesome.

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anthony_algmin: Just so,

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anthony_algmin: can you talk a little bit about the differences and the way data people work

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anthony_algmin: versus the software engineering side of the house, And like what you need to

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anthony_algmin: address differently is melt versus what Getitlap would do for first

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anthony_algmin: softwareers

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douwe_maan: Yeah, great question. uh, there are a number of differences that are relevant,

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douwe_maan: but the thing that surprised me the most is how similar the state of the data

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douwe_maan: spacease today is with the tooling available to it and the way teams collaborate

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douwe_maan: to where the software development space was maybe ten years ago. And looking

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douwe_maan: back at what Gitlap is doing, we realized that very early on, the biggest

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douwe_maan: challenge wasn’t necessarily in convincing companies to use Getlab for their

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douwe_maan: software development life cycle. The problem was convincing them that they

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douwe_maan: wanted develops at all that they wanted automated testing in automated

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douwe_maan: deployment at all, and that the waterfall based way of working in the past,

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douwe_maan: where it’s very much about different disciplines doing their work to perfection

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douwe_maan: and handing it over to the next team. Um didn’t work, and with the agile

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douwe_maan: methodogies of collaboration and and project management finding ground in

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douwe_maan: software development, the types of tooling that these teams needed also changed.

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douwe_maan: but in the beginning it getlap. we still to convince people they wanted the

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douwe_maan: stuff at all, and to these days that is kind of a given. and we see something

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douwe_maan: similar happening with data ups Where the challenge today isn’t even so much

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douwe_maan: about convincing people they want Montana, but convincing them that they won Da

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douwe_maan: Dos at all. It really means, which really means convincing data teams that they

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douwe_maan: are far more similar to software development teames then they might even

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douwe_maan: realize, and that they would actually benefit from Steing the work day. Do and

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douwe_maan: uh, you know the the data stack that they build by picking different tools and

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douwe_maan: lookoking them together and building custom components, And then you know having

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douwe_maan: a dashboard or some kind of notebook that a data scientist or Da liist works in,

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douwe_maan: We want them to see that this is way more similar to product building than they

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douwe_maan: might realize, And their data Stck is not just a combination of of purchasing

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douwe_maan: decisions, Like I’ll choose these five products and that’s to will use forever.

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douwe_maan: It is really you, building a bespoke piece of software of a spoke product suited

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douwe_maan: to the needs of the specific organization where your users are, your, your, your

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douwe_maan: colleagues in the company that wanna somehow benefit from the data that you make

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douwe_maan: available to them.

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douwe_maan: The features of the product are any way that they have of interacting with it.

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douwe_maan: So those are visualization methods, Uh, the U, the B, I, and Ellythtic tools and

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douwe_maan: Um. opportunities to run. You know, Jupiter, Notebooks and the data team, The

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douwe_maan: data engineers and analytics engineers. Their task is really to build a product

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douwe_maan: and continuously improve it day after day to more successfully meet Um, the

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douwe_maan: needs of their colleagues and the goals of the organization, and all of these

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douwe_maan: advantages of software development that have talked about where you wa to have

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douwe_maan: really high confidence that when you want to bring make a change, you don’t

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douwe_maan: accidentally break something down the line. A software development and defils

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douwe_maan: have answers to that that the data spacease and data tooling today isn’t really

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douwe_maan: set up for, because many times you end up with some Sas software running in your

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douwe_maan: browser and you make a change there. and the I. the effect immediately, Um

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douwe_maan: affects. You know. your c, f O was about to do a board meeting and wants to

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douwe_maan: present their dashboard, And if you accidentally made typo, then the live

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douwe_maan: Dashbard will be broken. So coming from the software development world, for, I

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douwe_maan: really haven’t been much exposed to data teams and the way they work until I

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douwe_maan: started looking into Milanno, and then I had this opportunity to join the team.

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douwe_maan: I was really surprised to see that the parallels to me were obvious. Data teams

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douwe_maan: themselves might not even realize that those barriels existed, but for me this

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douwe_maan: was just an opportunity to bring all of these ten years worth of lessons from

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douwe_maan: software development and the specific lessons we had learned at get building

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douwe_maan: this open source developer tools, Um. For and by software engineers into the

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douwe_maan: data spacee, Um, some of the differences, though one big difference is that over

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douwe_maan: the last five to ten years the data spacee has become incredibly horizontally

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douwe_maan: integrated when it comes to the tooling, so gone are the days of these big

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douwe_maan: integrated intertuent data platforms that companies were using them fiftyft

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douwe_maan: years ago, and the day companies are still using today. Instead, you have the

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douwe_maan: modern data stack where you make like six or seven tooling decisions to try to

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douwe_maan: get the best word, the task at hand, And then there iss a challenge for a

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douwe_maan: specific data team in hooking these up together and and making it all work

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douwe_maan: smoothly. Um,

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douwe_maan: what that means is that this vision we had of building an intuent platform for

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douwe_maan: the data life cycle cannot be realized just by building one massive tool and

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douwe_maan: saying, replace everything you currently have with this new thing Because as a

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douwe_maan: consequence of this versontal integration at every step of the life cycle at

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douwe_maan: every layer of the data stack, there are so many kind of really great tools

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douwe_maan: competing, and and focused so narrowly on a really specific problem to be

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douwe_maan: solved, that a single product can never really claim to do it all better or

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douwe_maan: even. Good enough. compared with the tools that spend every day just a training

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douwe_maan: on a particular area with Giitlab. the situation was a little bit different ten

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douwe_maan: years ago. Um, because there wasn’t really any platform that try to.

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douwe_maan: well, First of all, there wasn’t the platform that tried to do everything for

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douwe_maan: the entire dietal life cycle, But the horizontal integration also hadn’t evolved

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douwe_maan: to the extent that you have multiple really great competing solutions in every

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douwe_maan: step. So Gitlap’s approach was to just build one entertent platform and all of

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douwe_maan: the contributors in an open source ecosystem to contribute to this one tool. and

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douwe_maan: then we will build something amazing and get the best been able to do that in

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douwe_maan: data, because things are a little bit more fragmented and horizontally

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douwe_maan: integrated. Our opportunity is different. Uh. We have realized that in order to

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douwe_maan: bring develop best

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douwe_maan: practices to data which you can summarize as data, Os, uh, what’s really needed

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douwe_maan: is a new layer in the data stack, a foundational layer. If you will, would like

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douwe_maan: to call ourselves the foundation of Every’s ideal datatack, Because we

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douwe_maan: acknowledge that every’ data Se, a complete difference is going to look

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douwe_maan: different today versus six months from now and five years from now. All of the

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douwe_maan: tools might have been swapted out, but there is still advantage in having some

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douwe_maan: um part of that stack that brings it all together and allows teams to interact

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douwe_maan: with it as if they are building one data product that they can easily you know,

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douwe_maan: run and test locally without accidentally affecting production. They can have

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douwe_maan: automated tests running somewhere in their Devil platform, so that they will see

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douwe_maan: an error message that accidentally broke something before actually getting into

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douwe_maan: production. But all of this requires this, this loose combination of tools to

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douwe_maan: still have some foundational area that that brings it together and allows it to

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douwe_maan: be treated as one unit, as that’s a massive difference in the approach that we

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douwe_maan: are taking with Notano as a modular open source data platform versus a Getitlap

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douwe_maan: which was an all in one open source De platform.

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anthony_algmin: So

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anthony_algmin: oh man, there’s so much that I could get out of my soap box about in what

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anthony_algmin: you about and I, I will just say, I will just say. I absolutely agree that

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anthony_algmin: it. It seems like it’s history repeating itself, but in the datase versus

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anthony_algmin: the software engineering space, and I would argue that even for the last

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anthony_algmin: several decades, the walls that have differentiated the data spacease from

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anthony_algmin: the software engineering space have been crumbling slowly but surely, and

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anthony_algmin: that there are things that have kind of thrown the line over that wall. I

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anthony_algmin: think of things like No sequel, which is now the domain of more data people

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anthony_algmin: than it used to be. where the data people were like. Well, we relational.

