1 00:00:00,240 --> 00:00:03,379 Hello, data aficionados and tech enthusiast. 2 00:00:04,240 --> 00:00:07,839 Welcome to the first episode of season 8 of the data driven 3 00:00:07,839 --> 00:00:11,634 podcast. I'm your host, Bailey, your delightful 4 00:00:11,855 --> 00:00:15,315 AI guide through the fascinating world of technology and data. 5 00:00:15,775 --> 00:00:19,170 Now, before we dive into the data laden depth of today's 6 00:00:19,170 --> 00:00:22,930 episode, we've got something rather special for you. Brace 7 00:00:22,930 --> 00:00:26,470 yourselves for our new theme song, hot off the silicon press, 8 00:00:26,610 --> 00:00:30,355 entirely AI generated. Yes, even our theme 9 00:00:30,355 --> 00:00:33,015 music has joined the generative AI revolution. 10 00:00:33,795 --> 00:00:37,555 So plug in your headphones, turn up the volume and let's give 11 00:00:37,555 --> 00:00:38,295 it a listen. 12 00:00:50,695 --> 00:00:54,155 From trends to bikes, we light up your nights 13 00:00:54,295 --> 00:00:55,195 with inside 14 00:01:02,485 --> 00:01:05,545 it up loud. Let's get this party started. 15 00:01:08,005 --> 00:01:11,420 Well, what do you think? A symphony of zeros and 16 00:01:11,420 --> 00:01:15,020 ones, or should we stick to human composers? Feel 17 00:01:15,020 --> 00:01:18,795 free to drop your thoughts on our socials. Today's episode 18 00:01:18,795 --> 00:01:22,495 is a treat. We're joined by the brilliant Chris Cooney, 19 00:01:22,555 --> 00:01:26,235 a maestro in the realms of data, observability in production 20 00:01:26,235 --> 00:01:29,870 systems, and developer advocacy. We'll be delving into the 21 00:01:29,870 --> 00:01:33,390 intricacies of keeping an eye on your systems, the art of data 22 00:01:33,390 --> 00:01:36,925 observability, and why developer advocacy is crucial in 23 00:01:36,925 --> 00:01:40,284 today's tech landscape. So grab your favorite 24 00:01:40,284 --> 00:01:43,425 cuppa, get comfortable, and let's get data driven. 25 00:01:43,829 --> 00:01:47,509 Hello, and welcome to Data Driven, the podcast where we explore the 26 00:01:47,509 --> 00:01:51,030 emergent fields of AI, data science, and 27 00:01:51,030 --> 00:01:54,695 machine learning, and, of course, data engineering, because without data engineers, 28 00:01:54,995 --> 00:01:58,835 the world would stop revolving. And with me is Andy 29 00:01:58,835 --> 00:02:02,620 Leonard, my favoritest data engineer in the world. How is that for 30 00:02:02,620 --> 00:02:06,220 a new intro, Andy, for season 8? I like it, Frank, and 31 00:02:06,220 --> 00:02:10,035 welcome to season 8. Cool. Cool. Yeah. So 32 00:02:10,035 --> 00:02:13,095 I'm gonna tie in our guests, at least geography, 33 00:02:13,715 --> 00:02:17,495 with the theme of this season 8. And my promise to our listeners 34 00:02:17,920 --> 00:02:21,760 and viewers, is that we will not disappoint people like 35 00:02:21,760 --> 00:02:24,719 Game of Thrones season 8 did, and, 36 00:02:25,439 --> 00:02:29,174 our guest is nodding. And, as folks know, 37 00:02:29,314 --> 00:02:33,015 a lot of, Game of Thrones was filmed in and around Northern Ireland, 38 00:02:34,034 --> 00:02:37,599 where oddly enough, as as the coincidence would have it, I have a a 39 00:02:37,599 --> 00:02:41,040 family history there going back, well, 2 generations from 40 00:02:41,040 --> 00:02:44,795 me. But, our guest today is, 41 00:02:45,115 --> 00:02:47,855 Chris Cooney, who is a software engineer, 42 00:02:48,715 --> 00:02:52,075 SRE principal engineer, and he is 43 00:02:52,075 --> 00:02:55,870 now the head of developer advocacy at Coralogix. 44 00:02:56,170 --> 00:02:59,849 Hopefully I pronounced that right. And he's worked on everything from embedded 45 00:02:59,849 --> 00:03:03,355 systems, for controlling industrial battery 46 00:03:03,355 --> 00:03:06,095 units on the UK power grid, 47 00:03:07,035 --> 00:03:10,850 and to payment processing systems, that process, 48 00:03:11,390 --> 00:03:15,090 up to a 1000000000 a year, and just helping 49 00:03:16,805 --> 00:03:20,265 shape technology strategy, with 100 of millions 50 00:03:20,325 --> 00:03:23,765 worth of cloud and on premise infrastructure. Welcome to the show, 51 00:03:23,765 --> 00:03:27,420 Chris. Thank Thank you very much for having me. I'm super excited to be here. 52 00:03:27,720 --> 00:03:31,240 Awesome. Awesome. We had a great chat in the 53 00:03:31,240 --> 00:03:35,065 virtual green room, but, so tell tell us a little bit about yourself. 54 00:03:35,065 --> 00:03:38,685 You're currently located in, Northern Ireland now, but you 55 00:03:38,825 --> 00:03:42,365 you your entire career, from what I can tell, spans kind of the UK. 56 00:03:43,000 --> 00:03:46,840 And you were at I see on your LinkedIn that you did one 57 00:03:46,840 --> 00:03:50,600 time work at Sainsbury's, which if memory serves, because I used to live in Germany 58 00:03:50,600 --> 00:03:53,765 and I would go to UK quite a bit, that's a grocery chain? 59 00:03:54,385 --> 00:03:57,825 Yes. It's a it's a retailer. Yeah. It's the 2nd largest retailer in the 60 00:03:57,825 --> 00:04:01,470 UK. So my background, I've been engineering 61 00:04:01,770 --> 00:04:05,610 now for, oh, gosh, like, 11 years now, I 62 00:04:05,610 --> 00:04:09,415 think. I think I'm starting to get some gray hairs now. The, 63 00:04:10,275 --> 00:04:14,055 the the the gist of it is started out the application level Java, 64 00:04:14,595 --> 00:04:18,389 back then, then moved into React engineering because I realized 65 00:04:18,389 --> 00:04:22,150 I couldn't design a front end to save my life. And then, I 66 00:04:22,150 --> 00:04:25,205 real after a while, I thought, well, how do I run this thing? So then 67 00:04:25,205 --> 00:04:28,725 I moved started moving into the DevOps and SRE side of things and started 68 00:04:28,725 --> 00:04:32,425 reading a lot about SRE developer experience, 69 00:04:33,160 --> 00:04:36,919 the what was emerging then, which was platform engineering, which was slowly 70 00:04:36,919 --> 00:04:40,360 slowly coming to the forefront, and then ended up 71 00:04:40,360 --> 00:04:43,764 replatforming, got really into the whole Kubernetes 72 00:04:43,784 --> 00:04:47,504 space, and then started thinking really heavily about, well, how do I keep track 73 00:04:47,504 --> 00:04:51,264 of all this stuff? And after a few roles here and there, I went into 74 00:04:51,264 --> 00:04:55,090 engineering leadership. And, while I was in leadership for a 75 00:04:55,090 --> 00:04:58,770 few years as the principal engineer, I was responsible for there was, like, 22 76 00:04:58,770 --> 00:05:02,435 different teams. They all had very different portfolios. And I was just 77 00:05:02,435 --> 00:05:06,035 trying to, understand what each of them were trying to achieve and then 78 00:05:06,035 --> 00:05:09,669 maximize that outcome. I realized very 79 00:05:09,669 --> 00:05:12,949 quickly that the thing we were lacking desperately was, 80 00:05:13,349 --> 00:05:16,729 observability. And so when I started to look around, 81 00:05:17,335 --> 00:05:20,235 I spoke with Ariel Asarath, the CEO of CoreLogicix. 82 00:05:21,335 --> 00:05:25,175 And, if you have ever or will ever speak to him, you'll know he's 83 00:05:25,175 --> 00:05:28,950 a very compelling guy. And I pretty much signed up signed 84 00:05:28,950 --> 00:05:31,590 on the dot. I was like, let's do this for a 100%. I'm I'm ready 85 00:05:31,590 --> 00:05:34,870 to go. And then I've been there for 2 years now, started out as the 86 00:05:34,870 --> 00:05:38,705 advocate. And we the past few years have really just been about 87 00:05:38,705 --> 00:05:42,164 understanding what the what the what advocacy looks like both in the observability 88 00:05:42,384 --> 00:05:46,090 space, but also at CoreLogic specifically. We have a pretty good picture of that 89 00:05:46,090 --> 00:05:49,870 in terms of content, tone, speaking, that kind of thing. 90 00:05:50,250 --> 00:05:53,945 We're hiring more, and we're growing that advocacy function 91 00:05:53,945 --> 00:05:57,705 to be much more proactive and outreaching and have a lot more 92 00:05:57,705 --> 00:06:01,430 fun with it. And so, yeah. And and and, 93 00:06:01,430 --> 00:06:04,970 obviously, the reason for being so excited doing this podcast is 94 00:06:05,509 --> 00:06:09,130 the thing with observability. You know, most people will say, what's the problem with observability? 95 00:06:09,515 --> 00:06:13,115 Most often, the the thing that comes back is cost. People say, like, it's just 96 00:06:13,115 --> 00:06:16,795 so expensive. But, actually, like, if you deconstruct that 97 00:06:16,795 --> 00:06:20,550 slightly, really, the problem is data, volume, and how to manage it 98 00:06:20,550 --> 00:06:23,990 because that's what's driving the cost. And so then you go back and you analyze 99 00:06:23,990 --> 00:06:27,585 the behaviors and understand what decisions we made as an industry that drove 100 00:06:27,585 --> 00:06:30,625 that. And all of that has got me really into the data space now to 101 00:06:30,625 --> 00:06:34,225 try and understand better what we can do both as engineers and 102 00:06:34,225 --> 00:06:37,669 consumers to make sure that we feel we're getting the 103 00:06:37,669 --> 00:06:41,050 best return on investment for every dollar we spend on our observability. 104 00:06:41,990 --> 00:06:45,825 So it's fundamentally a data problem, and that's why this podcast was so 105 00:06:45,825 --> 00:06:49,505 exciting for me. Oh, awesome. Awesome. So, I'm gonna go 106 00:06:49,585 --> 00:06:53,010 was excited to talk to someone who's in developer advocacy. I worked 107 00:06:53,010 --> 00:06:56,530 in, event what we used to be called evangelism, for 108 00:06:56,530 --> 00:06:59,670 Microsoft for a number of years, but it's the same it's the same thing. Yes. 109 00:07:00,755 --> 00:07:04,435 What's fascinating is I think 110 00:07:04,435 --> 00:07:07,975 one of the key challenges of developer advocacy or evangelism 111 00:07:08,515 --> 00:07:12,259 was always the traceability. Now there's a number of problems. 