1 00:00:33,854 --> 00:00:36,734 W. Curtis Preston: Hi, and welcome to Backup Central's Restore it all podcast, 2 00:00:36,734 --> 00:00:39,134 I'm your host, W Curtis Preston, AKA Mr. 3 00:00:39,134 --> 00:00:39,614 Backup. 4 00:00:40,004 --> 00:00:44,284 And I have with me, my DIY commiserator Prasanna Malaiyandi. 5 00:00:44,284 --> 00:00:46,054 How's it going Prasanna? 6 00:00:46,469 --> 00:00:49,229 Prasanna Malaiyandi: Uh, I wish I did not have to commiserate. 7 00:00:49,234 --> 00:00:53,549 I wish I could cheer or celebrate, but. 8 00:00:53,714 --> 00:00:56,144 W. Curtis Preston: I, I wish you could come over and help me. 9 00:00:56,864 --> 00:00:57,604 Prasanna Malaiyandi: No, no. 10 00:00:58,214 --> 00:01:04,154 So for the listeners or the viewers of the podcast, Curtis is finally 11 00:01:04,159 --> 00:01:06,614 started his project to redo the floors. 12 00:01:07,154 --> 00:01:09,134 And he started this weekend. 13 00:01:09,139 --> 00:01:12,104 He's been prepping and doing all sorts of work before this. 14 00:01:12,104 --> 00:01:15,464 He finally started and he 15 00:01:15,659 --> 00:01:16,109 W. Curtis Preston: Yeah. 16 00:01:16,109 --> 00:01:16,619 And then I, 17 00:01:16,634 --> 00:01:17,954 Prasanna Malaiyandi: up just a little bit. 18 00:01:18,149 --> 00:01:20,369 W. Curtis Preston: yeah, I laid a couple of rows and then I realized I 19 00:01:20,369 --> 00:01:22,499 was actually laying them backwards. 20 00:01:22,679 --> 00:01:26,819 It would've worked it would've just made the whole job worse. 21 00:01:26,969 --> 00:01:27,419 Right. 22 00:01:27,479 --> 00:01:28,589 Um, harder. 23 00:01:28,799 --> 00:01:29,669 And, um, 24 00:01:29,804 --> 00:01:31,214 Prasanna Malaiyandi: it at the beginning, which is 25 00:01:31,229 --> 00:01:31,499 W. Curtis Preston: did. 26 00:01:31,504 --> 00:01:32,759 I caught it in the beginning. 27 00:01:32,819 --> 00:01:36,779 And, um, it was because there's this guy that I'm using to help me out. 28 00:01:36,779 --> 00:01:41,099 He has this, um, his name's Joe Letendre, he's actually up in the Midwest. 29 00:01:41,099 --> 00:01:45,959 And he, he actually has a service where like, he, he helps you lay 30 00:01:45,964 --> 00:01:47,249 out your stuff and all this stuff. 31 00:01:47,249 --> 00:01:48,509 And, and I needed did that. 32 00:01:48,509 --> 00:01:52,109 And I watched a bunch of videos, but so much time passed because. 33 00:01:52,604 --> 00:01:55,154 Everything that's happened in this house in the last few months 34 00:01:55,604 --> 00:02:00,374 that I had forgotten, uh, a really important, uh, part, which is 35 00:02:00,734 --> 00:02:03,464 which side of the, of the LVT 36 00:02:03,674 --> 00:02:04,844 Prasanna Malaiyandi: so simple, right? 37 00:02:05,054 --> 00:02:06,914 W. Curtis Preston: which side goes towards the wall. 38 00:02:07,094 --> 00:02:12,044 Um, and, uh, I, I had the, uh, I had the tongue. 39 00:02:12,644 --> 00:02:14,234 Let's see, I had the groove. 40 00:02:14,834 --> 00:02:19,604 Facing out instead of the, because to me, if you, for those of you that ever 41 00:02:19,604 --> 00:02:23,534 looked at L V T like there's a, there's a tongue and a groove, but to me, the 42 00:02:23,534 --> 00:02:27,594 groove looks like a tongue because it's sticking out really obvious. 43 00:02:27,614 --> 00:02:29,654 It looks like it's a tongue, but it's not a tongue. 44 00:02:29,684 --> 00:02:30,464 That's the groove. 45 00:02:30,674 --> 00:02:32,384 The tongue is the part that looks good. 46 00:02:32,654 --> 00:02:36,254 I don't understand why that is, but anyway, so, but, so it's good now. 47 00:02:36,254 --> 00:02:40,394 I've, I've got, I've gotten two rows, uh, laid and the first row is the absolute 48 00:02:40,394 --> 00:02:42,644 hardest, uh, cuz you gotta get it. 49 00:02:42,704 --> 00:02:46,484 You gotta measure it just so to get it, you know, to, to exactly everything. 