Dennis Will_mixdown.mp3-from OneDrive

Harpreet: [00:00:11] What's up, everybody, welcome to the artists of Data Science today, we've got a conversations episode where we get to hear from people who are doing interesting work, pursuing their dreams and adding value to the world. We're going to get inside their heads. See what makes them tick and walk away with a new perspective that'll help us in our journeys. These episodes are much less structured and formal than what you normally hear on the show. They're going to be raw, unedited unproduced for the most part, just like my intro music and stuff like that. But thanks for tuning in, and I love to hear what you guys think about these episodes. Feel free to shoot me an email at theartistsofdatascience@gmail.com with your thoughts.

Harpreet: [00:00:44] Our guest today is a highly skilled and seasoned veteran in Data Engineering who's been deployed on many big Data tours. He's continuously adapting to new technologies with the focus on data extraction, transformation analysis and data pipelines for developing optimal architectures. Like a true intrapreneur and linchpin, he's always eager to find the most lucrative solution, saving clients' valuable time and money. Like a true engineer, he's passionate about clean code and well documented code. Teamwork, and sharing his knowledge with our growing field. When he's not wrangling gnarly code, you can find him on Instagram under the alias Azure will, where he makes informative, easily digestible posts about Python and big data so you can keep improving your skills and develop big Data mastery. So please help me in welcoming our guest today. A Data engineer whose weapon of choice is Microsoft Azure Services, Dennis Will.

Harpreet: [00:01:54] Dennis, my friend. Thank you so much for taking time out of your schedule to be on the show today, man. Super excited to have you.

Dennis: [00:02:00] Thank [00:02:00] you for having me.

Harpreet: [00:02:01] Yeah, guys. So just a little bit of backstory. So I ran into to Dennis on Instagram, probably about a year ago, and we're both like, you know,

Harpreet: [00:02:09] I think, starting

Harpreet: [00:02:10] Our Instagram game. And he put out some of the most amazing bite sized bits of Python and big Data of wisdom and knowledge, and was able to grow his audience from like zero to twenty k. And how long how long did that take you? Couple of months? A couple of months, man. It's insane. And so he's been giving me tips

Harpreet: [00:02:29] On on Instagram

Harpreet: [00:02:31] And stuff, which I tried to implement, but I'm not as good as this guy is. But you guys got to check him out whenever you get a chance,

Harpreet: [00:02:37] Azure will on

Harpreet: [00:02:39] Instagram. But let's let's get to learn a little bit more about you, Dennis. So talk to us about where you grew up and what was it like there?

Dennis: [00:02:48] I actually grew up in Berlin, where I'm still living right now in Germany, and that's a pretty normal big city. So nothing too exciting. That's pretty much all about it.

Harpreet: [00:02:58] Have we've considered

Dennis: [00:03:00] Moving somewhere else but actually

Harpreet: [00:03:02] Been here for

Dennis: [00:03:02] Now twenty seven years and might be moving soon? I'm not sure about that, but some of them very happy.

Harpreet: [00:03:10] Yeah.

Harpreet: [00:03:10] Where would you possibly be moving to within Germany

Dennis: [00:03:13] Or plan to move within Germany for work?

Harpreet: [00:03:16] That's one thing I'd like

Dennis: [00:03:18] To get to Switzerland actually in the future, maybe a little far future, like five years or so. But for now, I'm staying in Germany.

Harpreet: [00:03:25] Nice. Yeah, I was in Berlin for maybe like three or four days back in 2008. I think so. Quite quite a while ago, quite a long time ago, man. Yeah, it was cool. We at this interesting hostel, I can't remember the name or anything, but I just remember there being just amazing like falafel everywhere. Like, Yeah,

Dennis: [00:03:45] That's that's part of the culture. Yeah. When you say everywhere, you'll be nevertheless, like like every second building you'll see is going to be like a falafel or a kebab building. That's normal. Yeah, yeah. But the thing about Berlin is that the price of the city itself, they are like [00:04:00] each their own individual city. That's mainly because the history was that they were different cities back then. So most people that are born in one area here, they generally tend to stay in that area. And that's where all your friends and most of the stuff.

Harpreet: [00:04:14] So it's a nice city. But that's true, too.

Harpreet: [00:04:18] Yeah, I liked it, man. I really enjoyed because I did a few walking tours when I was there, went

Harpreet: [00:04:21] Like saw pieces of the

Harpreet: [00:04:23] Old Berlin Wall, like Checkpoint Charlie and all that stuff. So it was cool getting the bit of that history. And like, I'm really, really into like street art, like graffiti type of street art.

Dennis: [00:04:32] Yeah, that's that's no excuse for that.

Harpreet: [00:04:34] Yeah, man. Berlin's got such amazing art out there. Do you like what do you think? Are you? Are you big into art? Do you go to like the museums? Check out art or anything like that?

Dennis: [00:04:43] Well, I use actually, there's a lot of museums here that I like more the history. But about art is nice to go. And as you mentioned, graffiti is everywhere here and actually part of the culture. So it's not like people don't dislike it. No, no, that has to go away. It's actually part of the culture. And I think that's very nice.

Harpreet: [00:05:00] Any specific type of history that you're into or just general world history just in general? Yeah. So talk to us. You're currently working as a Data engineer. Typekit interest in history. So like in high school, what did you think your future would look like? Were you thinking?

Dennis: [00:05:15] I you think it was going to become a Data and didn't actually want to become a pilot in high school and was always a dream. But I just got into programing like during late high school times, and I noticed that it's actually quite a lot of fun and it challenges you to think about problems and just kept doing that. And at the end I landed where I am right now.

Harpreet: [00:05:37] So in high school, when you were learning programing where you kind of teaching yourself or was there classes that you were taking,

Dennis: [00:05:42] You know, Germany is not the most were for that. We're not a little behind on the whole digital stuff. So when I went to school, you did have computer science. Classes which were like, this is how you turn on your PC. That's how you do this and then like word excel. So [00:06:00] you basically teach yourself, that's what you do. Watching YouTube videos, getting books and just learning and learning and learning and continuing like that. Small projects later. Bigger projects.

Harpreet: [00:06:10] And what was the language of choice back then?

Dennis: [00:06:13] Back then, it was actually starting with like how most people started C and Java, but I got into Python pretty quickly and I think it's good to do it this way around. Other people stop Python and then they get into C or Java, and then they notice, Oh, it's not that easy in other languages, but if you start to see, you actually have to understand how everything works in the back and that's that actually helps a lot to still know to this day.

Harpreet: [00:06:37] That's the interesting point, because I'm traditionally backgrounds of mine as as a statistician, right? Very academic kind of background, and

Harpreet: [00:06:43] I use primarily

Harpreet: [00:06:45] Sas and R when I was, you know, going through grad school and all that stuff. And the first few jobs I had recently started getting into Python. And I was wondering, I was like, You know, I do want to learn another programing language, and I do want to learn something that kind of help me develop intuition from the ground up. So for anybody out there who really wants to get that intuition from the ground up on how things work, do you think C would be the way to go?

