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hi everyone Welcome to our event this event is brought to you by data dos club which is a community of people who love
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data and we have weekly events and today one is one of such events and I guess we
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are also a community of people who like to wake up early if you're from the states right Christopher or maybe not so
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much because this is the time we usually have uh uh our events uh for our guests
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and presenters from the states we usually do it in the evening of Berlin time but yes unfortunately it kind of
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slipped my mind but anyways we have a lot of events you can check them in the
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description like there's a link um I don't think there are a lot of them right now on that link but we will be
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adding more and more I think we have like five or six uh interviews scheduled so um keep an eye on that do not forget
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as the one today and of course very important do not forget to join our community where you can hang out with
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other data enthusiasts during today's interview you can ask any question there's a pin Link in live chat so click
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on that link ask your question and we will be covering these questions during the interview now I will stop sharing my
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screen and uh there is there's a a message in uh and Christopher is from
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you so we actually have this on YouTube but so they have not seen what you wrote
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but there is a message from to anyone who's watching this right now from Christopher saying hello everyone can I
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call you Chris or you okay I should go I should uh I should look on YouTube then okay yeah but anyways I'll you don't
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need like you we'll need to focus on answering questions and I'll keep an eye
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I'll be keeping an eye on all the question questions so um
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yeah if you're ready we can start I'm ready yeah and you prefer Christopher
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not Chris right Chris is fine Chris is fine it's a bit shorter um
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okay so this week we'll talk about data Ops again maybe it's a tradition that we talk about data Ops every like once per
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year but we actually skipped one year so because we did not have we haven't had
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Chris for some time so today we have a very special guest Christopher Christopher is the co-founder CEO and
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head chef or hat cook at data kitchen with 25 years of experience maybe this
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is outdated uh cuz probably now you have more and maybe you stopped counting I
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don't know but like with tons of years of experience in analytics and software engineering Christopher is known as the
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co-author of the data Ops cookbook and data Ops Manifesto and it's not the
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first time we have Christopher here on the podcast we interviewed him two years ago also about data Ops and this one
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will be about data hops so we'll catch up and see what actually changed in in
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these two years and yeah so welcome to the interview well thank you for having
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me I'm I'm happy to be here and talking all things related to data Ops and why
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why why bother with data Ops and happy to talk about the company or or what's changed
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excited yeah so let's dive in so the questions for today's interview are prepared by Johanna berer as always
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thanks Johanna for your help so before we start with our main topic for today
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data Ops uh let's start with your ground can you tell us about your career Journey so far and also for those who
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have not heard have not listened to the previous podcast maybe you can um talk
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about yourself and also for those who did listen to the previous you can also maybe give a summary of what has changed
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in the last two years so we'll do yeah so um my name is Chris so I guess I'm
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a sort of an engineer so I spent about the first 15 years of my career in
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software sort of working and building some AI systems some non- AI systems uh
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at uh Us's NASA and MIT linol lab and then some startups and then um
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Microsoft and then about 2005 I got I got the data bug uh I think you know my
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kids were small and I thought oh this data thing was easy and I'd be able to go home uh for dinner at 5 and life
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would be fine um because I was a big you started your own company right and uh it didn't work out that way
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and um and what was interesting is is for me it the problem wasn't doing the
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data like I we had smart people who did data science and data engineering the act of creating things it was like the
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systems around the data that were hard um things it was really hard to not have
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errors in production and I would sort of driving to work and I had a Blackberry at the time and I would not look at my
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Blackberry all all morning I had this long drive to work and I'd sit in the parking lot and take a deep breath and
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look at my Blackberry and go uh oh is there going to be any problems today and I'd be and if there wasn't I'd walk and
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very happy um and if there was I'd have to like rce myself um and you know and
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then the second problem is the team I worked for we just couldn't go fast enough the customers were super
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demanding they didn't care they all they always thought things should be faster and we are always behind and so um how
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do you you know how do you live in that world where things are breaking left and right you're terrified of making errors
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um and then second you just can't go fast enough um and it's preh Hadoop era
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right it's like before all this big data Tech yeah before this was we were using
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uh SQL Server um and we actually you know we had smart people so we we we
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built an engine in SQL Server that made SQL Server a column or
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database so we built a column or database inside of SQL Server um so uh
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in order to make certain things fast and and uh yeah it was it was really uh it's not
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bad I mean the principles are the same right before Hadoop it's it's still a database there's still indexes there's
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still queries um things like that we we uh at the time uh you would use olap
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engines we didn't use those but you those reports you know are for models it's it's not that different um you know
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we had a rack of servers instead of the cloud um so yeah and I think so what what I
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took from that was uh it's just hard to run a team of people to do do data and analytics and it's not
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really I I took it from a manager perspective I started to read Deming and
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think about the work that we do as a factory you know and in a factory that produces insight and not automobiles um
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and so how do you run that factory so it produces things that are good of good
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quality and then second since I had come from software I've been very influenced
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by by the devops movement how you automate deployment how you run in an agile way how you
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produce um how you how you change things quickly and how you innovate and so
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those two things of like running you know running a really good solid production line that has very low errors
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um and then second changing that production line at at very very often they're kind of opposite right um and so
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how do you how do you as a manager how do you technically approach that and
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then um 10 years ago when we started data kitchen um we've always been a profitable company and so we started off
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uh with some customers we started building some software and realized that we couldn't work any other way and that
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the way we work wasn't understood by a lot of people so we had to write a book and a Manifesto to kind of share our our
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methods and then so yeah we've been in so we've been in business now about a little over 10
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years oh that's cool and uh like what
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uh so let's talk about dat offs and you mentioned devops and how you were inspired by that and by the way like do
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you remember roughly when devops as I think started to appear like when did people start calling these principles
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and like tools around them as de yeah so agile Manifesto well first of all the I
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mean I had a boss in 1990 at Nasa who had this idea build a
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little test a little learn a lot right that was his Mantra and then which made
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made a lot of sense um and so and then the sort of agile software Manifesto
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came out which is very similar in 2001 and then um the sort of first real
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devops was a guy at Twitter started to do automat automated deployment you know
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push a button and that was like 200 Nish and so the first I think devops
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Meetup was around then so it's it's it's been 15 years I guess 6 like I was
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trying to so I started my career in 2010 so I my first job was a Java
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developer and like I remember for some things like we would just uh SFTP to the
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machine and then put the jar archive there and then like keep our fingers crossed that it doesn't break uh uh like
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it was not really the I wouldn't call it this way right you were deploying you
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had a Dey process I put it yeah
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right was that so that was documented too it was like put the jar on production cross your
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fingers I think there was uh like a page on uh some internal Viki uh yeah that
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describes like with passwords and don't like what you should do yeah that was and and I think what's interesting is
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why that changed right and and we laugh at it now but that was why didn't you
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invest in automating deployment or a whole bunch of automated regression
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tests right that would run because I think in software now that would be rare
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that people wouldn't use C CD they wouldn't have some automated tests you know functional
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regression tests that would be the