112 00:07:12,259 --> 00:07:16,039 Right? But but one of them is the traceability of 113 00:07:16,180 --> 00:07:19,080 a a particular advocates activity 114 00:07:20,255 --> 00:07:24,095 to actual revenue. I think that was always that was always kind 115 00:07:24,095 --> 00:07:27,935 of a something that I know Microsoft struggled with. 116 00:07:27,935 --> 00:07:31,659 Right? Yeah. And I remember when they when, you know, you know, this 117 00:07:31,659 --> 00:07:35,020 was well, 2011, I interviewed and they were like, do you have any questions for 118 00:07:35,020 --> 00:07:38,765 me? I was like, yeah. Well, how was an individual evangelist tracked? 119 00:07:39,065 --> 00:07:42,905 Right? Yeah. And the answer to that was a really good 120 00:07:42,905 --> 00:07:46,650 stump speech by the hiring manager. His name is Dan. Shout out 121 00:07:46,650 --> 00:07:50,330 to Dan if you're listening. Was he goes, you know, you can't 122 00:07:50,410 --> 00:07:53,870 how do you track someone's individual activity? Right? At this time, 123 00:07:54,065 --> 00:07:57,585 Vista was still fresh in people's minds because, well, if you're a Windows evangelist, you 124 00:07:57,585 --> 00:08:01,105 can't blame you on Vista. Can't play 125 00:08:01,105 --> 00:08:04,820 Vista on you. Right? Okay. So how do you how do you kind of work 126 00:08:04,820 --> 00:08:07,060 that? And how do you how do you message that? How do you how do 127 00:08:07,060 --> 00:08:10,520 you track that from, you know, inception to purchase? 128 00:08:10,795 --> 00:08:13,755 And it was a very interesting kind of thing. I never look at it the 129 00:08:13,755 --> 00:08:17,595 same way again because on the outside, it looks like the 130 00:08:17,595 --> 00:08:20,570 funniest job in the world, and it is a fun job, but but there is 131 00:08:20,570 --> 00:08:24,250 a business side of it too. And, Andy and I actually 132 00:08:24,250 --> 00:08:27,875 got involved in developer community, and that's actually how we met through the 133 00:08:27,875 --> 00:08:31,235 efforts of another, evangelist. His name is 134 00:08:31,235 --> 00:08:34,834 Andrew. Okay. So, like, we got involved in developer 135 00:08:34,834 --> 00:08:38,350 community. At the time, the focus of Microsoft Evangelism was was 136 00:08:38,350 --> 00:08:42,190 building up a kind of a community of folks, user groups, and 137 00:08:42,190 --> 00:08:45,845 such. And it's fascinating to see how evangelism 138 00:08:46,145 --> 00:08:49,985 has evolved over the years. Yeah. Do you wanna talk a little bit about 139 00:08:49,985 --> 00:08:53,520 that? Because it's not. User groups are still important. They're still a thing, but it's 140 00:08:53,520 --> 00:08:56,320 actually, I think, a little different now. So you you mentioned writing and things like 141 00:08:56,320 --> 00:08:59,380 that. What what what makes up a the the the 142 00:09:00,905 --> 00:09:04,665 offerings of evangelism these days? Sure. So I think the early days 143 00:09:04,665 --> 00:09:08,285 of evangelism were where this reputation for it being remarkably 144 00:09:08,505 --> 00:09:12,050 fun job came from. Because as is often the case, people are quite 145 00:09:12,050 --> 00:09:15,810 idealistic when something's new. So when evangelism came about, it was 146 00:09:15,810 --> 00:09:19,495 like, we need to be able to talk to engineers. What do 147 00:09:19,495 --> 00:09:23,095 engineers like to talk about tech? We're gonna hire people that talk about tech, and 148 00:09:23,095 --> 00:09:25,894 that's their job. And that was that was the line of thinking, and then that 149 00:09:25,894 --> 00:09:29,300 spawned, you know, YouTube series and and and various different 150 00:09:29,300 --> 00:09:32,900 media that that that came from that. And then what happened over time is, like, 151 00:09:32,900 --> 00:09:36,545 as as it often does, economics came into play and was like, well, how what's 152 00:09:36,545 --> 00:09:40,225 the return on investment? Like, great. This guy just gave a talk on some very 153 00:09:40,225 --> 00:09:43,825 deep corner of Prometheus querying or something, or 154 00:09:43,825 --> 00:09:47,630 or, you know, Python. And what do we 155 00:09:47,630 --> 00:09:51,150 get for that? And so now what's what's what is still actually 156 00:09:51,150 --> 00:09:54,545 happening, I think, is, organizations are 157 00:09:54,545 --> 00:09:58,305 transitioning away from a model where evangelists just talk about open source 158 00:09:58,305 --> 00:10:01,125 tech, and they just, quote, unquote, connect with engineers. 159 00:10:01,660 --> 00:10:05,019 And there's something of a pipeline in place there 160 00:10:05,019 --> 00:10:08,860 where they they they are if you like, the toppest of the top 161 00:10:08,860 --> 00:10:12,685 funnel. They're right right at the top of the funnel. They are talking about 162 00:10:12,985 --> 00:10:16,765 open source technology or something that people will find interesting in general, 163 00:10:17,160 --> 00:10:21,000 And their goal is to talk to people often. Now when I speak 164 00:10:21,000 --> 00:10:24,654 to our advocates, the the the goal is to talk to people who might be 165 00:10:24,654 --> 00:10:27,935 interested in the product. They're not there to sell the product. They're not even there 166 00:10:27,935 --> 00:10:31,055 to market the product. They're just there to talk to those people and be like, 167 00:10:31,055 --> 00:10:34,480 okay. You're all interested in the observability space. For example, for 168 00:10:34,480 --> 00:10:37,920 me, here's a talk that's useful. Regardless of whether you're gonna buy 169 00:10:37,920 --> 00:10:41,220 CoreLogix or not, here's what's useful for you from an engineering perspective. 170 00:10:41,714 --> 00:10:44,375 And, normally, what I'd like to do is 90% 171 00:10:45,154 --> 00:10:47,894 independently valuable information, 10%, 172 00:10:49,680 --> 00:10:53,360 sort of, the by the way, I work at CoreLogix. This 173 00:10:53,360 --> 00:10:56,639 might solve some of the problems you've seen. So I give a talk, all over 174 00:10:56,639 --> 00:10:58,500 the place, which is kind of all about 175 00:11:00,575 --> 00:11:04,415 the summary of it is basically, like, how you can 176 00:11:04,415 --> 00:11:08,200 get the most value out of your observability spend. That's the kind of the the 177 00:11:08,200 --> 00:11:12,040 essence of it. I do not sell CoreLogic at any point 178 00:11:12,040 --> 00:11:15,855 throughout this talk. Yeah. But at the end of the talk, I say, by the 179 00:11:15,855 --> 00:11:19,695 way, if you're looking to cost optimize, CoreLogic has some great tools. It might solve 180 00:11:19,695 --> 00:11:23,350 a problem for you. And so I'm I'm I'm the pre product 181 00:11:23,350 --> 00:11:27,029 marketing person if you like. And that's kind of of ad where 182 00:11:27,029 --> 00:11:30,635 where I see advocacy at the moment. Yeah. That's one of the parts of it. 183 00:11:30,635 --> 00:11:34,475 And then there's the community building and the content creation, that kind of thing. And 184 00:11:34,475 --> 00:11:38,230 in terms of content creation, you know, the word content 185 00:11:38,850 --> 00:11:42,690 really has only been a a descriptor for the kind of media that 186 00:11:42,690 --> 00:11:46,145 we're creating, for a few years now. It's a relatively new word, 187 00:11:47,185 --> 00:11:51,025 And I think that the ability to create compelling content 188 00:11:51,025 --> 00:11:54,465 has is is becoming further and further to the foreground because the 189 00:11:54,465 --> 00:11:58,230 impact of one good video is, like, 190 00:11:58,850 --> 00:12:01,890 you know, it could be huge. It could be nothing. Could 2 views or you 191 00:12:01,890 --> 00:12:05,265 could get a million. You don't really know. And a lot of companies want to 192 00:12:05,265 --> 00:12:09,105 throw those dice. And I think that's where it's interesting. Well, 193 00:12:09,105 --> 00:12:12,760 you're fascinated by your by your that's okay. I'm fascinated by your 194 00:12:12,760 --> 00:12:16,520 division of, topics. They're 90% just trying to 195 00:12:16,520 --> 00:12:20,295 help Yeah. And, you know, share with people from your 196 00:12:20,535 --> 00:12:24,214 experience, which is impressive. It's vast. And, you 197 00:12:24,214 --> 00:12:27,890 know, doing someone who's who's come through the 198 00:12:27,890 --> 00:12:31,490 field as the field has matured. And I I think that's very easy to 199 00:12:31,490 --> 00:12:35,190 overlook. Yeah. You know, how you have to as a developer, 200 00:12:35,410 --> 00:12:39,225 especially, how you have to continually shift gears to grow with 201 00:12:39,225 --> 00:12:42,605 the technology as the new technology shows up and understand, 202 00:12:43,305 --> 00:12:47,130 okay. This is the problem it's trying to solve now. And that gives you 203 00:12:47,130 --> 00:12:50,810 a little bit of a, you know, a projection maybe or 204 00:12:50,810 --> 00:12:54,350 or a feel for maybe where it's going, a little bit of predictive 205 00:12:54,649 --> 00:12:57,995 analytics type thought. Sure. And then 206 00:12:58,775 --> 00:13:02,615 knowing that the company that you're working with, that the the 207 00:13:02,615 --> 00:13:06,460 platforms y'all are building and and making available 208 00:13:07,160 --> 00:13:10,760 also address some of the current problems and also some of the predictive 209 00:13:10,760 --> 00:13:14,505 ones. I like that 9, 8, 10 mix. I I find that makes 210 00:13:14,565 --> 00:13:18,405 for a compelling talk. And when when I'm sitting in one of those 211 00:13:18,405 --> 00:13:21,880 talks, I don't feel like I'm being sold something. Yeah. And 212 00:13:22,040 --> 00:13:25,399 No. And I I understand that. Yeah. What's funny is that, 213 00:13:26,040 --> 00:13:29,720 I've given the talks, and sometimes salespeople say that was very clever. And I said, 214 00:13:29,720 --> 00:13:33,385 what was very clever? And they say, you know, you you, I I 215 00:13:33,545 --> 00:13:35,865 you know, the sales pitch was there, but I didn't feel like I was being 216 00:13:35,865 --> 00:13:38,585 sold to. I was like, because you weren't being sold to. I wasn't I'm not 217 00:13:38,585 --> 00:13:42,330 trying to sell my job. I don't I'm not measured on sales or revenue. 