Dennis: [00:07:08] It's difficult to say. I mean, I think Python is actually a very good language to get into even as your first language, but you still should be open to what the language offers and what it's offered beyond Python. So I think C is a good starting point, but I think it might actually turn off a lot of people because it is very inherently difficult. Like a lot of concepts that seem like like the way pointers work, that's something that you don't even have to deal with in Python if you don't want to. And the whole way memory is managed, that stuff that you might not like too much. If you if you're someone who just thinks OK, programing is very cool and to do like data science and so on, that might not be the best time in language. But if you want to get into it, then yes, it is a good sign language.

Harpreet: [00:07:51] Yeah, that's

Harpreet: [00:07:52] Good. Yeah. So for anybody listening that wants that deep intuition that you know, the real rigorous kind of understanding of how computers work, then would be the way [00:08:00] if not just stick with Python, I guess.

Harpreet: [00:08:02] Yes.

Harpreet: [00:08:03] So you started getting involved in like software engineering and stuff back in high school, like how did you get involved in or how did you get interested in Data engineering?

Dennis: [00:08:13] How do you get interested in it? I didn't voluntarily get into Data Engineering. I always loved the concept of Data and how it's getting bigger and dealing with big data sets, but I didn't go out and look for a job that was discovering that I actually just doing my studies pretty early on and just wanted to get something a little more practical. So I was searching for some jobs online in my city, and I landed in a consulting company.

Harpreet: [00:08:37] Very small one

Dennis: [00:08:38] It wouldn't know.

Harpreet: [00:08:39] And pretty much was open

Dennis: [00:08:40] To anything as long as it was a little more technical. And then pretty much on my first day, I said, OK, this is the project. You will be working on this. You should do that. I started getting into it. I started working on the project was a whole lot of reading I had to do, and I just was so much more to uncover every single day I was just watching until 11 p.m. YouTube videos on new concepts. And there's so much to learn and was pretty good because the company gave me all the resources I needed to practice with big data. And then I just realized that's what I want to do and what I want to continue doing.

Harpreet: [00:09:17] So I wanted to ask what a day in your life is like as a data engineer. But before I get to that question, I'm curious because I think knowing a little bit of data engineering is important. Even for us data scientists, I think we definitely need to know some of the stuff. What are a couple of concepts, maybe two to three concepts that you think would be extremely beneficial for a data scientist to learn about data science so that we can help make each other's lives easier?

Dennis: [00:09:46] Oh, in general, maybe how all the content of Data engineering what the purpose is is important in order to work together. I mean, what we are doing is basically getting data out of various sources, [00:10:00] and this might be very structured data that might be already in the database. So it will be a little easier or it's something like a log file from a machine and has an awful structure. And what we're doing is trying to get all of that data into a unified structure somewhere. And what you do with that data after it's pretty much data science is, of course, a big option, and that obviously needs that structured data. But it's not just that, it's it's several other analysis that can be done. And I think for data scientists, it's important to know what you want to do, what kind of data you need. And yeah, maybe also how big the data is going to be because that is always a challenge. If you have smaller data sets, it's always easier for data engines to get that data. But if it's coming from a lot of machines everywhere, then you should know exactly what kind of data into the is that you want. If that makes any sense.

Harpreet: [00:10:55] Yeah, no. Definitely makes sense. So because you know, we're so used to working with like flat files and stuff when we are doing our analysis. So it's good to kind of understand the lineage of that data, how it goes from wherever it's created into this flat format. And it might. Some messiness in terms

Dennis: [00:11:13] Of how it's that's basically the process that as a Data engineer, the first thing you are is it is like a consultant. You talk to the person in business and they tell you that they have this data, they tell you what they want. And then your job is basically in an ideal

Dennis: [00:11:29] World. You have a big team and a realistic world. It's just you and then you have to plan and infrastructure that you want to build. And this is actually the biggest thing that you have to think about. It's generally not that difficult to get the data out, to extract the data, to process and transform it and to get the right shape. But it's also a little bit of a financial things. Probably the best word because you can use a lot of services that are very expensive and powerful and to get the job done very efficiently. But usually [00:12:00] companies don't want to spend that much money. So that's why you have to think up a solution that is very efficient, very memory efficient and in the end also gets the job done. But again, it's always what the customer wants, and if they want to do data science, then your first job is to think about how I can get the relevant data out of whatever system. We want to get the data out from now.

Harpreet: [00:12:22] Like, I like that point a lot. It's always about what the customer wants first. Always, right? Exactly. It's that, I think focus that we need to adopt for ourselves as

Dennis: [00:12:31] They don't know what they want, so you'll have to start getting that value as well.

Harpreet: [00:12:34] Yeah. So do you use any frameworks like or packages to help you with data engineering? Does anything like that exist that we should probably know about or I?

Dennis: [00:12:45] It's always depends on the type of data. I mean, for for big data, Apache Spark is a big thing, and it's gotten a lot more popular. But are people searching for when when you see a data entry position is always going to be? You have to know Apache Spark, you have to know like obviously that Azure or Google Cloud Services and Apache Spark is basically a it's a framework that allows parallel computing and it will enable you to get basically it used to be MapReduce, which is the idea of splitting up data into several parts and then reducing them to one part again. And it's basically working with clusters. You have one big cluster which has several workers, and this allows you to process data in parallel. And what I don't like is that it is a very useful to know, but most in most cases, you don't need that. It's very expensive. It's only for a very huge data sets and companies like to use for every single thing. So a lot of the things you can actually do with other services like like serverless functions that are a good example. Those are like a lot for Azure that called Azure functions and a W as it's called lambda. They are more difficult to set up, but [00:14:00] you can do pretty much the same things that you can do with that you would be able to do with Apache Spark. So those are two things I can think of and think about this field is that there is some new things every month and you have to get into the and you have to think about, Hey, this is something I could use. Is this useful? Sometimes it's a waste of time. Sometimes we actually learn something very valuable.

Harpreet: [00:14:22] That's it. Excellent point. There's always new stuff popping up, and you always have to try to try to keep up on stuff. How do you manage that? What's your for new tool? New tech comes out that you either hear about. Read about what's your process for determining? Is this something I spend my time

Dennis: [00:14:37] Going to your question and something that I think can make you very valuable if you know how to do it right. What I like to do is basically, I'm looking always for new news. That's something coming out

Harpreet: [00:14:49] If I am working

Dennis: [00:14:50] Mostly with other services, so I try to stick with those. But what you have to remember is that Azure and even Google Cloud, like all the services in there, they have their parallels and the other service, so it's not going to be something entirely new. So I'm looking out for those new ones. There's a lot of YouTube channels you can watch. Like for Azure, there's something called as of Friday. Every Friday, they just present something new and then I take a look at that. She can also obviously LinkedIn. It's always a very good source for that, and you can take a look at the videos first. And if you notice, hey, that's actually quite interesting, then it's very easy to set up a service like like a resource and it just test it out. It's also not that expensive most of the time, because if it's something that's very new, it's usually a preview like a private preview. The prices are very, very low. But in general, you will know when looking at the videos, looking at the documentation, you can generally guess this is something that we could use or it's something that's not that useful.