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anthony_algmin: this no sequel business, The application, folks that are literally had

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anthony_algmin: created it to do the kinds of things that the Web needed to be done. The

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anthony_algmin: search and retrieval of you. a lot of contextual information related to a

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anthony_algmin: specific account or specific person, specific entity that you know. it helps

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anthony_algmin: drive their engagement with that uh application, Uh, the web application or

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anthony_algmin: internet site. Um, you know it was. It was born out of necessity and

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anthony_algmin: eventually people on the data side are like. You know what. That’s pretty

354
00:23:10,708 –> 00:23:14,375
anthony_algmin: good at that particular thing. Like, Let’s understand what that happens and

355
00:23:14,458 –> 00:23:17,333
anthony_algmin: more data. People have gotten exposure to no sequal and have started to

356
00:23:17,333 –> 00:23:20,708
anthony_algmin: expand the horizons if you see similar patterns taking place with knowledge,

357
00:23:20,875 –> 00:23:25,250
anthony_algmin: graphs, and and other things that are the domain. Traditionally, I would

358
00:23:25,250 –> 00:23:28,875
anthony_algmin: argue of the application and software development side of the house, but I

359
00:23:29,000 –> 00:23:34,000
anthony_algmin: certainly agree with your assertions that the data space, if we cast a

360
00:23:34,000 –> 00:23:37,333
anthony_algmin: slightly different view than maybe the the data people traditionally would,

361
00:23:37,666 –> 00:23:40,208
anthony_algmin: especially enterprise data. People would have traditionally thought of

362
00:23:40,375 –> 00:23:44,875
anthony_algmin: themselves as it really is a software engineering application development

363
00:23:45,000 –> 00:23:48,791
anthony_algmin: type of function with just a few differences in what they’re working with.

364
00:23:48,958 –> 00:23:53,250
anthony_algmin: The their clay is different, but there’re still sculptors, and so I do find

365
00:23:53,416 –> 00:23:59,916
anthony_algmin: it. It’s very comparable and interesting to think about this as a. You know,

366
00:24:00,125 –> 00:24:02,708
anthony_algmin: it’s in some ways it it. It’s bringing together

367
00:24:03,833 –> 00:24:08,000
anthony_algmin: what are two sides of the coin already, and and just using more similar

368
00:24:08,291 –> 00:24:12,625
anthony_algmin: tools. Hey, we can learn from the software engineers who have solved this

369
00:24:12,791 –> 00:24:17,250
anthony_algmin: particular set of challenges before were not good at process and operations

370
00:24:17,416 –> 00:24:20,875
anthony_algmin: in the Dat spacease, where where we think about things andway it’s like we

371
00:24:21,000 –> 00:24:25,500
anthony_algmin: write ▁queries, using the same kind of iterative process that software

372
00:24:25,666 –> 00:24:29,833
anthony_algmin: engineers use to write code. But we don’t acknowledge the fact that there’s

373
00:24:30,000 –> 00:24:34,000
anthony_algmin: value in understanding those iterations. We try to make it so that that

374
00:24:34,125 –> 00:24:38,875
anthony_algmin: ▁querier those analytics get to the right answer, but then oftenims and this

375
00:24:38,875 –> 00:24:42,625
anthony_algmin: is where I do think there’s a difference oftenimes we. We don’t see the

376
00:24:42,791 –> 00:24:48,291
anthony_algmin: result from that until much later when a c f O is looking at two reports

377
00:24:48,458 –> 00:24:51,666
anthony_algmin: where the number should be the same and they’re different. Like, why is that

378
00:24:51,833 –> 00:24:55,000
anthony_algmin: and then you realize, Oh well, it, it was calculated differently, or the

379
00:24:55,166 –> 00:24:59,916
anthony_algmin: differences, s, or whatever it was. But there’s often that time delay

380
00:25:00,041 –> 00:25:03,666
anthony_algmin: because data starts to hide underneath the surface, which actually makes it

381
00:25:03,833 –> 00:25:08,708
anthony_algmin: that much more important to have rigor in the process of developing it.

382
00:25:09,000 –> 00:25:13,083
anthony_algmin: Because you want to catch these things up front. otherwise they remain

383
00:25:13,416 –> 00:25:16,791
anthony_algmin: buried. You’re not going to get that immediate feedback. Hey, we’re we’re

384
00:25:16,875 –> 00:25:21,333
anthony_algmin: going to do this presentation and Oh, the thing just broke. You’re going to

385
00:25:21,500 –> 00:25:25,916
anthony_algmin: find out later after you’ve already reported financial results to the market

386
00:25:26,208 –> 00:25:29,416
anthony_algmin: that now you’re going to have to go and restate things. Well, what’s more

387
00:25:29,666 –> 00:25:30,666
anthony_algmin: problematic?

388
00:25:31,500 –> 00:25:35,666
anthony_algmin: That kind of big problem that goes unseen for a long time or something, that

389
00:25:35,750 –> 00:25:39,666
anthony_algmin: ruins your morning with a bad demo, but can immediately be fixed and then

390
00:25:39,750 –> 00:25:42,208
anthony_algmin: you can move forward, And that’s what we want to do Is we want to reduce

391
00:25:42,375 –> 00:25:45,000
anthony_algmin: that cycle time. We want to get it to the point where we know immediately.

392
00:25:45,166 –> 00:25:49,250
anthony_algmin: Hey, we have a problem here. Let’s fix it versus having it be buried and

393
00:25:49,333 –> 00:25:51,166
anthony_algmin: then come up at the worst possible time. Later

394
00:25:51,875 –> 00:25:54,833
douwe_maan: Yeah, absolutely, and I would say that, Um,

395
00:25:56,041 –> 00:25:57,541
douwe_maan: things like having

396
00:25:58,750 –> 00:26:02,583
douwe_maan: consistent definitions of metrics so that you don’t have to. You know

397
00:26:02,666 –> 00:26:06,416
douwe_maan: contradictory numbers in the final dashboard and things like lineage, being able

398
00:26:06,500 –> 00:26:11,541
douwe_maan: to trace all the way back where some data point came from are um, go hand in

399
00:26:11,625 –> 00:26:15,875
douwe_maan: hand with this kind of platform way of thinking about data products like the

400
00:26:15,958 –> 00:26:21,458
douwe_maan: best data lineage tools today. Unfortunately, are these all in one platform? Is

401
00:26:21,541 –> 00:26:24,500
douwe_maan: that really do have everything under one roof, because they can easily relate

402
00:26:24,666 –> 00:26:28,333
douwe_maan: one day to point they to there. But if you are putting together a modern datatap

403
00:26:28,416 –> 00:26:31,791
douwe_maan: with five toling decisions, it becomes really difficult to have that information

404
00:26:32,166 –> 00:26:36,500
douwe_maan: be easily traceable between through the entire life cycle, so Motana, being kind

405
00:26:36,500 –> 00:26:40,416
douwe_maan: of an extrafational layer that has full visibility in entire datatack and all of

406
00:26:40,416 –> 00:26:45,083
douwe_maan: the different components also allows us to realize functionity like this that

407
00:26:45,208 –> 00:26:50,583
douwe_maan: previously would be hard to to realise in combination with the fact that since

408
00:26:50,833 –> 00:26:55,791
douwe_maan: we want to get data into the habit of versioning their change just and being

409
00:26:55,958 –> 00:26:59,291
douwe_maan: able to you know, describe with a Quickmit message why you made a certain

410
00:26:59,541 –> 00:27:03,291
douwe_maan: change, it will also be much more easy to trace back a particular error that

411
00:27:03,291 –> 00:27:07,208
douwe_maan: arose to the specific change that triggered it, which you can then trace back to

412
00:27:07,208 –> 00:27:10,125
douwe_maan: the Cod review conversation, where it was you know, signed off by another team

413
00:27:10,250 –> 00:27:13,458
douwe_maan: member, and they. Can trace expected specific issue that that prompted that

414
00:27:13,541 –> 00:27:17,708
douwe_maan: change to begin with, and just as this lineage it doesn’t just go through the Li

415
00:27:17,958 –> 00:27:21,625
douwe_maan: data through the data pipeline all the way to the point of data ingestion, but

416
00:27:21,708 –> 00:27:25,458
douwe_maan: actually also goes into the the meta level of the team. That that made this

417
00:27:25,625 –> 00:27:32,166
douwe_maan: change, and why is extremely powerful and seeing Um, your your data pipeline as

418
00:27:32,250 –> 00:27:36,583
douwe_maan: a type of software, Um, all the way from the ingestion piece to the the

419
00:27:36,583 –> 00:27:41,000
douwe_maan: Dashboard report definition, and the sequelqueries in Robia application, Um, is’

420
00:27:41,125 –> 00:27:44,250
douwe_maan: something that the software development space ten years ago hadn’t really caught

421
00:27:44,333 –> 00:27:47,291
douwe_maan: on too yet either, Like I remember when I started Know programming almost

422
00:27:47,375 –> 00:27:51,458
douwe_maan: eighteen years ago. now that I would just sign in over f t, p. To some life

423
00:27:51,625 –> 00:27:56,416
douwe_maan: production web serveurr download a ph P file, Make changes uploaded back and

424
00:27:56,500 –> 00:27:59,875
douwe_maan: then go through the main website to see if I did break something. and now it

425
00:28:00,333 –> 00:28:04,666
douwe_maan: unthinkable, but to me the way a lot of things work in Data Land still look like

426
00:28:04,916 –> 00:28:09,458
douwe_maan: something that I thought of as something of the past. But what I want to say?