218 00:13:42,330 --> 00:13:45,770 I don't get, commission. Nothing. Yeah. And so, 219 00:13:46,170 --> 00:13:49,795 what I measured on is actually, I have a personal KPI, which I really like, 220 00:13:49,875 --> 00:13:53,075 which is if I give a talk at a conference, I measure the number of 221 00:13:53,075 --> 00:13:56,675 people that come to the the the booth that we have at the conference and 222 00:13:56,675 --> 00:14:00,352 just ask questions about the talk, whether they're interested buyers or not. In other words 223 00:14:00,613 --> 00:14:04,263 Nice. Like, people are interested and engaged enough that they wanna come and 224 00:14:04,263 --> 00:14:07,914 talk to us afterwards. Like, whether they're buying or not is not my problem. 225 00:14:07,914 --> 00:14:11,135 But what is my problem is did I give a decent talk? And that that's 226 00:14:11,135 --> 00:14:14,175 usually a pretty good metric because the ones that I felt have gone really, really 227 00:14:14,175 --> 00:14:17,930 well, and then no one's come to the booth. And I've spoke you know, there's 228 00:14:17,930 --> 00:14:20,730 been drinks afterwards, whatever, and they were, oh, you were on stage. What were you 229 00:14:20,730 --> 00:14:24,090 talking about again? Like, no remembrance. And the ones that I felt were kind of 230 00:14:24,090 --> 00:14:27,735 okay, 30, 40, 50 people come to the booth. I I I just 231 00:14:27,815 --> 00:14:31,255 I'm I'm struggling to field all the questions, and then I have the drinks afterwards. 232 00:14:31,255 --> 00:14:33,960 And everyone's, oh, I saw your talk. It was brilliant. There were you know? So 233 00:14:34,120 --> 00:14:37,580 my own sense of how good a talk was is pretty off, basically, 234 00:14:38,200 --> 00:14:41,560 last time in the audio. So so so that that that's a nice little KPI 235 00:14:41,560 --> 00:14:45,314 for me personally. Yeah. But, yeah, the com yeah. Sorry. Go 236 00:14:45,314 --> 00:14:49,154 ahead. That's okay. I was just saying that's a great KPI. And I just like 237 00:14:49,154 --> 00:14:52,899 one quick follow-up, and I'll shut up and let Frank talk. Does anybody 238 00:14:52,960 --> 00:14:56,720 ever you mentioned the person that said, you know, that was clever. Does anybody 239 00:14:56,720 --> 00:15:00,320 in the audience ever provide feedback and say, oh, it was a big sales pitch. 240 00:15:00,320 --> 00:15:04,165 You ever get that? I so sometimes when the talks are very, 241 00:15:04,165 --> 00:15:07,845 very short, you know, I only have, like, sort of 10 minute lightning 242 00:15:07,845 --> 00:15:10,790 talks are different because I think it's a different head space. But when it's, like, 243 00:15:10,790 --> 00:15:14,630 a 10 minute, 15 minute slot. In 244 00:15:14,630 --> 00:15:17,510 the past, not so much these days, I'm better at it now, but a mistake 245 00:15:17,510 --> 00:15:21,245 I've made in the past is allowing that ratio to slip from 90:10 to 246 00:15:21,245 --> 00:15:25,085 kind of 50:50. And I think that I've come off and they've gone, 247 00:15:25,085 --> 00:15:27,725 you know, it would have been nice if you focused on the open source stuff 248 00:15:27,725 --> 00:15:31,510 a bit more. And so but I think that 90:10 ratio, 249 00:15:32,050 --> 00:15:35,170 even the people that say at the end, they noticed that that's 10% as a 250 00:15:35,170 --> 00:15:38,745 sales pitch. They go, well, yeah, but 90%, no one was trying to sell me 251 00:15:38,745 --> 00:15:42,425 anything. So whatever. That's fine. You know? Yeah. And I 252 00:15:42,425 --> 00:15:46,180 think as well, you just so much of it, like, engineers 253 00:15:46,180 --> 00:15:49,860 try 2 things that engineers are often, like, numb to now is 254 00:15:49,860 --> 00:15:53,540 1, recruiters and 2, salespeople because they are just 255 00:15:53,540 --> 00:15:57,185 everywhere. You know? Right. It's very difficult. So to not 256 00:15:57,265 --> 00:16:00,385 if you if you seem like a recruiter or a salesperson, you just become part 257 00:16:00,385 --> 00:16:04,130 of the background noise of an engineer's life. And so so 258 00:16:04,130 --> 00:16:07,889 my goal is always to make it really clear. I actually when people talk to 259 00:16:07,889 --> 00:16:11,015 me at the booth, I'm like, look, we could really solve your problems. And I 260 00:16:11,015 --> 00:16:14,375 can see them, like, go, oh god. Here we go. And I hold it quick. 261 00:16:14,375 --> 00:16:17,815 Just been saying, I don't work in sales. I'm not sales. Don't worry. This I 262 00:16:17,815 --> 00:16:21,540 have no best interest in you. Yeah. And so that's that I 263 00:16:21,540 --> 00:16:25,160 think from a from a from a sort of logical business perspective, 264 00:16:25,300 --> 00:16:28,740 that's a really powerful trust connection that you have with with engineers that, 265 00:16:29,325 --> 00:16:32,685 that businesses can use to get their message out. That's the economic side of it. 266 00:16:32,685 --> 00:16:36,145 And from a, from a just sort of human 267 00:16:36,285 --> 00:16:39,920 perspective, that's the really fun part of the job. It's just like finding 268 00:16:39,920 --> 00:16:43,519 something you're passionate about, being in a room with people who are also passionate about 269 00:16:43,519 --> 00:16:46,935 it, and then having a giant conversation. You know, and if you're a bit 270 00:16:46,935 --> 00:16:50,295 extroverted like I am, then being the center of attention is is good as well. 271 00:16:50,295 --> 00:16:53,355 It's always nice. So, yeah, it's it's it's it's wonderful. 272 00:16:54,200 --> 00:16:56,440 No. I think you said it best when you said it's the top of the 273 00:16:56,440 --> 00:16:59,640 funnel. Right? The tippy top of the funnel. Right? Because Yeah. One of the ways 274 00:16:59,640 --> 00:17:03,425 I've I I I've been I've had a 275 00:17:03,425 --> 00:17:07,265 number of roles that kind of dance between I've always been on 276 00:17:07,265 --> 00:17:10,625 the more evangelism side of sales when I've been in Sales Works. Right? Where I 277 00:17:10,625 --> 00:17:14,060 do create content. Because I gotta create content anywhere. You kinda just once you get 278 00:17:14,060 --> 00:17:17,520 the content creators bug, you have it. You know what I mean? Yes. And 279 00:17:18,300 --> 00:17:21,395 and to your point, you know, I don't when I make a video, I don't 280 00:17:21,395 --> 00:17:24,035 know. Is it gonna get 5 views or is it gonna get 5,000? I don't 281 00:17:24,035 --> 00:17:27,315 know. I really don't know. I haven't been able to figure out that, you know, 282 00:17:27,315 --> 00:17:30,559 try as I might, try as I try try to figure out some kind of 283 00:17:30,559 --> 00:17:34,240 algorithm for it. I I haven't really cracked that, and 284 00:17:34,240 --> 00:17:38,080 it's it's also true for, for speeches too. Like, I totally get 285 00:17:38,080 --> 00:17:41,725 it. But I think the best way that I've used to explain 286 00:17:41,725 --> 00:17:45,245 evangelism to non believers or advocacy to non believers is 287 00:17:45,245 --> 00:17:49,020 that, you know, salespeople go to the person in the corner office and ask for 288 00:17:49,020 --> 00:17:51,200 the deal. Mhmm. Evangelists 289 00:17:52,700 --> 00:17:54,845 warm up the crowd. Right? So they basically 290 00:18:01,245 --> 00:18:04,669 cubicles knock on the door of the corner office saying, hey. We need this. So 291 00:18:04,669 --> 00:18:08,510 that way when the salesperson does land, it's a warmer it's a 292 00:18:08,510 --> 00:18:11,635 warmer call. And and that's something that, 293 00:18:12,755 --> 00:18:16,515 I think that when you're talking to salespeople, they do get that notion of, 294 00:18:16,515 --> 00:18:20,240 like, you're the warm up act. Right? Yeah. Certainly, all the salespeople 295 00:18:20,299 --> 00:18:24,000 in in CoreLogix were a bit confused by advocacy initially. 296 00:18:24,460 --> 00:18:27,945 And then when you know, one of the things you'll know you'll 297 00:18:27,945 --> 00:18:31,565 you'll all know this from working at trade shows and and conferences, 298 00:18:32,184 --> 00:18:35,030 there's the you have people at the booths, and they're trying to snag people and 299 00:18:35,030 --> 00:18:38,790 be like, hey. Yeah. What are you interested in? And then suddenly that changes to 300 00:18:38,790 --> 00:18:42,424 30 or 40 people just turning up to the booth to ask questions. It's like 301 00:18:42,424 --> 00:18:45,625 it's so much easier for them. And then they kinda they okay. Right. No. I 302 00:18:45,625 --> 00:18:48,105 I get why this is good for me. Like, I the leads come to me. 303 00:18:48,105 --> 00:18:50,400 I don't have to, you know, I don't have to go out and find them. 304 00:18:51,040 --> 00:18:54,480 So that so so that that yeah. I think the salespeople are certainly the ones 305 00:18:54,480 --> 00:18:58,320 that I've worked with now get the value. But I I I 306 00:18:58,320 --> 00:19:01,975 still think we're only doing sort of 30, 307 00:19:01,975 --> 00:19:05,735 40% of of advocacy and evangelism and let us hire more people, and 308 00:19:05,735 --> 00:19:09,575 hopefully, we can do the other side of community building and all that sort of 309 00:19:09,575 --> 00:19:13,320 stuff, which is all gonna be new for the company as well. Yeah. And, 310 00:19:13,320 --> 00:19:16,280 you know, it's it's very avant garde that your company is even doing it. Right? 311 00:19:16,280 --> 00:19:19,640 Like, you know, it's it's it's it's something that even large companies don't 312 00:19:19,640 --> 00:19:23,275 really do. Obviously, FANG, to a certain extent, 313 00:19:23,275 --> 00:19:27,115 does. And I posit that because of Guy Kawasaki's work in the 314 00:19:27,115 --> 00:19:30,929 eighties. Okay. Right? He was the he was the first person that I'm 315 00:19:30,929 --> 00:19:34,690 aware of that had the title of evangelist. Right. Apple I think a 316 00:19:34,690 --> 00:19:38,414 big part of why the Macintosh has this cult following, and 317 00:19:38,414 --> 00:19:41,534 and I know all the Mac lovers go, don't call us a cult. Right? I 318 00:19:41,534 --> 00:19:44,745 know. I have a I have a MacBook. Right? But, 319 00:19:46,100 --> 00:19:49,940 it's because of his work, like, you know, 30, 40 years ago. Right? I I 320 00:19:49,940 --> 00:19:53,035 don't think that's a coincidence. And I think that, 321 00:19:53,755 --> 00:19:57,135 when Microsoft wanted to have the uptake of dot net, 322 00:19:57,755 --> 00:20:01,399 they reinvested heavily in in evangelism. And I 323 00:20:01,399 --> 00:20:04,279 think we saw the the fruits of that. So you no. I mean, it's it's 324 00:20:04,279 --> 00:20:07,000 one of those things where I've noticed that it kinda ebbs and flows. Right? It's 325 00:20:07,000 --> 00:20:10,695 like, you know, there's a the tide is up, everybody's all into 326 00:20:10,695 --> 00:20:13,495 it, and then the tide kinda goes away and people kinda Yeah. Down on it 327 00:20:13,495 --> 00:20:16,635 and they reorganize and they get rid of it. But I think that it is 328 00:20:17,290 --> 00:20:21,050 it's a field that I think has still maturing, I think, to your point. 