Harpreet: [00:15:46] I like that point you made about all of the cloud services have their parallels in Cross GCP. Azure would be like they all have. Essentially, they're all doing the same things. They just name it differently. They work a little bit different. So that's the excellent point because something [00:16:00] people always ask for. My mentees a day science dream job is which should I learn? And I'm always like, What's going to change depending on which company that you go to? And then I think the answer is right there. They're all the same as long as you understand the principles of what's going on.

Dennis: [00:16:15] It used to be job positions. They were asking for something particular, like just Azure. But most of them right now, they just ask for experience in the Azure, NWS or GCP. The thing is, it also depends on where you live here in Europe, as well as are most prominent, Google Cloud is a little bit lagging behind. Best example I have is Databricks is basically a very big platform. They are also basically making the patch as far as possible. It's a platform that's enabling you to integrate Apache Spark and they were available on Azure and us for a while. I think last week they started to be actually. This week they announced that they'd also be cooperating with Google Cloud. So that's one big step. And as you said, it doesn't really matter which one you get into. It's very easy to switch, of course.

Harpreet: [00:17:05] How did you find yourself getting into Azure? Was it just because the consulting company started off that that was the platform of choice?

Dennis: [00:17:12] That was pretty much what happened actually in the job

Harpreet: [00:17:15] Until we get this right here,

Dennis: [00:17:16] That's the Azure portal. Take a look at it. You'll be spending all day in that. And that's how I got into it. And then I started going to a few months and I started going to meetups. I like Meetup a lot. I'm not sure if you have them in over there. Well, Dave, they used to be before the whole corona, but I started going to those. I started going to conferences like Microsoft Ignite and it's just a great atmosphere.

Harpreet: [00:17:40] You can just go there and

Dennis: [00:17:42] Just settle this in meetings. At the same time, you can take a look, decide for yourself to something that you want. Is this a great Harp? And there's a lot of networking and other people that have experiences that tell you what they have built at their company, their architecture.

Harpreet: [00:17:56] And this is been I

Dennis: [00:17:58] Realized that, yeah, everyone's [00:18:00] actually offering a whole lot of services, but not everything is good. Of course, like one thing I would complain about is documentation. For Microsoft, it's always awful what I have to say. Yeah, yeah,

Harpreet: [00:18:11] It's very circular. Their documentation, I was at the link and then link and then have like, next thing, I try to figure out one thing and I've got like seven tabs open.

Dennis: [00:18:19] Because, yeah, to just keep going into the thing with Azure is that we have a subscription. We have like premium support and we think that the people working there would be experts at what they do. But realistically, they also only look at the documentation. So especially with the worst services coming out, you try them out and you have a question, and all they can do is look at the documentation and in the end, you just have to figure it out yourself. But I don't think that's going to be different for us, to be honest.

Harpreet: [00:18:47] How important do you think being resourceful has been in your career? Do you think that's a underrated skill for Data professionals?

Dennis: [00:18:56] I think that's actually a very good skill to have as a Data professional because you can do things by the book. You can do things as recommended, and I know a lot of companies that have been in hospital have worked with. They like to use like the Microsoft or Data because consultants and they basically tell them how to use their services. And that is, of course, a good thing. But it's always going to be a very expensive thing if you do it this way. And being resourceful for me means always taking a look at how can I do things in an alternative way? That doesn't mean you should build everything yourself, like the whole pipeline, the whole architecture. You don't have to build everything from scratch yourself. This is something that's already

Harpreet: [00:19:34] Been offered by

Dennis: [00:19:35] Microsoft, but it is something you should consider. And it is a lot of fun to do it this way. It may mean you have to maintain a lot more yourself instead of trusting like Data require Microsoft to do it. But at the same time, it gives you an amazing amount of insights on how the whole process is working. So it helps me a lot.

Harpreet: [00:19:55] Right? Oh, man, that's excellent point. I love that. Absolutely love that kind of attitude. So [00:20:00] question that's been making my head kind of scratch here is the difference between a Data architect and a Data engineer. So how are these two roles similar? How are they different?

Dennis: [00:20:11] They ask you the same about the difference between a data scientist and a data analyst, because there's also two things that seem to be the same to me sometimes. First of all, we have to we have to remember that for every company differently, some person is looking for a data analyst, but they actually need a data engineer or a scientist or data architect. Personally, to me and in my company right now,

Harpreet: [00:20:32] A data

Dennis: [00:20:34] Architect is the one that brings up the whole architecture like the building, the infrastructure, Data planning to build the infrastructure and what services that we would need. But they don't actually implement that. The person that is setting up the pipelines, the pipelines are the things that basically deploy the resources and the person that builds this architecture. That is what the Data engineer does. That's what I'm doing at the moment. But as I mentioned a lot of companies, those roles are merging. So for me right now, I'm pretty much sure the whole thing from the very start, I'm thinking of the infrastructure and building it, maintaining it. I'm thinking about how to optimize it. And that's just the reason that there's not that many Data professionals at the moment, but that's something that should grow in the future.

Harpreet: [00:21:19] And you're still in consulting, right?

Dennis: [00:21:21] I know actually went out of consulting about a year after I was in that company, and now I work at a somewhat big German company called Size. I think you should know that for glasses, they make glasses. They also make medical devices, which is what I'm working with at the moment and a lot of microscopes and so on. And what I do there is I, as I mentioned, I work with the medical devices that I extract the information from those actually operational and microscopes that you use in a hospital for neurosurgery. And I extract that information and analyze it, and it's basically coming in from all over the world.

Harpreet: [00:21:55] That's awesome, man. That's super cool. Microscopes for for [00:22:00] brain surgery, essentially

Dennis: [00:22:01] For brain surgeons, among others like this other devices. But yeah, in general, that's Data that we are getting from from our files all over the world. And it's pretty cool. You can pretty much track everything that happens during the operation. You can determine an error by error has happened, and it has actually helped a lot with the development of future devices and improvement of the current devices.

Harpreet: [00:22:25] That's awesome, man. Thanks for. Thanks for sharing that. I would ask more probing questions, but I'm sure there's confidentiality issues here. So much, so much. I want to learn about that.

Dennis: [00:22:34] That's really fascinating in medicine. That is a very big issue. It's also where things tend to move a little slowly, like when you try to improve something, it takes a while.

Harpreet: [00:22:43] But that's normal.

Dennis: [00:22:44] And not of the Data I'm dealing with is confidential Data. And it's always the first point. Like, you're not getting

Harpreet: [00:22:51] Like, you're not

Dennis: [00:22:52] Going to be able to see it in what patient they operated on or so. Yeah, something like that.