427
00:28:09,625 –> 00:28:12,666
douwe_maan: what I wa to add to, kind of ask some coolor to what you were saying earlier

428
00:28:12,833 –> 00:28:16,250
douwe_maan: about Wow, Data can learn from software development. I think in the other

429
00:28:16,500 –> 00:28:20,666
douwe_maan: direction too. I think there’s a big opportunity for software engineering teams

430
00:28:20,833 –> 00:28:25,291
douwe_maan: and data teams to start feeling more like you said. Like kind of two sites of

431
00:28:25,291 –> 00:28:29,458
douwe_maan: the same coin, or or way more similar than they are different, Um, data

432
00:28:29,708 –> 00:28:33,083
douwe_maan: engineering and and data work more as a subset of software engineering and

433
00:28:33,083 –> 00:28:36,666
douwe_maan: anything else, Because what I’m also seeing is that software engineers that need

434
00:28:36,833 –> 00:28:41,708
douwe_maan: to do things like pulling data from various Ap, is transforming it in some way,

435
00:28:41,875 –> 00:28:46,125
douwe_maan: and in presenting it to the user in U. I. They don’t know what the right terms

436
00:28:46,250 –> 00:28:50,666
douwe_maan: are to even Google to find the E t, ▁l solutions that data teams have been

437
00:28:50,750 –> 00:28:55,208
douwe_maan: working with for decades, and we are trying to build software that can tool that

438
00:28:55,291 –> 00:28:58,750
douwe_maan: can really narry these worlds that will feel at home. The software developers

439
00:28:58,916 –> 00:29:02,583
douwe_maan: who already know that the only kind of Datap plan they would ever build is one

440
00:29:02,666 –> 00:29:06,583
douwe_maan: that’s going to be viual, controllable and festible, and at the same time

441
00:29:06,750 –> 00:29:10,750
douwe_maan: bringing these data engineers into these expectations by showing how how

442
00:29:10,916 –> 00:29:14,500
douwe_maan: beneficial has been for software, and then a place where we end up, hopefully is

443
00:29:14,666 –> 00:29:18,416
douwe_maan: where every software engineer will be able to be more producactive with data

444
00:29:18,583 –> 00:29:24,916
douwe_maan: work, and Everyda engineer will also feel more empower to Um, use software to to

445
00:29:25,125 –> 00:29:29,708
douwe_maan: you know, automate things beyond just writing data pipelines, because, just a

446
00:29:29,791 –> 00:29:33,708
douwe_maan: mental aspect. Of, I’m software engineer, your data person creates some

447
00:29:34,041 –> 00:29:38,125
douwe_maan: expectation in those people’s heads of what I can and can do, and and where the

448
00:29:38,125 –> 00:29:42,916
douwe_maan: limits of my skills and I think that’s completely unnecessary. And um, there’s

449
00:29:43,125 –> 00:29:47,958
douwe_maan: so much to be gained from these worlds kind colliding and you combining.

450
00:29:49,583 –> 00:29:53,500
anthony_algmin: couldn’t agree more, and and time spent trying to understand at a deeper

451
00:29:53,666 –> 00:29:56,000
anthony_algmin: level, what the other side of that coin

452
00:29:56,208 –> 00:30:00,000
anthony_algmin: does is really important. I’ll never forget. early in my consulting career,

453
00:30:00,291 –> 00:30:04,000
anthony_algmin: I was more on the data side. I came from a business intelligence and and you

454
00:30:04,000 –> 00:30:07,000
anthony_algmin: know, data warehousing world where we’re doing data modeling, and like,

455
00:30:07,250 –> 00:30:11,250
anthony_algmin: there’s a lot of complexity in creating these massive calculations and

456
00:30:11,416 –> 00:30:14,875
anthony_algmin: models to do things like financial reporting and things like that. There’ a

457
00:30:14,958 –> 00:30:18,041
anthony_algmin: lot of complexity there. And and I was really familiar with that and I

458
00:30:18,041 –> 00:30:21,000
anthony_algmin: didn’t note the software engineering side of the house as well, but I was

459
00:30:21,000 –> 00:30:24,208
anthony_algmin: working in a consulting firm where we had our our Aptdev team who were

460
00:30:24,375 –> 00:30:27,583
anthony_algmin: talking about writing to Ap I, which I knew a little bit about. But I hadn’t

461
00:30:27,666 –> 00:30:32,041
anthony_algmin: done a ton of in the data spacease at that time, and and I remember them

462
00:30:32,291 –> 00:30:36,458
anthony_algmin: just completely dismissing how complicated the data warehousing component

463
00:30:36,625 –> 00:30:38,875
anthony_algmin: was were like Well, we just need to pull out the data, but the data part’s

464
00:30:38,958 –> 00:30:41,750
anthony_algmin: easy, so we don’t have to worry about that and I’m like wait, wait. it is

465
00:30:41,916 –> 00:30:45,250
anthony_algmin: not easy. It is not something that you can just dismiss, But that’s human

466
00:30:45,416 –> 00:30:50,041
anthony_algmin: nature. We tend to devalue or diminish the complexity of the things that we

467
00:30:50,125 –> 00:30:54,708
anthony_algmin: don’t understand because we’re humans like. That’s just a fundamental trait

468
00:30:55,000 –> 00:30:59,750
anthony_algmin: that we have, but I do feel like when it comes to being a data, uh, person

469
00:31:00,125 –> 00:31:04,000
anthony_algmin: or a software engineering person and finding ways to realize that Hey, these

470
00:31:04,125 –> 00:31:09,166
anthony_algmin: are all interconnected. These are all just different facets of the same

471
00:31:09,666 –> 00:31:13,916
anthony_algmin: collection of challenges that we face to using data to improve our

472
00:31:14,000 –> 00:31:18,125
anthony_algmin: businesses. That’s really what data leadership is all about, and so having

473
00:31:18,958 –> 00:31:22,625
anthony_algmin: that awareness is a great first step. So, for anybody who’s listening and

474
00:31:22,708 –> 00:31:25,083
anthony_algmin: these concepts and the things that we’re talking about today with the with

475
00:31:25,166 –> 00:31:28,958
anthony_algmin: Devbs and Data Os, like you’ve got years worth of learning in front of you

476
00:31:29,083 –> 00:31:32,708
anthony_algmin: because there’s these are very deep topics now. But just knowing that their

477
00:31:32,958 –> 00:31:36,375
anthony_algmin: things is a great place to start. it’s a great place to understand. Hey,

478
00:31:36,708 –> 00:31:39,416
anthony_algmin: there is a lot of thought being put into these areas and there’s an

479
00:31:39,416 –> 00:31:42,791
anthony_algmin: evolution happening. I mean, it’s kind of crazy to me to talk a like ten

480
00:31:43,000 –> 00:31:47,750
anthony_algmin: years ago. These things were barely being figured out on the software

481
00:31:48,041 –> 00:31:51,500
anthony_algmin: engineering side, and and that’s true, it just seems like it’s been so long

482
00:31:51,750 –> 00:31:55,250
anthony_algmin: because they’ve become so ingrained in in our processes. But it’s it’s