329 00:20:21,050 --> 00:20:24,810 Right? Like, you know, because how do you define it? So there was 330 00:20:24,810 --> 00:20:28,475 something going around on LinkedIn where a lot of folks 331 00:20:28,475 --> 00:20:31,835 who were advocates tagged in an avocado emoji on 332 00:20:31,835 --> 00:20:35,675 this. Yes. Yeah. Yeah. Could you explain that? Because I only know part of the 333 00:20:35,675 --> 00:20:39,200 story. Do you It's an Amazon thing. Oh, okay. 334 00:20:39,200 --> 00:20:42,179 Because Advocate and Avocado kind of sound similar. 335 00:20:43,200 --> 00:20:46,405 As far as I'm aware, that's the that's the depth of it. I mean, I 336 00:20:46,405 --> 00:20:50,245 think it was changed with Amazon, and then, people just started to do 337 00:20:50,245 --> 00:20:52,245 it. I did it for a while. I think I don't know if I still 338 00:20:52,245 --> 00:20:55,970 got it now, actually. But, but, yeah, it was just a nice thing to 339 00:20:55,970 --> 00:20:59,650 signal you as an advocate. And the advocacy community, I will say, 340 00:20:59,650 --> 00:21:03,250 is is singularly wonderful. They are such lovely 341 00:21:03,250 --> 00:21:06,915 people and, you know, all of them, I've been with competitor 342 00:21:06,915 --> 00:21:10,515 companies. You know, sat on on the front row ready ready to go up. And 343 00:21:10,515 --> 00:21:14,300 if someone's nervous, like, because no one's we're not we're not competing 344 00:21:14,300 --> 00:21:18,059 with each other. We're just giving talks. Everyone's super friendly, relaxed, talk blah blah blah. 345 00:21:18,059 --> 00:21:21,355 You know, everyone's like, the and the the way the community is, 346 00:21:22,135 --> 00:21:25,115 that that is one of you know, and you make friends pretty much instantly, 347 00:21:25,735 --> 00:21:28,900 with with with all these different people at the booth. So you suddenly you go 348 00:21:28,900 --> 00:21:32,260 to one conference. You just make 5 new friends. And, you know, 2 of them 349 00:21:32,260 --> 00:21:35,855 are have been doing this for 20 years, so they're just fountains of absolute 350 00:21:35,855 --> 00:21:39,695 wisdom. You know? And so it's the community actually makes 351 00:21:39,695 --> 00:21:42,735 a massive difference for it. And that avocado thing I might it's just a sing 352 00:21:42,815 --> 00:21:46,169 a signal, like, you know, we're we're in we're in the this weird little club 353 00:21:46,169 --> 00:21:49,850 with you. And and, you know, what is ostensibly a very strange niche, a 354 00:21:49,850 --> 00:21:53,585 soft niche of of software engineering. My boss, when he, when 355 00:21:53,585 --> 00:21:57,184 Ariel interviewed me, he said, congratulations, Chris. You're an extroverted software 356 00:21:57,184 --> 00:22:00,544 engineer. You're one of the 3. And I was like, yeah. Because it's 357 00:22:00,544 --> 00:22:03,090 it's, you know, it's it's a it's a weird space. 358 00:22:04,050 --> 00:22:06,770 And not that you have to be extroverted to be fair, but it it does 359 00:22:06,770 --> 00:22:10,290 kind of help, you know, going out and meeting people and shaking 360 00:22:10,290 --> 00:22:14,135 200 hands and and answering all the questions and being on stage, it does help 361 00:22:14,135 --> 00:22:17,735 if you get energy from that, within which I definitely 362 00:22:17,735 --> 00:22:21,490 definitely do, thankfully. Well, let's 363 00:22:21,490 --> 00:22:24,390 talk about the data part because I think that's the part that, 364 00:22:25,730 --> 00:22:28,905 so and I think it I think it also dovetails too. Right? Because there's certain 365 00:22:29,105 --> 00:22:32,865 you mentioned k p a KPI that you have. Right? And there there's clearly data 366 00:22:32,865 --> 00:22:35,745 that you have to collect as part of just being an evangelist to show your 367 00:22:35,745 --> 00:22:39,590 your and I'm sorry. I keep using the word evangelist. That's just the old 368 00:22:39,590 --> 00:22:42,649 habit. It's fine. Fire department. But, 369 00:22:44,044 --> 00:22:47,885 there's clearly data you track. But what's the core core core what's the 370 00:22:47,885 --> 00:22:51,325 core of CoreLogix? That sounds really weird. But, like, what's the core of the business? 371 00:22:51,325 --> 00:22:55,160 You mentioned observability. Right? And observability implies data. So talk to me 372 00:22:55,160 --> 00:22:58,700 about this. You mentioned Prometheus. So 373 00:22:59,320 --> 00:23:02,245 explain a little bit. I'm I'm giving you, like, a ton of questions together. Sorry. 374 00:23:02,245 --> 00:23:04,885 No. It's okay. Yeah. Yeah. No. I get it. So You're drinking Diet Coke. I 375 00:23:04,885 --> 00:23:08,085 have the monster energy drinks. I see. Okay. Right. Yeah. Okay. 376 00:23:08,645 --> 00:23:12,429 Something of a higher caffeine content, I imagine. So Right. The 377 00:23:12,570 --> 00:23:16,110 the the the gist of CoreLogix is this, full stack observability, 378 00:23:16,490 --> 00:23:20,190 processing logs, metrics, and traces, and we have a security offering. 379 00:23:21,305 --> 00:23:24,605 The the the essence of the platform can be distilled into, 380 00:23:25,465 --> 00:23:29,165 taking decent data science principles around how to manage your data 381 00:23:29,390 --> 00:23:32,990 and then baking them into observability. One of the 382 00:23:32,990 --> 00:23:36,530 problems that we so we actually did some investigation a few years ago, 383 00:23:37,635 --> 00:23:41,395 about how people are actually using their observability data. And we 384 00:23:41,395 --> 00:23:45,015 found some really, really surprising statistics. Like, for example, 385 00:23:45,509 --> 00:23:49,210 99% of index data in in a in a in an elastic 386 00:23:49,269 --> 00:23:52,629 search, or an open search cluster, for example, is never 387 00:23:52,629 --> 00:23:56,434 queried. So 99% of it is is ingested into the cluster and then just never 388 00:23:56,434 --> 00:23:59,955 touched. And and it's one of those things where it's like, wait a minute. Like, 389 00:23:59,955 --> 00:24:03,480 why? And then we realized, well, it's not queried, but they're maybe 390 00:24:03,480 --> 00:24:07,080 visualizing in dashboards. So I was, okay. So that's 391 00:24:07,080 --> 00:24:10,905 interesting. Then another one was, a large volume of data. I can't remember 392 00:24:10,905 --> 00:24:14,265 the exact number, but a large volume of data is only interesting for a single 393 00:24:14,265 --> 00:24:17,865 number in the log. This is primarily focused on logs. It's 394 00:24:17,865 --> 00:24:21,500 just one log has a latency field, and that's what people really care about. The 395 00:24:21,500 --> 00:24:25,260 rest of it is just noise. For example, the 396 00:24:25,260 --> 00:24:28,875 the average historical query length is a week, so people 397 00:24:28,875 --> 00:24:32,495 tend to query back a week and not much further. The retention 398 00:24:32,554 --> 00:24:36,390 time is between 2 4 weeks, on average. So at 399 00:24:36,390 --> 00:24:39,670 the very least, we retain in high performance storage for twice as long as we 400 00:24:39,670 --> 00:24:43,510 need to, on average in 4 weeks, the upper end 401 00:24:43,510 --> 00:24:47,295 of that. When I do this at conferences, I say, hands up if you 402 00:24:47,295 --> 00:24:51,055 retain for a week, 2 weeks, 3 weeks, 4 weeks. And inevitably, there's 403 00:24:51,055 --> 00:24:54,720 always some poor guy with his hand up who's like, you know, we're on 404 00:24:54,720 --> 00:24:56,960 3 months at this point, and I'm like, can you just tell me how long 405 00:24:56,960 --> 00:24:59,440 you retained? Was that a year? And I'm like, okay. We would've been here for 406 00:24:59,440 --> 00:25:03,095 a while. So so so what we found in the 407 00:25:03,095 --> 00:25:06,695 industry was that there was this perception of we need to send less 408 00:25:06,695 --> 00:25:09,815 data. We need to, we need to we need to do more with less, I 409 00:25:09,815 --> 00:25:13,630 suppose. And what we found was, no, the problem isn't 410 00:25:13,630 --> 00:25:17,390 data volume as such. We have this data because it has a 411 00:25:17,390 --> 00:25:20,414 purpose. It has a function. It has a reason for being. We don't just generate 412 00:25:20,414 --> 00:25:24,115 the data for the fun of it. We data we generate data because our infrastructure's 413 00:25:24,174 --> 00:25:28,010 becoming more complex. Our, the the 414 00:25:28,010 --> 00:25:31,610 solutions that we have to come up with micros microservices. You know, at some point, 415 00:25:31,610 --> 00:25:35,130 somebody had, you know, decided that they have a 200 user a month CMS, and 416 00:25:35,130 --> 00:25:38,144 they were like, we need 15 microservices to run this thing. I don't know when 417 00:25:38,144 --> 00:25:41,505 that, like, gripped the the popular consciousness, but it has become a big thing 418 00:25:41,505 --> 00:25:44,980 now. So all these practices drive up the 419 00:25:44,980 --> 00:25:48,820 data. We need the data. So instead of deciding what data we're 420 00:25:48,820 --> 00:25:52,115 gonna chop and change and and get rid of and that kind of thing, Let's 421 00:25:52,115 --> 00:25:55,955 say, how would we go about retaining everything? How would we go about keeping all 422 00:25:55,955 --> 00:25:59,100 of our data? And then the question was, how do you manage the cost? The 423 00:25:59,100 --> 00:26:02,139 answer is to be use case driven with how the data is stored and how 424 00:26:02,139 --> 00:26:05,919 the data is accessed. So as I mentioned, some data 425 00:26:05,980 --> 00:26:09,755 is only useful for a single number converted into a metric. Metrics 426 00:26:09,755 --> 00:26:13,515 are a fraction of the the cost to retain. So there you go. 427 00:26:13,515 --> 00:26:17,040 You just you just shaved off the vast majority of that document's overhead, 428 00:26:17,340 --> 00:26:20,880 convert it into a Prometheus metric or a Victoria metrics or whatever works. 