Harpreet: [00:22:56] I was in pharmaceuticals for five years. I worked as a biostatistician, so I'm very familiar with the regulations and things like that.

Dennis: [00:23:04] So get in the way, but it's obviously necessary.

Harpreet: [00:23:06] Oh yeah, definitely. So when you were in consulting this kind of thing back then, when you're thinking about how to come up with a plan for how you're going to solve a tough problem, I guess what is your problem solving process like, right? Let's say you just climb, comes up to you and says we need Data architecture in place. Like, what are some of the things that you would ask your client so that you can figure out what it is that you need to go do?

Dennis: [00:23:30] Well, first ask them what their end goal is with that Data like in general, what the process is going to be. They're going to tell you, OK, be able to analyze that data and maybe put it in the dashboard or something. And then I'll take that information, present them with the first draft. And here the good thing is that if you if you were consulting and I have a lot of friends that still work in consulting,

Harpreet: [00:23:52] It's

Dennis: [00:23:53] Different companies, different projects. But basic skeleton of the project might be very similar for us. It might be something [00:24:00] like a data lake that's a center, like a data lake. You can imagine just all the data is coming in at one central place, and from there you can build the pipelines. You can analyze that data, you can transform that data and push it, push it elsewhere. So often you can actually suggest something similar for people. But then there might be some use cases where it just doesn't work because it's a very special project. And one advice I can give is that you should always be honest and don't overpromise that don't oversell. Don't try, try to tell them, OK, you can do this and that and all of that. Sometimes it really is not possible. Sometimes the things that they want are not possible. So often as a consultant, you first have to tell your client what it is that they really want, even though that sounds a little bit stupid. That's one thing I can say about that.

Harpreet: [00:24:45] Thank you very much, man. I appreciate that. It's always, always interesting for me to hear kind of what people's problem-solving processes

Harpreet: [00:24:52] Like and really helps

Harpreet: [00:24:53] Me build my own problem solving technique. And I know the audience analogy that. So where do you see the field of Data Engineering headed in the next two to five years

Dennis: [00:25:04] And somewhere you're looking for growth? There's still a lot of companies that have Data.

Harpreet: [00:25:09] Engineering was built

Dennis: [00:25:11] On something that used to be called business intelligence first, and that was it's a lot of SQL stuff. And that's what also a lot of people think Data engineer does this do sequel stuff and databases. And while it's true to some extent, these Data as well, the thing that's changed everything and I think it's going to be a big step that most companies will accept that the way they have set up right now, which is some on premise database that's not going to be the future. And they have a lot of data and they want to analyze so they will move to the cloud, they will move to big data architectures. And there's the need for a lot of data professionals and data science thing. A very good example that in the previous years has grown and a lot of companies say, we want to do machine learning, we want to do data science, but they don't even know what that means. That's why I think that it's going to get more important because for data science [00:26:00] to be able to work properly, you need a good architecture to Data in the right place. And I think only going to get bigger from here.

Harpreet: [00:26:07] I absolutely agree with you. Yes, I read something. I mean, I'm sure you've probably seen this thing pop up. As well, Data engineering jobs are going up and Data science jobs are going down like that's not necessarily a bad thing. I think it's a good thing that there are more Data engineering roles because the more Data engineering roles you have than a byproduct of that will be more data science roles. You guys are now data scientists can

Dennis: [00:26:30] Be like a cycle. It means that right now there's more data engineering at the Met, but in five to 10 years, it might be more data science again, because there are more architectures. But it's a good thing that you mentioned like, especially last year. I've heard from a lot of friends and also colleagues that both companies last year are afraid of what the future would bring. They would stop data science projects, and a lot of data science would have not much to do at this place. And that changed quite quickly, like half a year after everything was back to normal. But it was all you can see the relationship between data science, the data entry, because I was doing more than ever last year because my my projects didn't change that much. But I think it's as I mentioned, it's going to be a cycle like it's going to be right now. Ais Data engineering in a few years is definitely going to be data science again.

Harpreet: [00:27:20] So what can aspiring Data engineers do now to help prepare themselves for the future?

Dennis: [00:27:28] That's actually a very good question, and I have to think of what meme that I saw a while back, but it was a data science where people were asking, What does a data scientist need? And then there's going to be a lot of math related things. But the real thing they do is Python, because most people just ask for Python in both data engineering and data science. So if you want to get into data engineering right now, I would recommend taking a look at three things one is still going to be databases and SQL, not because you're going to use it a lot. I don't use SQL that much. I [00:28:00] set up databases and I built them, but everything happens automatically. Like the things I insert into the database and everything is happening via Python code using an OEM, and you still have to understand the structure of a database and how you can design it. Because the data I have right now, it

Harpreet: [00:28:17] Grows exponentially

Dennis: [00:28:19] Every day. There's just so much more coming in. And if you set up a database or any data storage that is not optimal, you would regard it in the future because it's going to be too much to maintain. So get into that into how to set up a database. Even the basics of relational database will help a lot. Second thing, as I mentioned, Python is a very good language to get into that. And the third thing is that you mentioned before, like the big data frameworks like Apache

Harpreet: [00:28:43] Spark, they are very good to

Dennis: [00:28:45] Know because a lot of companies and internet, they'll ask questions about that, for sure.

Harpreet: [00:28:49] Yeah, I've been upping my game and knowledge about databases with this book in particular, it's the the main go to databases. I don't know if you've seen this

Dennis: [00:28:58] Before, but that's the top top notch state of the art material. It is, and it is.

Harpreet: [00:29:04] This is a good book, especially if you're like me and that stuff kind of boards you got.

Dennis: [00:29:08] Sometimes it is. That can be very boring. And when I take a look at like when I worked in consulting, I with the of them. But it's something I had to talk about when I worked in consulting. It's the same as it is there was three years ago, and right now in a lot of companies, it's still the same thing like this. Consulting companies, they try to offer Data engineering, but most of it is again, this business intelligence stuff like people maintaining sequel, not just databases, but also data warehouses, which is basically just this huge Data database. And then I would take a look at what my colleagues are doing, and it's like a thousand lines of sequel queries and I'm thinking, Oh my God, I'm so glad I don't have to deal with that. That's one thing that is very boring, and we will never have to write this big query. That's one thing I can promise you, but you should be able to understand

Harpreet: [00:29:53] What that query would

Dennis: [00:29:54] Do. And just the concept of, you know, of a relational database [00:30:00] works is very important.

Harpreet: [00:30:01] Yeah, 100 percent agree. Speaking of, we're talking about some boring aspects of our jobs. Maybe I guess it might be leading the question too much here, but I know.

Dennis: [00:30:12] Go ahead.

Harpreet: [00:30:13] What? What are some of your favorite misconceptions about what it is that a Data engineer does?