483
00:31:55,416 –> 00:31:59,083
anthony_algmin: something too that you know they’ve evolved naturally and I think the data

484
00:31:59,250 –> 00:32:04,708
anthony_algmin: Baase has an interesting history because a lot of the analysis a lot of the

485
00:32:05,750 –> 00:32:09,083
anthony_algmin: understanding of what makes the data valuable and how it can be combined to

486
00:32:09,083 –> 00:32:11,750
anthony_algmin: create things that are relevant to the business happens on the business side

487
00:32:12,000 –> 00:32:15,166
anthony_algmin: itself, the data analyst, the financial analyst, Those are people that I

488
00:32:15,166 –> 00:32:20,000
anthony_algmin: wouldn’t call traditional I, t, or technology side of the house folks, but

489
00:32:20,541 –> 00:32:25,083
anthony_algmin: they, because of their need to leverage technology created these data groups

490
00:32:25,166 –> 00:32:28,958
anthony_algmin: that kind of came. Up in between that software engineering, sit on the very

491
00:32:29,083 –> 00:32:32,958
anthony_algmin: deep technical programming side and the data analyst, which were very

492
00:32:33,083 –> 00:32:35,916
anthony_algmin: business centr creating dashboards and things like that, and so you started

493
00:32:36,000 –> 00:32:41,333
anthony_algmin: to see to your point The the tooling started to cr, you know, create these

494
00:32:41,666 –> 00:32:46,958
anthony_algmin: these greater horizontal coverages that could be used by people that sat

495
00:32:47,083 –> 00:32:51,083
anthony_algmin: firmly on the business side of the house, but were able to exploit

496
00:32:51,083 –> 00:32:52,083
anthony_algmin: technology

497
00:32:52,708 –> 00:32:56,375
anthony_algmin: enhancements that went really deep into into the technical side of the

498
00:32:56,375 –> 00:33:00,041
anthony_algmin: house. So it’s it’s It’s an interesting paradigm that we find ourselves

499
00:33:00,208 –> 00:33:04,708
anthony_algmin: because to do this effectively, and and like what Maltono’s trying to do is

500
00:33:04,791 –> 00:33:08,625
anthony_algmin: to say, Okay, Well, you’re going to have a different collection of tools. Or

501
00:33:08,708 –> 00:33:11,250
anthony_algmin: what have you? We’re not going to try to replace that and I have a question

502
00:33:11,583 –> 00:33:14,625
anthony_algmin: related to that. But it’s it’s you’re You’re going to just try to plug in

503
00:33:14,791 –> 00:33:19,166
anthony_algmin: and explain it Because I think things like data lineage remain an incredibly

504
00:33:19,208 –> 00:33:20,208
anthony_algmin: difficult challenge

505
00:33:20,458 –> 00:33:23,833
anthony_algmin: because we’re looking at changes happening over time and that being able to

506
00:33:23,916 –> 00:33:28,125
anthony_algmin: retroactively go back and explore them. And we’ve already talked about how

507
00:33:28,375 –> 00:33:32,791
anthony_algmin: the development process of these data pipelines and andate movements we

508
00:33:32,875 –> 00:33:36,541
anthony_algmin: aren’t very good at showing our work and those by themselves, let alone

509
00:33:36,708 –> 00:33:40,041
anthony_algmin: looking back on it from the data perspective and saying where did this

510
00:33:40,125 –> 00:33:43,500
anthony_algmin: number come from? How did this get created? If we haven’t done our jobs in

511
00:33:43,666 –> 00:33:47,583
anthony_algmin: in documenting the flow of that that knowledge may be lost, let alone

512
00:33:47,833 –> 00:33:52,958
anthony_algmin: difficult to find. So the question that I have for you though is because we.

513
00:33:53,166 –> 00:33:56,125
anthony_algmin: we can certainly talk some more about lineage, and and that, because I think

514
00:33:56,208 –> 00:34:01,416
anthony_algmin: that’s a an area we’re talking about. But also how do you? for someone who’s

515
00:34:01,583 –> 00:34:05,166
anthony_algmin: in deep in the data spacease, that maybe doesn’t have as much of the that

516
00:34:05,250 –> 00:34:09,166
anthony_algmin: software engineering or programming background? how do you differentiate

517
00:34:09,500 –> 00:34:10,708
anthony_algmin: what Meltonno does

518
00:34:11,916 –> 00:34:17,500
anthony_algmin: versus being just another e t ▁l type of solution where you’re facilitating

519
00:34:17,666 –> 00:34:21,250
anthony_algmin: those workflows like, Help me draw that contrast between those Because I

520
00:34:21,333 –> 00:34:24,458
anthony_algmin: know there’s a relationship there, but I would understand better, the for

521
00:34:24,541 –> 00:34:27,833
anthony_algmin: the audience out there, I want them to understand better where does Meltonno

522
00:34:28,041 –> 00:34:34,125
anthony_algmin: sit in relation to what we would consider the e t ▁l um functionality that.

523
00:34:34,208 –> 00:34:35,666
anthony_algmin: We may be more accustomed to

524
00:34:36,041 –> 00:34:38,666
douwe_maan: Yeah, a great question. Theres a couple of things you were mentioned earlier.

525
00:34:38,833 –> 00:34:40,416
douwe_maan: I’d love to get back to one face that

526
00:34:40,500 –> 00:34:44,333
douwe_maan: kept coming up in my head while you were talking was just the importance of a

527
00:34:44,333 –> 00:34:48,750
douwe_maan: share vocabulary within data teams, but also between De and and software

528
00:34:48,916 –> 00:34:54,500
douwe_maan: engineering teams. But yeah, let’s talk a little bit first about Hamana relates

529
00:34:54,666 –> 00:35:01,208
douwe_maan: to T tos. For example, so um y ▁l t, you know e t, ▁l uh, whichever way you’re

530
00:35:01,291 –> 00:35:05,208
douwe_maan: going, data integration and transformation, uh is is, in many cases the first

531
00:35:05,291 –> 00:35:08,833
douwe_maan: step of the La. Data life cycle got to get the data from somewhere noted in some

532
00:35:08,916 –> 00:35:12,250
douwe_maan: data warehouse, and there’s a lot of solutions that have been built to do that,

533
00:35:12,416 –> 00:35:17,000
douwe_maan: Um. but we think they’re lacking in various ways. Um. they are not set up with

534
00:35:17,125 –> 00:35:21,083
douwe_maan: these des kind of best practices and principles. Mind, they don’t really allow

535
00:35:21,291 –> 00:35:25,458
douwe_maan: you to version your configuration or to test things locally before you. you

536
00:35:25,458 –> 00:35:29,458
douwe_maan: know. Um. have them take effect in the life environment. So one of the third

537
00:35:29,625 –> 00:35:30,916
douwe_maan: first things we started doing with

538
00:35:30,916 –> 00:35:32,041
douwe_maan: Miltano, and this is this

539
00:35:32,125 –> 00:35:35,000
douwe_maan: bivot about a year ago that I was talking about where I had to kind of figure

540
00:35:35,125 –> 00:35:39,458
douwe_maan: out how to turn the product around and find traction in the short term Was for

541
00:35:39,625 –> 00:35:43,541
douwe_maan: Melana which started with is really broad. One two for the entire entire data

542
00:35:43,708 –> 00:35:48,833
douwe_maan: life cycle. Uh, goal to focus on statches on that very first step and bring data

543
00:35:49,000 –> 00:35:52,916
douwe_maan: ups into it and and bring the atdtentages also of open source software into it.