50 00:02:47,509 --> 00:02:50,359 And so, you know, uh, now I just have to deal with the fact 51 00:02:50,364 --> 00:02:52,429 that my knee is 56 years old. 52 00:02:52,619 --> 00:02:57,029 Knee padding and, and Motrin is what it's better living through chemistry. 53 00:02:57,339 --> 00:03:02,469 I throw out our usual disclaimer, Prasanna and I work for different companies. 54 00:03:02,469 --> 00:03:03,219 He works for Zoom. 55 00:03:03,219 --> 00:03:04,009 I work for Druva. 56 00:03:04,269 --> 00:03:08,289 This is not a podcast of either company and the opinions that you hear are ours. 57 00:03:08,739 --> 00:03:12,279 Uh, be sure to rate us at ratethispodcast.com/restore. 58 00:03:12,769 --> 00:03:15,854 If you wanna talk about the kind of stuff we like to talk about, backups, 59 00:03:15,854 --> 00:03:23,264 archives, uh, security storage, uh, you know, barbecue, uh, you know, 60 00:03:24,759 --> 00:03:25,159 Prasanna Malaiyandi: scuba diving. 61 00:03:26,460 --> 00:03:30,224 W. Curtis Preston: scuba diving, uh, @wcpreston on Twitter or. 62 00:03:31,394 --> 00:03:33,464 Uh, w Curtis Preston at Gmail 63 00:03:33,944 --> 00:03:36,854 . Uh, so let's bring on our guest today. 64 00:03:36,859 --> 00:03:41,114 He has been in the it industry since the late nineties running HP's enterprise 65 00:03:41,114 --> 00:03:43,754 server business for a while, which means I might have actually been a 66 00:03:43,754 --> 00:03:47,484 customer of him back in the day, before founding a startup that was actually 67 00:03:47,489 --> 00:03:50,024 acquired by HPE for the last four years. 68 00:03:50,024 --> 00:03:53,234 He's been the CEO of Datacore, a software defined storage 69 00:03:53,234 --> 00:03:54,614 company in Fort Lauderdale. 70 00:03:54,944 --> 00:03:57,364 Welcome to the podcast, Dave Zabrowski. 71 00:03:57,974 --> 00:04:00,974 Dave Zabrowski: I'm glad to be here, Curtis and Prasanna. 72 00:04:01,064 --> 00:04:01,664 Nice to have you. 73 00:04:01,664 --> 00:04:05,554 I, I, uh, I have bad memories of doing my own floors. 74 00:04:06,539 --> 00:04:12,419 Way, way, way before I had any money, I rented a ceramic saw and 75 00:04:12,419 --> 00:04:13,889 it was like, it was a disaster. 76 00:04:13,889 --> 00:04:14,459 So yes. 77 00:04:14,459 --> 00:04:15,209 Good, good for you. 78 00:04:15,359 --> 00:04:18,809 W. Curtis Preston: on, um, I'm doing luxury vinyl tile, and I will also 79 00:04:18,809 --> 00:04:21,869 have bad memories, but I, you know, I'm in it, I'm in it to win it. 80 00:04:21,899 --> 00:04:22,289 You know what I 81 00:04:22,489 --> 00:04:22,779 Dave Zabrowski: yeah. 82 00:04:22,784 --> 00:04:25,259 Well, Curtis, you know, when you get to be our age, you gotta be like 83 00:04:25,259 --> 00:04:28,229 the pharaohs who built the pyramids quote, unquote, built the pyramids, 84 00:04:28,229 --> 00:04:30,209 you outsource that stuff, you know, 85 00:04:30,689 --> 00:04:31,889 W. Curtis Preston: I, I, yeah. 86 00:04:31,889 --> 00:04:34,559 You know, a good, a good buddy of mine, this breakfast place 87 00:04:34,559 --> 00:04:36,299 that I go to all, all the time. 88 00:04:36,359 --> 00:04:40,739 I I've been going there 20 years and I was talking to him about DIY 89 00:04:40,744 --> 00:04:42,029 stuff and he he's a Curtis Curtis. 90 00:04:42,059 --> 00:04:44,519 He goes, my dad taught me something a long time ago. 91 00:04:44,819 --> 00:04:49,109 Be really good at what you do so you could pay other people to do what they do. 92 00:04:49,829 --> 00:04:50,099 and I'm. 93 00:04:50,954 --> 00:04:52,304 Oh, that's just that's. 94 00:04:52,364 --> 00:04:54,164 That is a way to live your life. 95 00:04:54,164 --> 00:04:54,344 That 96 00:04:54,419 --> 00:04:55,709 Dave Zabrowski: Haven't learned that lesson yet. 97 00:04:56,024 --> 00:04:56,984 W. Curtis Preston: haven't learned that yet. 98 00:04:57,074 --> 00:04:58,094 It's coming up though. 99 00:04:58,364 --> 00:05:03,644 This, this, this one hurts, uh, nowhere near as painful as my last DIY project. 