Dennis: [00:30:18] That's a good question. Something I talk a lot about colleagues and comes up a lot. One thing I just mentioned, I think that all we do is to SQL and maintain databases and so on. That's a very popular one. And this is when I take a look at there's going to be a lot of recruiters contacting me and a lot of job offers that I take a look at. And I'm just thinking, you're not really looking for a data engineer. You're looking for someone to maintain your data warehouse on premise. Like, that's one thing. And that's also something I can highly recommend to not get turned off by because most people now realize what a data engine is. That was a lot worse a few years ago. The other thing is, and I think that also happens to data scientists or Data analysts like they think that, like all you do is build a dashboard. I personally, I have built some dashboards, but we leave. What we do is provide the data for that. So it can run optimally and it's really filling, and that's what is often a drag and drop thing. So that actually bothers me a little bit, but I also didn't find it very funny because it's it's often what people think about the job.

Harpreet: [00:31:27] So we talked about this a little bit earlier. Maybe you might have touched on it, but what can a Data scientists do to make the lives of their Data engineering colleagues easier?

Dennis: [00:31:39] I guess many things you could say here. And one of the thing that I mentioned earlier was to understand what you actually need, what kind of data you what was the format? Where should it be? And don't be afraid to ask questions, and I don't know how it is over there, Air Canada or the United States. But here, most of the people that I work in as a data scientist at PhD, it's [00:32:00] just going to be like that. And they don't come from a computer science background. They come like they came from a background or a physics and so on. At my company, most people reckon that physicists and

Harpreet: [00:32:12] I think it's

Dennis: [00:32:13] Really great and they bring in a lot of knowledge, and a lot of them are also. I like that they can adapt and realize, OK, you need this kind of knowledge and they do ask good questions. And this is one thing I can advice always ask questions if you don't understand something. If you don't know how cloud architecture is working, even if it's a very simple thing, if you don't know how a version control system like it is working and and a lot of people don't just just ask, it's everybody has to start somewhere and you have to learn, and nobody's going to mind a question like that. Try to not just adapt, but try to also make your own suggestions because the Data engineer won't always know what data science looks like. So it's always working together. It's always working hand in hand.

Harpreet: [00:32:55] That's an important point you made about asking questions like just ask like if you're stuck, don't pretend like you understand it, don't pretend like everything's OK because you're just going to delay your entire team. So do you have any words of encouragement or advice to share with anyone who's afraid to ask questions because they don't want to look stupid?

Dennis: [00:33:17] Maybe something that every teacher said is that they are no stupid questions, but that's actually not true. That's one thing I have to say. And you should do a minimum of knowing how to use Google and look something up yourself. So somebody comes to me and they just ask the question they literally could have Googled. And I'm going to say, maybe you should have looked at that yourself. But what I noticed is that most people, they actually Google things themselves. And then they come with the results of Google from Stack Overflow. So they come up with that and then they ask something a lot more in depth. And then I realize that's a very good question, and that's what I like to see that people have dealt. They have tried to solve the problem before. In general, there is really no there are no stupid questions. If [00:34:00] you're starting off, you can ask with anything like if you don't understand how the most basic things are working, just ask. I'm not going to mine it. I'm actually going to. I like the thing is, if you teach something, if you can answer something, you always learn a little bit of yourself as well. And that's why I like to answer questions. But if you want to make an impression on the more senior people in your company, you come with a really good question. You get your first draft and then you come ask you that question. Then the person is going to be very invested in helping you

Harpreet: [00:34:29] Look that if you teach something, you can learn it twice, right? Yeah. And speaking of teaching, Azure will Instagram page. Talk to us about how you kind of got into that.

Dennis: [00:34:39] Let's say one thing first, I haven't done much with Instagram lately, like I try to do it every now and then, but it's just that the work gets too much. Then sometimes you lose a little inspiration. But what I first did was actually, and I think that was now actually pretty much exactly one year ago, a little more than that I

Harpreet: [00:34:56] Just thought

Dennis: [00:34:58] About to Instagram in general, and I thought about with the whole big Data trend going up and up. There's not that many channels offering. I guess there's always going to be Python channels or programing channels, but there's no one at that point that was offering to little more than that. So I thought that's actually an interesting thing to teach people. And with Python being such a great language, such a fantastic community, I also thought that's something that I have to teach people and something I just like to get into. But as you mentioned as well, Instagram to start it can be very annoying because nothing happens for a month or so and it can be a bit of a turn off. But if

Harpreet: [00:35:37] Someone is planning to get

Dennis: [00:35:38] Into that, that's all I can say. Just keep going. It eventually will happen.

Harpreet: [00:35:42] Yeah, as long as you're passionate, yeah, I kind of like paused on my Instagram posting. I guess I just have a bigger following on LinkedIn a lot more engagement.

Dennis: [00:35:49] So that's great. I mean, you have to know your medium and whether or express it likes work best. So that's all I can say to encourage. Just maybe if it doesn't [00:36:00] work after a few months on something that Instagram or YouTube, maybe just try to find a new medium. That's something that can help as well.

Harpreet: [00:36:06] And the way you plan out your post is very, very thoughtful, right? Because you could tell right from the first because you do a lot of carousel posts in the first post and that carousel based on the color based on the different type of crown you have on there, it signifies a different type of post, right? So just for for anybody who's listening here, I know they're going to go and check out your page. Do you mind telling us how you structure that, how you've planned that out for your carousels?

Dennis: [00:36:32] And it's been a while, actually, but generally I had these different colors. I had the dark post and the brighter process, and I was at first I tried to make the dark post exclusively. Python and the bright posts something more data science, big Data, Data engineering. That's awesome because I would use Jupyter Notebooks for the bright posts, and I would use Python for the back post. And, you know, it just looks better. So that was one thing. Later, I experimented with the crowds that tried to symbolize a little bit of difficulty of the post. I get the bronze one, the silver one, and it wasn't silver or gold and diamond tron. And I thought people would immediately be able to tell, Hey, this is a little higher level, this is a little lower level. And I got that feedback as well. But I also got the feedback both of the crowd queen, so it went both ways. But in general, the starting page is going to be what the topic is about, and I always try

Harpreet: [00:37:25] To have a very

Dennis: [00:37:26] Catchy title and ask the question like, I have a subtitle that kind of makes you think a

Harpreet: [00:37:31] Little bit.

Dennis: [00:37:31] And yeah, then I try to basically design my post so that anyone could understand them. And the difficult thing about Instagram is you have a maximum of 10 slides, 10 carries you can put in. Realistically, it's only going to be eight or nine because the first is going to be the starting page at the last is going to be the thank you for looking at my first page. So with eight pages and if you make the. On too small, I would make it fun to follow up going to like that. So [00:38:00] you have to make it big enough to be readable and you have to put in some images and some some code as well, and I thought it was quite challenging. And yeah, that's why I actually liked it about it, that you could put a complicated problem in just eight carousels with rather big font. And as people can understand something there, then you know you're doing the right

Harpreet: [00:38:20] That your posts are really, really awesome.

Harpreet: [00:38:23] I really enjoyed them.