544
00:35:53,083 –> 00:35:57,125
douwe_maan: So, Malana, if you go look at the websites, Um, you know in October, let’ saying

545
00:35:57,375 –> 00:36:00,166
douwe_maan: today, because we’ll have to see when this gets published. But as of October, on

546
00:36:00,250 –> 00:36:05,208
douwe_maan: the Montana website we position ourselves as E ▁l t, for the Data Os Era, and

547
00:36:05,625 –> 00:36:10,333
douwe_maan: Um, the way we compare to other E ▁l T tools. The open Tourt aspect makes a

548
00:36:10,333 –> 00:36:14,333
douwe_maan: really big difference, Um Melanno from They one has always been about I,

549
00:36:14,750 –> 00:36:19,625
douwe_maan: adopting and supporting existing, if possible, best in class open source

550
00:36:19,875 –> 00:36:24,833
douwe_maan: technologies and tools. So for E, ▁l, we chose Singer, which is a standard for

551
00:36:24,916 –> 00:36:28,166
douwe_maan: data connectors that has a library currenturly of about three hundred connectors

552
00:36:28,333 –> 00:36:31,791
douwe_maan: for different sources and destinations. Um. In that sense, it can rival the

553
00:36:31,791 –> 00:36:35,208
douwe_maan: libraries of tools like Five trend, but this is completely open source. All the

554
00:36:35,208 –> 00:36:38,666
douwe_maan: source codes is available. Anyone in the world can build a new connector with E,

555
00:36:38,833 –> 00:36:42,041
douwe_maan: and we build an s. T case or for development kit to make that extremely easy,

556
00:36:42,583 –> 00:36:48,250
douwe_maan: and Um. People can improve extent iterate all these existent connectors as they

557
00:36:48,333 –> 00:36:51,791
douwe_maan: see fits to make them more appropriate for their use case. so when it’s just

558
00:36:51,958 –> 00:36:55,958
douwe_maan: about e t, ▁l Motanno has massive advantages over other solution because of its

559
00:36:56,041 –> 00:37:00,125
douwe_maan: much larger library of connectors and the flexibility it gives teams to go

560
00:37:00,250 –> 00:37:04,750
douwe_maan: beyond for the support out of the box, Um. And as an open source platform, it

561
00:37:04,916 –> 00:37:09,291
douwe_maan: can be self managed that you have complete control over uh, where the data goes

562
00:37:09,458 –> 00:37:13,541
douwe_maan: and where it doesn’t go, perhaps more importantly of for privacy security, or

563
00:37:13,625 –> 00:37:16,583
douwe_maan: even compliance reasons, if you’re working with healthcare data, for example,

564
00:37:16,583 –> 00:37:17,583
anthony_algmin: Hey

565
00:37:17,583 –> 00:37:21,958
douwe_maan: But for Meltao’s perspective, Singer for data connectors and d, b, t, uh. This,

566
00:37:22,125 –> 00:37:24,916
douwe_maan: this amazing Da of transformation told than I’m sure most businessers are

567
00:37:25,000 –> 00:37:29,208
douwe_maan: familiar with are just a couple of the plug ins that our Daate out platform

568
00:37:29,541 –> 00:37:32,666
douwe_maan: supports, and the goal of Melannos to provide that platform that you can bring

569
00:37:32,833 –> 00:37:36,500
douwe_maan: different components into, and then Melana provides the glued. The intermediate

570
00:37:36,583 –> 00:37:41,000
douwe_maan: tissue between the different components gives you a uh, consistent configuration

571
00:37:41,125 –> 00:37:45,708
douwe_maan: layer for the entire the thing, and also allows the different components to Um,

572
00:37:45,958 –> 00:37:49,375
douwe_maan: have their configurations synchronized automatically so that when you point at a

573
00:37:49,375 –> 00:37:52,750
douwe_maan: different snowfake warehouse, all the tools, including potentially, your, you

574
00:37:52,750 –> 00:37:58,250
douwe_maan: know, your B, I or analysis tool knows where to look now. Um, So for E ▁l T

575
00:37:58,333 –> 00:38:02,333
douwe_maan: alone, the combination of Singer and d B T managed with Mel Anno, can directly

576
00:38:02,500 –> 00:38:05,541
douwe_maan: compete with five trainn, or all these other tools people might be using today.

577
00:38:05,791 –> 00:38:09,708
douwe_maan: But what Montana really adds is that layer that brings together great open

578
00:38:09,875 –> 00:38:13,000
douwe_maan: source tools and technologies. So when you’re thinking about your modern, your

579
00:38:13,125 –> 00:38:17,375
douwe_maan: datateck today, Uh, and you have five tooling decisions. Some of them, you might

580
00:38:17,458 –> 00:38:20,833
douwe_maan: have gone for some proprietary Sa software, but there exist really great

581
00:38:21,083 –> 00:38:26,041
douwe_maan: open source. Uh, the itols like supers sett metaabase today as well, Um, you

582
00:38:26,125 –> 00:38:30,666
douwe_maan: know, on the data orchestration, Uh, side of things, for example, when you are

583
00:38:30,833 –> 00:38:35,000
douwe_maan: talking about these workflow managers like Airflow and Dexter and Prefect, this

584
00:38:35,083 –> 00:38:37,958
douwe_maan: is another piece of software that’s open source and you mighting into the place

585
00:38:38,166 –> 00:38:41,625
douwe_maan: somewhere, Um, and then if you want to have d v T running somewhere with your

586
00:38:41,708 –> 00:38:45,291
douwe_maan: transformation pipeps, then again, that’s another piece to deploy, and this

587
00:38:45,541 –> 00:38:48,333
douwe_maan: become this becomes really complicated very quickly because you need all of

588
00:38:48,333 –> 00:38:49,333
douwe_maan: these tools to

589
00:38:49,333 –> 00:38:52,666
douwe_maan: have some shared understanding of what. We’re all about, and it is Meltano that

590
00:38:52,750 –> 00:38:55,958
douwe_maan: provides that stable foundation on top of which you and built your ideal

591
00:38:56,041 –> 00:39:00,750
douwe_maan: datastack, And Malano allows teams to treat all of this as if it is one product

592
00:39:01,000 –> 00:39:05,000
douwe_maan: that they build with one repository where they can do this version, control and

593
00:39:05,083 –> 00:39:08,750
douwe_maan: coach review work. And it allows entire data teams, all the different

594
00:39:09,000 –> 00:39:12,500
douwe_maan: disciplines that might exist from data engineers, andliytics, engineers and

595
00:39:12,583 –> 00:39:16,625
douwe_maan: analysts and scientists to come together and have a single source of truth in a

596
00:39:16,625 –> 00:39:17,625
douwe_maan: single place

597
00:39:17,708 –> 00:39:22,041
douwe_maan: to discuss and collaborate and give each other feedback on anyone’s idea for how

598
00:39:22,166 –> 00:39:26,166
douwe_maan: this data product could be better. Um, kind of to tiit us back to what you were

599
00:39:26,250 –> 00:39:29,375
douwe_maan: saying about the share, or what I kind of heard you say about the shared

600
00:39:29,458 –> 00:39:34,333
douwe_maan: forcovery and the importance of it just by bringing people together in one

601
00:39:34,916 –> 00:39:39,083
douwe_maan: place. One website that defines the entirety of the data products all way from

602
00:39:39,125 –> 00:39:44,500
douwe_maan: integration to Uh. analysis, allows them to ask each other questions and make

603
00:39:44,583 –> 00:39:47,958
douwe_maan: suggestions and go find the relevant code and see like Hey, is this where I

604
00:39:48,041 –> 00:39:50,750
douwe_maan: should make a change if I want ▁x. that. Instead of saying well, I’m a date

605
00:39:50,916 –> 00:39:54,500
douwe_maan: analyst, I have no idea about this engineering stuff if I need anything, even if

606
00:39:54,583 –> 00:39:57,708
douwe_maan: it’s just another column or another, a p. i, N point that I would love to see in

607
00:39:57,791 –> 00:40:01,375
douwe_maan: my database. I need to create a ticket and throw through the data engineers, and

608
00:40:01,458 –> 00:40:05,000
douwe_maan: then they are going to be working in this kind of like ticket response workflow,

609
00:40:05,125 –> 00:40:08,333
douwe_maan: just like analysts often do when they just get questions from the business. And

610
00:40:08,416 –> 00:40:11,625
douwe_maan: and they basically its working through tickets all day, But it would be far more

611
00:40:11,875 –> 00:40:15,208
douwe_maan: effective for all of these stakeholders to be able to have a shaft for caavalry.