100 00:05:03,644 --> 00:05:07,514 If you can believe this, actually, uh, put solar up on my roof, if 101 00:05:07,519 --> 00:05:09,464 you can believe that that was, that 102 00:05:09,479 --> 00:05:10,469 Dave Zabrowski: I'm afraid of Heights. 103 00:05:10,469 --> 00:05:12,599 I wouldn't, I wouldn't dig that project at all. 104 00:05:12,734 --> 00:05:13,064 W. Curtis Preston: no. 105 00:05:13,394 --> 00:05:13,814 Yeah. 106 00:05:13,994 --> 00:05:16,874 Um, I don't, I'm not gonna say I dug it, but, but yeah. 107 00:05:16,874 --> 00:05:20,714 Anyway, uh, and ultimately ended up having to call the guy towards the 108 00:05:20,714 --> 00:05:24,314 end of the project, cuz I, I wanted to finish by the end of the year cuz I 109 00:05:24,569 --> 00:05:25,979 Dave Zabrowski: The call of shame. 110 00:05:26,444 --> 00:05:26,834 W. Curtis Preston: Yeah. 111 00:05:26,864 --> 00:05:27,074 Yeah. 112 00:05:27,074 --> 00:05:27,884 The call of shame. 113 00:05:27,884 --> 00:05:28,214 Exactly. 114 00:05:28,394 --> 00:05:31,064 Well the worst part, the worst part and listeners will know this 115 00:05:31,064 --> 00:05:35,084 already, but the worst part was like, he charged me like it was. 116 00:05:35,999 --> 00:05:41,369 $800 to finish, uh, which was the fi I had put all the, uh, all the 117 00:05:41,369 --> 00:05:45,329 posts in, and then he just had to put the, the panel just had to put 118 00:05:45,329 --> 00:05:47,189 up the panels and do all the wiring. 119 00:05:47,549 --> 00:05:49,319 And so he charged me $800 for that. 120 00:05:49,324 --> 00:05:51,719 His team came out and they were done in a, like a day. 121 00:05:51,779 --> 00:05:52,169 Right. 122 00:05:52,799 --> 00:05:57,989 And, and I said, just curious, um, for the part that I had done already, how 123 00:05:57,989 --> 00:05:59,909 much more would you have charged me? 124 00:06:00,644 --> 00:06:01,604 To do that part. 125 00:06:01,604 --> 00:06:03,644 He's like, oh, another $300. 126 00:06:03,694 --> 00:06:07,534 I spent months, it took me months doing it because it's up high. 127 00:06:07,834 --> 00:06:11,524 You can't work in the afternoon cuz you know, I live in Southern California. 128 00:06:11,524 --> 00:06:12,964 It's hot as hell up there. 129 00:06:12,964 --> 00:06:17,224 And anyway, so sometimes DIY is not the way they go, but um, 130 00:06:18,659 --> 00:06:21,149 we're just, we're glad you're here. 131 00:06:21,359 --> 00:06:24,749 And, and thanks for, uh, also commiserating with me here. 132 00:06:25,094 --> 00:06:25,934 Dave Zabrowski: Yeah, of course 133 00:06:26,069 --> 00:06:28,469 W. Curtis Preston: I, I I've been aware of Datacore, you know, a 134 00:06:28,469 --> 00:06:30,149 lot longer than you've been there. 135 00:06:30,599 --> 00:06:32,609 Uh, how, how long have they been around? 136 00:06:33,569 --> 00:06:35,694 Dave Zabrowski: Datacore since 1998. 137 00:06:36,024 --> 00:06:38,214 They were founded by 11 founders. 138 00:06:38,214 --> 00:06:39,264 If you can believe that. 139 00:06:39,894 --> 00:06:43,674 And they came outta the high performance computing business, believe it or not. 140 00:06:43,674 --> 00:06:48,384 Fort Lauderdale Boca Raton area in the heyday was, was one of the places 141 00:06:48,389 --> 00:06:49,734 for high performance computing. 142 00:06:50,394 --> 00:06:54,624 And, uh, they came out of that world and, uh, built a company that was 143 00:06:54,624 --> 00:06:58,644 very successful, very profitable, and barely anybody knew about it. 144 00:06:59,014 --> 00:07:02,544 so they, they were very much technologists and not marketeers that's for. 145 00:07:03,369 --> 00:07:03,819 Wonderful. 146 00:07:03,819 --> 00:07:04,599 Wonderful people. 147 00:07:04,659 --> 00:07:05,469 Wonderful founders. 148 00:07:05,469 --> 00:07:05,739 Yep. 149 00:07:06,354 --> 00:07:09,714 W. Curtis Preston: Yeah, by the way, super jelly, uh, love Fort Lauderdale. 150 00:07:09,714 --> 00:07:10,914 I actually grew up in Orlando. 