Harpreet: [00:38:24] So you made a post a while back on the same Instagram page about why we shouldn't use functional programing. Yeah, yeah. So I mean, like, I don't know much about this. It's just I was recently rereading a book called The Unicorn Project. I'm not sure if you've read it, but it's a fictional book. You'll definitely, really, really enjoy it. Fictional book about software development team working on a essentially a Data project. And actually the main protagonist she's all about, like functional programing and using Lisp in all of this. And I was like kind of reading into it, just trying to understand what it was all about. And then I guess just coincidentally, I was researching you the same day preparing the questions, and I saw that post about the functional programing. So I figured, why don't we get into the. So talk to us about what functional programing is and why we shouldn't use it.

Dennis: [00:39:09] First thing I have to say is that the folks actually made me realize the bad side of Instagram. I actually got a lot of hate for that and a lot of people complaining about it, the people that was actually what I mentioned. The title has to be a little catchy to get people's attention, but at the end, what I was saying in the post that that obviously it's up to everyone. But to get back to your question, the idea of functional programing is, as the name is saying, it's a very big field of the program paradigm, just like object oriented programing would be functional programing. The idea is that

Harpreet: [00:39:39] You apply a

Dennis: [00:39:40] Function to to objects to basically modified to mutate them, but you don't want to have something like a shared state like like it would have an object or classes and so on. You basically focus on it's a very declarative idea of programing. It's a big field. And I was talking about Python in that post and [00:40:00] I was talking about some of the concepts I just mentioned, like applying functions and python. We have the map, the filter and also the reduced functions and maybe so quickly explain what they are. It's actually a very simple concept like map. You can imagine you have the idea of you have any trouble, which is a collection like a list or an array. And the idea of map is that you apply some sort of function on every element in that list. It could be something as simple as squaring all the elements in one list, and that is what Map does and fill it, on the other hand, is very similar. You have the idea of you basically define a function, and it should always be a function that returns a truth value that true or false. And then you check that condition on every element and that the trouble and the list and if it's true or false and the result will be a filter like the name says of only the truth, the values from the fountain. So again, the idea is always very similar, like use a function and apply it on some sort of object or something terrible. And it's very, very useful. And a lot of people that went to university, they will have covered these concepts to no end. If you study computer science, you learn them in Java, you go to them in JavaScript and they are very useful, as I mentioned.

Harpreet: [00:41:14] But it's not very clean code.

Dennis: [00:41:16] And one thing about Python and the thing I love about is that the code is very concise. It's very clean. Like we can read, it can take a look at it and you will know immediately this is what the code is doing. And in Python, it would be nice if at the post, nothing could show that the little petabyte in Python, if you want to use map or filter, often you're going to be filtering something and then mapping the result of that filter. The trouble? And again, it's going to be so nested, so deeply nested. It's just not going to be readable. And if I look at code,

Harpreet: [00:41:46] I need to

Dennis: [00:41:46] Be able without comments. Actually don't like to use comments. It has to be very basically the first look. You need to know what the code is doing and that is not possible with these concepts. And Python offers is something like this comprehension [00:42:00] or generate expressions, and they are very, very cool. They can be done in one line. And the most important thing is is that they are like English language. It's basically

Harpreet: [00:42:08] Saying, do this

Dennis: [00:42:09] For all the elements in that list. It's very easy for people to understand and it's by a lot of people love Python.

Harpreet: [00:42:17] Yeah, I'll be sure to link to that post in the at this timestamp in the show notes that people can check that out so they won't miss out on that.

Dennis: [00:42:24] But actually, the bottom line was at the end that no one is a bad programmer as they use this content like map or filter. And one big thing is recursion. It's also something that's a big part of functional programing. Recursion is very cool. It makes code very clean. It's also a very complicated concept that in my university, they're just like from day one, they told you recursion is going to be the very big thing. It's the best thing ever. You have to understand it. I don't think that's much recursion since then. The idea is that the function is going to be able to call it. So you have a base case and then the families are going to keep calling itself, and it's very cool for something like I think the best example is the Fibonacci numbers or calculating the

Harpreet: [00:43:03] Factorial of a number.

Dennis: [00:43:06] The problem with recursion is that it can get very, very slow. We to imagine the phone keeps calling itself, and it's just going to blow up at some point for the Fibonacci numbers as well. If you have a bigger number, you always have to calculate the smaller Fibonacci numbers again and again and again and just going to blow up. It's very slow. It's very inefficient. That's what a lot of people don't seem to realize that go a little broader here and go back to Data Engineering. If you build an architecture

Harpreet: [00:43:32] And

Dennis: [00:43:33] You have to process a lot of data, something that may seem very small in your line of code can blow up quite quickly. Because if you have millions or billions of files coming in and if the same thing over and over and over and over again, every second you can save there, it's going to be very valuable at the end. So it's always important to know how to design new code to be most efficient. And yeah, that's one thing I wanted to convey in the [00:44:00] process.

Harpreet: [00:44:01] Wonderful, man, thank you so much, I appreciate that deep Dove. There's a lot to learn there. So for people out there who want to build out like a Data engineering project, Data science projects, everybody kind of has an idea of what to do for those. But yeah, I think you have to admit. Yeah, but what about like Data engineering project? Like, how do we what do you have like any tips, any ideas on on know a project?

Dennis: [00:44:24] It's actually not that easy to build that up, because if you have to work with big data, you likely don't have access to those kind of data sets. Microsoft and not just Microsoft. Actually, there's going to be a lot of offerings you can look up on Google, even like you can get these datasets like from everywhere. That's like something like flight data or anything like that available publicly as CSV files, you can download those

Harpreet: [00:44:45] And build your project

Dennis: [00:44:47] Around that. What I would recommend, however, is to not go that big yet to just start with very small files that can be something as a few megabytes of just text files and you see files and maybe even some log files that are very unstructured and think about how can I put some structure to that data? And the end goal should be to get it into some sort of data storage. It doesn't have to be a database, but it can also set up something like in-memory database like SQL Light. It's very popular, and you can talk to that from the Python easily, and you can also set up something like a PostgreSQL. That's a very, very popular format, a very popular database. You can also set all that up locally. It's not going to cost you any money, and you can play around with that. If you want to get into something like Apache Spark used to be very difficult because you have to set up the cluster yourself you to your computer into a cluster. And setting that up is very difficult. If you have the time, I can highly recommend it because it will teach you so much about setting up concepts and frameworks like that if you don't have the time.

Harpreet: [00:45:51] Data Breaks

Dennis: [00:45:51] Is actually offering you

Harpreet: [00:45:53] To try out

Dennis: [00:45:55] The Data on its platform for free. They will offer you a cluster which usually cost a lot of money. [00:46:00] It's a very small cluster, but if you're a student, I think it's going to be completely free and that's what most people can use to get into it. And that's something I might actually send you via email after the podcast, and you can link that because I think it's a great offer. I know a lot of people know about it.

Harpreet: [00:46:16] Data breaks is is amazing, and you don't even have to be a student to get the free tier, at least. Oh, OK, OK.