612
00:40:15,458 –> 00:40:19,083
douwe_maan: be able to trace. Okay, I’m looking at this dashboard here. I can find the code

613
00:40:19,125 –> 00:40:22,583
douwe_maan: that pulled that data from the rel of a B. I. If I’m a little bit familiar with

614
00:40:22,583 –> 00:40:25,708
douwe_maan: a sequel, or maybe a little bit of Python or Yammo files, where you can

615
00:40:25,791 –> 00:40:29,208
douwe_maan: configure the stuff, I can figure out how to add another attribute. And because

616
00:40:29,458 –> 00:40:32,416
douwe_maan: this is not just immediately going to go life, but it’s going to go through this

617
00:40:32,500 –> 00:40:36,125
douwe_maan: Sce review process. You can have the confidence to propose changes even if

618
00:40:36,125 –> 00:40:37,458
douwe_maan: you’re not super confident what

619
00:40:37,458 –> 00:40:40,416
douwe_maan: you’re doing because you know it’s going to be reviewed and approved by someone

620
00:40:40,583 –> 00:40:43,125
douwe_maan: who knows better, and not just a person who knows better with an actual

621
00:40:43,291 –> 00:40:45,708
douwe_maan: automated testing pipeline. And it will tell you if you

622
00:40:45,791 –> 00:40:51,625
douwe_maan: accidentally broke something or not before. Um, you know, as an alternative to

623
00:40:51,958 –> 00:40:54,916
douwe_maan: saying, I don’t even want to touch this, because I’ll break things. You guys do

624
00:40:55,000 –> 00:40:56,583
douwe_maan: it and free

625
00:40:56,833 –> 00:40:59,708
douwe_maan: these people closer together. Not just on the data, Cyle on the soffign,

626
00:40:59,875 –> 00:41:04,125
douwe_maan: Engineerys is is extremely valuable and a really big part of trying to

627
00:41:04,125 –> 00:41:06,916
douwe_maan: accomplish and and what theffs has accomplished. And so for development.

628
00:41:08,708 –> 00:41:12,291
anthony_algmin: Well in and it. it encourages what we were talking about earlier around.

629
00:41:12,625 –> 00:41:17,583
anthony_algmin: learning more on the other side of the coin is is that if you can feel free

630
00:41:17,916 –> 00:41:24,000
anthony_algmin: and encouraged to suggest potential solutions, I think of it as a as a great

631
00:41:24,208 –> 00:41:28,625
anthony_algmin: way on the technology side of things, to do what I do in in business and

632
00:41:28,875 –> 00:41:33,916
anthony_algmin: consulting all the time. It’s to say hey, half of consulting is suggesting

633
00:41:34,125 –> 00:41:38,458
anthony_algmin: really bad ideas for people to react to and and going from them. just

634
00:41:38,541 –> 00:41:42,375
anthony_algmin: because of a blank piece of paper, is a much more difficult starting place

635
00:41:42,791 –> 00:41:46,708
anthony_algmin: than a terrible idea. Like a terrible idea You can react to very quickly and

636
00:41:46,791 –> 00:41:51,000
anthony_algmin: iterate fast and you’ll get somewhere Uh, remarkably quick. If I know I’m

637
00:41:51,083 –> 00:41:53,916
anthony_algmin: looking for something and I can say Hey, it should look something like this,

638
00:41:54,375 –> 00:41:58,708
anthony_algmin: And then there’s a way for me to propose that, and for the system or for the

639
00:41:58,791 –> 00:42:01,583
anthony_algmin: people to react to it and make it. Oh, you know what we should do it this

640
00:42:01,666 –> 00:42:06,541
anthony_algmin: way Because of ▁x, You could be so much closer, so much faster with that

641
00:42:06,708 –> 00:42:12,000
anthony_algmin: kind of interaction and it helps self document. Like, if I can just show you

642
00:42:12,125 –> 00:42:16,875
anthony_algmin: what I’m talking about and then we can react, It saves me the trouble of

643
00:42:17,000 –> 00:42:20,791
anthony_algmin: trying to figure out the words to describe the thing that I could otherwise

644
00:42:20,958 –> 00:42:24,625
anthony_algmin: draw you a more direct picture of. And And that’s the kind of. I think

645
00:42:24,708 –> 00:42:28,875
anthony_algmin: that’s where Dev Ops is is at its finest right where we collectively bring

646
00:42:29,083 –> 00:42:34,208
anthony_algmin: our wisdom to a shared. You know it’s it’s common vocabulary, C, common

647
00:42:34,541 –> 00:42:39,833
anthony_algmin: vocabulary, but it’s also a common working area where we together solve

648
00:42:40,041 –> 00:42:43,166
anthony_algmin: these problems using our different perspectives, our differentvantage

649
00:42:43,333 –> 00:42:48,041
anthony_algmin: points, or different skill sets, to then collectively solve whatever it is

650
00:42:48,208 –> 00:42:52,000
anthony_algmin: that needs to be done. Get it done in the fewest steps necessary.

651
00:42:53,000 –> 00:42:58,458
anthony_algmin: have it be validated and then codified Put in production. You’re done. You

652
00:42:58,541 –> 00:43:01,333
anthony_algmin: move on and and you get to the next thing that much faster, And that to me

653
00:43:01,666 –> 00:43:06,041
anthony_algmin: is is the key takeaway for folks that are are new to the space. is to say,

654
00:43:06,875 –> 00:43:14,000
anthony_algmin: this is all about evolving how we work to get to the core essence of what’s

655
00:43:14,125 –> 00:43:19,000
anthony_algmin: really necessary, and remove as much of the redundancy as much of the than

656
00:43:19,250 –> 00:43:23,833
anthony_algmin: unnecessary steps as possible. And this is a continued evolution, right,

657
00:43:24,208 –> 00:43:28,375
anthony_algmin: this is, we’ve got. We’ve come a long way in the last ten years, Right.

658
00:43:28,708 –> 00:43:30,375
anthony_algmin: What? What are these next ten years?

659
00:43:31,500 –> 00:43:34,458
anthony_algmin: What will they have in stores? So that that’s my next question for you, Like

660
00:43:34,625 –> 00:43:38,208
anthony_algmin: where do you see this going? What’s the natural evolution of this And and

661
00:43:38,291 –> 00:43:42,000
anthony_algmin: how does Meltano Um expect to play a role in that?

662
00:43:42,916 –> 00:43:47,291
douwe_maan: Yeah, yeah, left. to answer that question. I also love to start by kind of tying

663
00:43:47,458 –> 00:43:51,000
douwe_maan: into what you said about the importance of the common working area, and that’s

664
00:43:51,083 –> 00:43:54,833
douwe_maan: really what I’m talking about when we say you know one repository, which is kind

665
00:43:54,916 –> 00:43:58,250
douwe_maan: of a software development way of saying you, a project, a collection of files

666
00:43:58,416 –> 00:44:02,041
douwe_maan: that come together to create something, a software product, in many cases, or

667
00:44:02,166 –> 00:44:06,125
douwe_maan: the single source of truth, where you give the entire team one place where they

668
00:44:06,166 –> 00:44:10,833
douwe_maan: know they can find the actual state of things, and also knowing that everything

669
00:44:11,208 –> 00:44:15,000
douwe_maan: that comes together to create that. Dashboard’re looking at a Yourbi tool is in

670
00:44:15,083 –> 00:44:18,916
douwe_maan: one place, and you could theoretically, if you as an analyst, detected, or some

671
00:44:19,083 –> 00:44:25,625
douwe_maan: Vaian database, use Melttao to um. find out within one place. the code that was

672
00:44:25,708 –> 00:44:29,375
douwe_maan: responsible for pulling out of the api. The code was responsible for from

673
00:44:29,625 –> 00:44:34,125
douwe_maan: transforming it into the required data. Schma, tools that were responsible for

674
00:44:34,500 –> 00:44:38,583
douwe_maan: you know, having anomeditated validation tests while the pipeline was running

675
00:44:38,750 –> 00:44:42,750
douwe_maan: all the way to the actual sequel Creer that generates report, giving people the

676
00:44:42,833 –> 00:44:45,791
douwe_maan: confidence that they know where to find it. Instead of this scary feeling of

677
00:44:45,875 –> 00:44:50,041
douwe_maan: like, Well, I’m an analyst. I know byb I tool, but anything that comes before it

678
00:44:50,125 –> 00:44:53,708
douwe_maan: is weird, tools never used and none of the accounts. I don’t know which buttons

679
00:44:53,791 –> 00:44:57,625
douwe_maan: to click. I’m afraid to break things. Um, giving them the confidence they can