429 00:26:21,500 --> 00:26:25,295 But the thing is, we don't wanna we wanna retain data for 430 00:26:25,295 --> 00:26:28,735 a really long time because archiving and rehydrating is both 431 00:26:28,735 --> 00:26:32,510 expensive and painful. So instead of that, let's break the rehydration 432 00:26:32,570 --> 00:26:36,010 paradigm and directly query the data in your archive with no 433 00:26:36,010 --> 00:26:37,950 dependency on indexing or reindexing. 434 00:26:40,305 --> 00:26:42,865 Some of our data is queried. Some of our data is never used. Some of 435 00:26:42,865 --> 00:26:46,500 our data we ingest just because we might need it in the future. Okay. 436 00:26:46,500 --> 00:26:50,179 So make different levels of pricing based on the use case for that 437 00:26:50,179 --> 00:26:53,620 data. And so that was the that was the, the 438 00:26:53,620 --> 00:26:57,255 pinnacle of the sort of the the the what what was distilled 439 00:26:57,255 --> 00:27:01,095 down into what became the CoreLogic stream architecture. So I 440 00:27:01,095 --> 00:27:04,875 find myself talking about that the most. There's a tonne of, you know, tracing 441 00:27:04,934 --> 00:27:08,730 and, database APM and serverless APM, and 442 00:27:08,730 --> 00:27:12,490 q Kubernetes and so on in the platform. But, ostensibly, at its 443 00:27:12,490 --> 00:27:16,105 very, very core, it's just some smart data science and data engineering 444 00:27:16,105 --> 00:27:19,565 principles, including the fact that we built everything on a streaming based architecture. 445 00:27:19,945 --> 00:27:23,625 So, rather than doing everything in, like, a batch mode or 446 00:27:23,625 --> 00:27:27,430 triggering everything from a database, we use Kafka and Kafka streams to process the 447 00:27:27,430 --> 00:27:31,190 data and make decisions in flight rather than sort of 448 00:27:31,190 --> 00:27:34,665 various intermediary storages that have IO bottlenecks and all the rest of 449 00:27:34,665 --> 00:27:38,205 it. Makes it very scalable and also very efficient to run. 450 00:27:38,745 --> 00:27:42,185 It's less which means less expensive for us to run the platform, less expensive for 451 00:27:42,185 --> 00:27:45,980 the customer. So we drive enormous cost savings as well based on that. 452 00:27:45,980 --> 00:27:48,940 So all those things, there's a lot of information there, but all those things kind 453 00:27:48,940 --> 00:27:52,485 of, like, come together to form this platform that gives you 454 00:27:52,705 --> 00:27:56,304 all the traditional observability things that you want and some really, really 455 00:27:56,304 --> 00:27:59,525 advanced stuff. And it's also possible to query 456 00:27:59,850 --> 00:28:03,290 your archive, which is actually an s three bucket or cloud storage in your own 457 00:28:03,290 --> 00:28:06,350 account, directly from things like 458 00:28:07,625 --> 00:28:11,305 you can query your metrics directly from your archive. You can query your 459 00:28:11,305 --> 00:28:14,525 logs directly from your archive, your traces, but, also, you can 460 00:28:14,825 --> 00:28:18,400 visualize stuff in dashboards alongside index stuff 461 00:28:18,620 --> 00:28:22,460 from your archive. So it's it's all about and the the difference is 462 00:28:22,460 --> 00:28:26,274 you wait maybe 2 seconds instead of a sub second rendering time. You know? 463 00:28:26,274 --> 00:28:29,955 It's not much of a cost, but a massive, massive saving opportunity. And that's 464 00:28:29,955 --> 00:28:33,760 that's kind of where we've gone. Now we're just building this wonderful platform 465 00:28:33,760 --> 00:28:37,360 that's really, really fully featured and really mature. So interesting. Yeah. 466 00:28:37,360 --> 00:28:40,960 Absolutely. When it terms the, like, the the the 467 00:28:40,960 --> 00:28:44,014 record keeping of logs in terms of history, is there 468 00:28:44,955 --> 00:28:48,794 a difference between different industries? Like, there's certain regulatory things. 469 00:28:48,794 --> 00:28:51,695 Is it come up in digital forensics, I would assume? 470 00:28:52,380 --> 00:28:55,900 Yes. So we have some really fun use cases. We have some companies that are 471 00:28:55,900 --> 00:28:59,615 using us. So as I said, the core of CoreLogic is 472 00:28:59,695 --> 00:29:03,235 just great data engineering principles. That's what our architecture is all about. 473 00:29:03,455 --> 00:29:07,075 And, that's illustrated best by the fact that we've 474 00:29:07,375 --> 00:29:11,000 got some companies that are sending, you know, maybe 475 00:29:11,000 --> 00:29:14,760 40 or 50 terabytes of data through us a day. And they're using 476 00:29:14,760 --> 00:29:18,520 us purely as a transformation and analytics engine. They don't index any of 477 00:29:18,520 --> 00:29:21,375 their data. It actually goes back to the s three bucket. In this case, an 478 00:29:21,375 --> 00:29:24,975 s three bucket in their account. And we 479 00:29:24,975 --> 00:29:28,679 transform process and analyze that data. This particular company is in the 480 00:29:28,679 --> 00:29:32,360 financial industry. So heavy, heavy regulation. Everything 481 00:29:32,360 --> 00:29:36,125 has to be retained. Everything has to be accessible. They get regular audits where 482 00:29:36,125 --> 00:29:39,325 they have to demonstrate. Like, someone will come in and be like, tell me what 483 00:29:39,325 --> 00:29:42,705 happened with this user on the 1st June 484 00:29:43,210 --> 00:29:46,890 2022, you know, to have to have the data that far back. If they wanted 485 00:29:46,890 --> 00:29:50,330 to do that with any other platform, things would get very pricey very 486 00:29:50,330 --> 00:29:54,085 quickly, and we offer that archive query at no additional cost. It doesn't 487 00:29:54,085 --> 00:29:57,145 cost anything per query. It's purely based on gigabytes ingested. 488 00:29:57,685 --> 00:30:01,460 So they get basically enormous retention, enormous 489 00:30:01,460 --> 00:30:05,220 scalability without needing to pay the cost. And that that digital 490 00:30:05,220 --> 00:30:08,705 forensics thing is a really, really interesting part because people think of 491 00:30:08,705 --> 00:30:12,304 observability as a purely DevOps or resiliency kind 492 00:30:12,304 --> 00:30:16,144 of discipline. Yeah. It's not. It it's it's all about understanding what you've got, 493 00:30:16,144 --> 00:30:19,940 data observability, measuring the freshness, distribution volume, and so on 494 00:30:19,940 --> 00:30:23,700 of your data, is within that observability realm. And it's 495 00:30:23,700 --> 00:30:27,355 possible to do it in the same platform provided that platform has been built with 496 00:30:27,355 --> 00:30:31,035 those decent data engineering principles in mind. And so, yes, 497 00:30:31,035 --> 00:30:34,810 the data forensics piece, analytics sort 498 00:30:34,810 --> 00:30:38,650 of, you know, aggregations across 1,000,000,000 and 1,000,000,000 of 499 00:30:38,650 --> 00:30:42,434 documents to get some really some insights that would otherwise be hidden away. Yeah. Those 500 00:30:42,434 --> 00:30:46,115 are all things that we run into regularly. I I think it's really 501 00:30:46,115 --> 00:30:48,855 interesting that you took the approach of streaming first. 502 00:30:49,850 --> 00:30:53,530 And I could see several advantages to doing that. Often, people architect 503 00:30:53,530 --> 00:30:56,350 data engineering, even near real time analytics, 504 00:30:57,290 --> 00:31:00,915 collection to be more focused on 505 00:31:00,915 --> 00:31:04,675 historical and being able to move the window around. And 506 00:31:04,675 --> 00:31:08,360 then they have an option that they kinda bolt on, And, 507 00:31:08,360 --> 00:31:12,020 you know, they're just trying to pound on the server 508 00:31:12,640 --> 00:31:16,080 Yep. Pull the server, throw it into a loop where every second is 509 00:31:16,080 --> 00:31:19,905 grabbing the most current data. And if you do it 510 00:31:19,905 --> 00:31:23,265 that way, I mean, you're you all are nodding your heads. You you get it. 511 00:31:23,265 --> 00:31:26,920 It's you've gotta architect from the ground up, I 512 00:31:26,920 --> 00:31:30,680 mean, you know, to have that perform at all. But if you take 513 00:31:30,680 --> 00:31:34,385 that approach of reading real time data, to 514 00:31:34,385 --> 00:31:37,905 start with, that's your focus. It's very easy, I would think, or 515 00:31:37,905 --> 00:31:41,650 easier to expand the window and say, no. Let's look at 516 00:31:41,650 --> 00:31:45,490 the last hour, the last day. Yeah. And and more more 517 00:31:45,490 --> 00:31:49,175 than that as well. The it's not just so so the streaming architecture, 518 00:31:49,175 --> 00:31:53,015 basically, as far as, as far as I 519 00:31:53,015 --> 00:31:55,975 see it, then I I I this may be more of an opinion than an 520 00:31:55,975 --> 00:31:59,799 engineering position as such, but the streaming gives you the ability 521 00:31:59,799 --> 00:32:03,400 to make decisions in flight, and it makes it really easy to 522 00:32:03,400 --> 00:32:07,215 perform transformations. And, as long as you're disciplined 523 00:32:07,215 --> 00:32:10,655 about minimizing side effects, it also unlocks, the 524 00:32:10,655 --> 00:32:13,875 scalability. But it's the things that sit around 525 00:32:14,370 --> 00:32:18,130 the stream, which is so we have this concept of, source, 526 00:32:18,130 --> 00:32:21,970 stream, and sync. And, Yoni Farrin, the CTO of the 527 00:32:21,970 --> 00:32:25,794 company, and I believe his team kind of came up with this. But a source 528 00:32:25,794 --> 00:32:29,395 is like, you know, a a a the data source, the actual database, that kind 529 00:32:29,395 --> 00:32:32,330 of thing. The stream is obviously the Kafka stream that's processing it, and the sync 530 00:32:32,330 --> 00:32:36,010 is where the data ends up eventually. It's a pretty normal thing in data science 531 00:32:36,010 --> 00:32:39,755 anyway. But the the key thing here is that 532 00:32:39,895 --> 00:32:43,735 none of our, sort of in stream transformations are reading 533 00:32:43,735 --> 00:32:47,440 from external sources. All the data is loaded first, pushed into 534 00:32:47,440 --> 00:32:51,040 the stream, and then written. And what that means is that the 535 00:32:51,040 --> 00:32:54,820 individual streaming processes can be horizontally scaled very, very easily. 