Dennis: [00:46:23] That's really cool. Yeah, that that's even better.

Harpreet: [00:46:25] Yeah, it's definitely very useful. Very cool thing to to try out and learn. So what are some tips you can leave with our audience on how we can be more valuable in our jobs?

Dennis: [00:46:36] I think it's something I talked about already. You should never stop learning and you should never take anything for granted just because your current infrastructure, your current architecture is working doesn't mean it will be the best thing in a year from now or five years from now. So those are two things I would recommend. First of all, keep optimizing your current code, keep optimizing and current infrastructure, evaluate new services, but also improve on just the basics, like your code. You always have to get better. You should always learn more things. The second thing is never be afraid to look elsewhere. As you mentioned, we have Azure and GCP. They do offer pretty much the same things, but some of them do it a little differently. Like as I might offer

Harpreet: [00:47:18] Something in a different format

Dennis: [00:47:20] That we use might

Harpreet: [00:47:20] Not and never be afraid

Dennis: [00:47:22] To switch never stayed in one position. That's the worst thing you could do, because then you end it like some other people that have maintained some safety code for forever. I think SAP is the best example. It's very difficult to get into. It's been stagnant for 20 years. I have one colleague that came from an SAP field, and if you want to get into sap, you have to learn the things that were there 30 years ago and you'll make a lot of money if you get into S&P. And I think that's the main reason that you have to learn all that stuff. So always keep improving. Never take anything for granted.

Harpreet: [00:47:56] I love that man. Great advice. Thank you very much. So last formal [00:48:00] question before we jump into our random round. It's 100 years in the future. What do you want to be remembered for?

Dennis: [00:48:08] I that it in the future. Well, well, first off, with modern medicine, I might still be alive by then. I don't know if that's a profession. And if I'm not alive at that point, then I don't think it has to be something very big. But as long as he made a change, as long as he made even a small change in some person's life, I think that's going to be something worth remembering by when you're gone and someone will still remember something you did for them. I think that's actually a very good thing to have to be able to say.

Harpreet: [00:48:40] I love it, man. I actually love it. Hopefully, 100 years in the future. Youtube is still around and you know, people can can come back and listen to you, talk about all this wonderful advice.

Dennis: [00:48:49] You can buy that question I would actually send right back to you. What do you want to be remembered for

Harpreet: [00:48:55] 100 hundred years from now? Man, that's crazy, because I asked this question a lot like it's one of it's like the closing question to every one of my interviews, and it's not really one that I've really thought about myself. What I do is that I actually would want to be remembered for. And I hope that I'm just remembered for somebody who's been able to connect people and bring people together. That's one thing I hope I get remembered for is just being a connector, being somebody who is able to bring people together because I feel like that's been a recurring theme in my life, right? So any time I went back home to visit friends and stuff, right, because I moved away from Sacramento and was gone for a while and I always came back, and every time I came back, everybody would say, Oh, you're the reason that we were coming together again or every job I've been at be getting people to hang out together that didn't hang out together. Now I've got these open office hours that I do and and people are coming together from all parts of the world to meet and share ideas. And I guess that's the main thing I want to be remembered for is being a connector.

Dennis: [00:49:55] Very cool.

Harpreet: [00:49:57] Thanks, man. So let's jump into the random around here. [00:50:00]

Dennis: [00:50:00] Well, let's go ahead.

Harpreet: [00:50:01] So when do you think the first video to hit one trillion views on YouTube will happen? And what will that video be about?

Dennis: [00:50:11] The first question I have to ask is what's the maximum use right now on YouTube about things like 10 billion or something like that?

Harpreet: [00:50:17] Yeah, not quite. 10 billion is Baby Shark. And it has roughly eight billion ish billion.

Dennis: [00:50:25] That's quite a lot. One trillion depends on how we keep growing, but I think it might be possible in the next 10 years. It could always be something that blows out there, but it's going to be about, I think, the top 10 right now. They're like 90 percent music videos or maybe 100 percent music videos. That's going to be likely something like that or like something that's like Baby Shark.

Harpreet: [00:50:49] Yeah. One trillion views and just that number to me is just insane. Like one trillion. What would that be in terms of like gigabytes or petabytes? Like what is that a trillion

Dennis: [00:51:01] Think one trillion would be? That would be a whole lot. I mean, but even thinking about petabytes, it's, I think, going to be the big weight of those.

Harpreet: [00:51:11] Yeah, that's crazy, huh? What a huge number. So.

Harpreet: [00:51:15] Next question here, in your opinion, what do most people think within the first few seconds when they meet you for the first time?

Harpreet: [00:51:23] That's a very interesting question. It's hard to answer.

Dennis: [00:51:26] I think there will be one day where I was smiling so much. I like to smile a lot and say, I know I'm very open to people I need, and I try to get personal quite quickly. Sometimes maybe a little too quickly, even in a work environment. I don't know how it is with your company, but in my company there's still a lot of hierarchies and some people are very informal and I've never liked that. I'm very upfront and personal, by the way. And I've noticed that a lot of people appreciate that and even the ones that are more conservative, they actually like that you open. So, yeah, I think people think either on too direct [00:52:00] or I'm smiling, too much work with you.

Harpreet: [00:52:02] That's good, man. That's good friendly disposition. And I mean, you're like that. When we first crossed paths on Instagram, you're just ready to help me and offer advice and share information and stuff like that. So I could definitely see you being like that in the workplace, man. That's that's awesome. So this question here next one is, do you think you have to achieve something in order to be worth something?

Dennis: [00:52:27] Also, quite a good question when you think about it and not one I could give a direct and perfect answer to. It depends on how you define to achieve something. Or rather, how do you find out being worth something? I think everybody can be worth something. Everybody is for something and you don't have to be the very best at something and you don't have to keep improving like you and I do. I don't think that's necessary to be worth something. Definitely, not everybody should be proud of what they do. And as long as you're happy with where you been at in life, I think then you're definitely worth something. But I think I can recommend everyone to just keep trying the best they can to improve because it's a very, very satisfying feeling when you get something done, when you are ahead of the curve. And again, I can only recommend it.

Harpreet: [00:53:13] Yeah, constantly improving yourself, even if just a little bit it pays dividends. It's the compound interest. So what are you currently reading?

Dennis: [00:53:22] Well, I wish I could read a little more at the moment, having read something a while, to be honest, because it's just so much to do. The last book I read was, I think, must have been late January. I was reading a book called I Think It Was Your Money and Your Brain was nothing about the stock market and the psychology and the way people take a look at money and wealth and a lot of people that are not

Harpreet: [00:53:45] Wealthy, they get

Dennis: [00:53:46] Very envious and very negative towards people that are wealthy. And that actually that is what's holding them back to get to this point as well. And I thought it was a very good book. And I think what's called your money and your brain

Harpreet: [00:53:58] Definitely check that out. I could see that being true, [00:54:00] man. Like, if you think that money is the root of all evil, you're going to have this disdain for it and you just actively going. I mean, not maybe not actively, but subconsciously work against yourself for that.