680
00:44:57,625 –> 00:44:59,458
douwe_maan: find it, and like you said, that they can suggest the change

681
00:44:59,625 –> 00:45:03,375
douwe_maan: no matter how bad it is, and not accidentally break things is massive and in

682
00:45:03,458 –> 00:45:07,291
douwe_maan: Soware development, what we saw is in just by bringing product managers and U, y

683
00:45:07,458 –> 00:45:11,958
douwe_maan: u, Y, designers and software engineers. And and you know, K, a quality testers

684
00:45:12,166 –> 00:45:17,083
douwe_maan: to one place and giving them um access to everything the others are seeing,

685
00:45:17,541 –> 00:45:21,875
douwe_maan: allows this collaboration to turn from a kind of walled system where you just

686
00:45:21,875 –> 00:45:25,208
douwe_maan: throw stuff over fences because you’re afraid to even touch it. Because you know

687
00:45:25,291 –> 00:45:28,833
douwe_maan: you could break things. That’s kind of the reality of things today. Um, we want

688
00:45:28,916 –> 00:45:32,041
douwe_maan: to throw that on its head. So, when we’re talking about where the next ten years

689
00:45:32,166 –> 00:45:36,166
douwe_maan: are going, like I was saying earlier, Um, in data, we’re still at the space now

690
00:45:36,333 –> 00:45:40,125
douwe_maan: where it’s not even so much about convincing people that Nottana is amazing.

691
00:45:40,250 –> 00:45:43,208
douwe_maan: It’s about convincing that the data Os is something they want at all. That

692
00:45:43,875 –> 00:45:47,458
douwe_maan: deas to data makes sense that software development teams have a lot more in

693
00:45:47,541 –> 00:45:51,708
douwe_maan: common with data teams than they might realize. Um. some of this kind of wave

694
00:45:52,250 –> 00:45:57,791
douwe_maan: towards a horizontally integrated data tooling space with besting class tools.

695
00:45:58,250 –> 00:46:03,083
douwe_maan: Um, is already kind of revolutionizing the way things work. but something has

696
00:46:03,208 –> 00:46:07,291
douwe_maan: gone missing because there’s no longer that that single place people know to go.

697
00:46:07,875 –> 00:46:12,500
douwe_maan: So we, in the next ten years are going to be seeing a combination of I think,

698
00:46:13,208 –> 00:46:18,041
douwe_maan: really, Um. Focused tools for really specific problems, because there can be ten

699
00:46:18,166 –> 00:46:21,291
douwe_maan: competitors and everyone can kind of bring in their new idea to make

700
00:46:21,458 –> 00:46:25,083
douwe_maan: transformation better than ever was before, while trying to

701
00:46:25,125 –> 00:46:29,541
douwe_maan: fit this into a world where there’s clearly still value in having a consistent

702
00:46:29,875 –> 00:46:32,916
douwe_maan: foundation. That that pulls it all together. Of course that’s exactly what

703
00:46:33,000 –> 00:46:37,083
douwe_maan: Moltana is, but this is a challenge that will take years and years to kind of

704
00:46:37,208 –> 00:46:41,125
douwe_maan: roll out in in many organizations, and and some of this will also just have to

705
00:46:41,375 –> 00:46:45,875
douwe_maan: become companies getting comfortable getting off of their maybe ten year old an

706
00:46:46,041 –> 00:46:50,416
douwe_maan: to end data platforms, adopting specific tools for each step of the way in

707
00:46:50,500 –> 00:46:55,958
douwe_maan: combination with it to like moanno, and data teams that need to not sly, retrain

708
00:46:56,125 –> 00:46:58,500
douwe_maan: their people, because if these people are productive today than they can ever

709
00:46:58,583 –> 00:47:01,875
douwe_maan: be, but at least realizing there’s additional skills. They can learn and there

710
00:47:02,041 –> 00:47:05,208
douwe_maan: are things from software development that are really not specific to software

711
00:47:05,375 –> 00:47:09,625
douwe_maan: development. Um. Get Lab. Their mission do, mission for Getlab has always been,

712
00:47:09,958 –> 00:47:12,166
douwe_maan: everyone can contribute, and the the

713
00:47:12,250 –> 00:47:15,708
douwe_maan: broader vision has been to build a platform that allows knowledge workers in any

714
00:47:15,875 –> 00:47:19,875
douwe_maan: profession to more effectively coliaborate to build better things together. And

715
00:47:20,041 –> 00:47:23,458
douwe_maan: in this mission division software is explicitly mentioned nowhere. like

716
00:47:23,541 –> 00:47:27,125
douwe_maan: obviously today Getit Lab is still primarily focused in software development

717
00:47:27,291 –> 00:47:31,791
douwe_maan: teams E. but now Getlab and Devts in general, is it disposition where it can

718
00:47:31,916 –> 00:47:32,916
douwe_maan: start moving

719
00:47:33,000 –> 00:47:36,041
douwe_maan: into more and more and more industries, starting with the ones that are more

720
00:47:36,166 –> 00:47:40,500
douwe_maan: closely aligned more adjacent to software development. But the goal is very much

721
00:47:40,750 –> 00:47:44,833
douwe_maan: to find a way to bring it advantages to any knowledge industry in the world, and

722
00:47:45,125 –> 00:47:49,208
douwe_maan: part of the challenge with Moltanno is to kind of provide that layer between the

723
00:47:49,291 –> 00:47:52,666
douwe_maan: software development way of looking at things and the data away of looking at

724
00:47:52,833 –> 00:47:58,750
douwe_maan: things, Um, and and doing it in such a way that the Defap’s best practices will

725
00:47:58,833 –> 00:48:03,875
douwe_maan: feel natural to data teams, and in the same sense that now data work will feel

726
00:48:04,041 –> 00:48:08,125
douwe_maan: natural to software development teams, so over the, that’s part of what I see

727
00:48:08,250 –> 00:48:11,375
douwe_maan: happening this is. Really big data of revolution, which is going to take ten

728
00:48:11,541 –> 00:48:15,875
douwe_maan: years to pen out, Um. The other thing that I think is really key is open source

729
00:48:16,125 –> 00:48:17,458
douwe_maan: software. One of the many

730
00:48:17,625 –> 00:48:22,833
douwe_maan: reasons why meltaa’ open source um is because I just very strongly personally

731
00:48:23,083 –> 00:48:29,375
douwe_maan: believe that the most, the best most useful and most effective tool will always

732
00:48:29,541 –> 00:48:33,708
douwe_maan: be a tool built in very close collaboration with the users of that tool. The

733
00:48:33,708 –> 00:48:37,458
douwe_maan: feedback loop needs to be incredibly tight, and it does not get tighter than

734
00:48:37,625 –> 00:48:41,791
douwe_maan: inviting your users into your issue, Tcker into your code base, allowing them to

735
00:48:41,791 –> 00:48:45,208
douwe_maan: make suggestions like you, said Anthony. Allowment makes suggestions. even if

736
00:48:45,208 –> 00:48:48,333
douwe_maan: they will be. the code will not be up to par or whatever, which is. giving that

737
00:48:48,500 –> 00:48:52,750
douwe_maan: ability to express themselves and start a conversation. Allows us to build tools

738
00:48:52,916 –> 00:48:57,791
douwe_maan: that make its user more effective than ever, and in Getlab itself it was always

739
00:48:57,875 –> 00:49:00,583
douwe_maan: quite special that the users of the product for software engineers, and

740
00:49:00,583 –> 00:49:03,458
douwe_maan: obviously the people building the protocols software engineers. But what

741
00:49:03,541 –> 00:49:07,541
douwe_maan: foreseeing in data now similarly is that Data engineers and Olympics engineers

742
00:49:07,625 –> 00:49:11,000
douwe_maan: are increasingly becoming more confident with little bits of programming and a

743
00:49:11,000 –> 00:49:15,083
douwe_maan: little bits of Python, and having the opportunity to build your own ideal tool

744
00:49:15,208 –> 00:49:19,458
douwe_maan: to make you most productive. Um, it doesn’t get better than that, and we can

745
00:49:19,541 –> 00:49:23,458
douwe_maan: bring together the ideas and the perspectives of of thousands, if not a millions

746
00:49:23,541 –> 00:49:27,625
douwe_maan: of data engineers, instead of having to defer to one product manager and their