536 00:32:54,825 --> 00:32:57,565 We can so what it means is that we could scale up and scale down 537 00:32:57,945 --> 00:33:01,705 very, very effectively and very efficiently. That's part of that cost 538 00:33:01,705 --> 00:33:05,470 optimization on our side, but it means that that's a thing that, 539 00:33:05,930 --> 00:33:09,610 I think is really important to highlight because people often I worked in Sainsbury's for 540 00:33:09,610 --> 00:33:13,205 a long time and I saw many Kafka projects appear and fail. 541 00:33:13,345 --> 00:33:16,945 And it was because while they built the Kafka someone built some 542 00:33:16,945 --> 00:33:20,520 Kafka solution, They constrained it left, right, and center 543 00:33:20,520 --> 00:33:24,120 with IO and database sort of operations. And so when it came to 544 00:33:24,120 --> 00:33:27,735 scaling it, it was like, well, this isn't the bottleneck isn't this. The bottleneck 545 00:33:27,735 --> 00:33:31,415 is the 30 or 40 database and Redis clusters that are sitting around this 546 00:33:31,415 --> 00:33:35,255 thing. So it's not just the foresight to build a streaming architecture. It's a foresight 547 00:33:35,255 --> 00:33:38,919 to minimize side effects in your architecture, and that makes it much 548 00:33:38,919 --> 00:33:42,600 more scalable and, ironically, much more observable as well because the the 549 00:33:42,679 --> 00:33:46,475 everything's just an in out transformation. Makes it a lot easier to monitor and maintain. 550 00:33:47,175 --> 00:33:50,935 Cool. That's a lot that's really 551 00:33:50,935 --> 00:33:54,440 clever, actually. Like, the more you the more you explain it, I'm like, that's 552 00:33:54,440 --> 00:33:58,280 brilliant. Like, what? Why didn't I think of that? Just like I 553 00:33:58,280 --> 00:34:01,320 tell you, like, these the part of the reason I joined this company was because 554 00:34:01,320 --> 00:34:04,184 I had a call with I had a call with Yoni, the CTO. I had 555 00:34:04,184 --> 00:34:07,705 called Ariel, the CEO, and, I left both of those 556 00:34:07,705 --> 00:34:11,409 calls feeling like I was just like a chimp banging 2 rocks together. And 557 00:34:11,409 --> 00:34:15,010 I was like, right. Okay. This is where I need to be. I need to 558 00:34:15,090 --> 00:34:18,370 this is this is how I level up now. So and it's I've learned so 559 00:34:18,370 --> 00:34:21,275 much since joining, not just as a I love the architecture. 560 00:34:21,975 --> 00:34:25,335 Sorry. I didn't mean to cut you off. I I love the architecture, and I 561 00:34:25,335 --> 00:34:28,880 love the patterns in both of those kind of appeals. I mean, but, you know, 562 00:34:28,880 --> 00:34:31,699 I I would imagine that the IO on the data 563 00:34:32,400 --> 00:34:36,239 store is critical. That's gotta be mission critical at that 564 00:34:36,239 --> 00:34:40,074 point because you you wanna catch all the data as fast as possible. 565 00:34:40,534 --> 00:34:44,054 And at the same time, you wanna be able to serve that data as fast 566 00:34:44,054 --> 00:34:47,640 as possible. It's, you know, that would be the 567 00:34:47,640 --> 00:34:51,160 bottleneck, I would I would imagine, and I'm sure y'all have solved 568 00:34:51,160 --> 00:34:54,795 that at the, you know, tuning the hardware up. You 569 00:34:54,795 --> 00:34:58,415 know? Yes. Precisely. Yeah. It's it's it's it's it's a comp it's essentially, 570 00:35:01,515 --> 00:35:05,180 partially an architectural decision and partially an engine partially an engineering decision. So 571 00:35:05,180 --> 00:35:08,940 the architectural decision is, for example, because of the 572 00:35:08,940 --> 00:35:12,515 way Kafka works, we can parallelize a great deal of the processing. So to give 573 00:35:12,515 --> 00:35:16,115 you an idea, a typical observability platform, one of the large 574 00:35:16,115 --> 00:35:19,494 vendors now, you can expect an alarm to fire. 575 00:35:20,080 --> 00:35:23,840 Let's say a metric crosses a threshold. It's anywhere from 576 00:35:23,840 --> 00:35:27,360 2 to 4 minutes on the on the upper end before the alarm actually 577 00:35:27,360 --> 00:35:31,155 fires. Wow. That's a big delay. That's huge. It's enormous. You 578 00:35:31,155 --> 00:35:34,915 know? And if if you imagine you're a financial trading company, like, 4 579 00:35:34,915 --> 00:35:38,375 minutes is, like, is is huge. You know? If you're if it's a security 580 00:35:38,435 --> 00:35:42,170 alarm, that's massive. And that's because what they do is they 581 00:35:42,170 --> 00:35:46,010 ingest the data, store it, normalize it, and index it. And then they trigger a 582 00:35:46,010 --> 00:35:49,835 series of processes. And this normalizing and indexing process only gets worse with 583 00:35:49,835 --> 00:35:53,675 time. That's an infinitely scaling dataset. What we do is instead is we do all 584 00:35:53,675 --> 00:35:57,180 the analytics upfront, and then if you want, we store it 585 00:35:57,420 --> 00:36:00,619 in high performance index storage at the end. Otherwise, it just goes straight to the 586 00:36:00,619 --> 00:36:04,220 s three bucket archive or to the, to the cloud storage in your 587 00:36:04,220 --> 00:36:08,055 account. Okay. So I love the flexibility of that. I I do. I could 588 00:36:08,055 --> 00:36:11,734 see how that serves the architecture because, you know, often, 4 minutes 589 00:36:12,340 --> 00:36:15,860 being notified 4 minutes after, a hack starts, it's 590 00:36:15,860 --> 00:36:19,620 over. It's useless. It's some so we have this type of alarm called an 591 00:36:19,620 --> 00:36:23,405 immediately type alarm, and I run some use cases with it where, 592 00:36:24,105 --> 00:36:27,485 a an IP address appears in the logs from AWS, 593 00:36:28,025 --> 00:36:31,760 web application firewall. We have an ability to 594 00:36:31,760 --> 00:36:34,960 enrich data as it comes in, so you can we actually look at the top 595 00:36:34,960 --> 00:36:38,640 15 threat databases when an IP address comes in and we say, oh, 596 00:36:38,640 --> 00:36:42,385 is this malicious? And then the alarm was triggering every time there was a malicious 597 00:36:42,385 --> 00:36:45,905 IP, and the immediately type alarm was triggering it under a second. So it was, 598 00:36:45,905 --> 00:36:49,559 like, firing pretty much instantly. And in within 18 seconds, 599 00:36:49,559 --> 00:36:52,859 it had invoked a Lambda function, which, 600 00:36:53,400 --> 00:36:56,940 updated the IP set in the in the WAF instance. 601 00:36:57,105 --> 00:37:00,785 And so it's like sub WAF itself does that in 602 00:37:00,785 --> 00:37:04,404 in over a minute. So the data was leaving AWS. 603 00:37:05,330 --> 00:37:08,850 Oh, that CoreLogic is in Amazon, actually. So so but but it was it was 604 00:37:08,850 --> 00:37:12,450 going to CoreLogic's being processed along this extremely complex 605 00:37:12,450 --> 00:37:15,974 pipeline. The alarm was firing, and the Lambda function was being 606 00:37:15,974 --> 00:37:19,494 invoked in a 5th of the time it was taking for WAF to even measure 607 00:37:19,494 --> 00:37:23,250 the data in the first place. That's huge. Yeah. It's enormous. And 608 00:37:23,250 --> 00:37:27,089 that's that's like that it to me, that's the best demonstration of our architecture. 609 00:37:27,089 --> 00:37:30,710 That's like Yeah. That that that's one of the reasons why it's just so remarkable. 610 00:37:31,435 --> 00:37:34,155 And and it's also it's a it's like I say, it's a lesson in data 611 00:37:34,155 --> 00:37:37,595 engineering. And when I when I go to conferences and talk about cost 612 00:37:37,595 --> 00:37:41,410 optimization, I find myself talking more and more and more about just 613 00:37:41,410 --> 00:37:44,850 good practice of managing your data, as opposed to 614 00:37:44,850 --> 00:37:48,664 any secret observability magic source. It's always the way, isn't it? Like, a 615 00:37:48,664 --> 00:37:52,505 relatively new industry has a lot to learn from the adjacent industries and almost 616 00:37:52,505 --> 00:37:56,295 never does. It takes a lot of time to that passion. So yeah. 617 00:37:57,660 --> 00:38:01,040 No. That's very true. Like, there's a lot of good lessons to learn from, 618 00:38:02,140 --> 00:38:05,735 like you said, adjacent industries because we a lot a lot of the 619 00:38:05,735 --> 00:38:09,515 problems we're facing are not necessarily new. The context is definitely 620 00:38:09,575 --> 00:38:13,240 new, but the Yeah. Laws of physics are persistently 621 00:38:13,620 --> 00:38:16,200 stubborn. Yes. Precisely. Precisely. 622 00:38:17,860 --> 00:38:21,655 Interesting. Well, are we at that point, Frank, where we're ready to 623 00:38:21,655 --> 00:38:25,255 pull up the questions? Okay. Alright. So we'll ask the pre 624 00:38:25,255 --> 00:38:29,015 canned questions. They're none of them are real brain teasers. 