Dennis: [00:54:11] I mean, I'm not saying that everyone who is rich is going to be a saint or a person. There's a lot of people that aren't, but I don't think that should define who is a good person, and I think he should not be envious of someone who has more than you.

Harpreet: [00:54:25] You should always be happy

Dennis: [00:54:26] For them because I'm pretty sure that did something right to get to this point.

Harpreet: [00:54:30] Yeah, definitely. Definitely. What song do you currently have on repeat?

Dennis: [00:54:37] Yeah. So that's a good question. Especially, I'm not sure about you, but when I write programs, I actually like to listen to music a lot. But sometimes I get too serious, but I have to turn all the music off because otherwise I can focus right now on repeat. I have taken me by on how I feel about, Oh yeah, that's that's. You can't get out of my head, to be honest.

Harpreet: [00:54:55] That's not going to be out of my head all day now, too. That's a great song. That's a good one. I've been listening to a lot of deep house recently, instrumental deep house, and it's just been like nice and relaxing. And just, yeah, I get that.

Dennis: [00:55:07] It's that's what I love about music as well. It's again, depending on what I need to do if I need to focus, you can listen to different music, but if something is getting a little more intense, it just just helps a lot and I can recommend that to everyone as well. If you have a coding problem, you can solve it. Don't stick too much to it, but turn out some good music. It might help. And if not, just go to bed and you'll wake up and realize, Oh, OK, that's how I told that.

Harpreet: [00:55:31] Yeah, this is actually true. Like, this is a big thing, like taking a break from stuff, stepping away from stuff. It really does help you get some new insight. We're going to go ahead and jump into the random question generator.

Dennis: [00:55:45] All right, let's go ahead.

Harpreet: [00:55:46] All right, here we go. What incredibly strong opinion do you have that is completely unimportant in the grand scheme of things?

Dennis: [00:55:56] Think about that one who incredibly strong opinion that [00:56:00] is completely unimportant. I sometimes think way too much about food, which is going to come out of it, about the things that I want to cook, and I put a lot of emphasis on the way I prepare my meals. I like to cook a lot AIs, and sometimes I just put so much effort into that, and I don't think it's going to get a whole lot to the grand scheme of things, but that's my life or the life of my family. But I still have a very strong opinion about the way I want to prepare my food.

Harpreet: [00:56:30] Do you have a signature dish that you're well known for?

Dennis: [00:56:33] Generally, I like to prepare a lot of Asian dishes because my girlfriend's Chinese and I got into that quite a lot. But yeah, it's going to be a mixture of those either something Asian or something very western

Harpreet: [00:56:44] That involves a lot of meat. Nice.

Harpreet: [00:56:47] What is one of your favorite smells?

Dennis: [00:56:50] That's a good question. I mean, that's great. I never said it was a little kid like vanilla ice cream was always my favorite, but just the smell of vanilla. If you actually have an actual vanilla bean, not just like the like the, you know, the chemical stuff and actual vanilla bean is such an amazing smell. And just I don't think anything can be that.

Harpreet: [00:57:08] I've never actually seen a vanilla bean in real life, only just the

Dennis: [00:57:13] It's like some of the recipes asks you to actually. And if you bake a lot of recipes, ask you to put in a vanilla bean and you have to scratch out like the black stuff from the bean. At the moment you do that. It just smells like heaven.

Harpreet: [00:57:26] I can only recommend it. Can I get my hands on some vanilla bean? What story does your family always tell about you?

Dennis: [00:57:36] It's also a very good question, actually. I personally don't think I'm able to think of anything right now.

Harpreet: [00:57:42] Yeah, no worries. Let's do one last one here. Sure. What's one of your favorite comfort foods.

Dennis: [00:57:47] That's a good question. Actually, Comfort Foods, I mean, sweets, that is helpful, but I like to get too much into them.

Harpreet: [00:57:55] I do like

Dennis: [00:57:56] Salted caramel a lot. I don't know what it's about that, but it's just you get [00:58:00] into it. I like the idea of sweet and salty. You know, a lot of recipes ask you to put a little bit of salt into your cake. And I always wondered what? Why would you do that? Why would you put salt in your cake? But ever since I have salted caramel, I realize why. Yeah, thank you. Because it's two opposites, and I think the

Harpreet: [00:58:17] Salt makes

Dennis: [00:58:18] The sweet taste even more intense because it's just distracting you from it. You wouldn't expect that.

Harpreet: [00:58:24] Yeah, salted caramel is delicious and actually, like, I was making pancakes for breakfast this morning, and actually, I put a little bit of salt in my pancakes and

Dennis: [00:58:31] Nice, and I'm hoping as the Canadian to put a maple sirup.

Harpreet: [00:58:35] Exactly, yes. You know, real

Dennis: [00:58:37] Maple stuff is so hard to get by here. I really like the best example is the brown sugar you have. I'm sure it's the same in Canada, but in the United States, the brown sugar, you have this like dark brown sugar and light brown sugar, and it's this very thick sugar. It's like, I think they put what it's called, but I think they put the syrupy stuff into the sugar to make it like this. This just doesn't exist over here and find it. But all of the recipes that I've I was in the United States quite a while. All of the things that I had there that required this blood sugar, like all the recipes, I just can't prepare them here.

Harpreet: [00:59:11] Yeah, yeah. I think the molasses, I think of that.

Dennis: [00:59:15] That's cool. Yeah. Well, you can make it yourself, I guess. But I think that's too much work for something like sugar.

Harpreet: [00:59:20] Dennis, how can people get in touch with you? Where can they find you online? How can they connect with you?

Dennis: [00:59:26] Oh, in general, you can still e-mail Instagram. Even though I don't post that much anymore or someone writes me there, I'm always going to reply. And if people have questions and I sometimes wonder how they still find me if I haven't posted in three months and they still ask me questions and I'm still happy about that, and I love to answer those. So feel free to shoot me a question there, but also a LinkedIn very much available. And I like it when you know I like it, when people like me, they had it and recruiters, that always helps. And it's just just have some something to discuss about it. And I love seeing what other people do. [01:00:00] I know not everyone is going to share the way they designed the architectures, but just talking about the stuff I think is very interesting, and I'm looking very much forward to the day we can have a life meetups and life conferences again.

Harpreet: [01:00:12] Definitely, man. Well, you know, you can always join in on one of my open office hours. I've got, you know, got a couple of them a week one.

Dennis: [01:00:19] I see. I see your LinkedIn posts a lot and I always take a look at those videos and I think of the Postbank. I think they're very inspirational

Harpreet: [01:00:26] And I

Dennis: [01:00:27] Hope you keep doing what you're doing.

Harpreet: [01:00:28] Thank you very much, man. I appreciate that. Dennis, thanks so much for taking time out of schedule to come onto the show, man. Really appreciate having you here.

Dennis: [01:00:35] I was very happy to be here.