747
00:49:27,791 –> 00:49:32,125
douwe_maan: team. However amazing they might be, It’s different from saying this is a

748
00:49:32,250 –> 00:49:35,291
douwe_maan: product. Four data people by data people, Um,

749
00:49:36,416 –> 00:49:41,083
douwe_maan: our mission very explicitly is to not force our perspective on you, especially

750
00:49:41,208 –> 00:49:44,583
douwe_maan: because I coming from software development. Never having worked in data are very

751
00:49:44,750 –> 00:49:49,291
douwe_maan: aware of how little I know, but I really want to listen and create a space for

752
00:49:49,541 –> 00:49:53,000
douwe_maan: people to come together and build really awesome data to link together. And I

753
00:49:53,083 –> 00:49:58,583
douwe_maan: think that in any field, any industry where the users of tools have the

754
00:49:58,750 –> 00:50:02,583
douwe_maan: capability of programming even if it’s just as little as no. I don’ know how to

755
00:50:02,583 –> 00:50:07,000
douwe_maan: write simple code in an editor, and I’ so beginning to use Get. Once you have

756
00:50:07,208 –> 00:50:11,291
douwe_maan: that level of technical skills in your users, open source software will always

757
00:50:11,458 –> 00:50:16,666
douwe_maan: win. period, because this model of we chargear for it, we you know, build it to

758
00:50:16,666 –> 00:50:20,333
douwe_maan: our own liking. If you don’t like it, go to a competitor just does not skill. If

759
00:50:20,416 –> 00:50:24,166
douwe_maan: you have a user base that is empowered to say, Oh, we’re fed up’re, just going

760
00:50:24,166 –> 00:50:26,500
douwe_maan: to buil something yourself, and it’s going to be better because they can

761
00:50:26,666 –> 00:50:31,000
douwe_maan: actually make it happen. So, Um, more and more, I think that that, um, the

762
00:50:31,208 –> 00:50:34,583
douwe_maan: entire space will move to open source software. To some extent we’re already

763
00:50:34,750 –> 00:50:38,416
douwe_maan: seeing that. of course, with Um, you know, work for orchanation, stuff like

764
00:50:38,500 –> 00:50:43,125
douwe_maan: Airflo Exxtra and Prefect d B T for transformation, Uh, and especially now that

765
00:50:43,291 –> 00:50:47,000
douwe_maan: the space is becoming increasingly horizontally integrated, it’s going to be

766
00:50:47,000 –> 00:50:49,875
douwe_maan: much easier for anyone to step up and say Well, I built an open tource

767
00:50:50,041 –> 00:50:54,250
douwe_maan: alternative to fancy expensive tool, ▁x, And then that’s open tour. Our tool

768
00:50:54,416 –> 00:50:57,958
douwe_maan: might actually be much better than the alternative places, especially if it can

769
00:50:58,041 –> 00:51:02,041
douwe_maan: rally around another couple hundred people to bring in their ideas, perspectives

770
00:51:02,125 –> 00:51:07,125
douwe_maan: and en coding skills. So ten years from now, just like we see in software

771
00:51:07,291 –> 00:51:10,583
douwe_maan: development, and the database is going to be built around open force. Sting.

772
00:51:10,833 –> 00:51:15,541
douwe_maan: Devil practices are going to be built in from day one, and these data teams are

773
00:51:15,625 –> 00:51:19,000
douwe_maan: going to be functioning and thinking of themselves, much more like teams

774
00:51:19,125 –> 00:51:23,791
douwe_maan: building a product. Then you know I, I work in two. A you working to be working

775
00:51:23,875 –> 00:51:27,083
douwe_maan: to C, and the responsibility of the data team is to make good product of

776
00:51:27,083 –> 00:51:30,250
douwe_maan: purchasing decisions, which I think is a really kind of sad way of thinking

777
00:51:30,416 –> 00:51:34,750
douwe_maan: about this. this massive responsibility of uh, setting up all the infrastructure

778
00:51:34,916 –> 00:51:39,541
douwe_maan: a company needs to use data to the full potential and tying together an open

779
00:51:39,708 –> 00:51:43,208
douwe_maan: source and the full potential. Bits. Uh, you, you end up at what our mission is

780
00:51:43,291 –> 00:51:47,375
douwe_maan: as Mofana, which is to enable everyone to realize the full potential of their

781
00:51:47,458 –> 00:51:53,000
douwe_maan: data And everyone here is intentionally broad and we want to include anyone who

782
00:51:53,083 –> 00:51:55,958
douwe_maan: could benefit from doing anything with their data which we know is literally

783
00:51:56,125 –> 00:52:00,583
douwe_maan: everyone, and give them the ability to do so in an open source, Ties to that in

784
00:52:00,583 –> 00:52:01,583
douwe_maan: a really big way.

785
00:52:03,666 –> 00:52:07,750
anthony_algmin: thank you so much for sharing that. Unfortunately we’re out of time. I’m I.

786
00:52:07,750 –> 00:52:12,958
anthony_algmin: I’m so excited about what Meltono is doing about how you’re bringing open

787
00:52:13,250 –> 00:52:15,583
anthony_algmin: source and data opts to everybody.

788
00:52:16,875 –> 00:52:21,083
anthony_algmin: I. I’m thrilled with your business model. I think this is amazing stuff and

789
00:52:21,166 –> 00:52:27,083
anthony_algmin: I think it’s really going to help drive that next evolution of what kind of

790
00:52:27,250 –> 00:52:32,458
anthony_algmin: open source and and project management and the way we collaborate in the Dat

791
00:52:32,625 –> 00:52:36,208
anthony_algmin: spacease. I think it’s It’s overdue and and it’s great to see you putting

792
00:52:36,375 –> 00:52:40,291
anthony_algmin: some real energy behind that. I can’t wait to see what’s next for you in

793
00:52:40,375 –> 00:52:44,458
anthony_algmin: Milta, and to watch this growth over coming years. I think it’s going to be

794
00:52:44,541 –> 00:52:47,750
anthony_algmin: an amazing ride. So thank you for being on the show and sharing some of that

795
00:52:48,250 –> 00:52:51,791
douwe_maan: Yeah, thank you, Anthony so much for the opportunity and for everyone Listening.

796
00:52:51,958 –> 00:52:56,416
douwe_maan: If this story of data people building tools for data people resonates, please

797
00:52:56,666 –> 00:53:00,500
douwe_maan: come join our Slect community where we currently have over eighteen hundred data

798
00:53:00,666 –> 00:53:03,625
douwe_maan: professionals. Uh, doing exactly what I’ve been describing coming together,

799
00:53:03,875 –> 00:53:06,833
douwe_maan: sharing ideas and perspectives and trying to build the next generation of

800
00:53:06,833 –> 00:53:07,833
douwe_maan: dataing,

801
00:53:08,500 –> 00:53:10,666
douwe_maan: and you’re more welcome to come join the party.

802
00:53:11,916 –> 00:53:12,500
anthony_algmin: And we will definitely include Uh, more information and links uh to that in

803
00:53:12,500 –> 00:53:17,333
anthony_algmin: And we will definitely include Uh, more information and links uh to that in

804
00:53:17,416 –> 00:53:21,166
anthony_algmin: the show note. So so check out the show notes for Uh directions on how to

805
00:53:21,166 –> 00:53:25,000
anthony_algmin: get to Meltano and and where you can a join that community. So thank you all.

806
00:53:25,250 –> 00:53:29,000
anthony_algmin: Uh for joining us today. Uh, dive deeper with my book at Data Leadership

807
00:53:29,166 –> 00:53:33,416
anthony_algmin: Book Dot Com and use Promocode Almondel at the Diversity Online Trainding

808
00:53:33,500 –> 00:53:37,000
anthony_algmin: Center for twenty percent off your first purchase. and if you enjoy our show

809
00:53:37,166 –> 00:53:40,208
anthony_algmin: and would love your own, but don’t know where to start, visit Algmin.com

810
00:53:40,291 –> 00:53:44,000
anthony_algmin: to learn how we make having your own video podcast as easy as joining a

811
00:53:44,000 –> 00:53:47,916
anthony_algmin: call and sending an email. Stay safe during these unusual times and go make

812
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anthony_algmin: an impact

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