625 00:38:29,015 --> 00:38:32,570 Right? They're we're not we're not trying to be Mike Wallace, and I don't even 626 00:38:32,570 --> 00:38:35,370 know if anyone get that reference these days. Okay. 627 00:38:36,170 --> 00:38:39,984 But you can always ask chat gbd who 628 00:38:39,984 --> 00:38:42,565 Mike Wallace was. I will. 629 00:38:46,030 --> 00:38:49,790 But in order to save the environment from hitting those GPUs, all these 630 00:38:49,790 --> 00:38:53,390 people hitting the GPUs, Mike Wallace was a journalist for 631 00:38:53,390 --> 00:38:57,185 TV show called 60 Minutes who was notorious for, you know, 632 00:38:57,185 --> 00:39:00,945 if there was a corrupt executive or politician, he was notorious for, like, sneaking up 633 00:39:00,945 --> 00:39:04,510 on him and asking them, like, while they're, like, getting groceries or whatever. Maybe even 634 00:39:04,510 --> 00:39:08,190 at Sainsbury's. Who knows? And saying, like, you know, hey. You know, 635 00:39:08,190 --> 00:39:11,870 why did you embezzle $5,000,000? Like, what's going 636 00:39:11,870 --> 00:39:15,655 on? It's a bad day if you walked into your office and 637 00:39:15,655 --> 00:39:18,075 Mike Wallace was waiting. Just sat there. 638 00:39:21,900 --> 00:39:24,220 We could do with some of that now. I think we should resurrect that practice 639 00:39:24,220 --> 00:39:27,099 in the UK. We need more of my gualas', I think. Yeah. I think you're 640 00:39:27,099 --> 00:39:30,605 right about that. Yeah. So the first question 641 00:39:30,605 --> 00:39:34,285 is, how did you find your way into data? Did data find you, 642 00:39:34,285 --> 00:39:38,020 or did you find data? Data very much 643 00:39:38,020 --> 00:39:41,780 found me. I as I said before, like, I was primarily interested in 644 00:39:41,780 --> 00:39:45,494 SRE, and then I realized, like, then observability became a real 645 00:39:45,494 --> 00:39:48,855 passion of mine. And then I realized, well, what is the biggest problem? Oh, god. 646 00:39:48,855 --> 00:39:52,075 It's data. Okay. This is okay. So that's that's how I ended up here. 647 00:39:53,950 --> 00:39:56,850 So what would you say is the favorite part of your current job? 648 00:39:58,350 --> 00:40:01,915 Every time I finish a 649 00:40:01,915 --> 00:40:04,955 talk and someone comes to the booth and says, you know, I've been really struggling 650 00:40:04,955 --> 00:40:08,230 that for a long time, and that thing you said there has just given me 651 00:40:08,230 --> 00:40:11,450 a really great idea. And I that is, like, 652 00:40:11,670 --> 00:40:15,270 amazing. That's just just, like, direct dopamine to me because it's, like, 653 00:40:15,270 --> 00:40:18,975 engineer to engineer having a conversation and just solving problems is, yeah, 654 00:40:18,975 --> 00:40:22,355 brilliant. So, yeah, right now, that's that's the the best. 655 00:40:24,255 --> 00:40:26,850 Nice. We have 3 complete sentences. 656 00:40:27,870 --> 00:40:30,210 When I'm not working, I enjoy blank. 657 00:40:31,845 --> 00:40:34,724 Oh, god. Is this just like what I do in my in my normal Yeah. 658 00:40:34,724 --> 00:40:38,565 In your spare time. Yeah. Yeah. Yeah. Spare time. So I 659 00:40:38,885 --> 00:40:42,730 Yeah. Well, whatever that whatever spare time. Yeah. Yeah. I I 660 00:40:42,950 --> 00:40:46,630 try and, I try and spend as much time with my daughter as possible. She's 661 00:40:46,630 --> 00:40:50,345 10 months old and she's, just crawling and everything else. So 662 00:40:50,585 --> 00:40:54,425 just absolutely fantastic. And, yeah, it's a level of, like, 663 00:40:54,425 --> 00:40:58,260 it's I'm tired of doing way wrong, and I'm a bit stressed, but, it's 664 00:40:58,260 --> 00:41:02,100 like the the the peaks of joy are just, like, unbelievably, like, 665 00:41:02,100 --> 00:41:05,825 incomparable. As one, I like philosophy, and I like, I play 666 00:41:05,825 --> 00:41:09,445 some guitar as well. So I've been kind of getting into that more as well. 667 00:41:09,985 --> 00:41:13,425 Cool. Excellent. Yeah. Dad, Frank, and I are both 668 00:41:13,425 --> 00:41:17,109 dads. We set up. And I'll tell you as a dad 669 00:41:17,109 --> 00:41:20,869 of, daughters, I have 3 daughters and 2 670 00:41:20,869 --> 00:41:24,665 sons. And I was I'm the oldest I'm I'm 671 00:41:24,665 --> 00:41:28,445 the oldest of, like, 5 boys, so I had no clue 672 00:41:28,585 --> 00:41:32,329 about either daughters, sisters, anything like that. But, yeah, 673 00:41:32,329 --> 00:41:36,010 it was it's an it's an amazing experience. Yeah. 674 00:41:36,010 --> 00:41:39,849 And watching them grow up and my baby girl is in college right now 675 00:41:39,849 --> 00:41:43,234 at Virginia Tech. And so you I saw that 676 00:41:43,234 --> 00:41:46,835 look, and it's like, yeah. That's gonna feel like about a month from 677 00:41:46,835 --> 00:41:50,214 now when you look back when she gets there. 678 00:41:50,829 --> 00:41:54,589 Yeah. For sure. The days are long, but the years are short. That's the Yeah. 679 00:41:54,589 --> 00:41:58,130 Yeah. Yeah. I have 3 boys. 14 is the oldest, 680 00:41:58,910 --> 00:42:02,755 9 18 months. So yeah. 681 00:42:02,895 --> 00:42:06,735 It's pretty chaotic. That's great to hear that. Our our second 682 00:42:06,735 --> 00:42:10,570 complete sentence, I think the coolest thing in technology today 683 00:42:10,570 --> 00:42:14,250 is fun. I hate giving this 684 00:42:14,250 --> 00:42:16,835 answer because I feel like it's everyone's gonna say the same thing, but it's it's 685 00:42:16,835 --> 00:42:19,974 it's gotta be Gen AI. Right? Like, right now, it's it's it's just 686 00:42:20,595 --> 00:42:24,275 like some of the thing. I so I, I'm half, Arab, and I've been learning 687 00:42:24,275 --> 00:42:27,890 Arabic for the past few years. And the other day, one one of the problems 688 00:42:27,890 --> 00:42:31,730 with Arabic is that, online, you basically find all the 689 00:42:31,730 --> 00:42:35,555 lessons in classical Arabic, but you'll never find lessons in what's called the dialect. 690 00:42:35,555 --> 00:42:39,395 So, like, I'm Jordanian. So the Jordanian dialect is is is it's not 691 00:42:39,395 --> 00:42:43,095 completely different. It's very similar, but it's there's lots of details that are different. 692 00:42:43,619 --> 00:42:46,740 And I went on to the new chat gpt model the other day, you know, 693 00:42:46,740 --> 00:42:50,579 4 o, and I, opened it up and said, I hit the 694 00:42:50,579 --> 00:42:54,425 the headphone thing to have a conversation. And I said in Arabic. I said, I 695 00:42:54,425 --> 00:42:56,984 want you to speak in Jordanian dialect to me, and I don't want you to 696 00:42:56,984 --> 00:43:00,444 speak in in in classical Arabic. And it responded 697 00:43:00,505 --> 00:43:04,290 perfectly. And I thought Wow. Like Wow. I don't I don't even 698 00:43:04,290 --> 00:43:08,070 know what resource you went to to get this information. But, 699 00:43:08,210 --> 00:43:11,815 like, it that you know? And it was it was the the accent, like, 700 00:43:11,815 --> 00:43:14,934 wasn't right. Obviously, the the release in the new voice model, I imagine that might 701 00:43:14,934 --> 00:43:18,670 change things a bit. But just the grammar, the inflection, the 702 00:43:18,670 --> 00:43:22,030 phrasing, the slang was was all that. And I thought this is 703 00:43:22,030 --> 00:43:25,684 like, I can't find this information. I've tried I've 704 00:43:25,684 --> 00:43:29,525 tried for for a long time to find this information in written form or 705 00:43:29,525 --> 00:43:32,905 in just a one place where I can go to get a, like, a full 706 00:43:33,350 --> 00:43:37,110 sense of Jordanian dialect, and it just doesn't exist. So so 707 00:43:37,110 --> 00:43:40,650 I just thought, wow. That's that's pretty pretty crazy. 708 00:43:41,244 --> 00:43:44,365 And that's yeah. So that's probably the coolest thing I've seen in a while technology 709 00:43:44,365 --> 00:43:48,205 wise. I'm from the villages, so I should say that. Yeah. But Well yeah. But 710 00:43:48,285 --> 00:43:51,380 no. But I mean, language learning is one of those things where I I did 711 00:43:51,380 --> 00:43:54,900 take a couple of courses on Arabic, and the teacher was from 712 00:43:54,900 --> 00:43:58,520 Syria. Yeah. But everybody in the class were 713 00:43:59,225 --> 00:44:02,845 as was this was Jersey. Yeah. The state, not the island. 714 00:44:03,385 --> 00:44:06,985 And most of the most of the other, participants were from 715 00:44:06,985 --> 00:44:10,730 Egypt, and they would always argue over how to say things. Yeah. 716 00:44:10,730 --> 00:44:14,190 And as a non native speaker, I'm kinda like 717 00:44:15,369 --> 00:44:18,945 completely lost. Oh, mate. You know? And they 718 00:44:19,265 --> 00:44:22,945 you even like, one of the things that's we we say Arabic in English, and 719 00:44:22,945 --> 00:44:26,005 we what we're essentially describing is a whole family of languages. 720 00:44:26,529 --> 00:44:30,289 Right. There are lots of Egyptians who if you ask them, do you speak 721 00:44:30,289 --> 00:44:33,650 Arabic? They say no. I speak Egyptian. And if are you an Arab? No. I'm 722 00:44:33,650 --> 00:44:37,495 Egyptian. And they because there's a 40% of the Egyptian 723 00:44:37,495 --> 00:44:40,315 language is is influenced by Coptic language. 724 00:44:41,095 --> 00:44:44,150 So and and then if you go to, like, Morocco and you ask them, do 725 00:44:44,150 --> 00:44:47,270 you speak Arabic? They say, no. I speak a Tarija. Like, the I speak a 726 00:44:47,349 --> 00:44:51,190 and and and it's like everything, you know, even even, like, Lebanese, they say the 727 00:44:51,190 --> 00:44:54,845 Canaanites rather than rather than Arabs. Now there's obviously 728 00:44:54,845 --> 00:44:57,964 politics and things mixed up in that, but it's also embedded in the language as 729 00:44:57,964 --> 00:45:01,105 well. The Lebanese will use a lot of French, for example, 730 00:45:01,710 --> 00:45:04,450 because of the history there, but also because of the 731 00:45:05,309 --> 00:45:09,150 it differentiates them slightly from from their neighbors. You know, in Jordan, they use 732 00:45:09,150 --> 00:45:12,765 a lot more classical Arabic in how they speak, but also use a lot of 733 00:45:12,765 --> 00:45:16,605 English in how they speak as well. Again, because of the history, but also, again, 734 00:45:16,605 --> 00:45:20,270 to differentiate themselves. They say different work, different letters differently, and so 735 00:45:20,270 --> 00:45:23,650 on. So so when we say Arabic, 736 00:45:24,190 --> 00:45:27,915 you know, the the general perception is that it's it's a one monolithic language, 737 00:45:27,915 --> 00:45:31,455 and, actually, it's a very, very wide, the glossier of languages. 738 00:45:33,275 --> 00:45:36,900 And, yeah, I've I've very rarely seen resources acknowledge 739 00:45:36,900 --> 00:45:40,520 that. I've never seen a resource ever automatically 740 00:45:40,740 --> 00:45:44,295 talk back to me in Jordanian Arabic. I've never ever seen that ever. So, 741 00:45:44,295 --> 00:45:47,974 yeah, remarkable. Mostly remarkable. That is cool. Yeah. So the last 742 00:45:47,974 --> 00:45:51,734 complete sentence, I look forward to the day when I 743 00:45:51,734 --> 00:45:54,030 can use technology to link. 744 00:45:57,690 --> 00:46:00,750 I am really looking forward to where 745 00:46:01,975 --> 00:46:05,495 the VR headsets get to. I worked in VR for, like, a 746 00:46:05,495 --> 00:46:09,095 week, and it was the Quest 2, so there was 747 00:46:09,095 --> 00:46:12,750 no HD pass through. But it was still pretty great, and I 748 00:46:12,750 --> 00:46:16,430 I I had to stop every few hours because I was getting headaches and 749 00:46:16,430 --> 00:46:20,105 stuff, and I had to kind of, like, take it easy. And 750 00:46:20,484 --> 00:46:24,005 we I really just feel like we've got the tracking sorted, we've got the 751 00:46:24,005 --> 00:46:27,444 processing sorted. Almost everything's there. It's just 752 00:46:27,444 --> 00:46:31,130 the comfort factor that we have to work on. That's just gonna come with 753 00:46:31,130 --> 00:46:34,810 lighter and lighter components until it yeah. It was like the visor being made by 754 00:46:34,810 --> 00:46:38,155 a company called the MERST right now, and they haven't done a demo of it 755 00:46:38,155 --> 00:46:41,994 yet. I'm very excited for that demo, but, I I'm I'm 756 00:46:41,994 --> 00:46:44,890 really looking forward to the day when I can just have an empty desk and 757 00:46:44,890 --> 00:46:48,010 and just put the visor on and I've got all my screens and everything that 758 00:46:48,010 --> 00:46:50,589 I that that's that's really what I'm excited about. 759 00:46:52,385 --> 00:46:56,225 Yeah. Absolutely. Like, the the sweat that you build up around 760 00:46:56,225 --> 00:46:59,910 the is is is is really a limiting factor. That 761 00:47:00,069 --> 00:47:03,430 Yeah. That a little bit of motion sickness, but it depends on the game you 762 00:47:03,430 --> 00:47:07,165 play, I found. Yeah. Yeah. For sure. So 763 00:47:07,165 --> 00:47:10,925 share something different about yourself. But do you remember it is a family 764 00:47:10,925 --> 00:47:14,605 podcast? Something different about myself. I 765 00:47:14,605 --> 00:47:18,380 I boxed. Oh, really? Yeah. Not professionally 766 00:47:18,380 --> 00:47:22,220 or anything. Just, I go into it for the fitness to start with 767 00:47:22,220 --> 00:47:24,560 and then just got more and more obsessed with it. 768 00:47:25,875 --> 00:47:29,555 Greatly enjoyed it, and, and I only stopped recently because I moved 769 00:47:29,555 --> 00:47:32,855 house, so there's no gym near me. But I 770 00:47:33,619 --> 00:47:37,160 absolutely loved it, met some really wonderful people, and learned a great deal about myself. 771 00:47:37,780 --> 00:47:40,580 That's a quote fight club. You don't really know yourself until you get punched in 772 00:47:40,580 --> 00:47:44,285 the face. And and you really do find out a lot 773 00:47:44,285 --> 00:47:47,345 about yourself. So yeah. Excellent. 774 00:47:49,220 --> 00:47:52,819 So Audible is a sponsor of Data Driven. Do you do 775 00:47:52,819 --> 00:47:56,484 audiobooks? And if so, can you recommend a good one? I do audiobooks 776 00:47:56,785 --> 00:48:00,625 all the time. Nice. Whenever I run, whenever I walk the dog, it's either 777 00:48:00,625 --> 00:48:02,565 a podcast or an audiobook almost guaranteed. 778 00:48:04,660 --> 00:48:08,500 Recommend a good book. I'll do 2, one tech and one non tech just because 779 00:48:08,500 --> 00:48:11,560 life's too short for only tech books. For 780 00:48:11,860 --> 00:48:15,705 tech, I will say Team Topologies, I think is one of 781 00:48:16,245 --> 00:48:20,085 the books that impacted me the most when I read it and made me think 782 00:48:20,085 --> 00:48:23,829 about how people collaborate with one another. That or Team of Teams by, 783 00:48:24,069 --> 00:48:27,130 Stanley McChrystal is just, oh, amazing as well. 784 00:48:28,150 --> 00:48:31,405 Both of those are kind of in the tech realm. Non tech, I would say 785 00:48:31,405 --> 00:48:35,025 The Master and, Margarita by Mikhail Bulgakov was, 786 00:48:35,965 --> 00:48:39,250 it's just I read it and I wanted to I read it. I've read about 787 00:48:39,250 --> 00:48:42,150 5 times and I just loved it every single time. I think it's just wonderful. 788 00:48:43,490 --> 00:48:46,915 And, also kind of morbid book Crime and Punishment by 789 00:48:46,915 --> 00:48:50,755 Dostoevsky, I just think is, again, I read it 790 00:48:50,755 --> 00:48:53,315 and I was greatly impacted by it whenever I read it. I thought it was 791 00:48:53,315 --> 00:48:56,859 wonderful. So, yeah, kind of too dusty Russian writers there. But, 792 00:48:56,859 --> 00:49:00,700 yeah, those are the, those are the recommendations. Well, that's the beauty of the 793 00:49:00,700 --> 00:49:04,435 time we live in. Like, you could get an audio book on just about 794 00:49:04,895 --> 00:49:08,735 anything. Anything. And listen to it just about anywhere. Yeah. 795 00:49:08,735 --> 00:49:12,010 That's the thing. That's the thing. You know, when time is limited, I can take 796 00:49:12,010 --> 00:49:15,710 the dog out for a walk, audiobook, and I'm I get to 797 00:49:15,930 --> 00:49:19,605 hear about the, you know, some crazy you 798 00:49:19,605 --> 00:49:23,205 know, some detail of the Napoleonic war while my dog's chasing a stick on the 799 00:49:23,205 --> 00:49:26,565 beach. You know, it's it's like that level of convenience. You can't beat it. You 800 00:49:26,565 --> 00:49:30,350 really can't beat it. And on the as well. Like, no. It's wonderful. For 801 00:49:30,350 --> 00:49:34,030 sure. If you go to the data driven book, which I think 802 00:49:34,030 --> 00:49:36,930 Andy is testing right now, see if it's a DNS that works. It's working. 803 00:49:38,155 --> 00:49:41,995 You know, for 2 tech guys, we we have a lot of infrastructure challenges. We 804 00:49:41,995 --> 00:49:45,755 definitely need some SRE. What do they say about Shoemaker's 805 00:49:45,755 --> 00:49:46,255 children? 806 00:49:50,240 --> 00:49:53,140 Where can people find more about you and CoreLogix? 807 00:49:54,555 --> 00:49:56,494 So the easy one for CoreLogix, corelogix.com. 808 00:49:58,315 --> 00:50:01,755 And there's, there's a whole host of different ways you can learn about 809 00:50:01,755 --> 00:50:05,230 CoreLogix, video courses, Mostly me, so I 810 00:50:05,230 --> 00:50:08,270 apologize if you're sick of the sound of my voice because it's gonna be a 811 00:50:08,270 --> 00:50:11,790 lot more of it, I'm afraid. And then, we're on 812 00:50:11,790 --> 00:50:15,295 YouTube. We have a blog and all sorts. That's 813 00:50:15,295 --> 00:50:18,974 CoreLogic. For me, personally, I'm mostly on LinkedIn these days. I 814 00:50:18,974 --> 00:50:22,810 have other social media, but I don't really use it. So, if 815 00:50:22,810 --> 00:50:26,170 you just search LinkedIn for Chris Cooney, I think I'm the top one. I think 816 00:50:26,170 --> 00:50:29,950 that's my accolade now. But if not, if I'm not coming up, Chris Cooney, CoreLogic 817 00:50:30,010 --> 00:50:33,464 is guy. So that For me, you were the 3rd. For you, me, you were 818 00:50:33,464 --> 00:50:36,904 the 3rd one. Yeah. But, I mean, you're first in my mind and my heart, 819 00:50:36,904 --> 00:50:40,730 of course. But, I when I saw it when I saw the picture 820 00:50:40,730 --> 00:50:43,430 of the guy that first come up, I'm like, doesn't look like you. And 821 00:50:44,290 --> 00:50:47,525 she, I don't think you live in Miami. So 822 00:50:48,224 --> 00:50:51,365 so I wish. It would be nice, but I'll yeah. 823 00:50:52,464 --> 00:50:56,170 Miami is awesome in the winter. In the summer, it 824 00:50:56,170 --> 00:50:59,630 requires a certain type of person. That's all I'll say. 825 00:50:59,849 --> 00:51:03,595 K. I I I struggle in heat above 25 degrees Celsius, so I imagine 826 00:51:03,795 --> 00:51:07,155 Yeah. Miami is probably not for you. No. I agree. Not for you in the 827 00:51:07,155 --> 00:51:08,615 summer. Yeah. 828 00:51:11,349 --> 00:51:14,710 But I love Miami. Big shout out to, Miami. I know a lot of folks 829 00:51:14,710 --> 00:51:18,250 who live there and love it. Noel and Bill are the first two, 305 830 00:51:18,470 --> 00:51:21,975 people that comes to mind. Of course, Pitbull, the the musician, but that's 831 00:51:21,975 --> 00:51:25,815 a that's a more of an insight, I think, to my musical taste that 832 00:51:25,815 --> 00:51:26,555 people want. 833 00:51:29,570 --> 00:51:33,010 And with that, will it barely finish the show? And just like 834 00:51:33,010 --> 00:51:36,770 that, we've reached the end of the first episode of season 8 of the 835 00:51:36,770 --> 00:51:40,115 data driven podcast. We've traversed the fascinating 836 00:51:40,255 --> 00:51:43,935 terrain of data, observability in production systems, and 837 00:51:43,935 --> 00:51:47,555 developer advocacy, all thanks to the insightful Chris Cooney. 838 00:51:47,980 --> 00:51:51,420 A big thank you to Chris for sharing his expertise and making the 839 00:51:51,420 --> 00:51:55,180 complex sound oh so simple. Now, a quick note on our 840 00:51:55,180 --> 00:51:58,755 new theme song. We know it's a bit lengthy, but fear 841 00:51:58,755 --> 00:52:02,195 not, we'll be trimming it down for future episodes. Your 842 00:52:02,195 --> 00:52:05,815 listening experience is our top priority after all. 843 00:52:06,150 --> 00:52:09,609 As always, we'd love to hear your thoughts on today's episode. 844 00:52:10,230 --> 00:52:14,069 Feel free to reach out on our social media channels or leave a comment on 845 00:52:14,069 --> 00:52:17,675 our website. Don't forget to subscribe, rate, 846 00:52:17,735 --> 00:52:21,575 and review us on your favourite podcast platform. Until next 847 00:52:21,575 --> 00:52:25,258 time, stay curious, stay data driven and remember 848 00:52:25,718 --> 00:52:28,538 the future is data shaped. Cheers.