151 00:07:11,124 --> 00:07:16,524 Um, and, um, I, the, the scuba diving in Fort Lauderdale is, is amazing. 152 00:07:16,869 --> 00:07:18,009 Dave Zabrowski: I was just doing it on Sunday. 153 00:07:18,009 --> 00:07:18,699 It's spectacular. 154 00:07:18,699 --> 00:07:21,639 I, I spent almost my whole career in Silicon valley, so it's it's, 155 00:07:21,729 --> 00:07:25,269 it's nice to go on the ocean when it's actually above 60 degrees. 156 00:07:26,064 --> 00:07:29,364 W. Curtis Preston: yeah, I mean, you know, you're, you're looking at 80, 85, right? 157 00:07:29,409 --> 00:07:29,739 Dave Zabrowski: yep. 158 00:07:29,769 --> 00:07:30,309 For sure. 159 00:07:31,374 --> 00:07:32,094 Prasanna Malaiyandi: Curtis is so 160 00:07:32,154 --> 00:07:33,744 W. Curtis Preston: um, Yeah. 161 00:07:33,834 --> 00:07:34,644 Super jealous. 162 00:07:34,644 --> 00:07:34,884 Yeah. 163 00:07:34,914 --> 00:07:36,864 Cuz you know, the temps that we're dealing with out here. 164 00:07:36,984 --> 00:07:37,314 Right. 165 00:07:38,814 --> 00:07:42,984 So, uh, what, why don't, why don't you give a, an overview? 166 00:07:43,014 --> 00:07:47,154 Uh, I know it's a software defined, uh, storage company, but you 167 00:07:47,154 --> 00:07:49,254 you've really looks like you've. 168 00:07:49,914 --> 00:07:53,964 Uh, in the last couple of years you've really been looking at this 169 00:07:53,964 --> 00:07:58,344 problem of, uh, ransomware and, and cyber attacks and things like that. 170 00:07:58,349 --> 00:08:00,024 So why don't you give an overview of Datacore? 171 00:08:00,519 --> 00:08:00,879 Dave Zabrowski: Sure. 172 00:08:00,879 --> 00:08:01,119 Sure. 173 00:08:01,119 --> 00:08:03,369 So my last company, as you mentioned, it was in the cloud 174 00:08:03,369 --> 00:08:04,899 analytics consumption space. 175 00:08:04,899 --> 00:08:06,039 We had a SaaS product. 176 00:08:06,879 --> 00:08:10,479 We sold that to Hewlett Packard Enterprise in 2017. 177 00:08:10,539 --> 00:08:15,309 And if you're familiar with that, uh, with HP's lineup called GreenLake, that's 178 00:08:15,309 --> 00:08:20,799 essentially where cloud cruiser ended up going and, and growing that, uh, HPE 179 00:08:20,799 --> 00:08:23,169 was our largest customer at the time. 180 00:08:23,259 --> 00:08:26,829 And, um, in fact, many of our Cloud Cruiser employees, that was the name 181 00:08:26,829 --> 00:08:30,849 of the company, um, are still there and take on more and more responsibility. 182 00:08:30,849 --> 00:08:32,769 So that, that was a really interesting experience. 183 00:08:33,474 --> 00:08:36,084 And, uh, and a good partnership with HPE. 184 00:08:36,234 --> 00:08:41,394 So Antonio was president at the time and shortly after we acquired, he became CEO. 185 00:08:41,484 --> 00:08:43,344 So, uh, so that's good. 186 00:08:43,344 --> 00:08:49,674 So Datacore, as the software defined storage, we really focused on a vision 187 00:08:49,704 --> 00:08:52,164 that we called at the time, Datacore one. 188 00:08:52,764 --> 00:08:58,614 And what that meant was a single solution for all your storage needs 189 00:08:58,614 --> 00:09:00,564 based upon a virtualized approach. 190 00:09:01,174 --> 00:09:05,799 One of the things that was very obvious to me prior to my cloud company, 191 00:09:06,309 --> 00:09:12,399 uh, we had, uh, I was in the storage business, um, in 2002, I left it. 192 00:09:13,029 --> 00:09:18,249 And when I exited the company in 2017, I came back into the storage business and 193 00:09:18,279 --> 00:09:19,749 poked around and not much had changed. 194 00:09:20,559 --> 00:09:24,939 I mean, it was, uh, kind of an innovation, you know, desert, if you will. 195 00:09:25,929 --> 00:09:29,859 A lot of the big guys that were big in 2002 were still more 196 00:09:29,859 --> 00:09:31,089 or less doing the same thing. 197 00:09:31,089 --> 00:09:32,409 Wasn't a lot of innovation. 198 00:09:33,129 --> 00:09:38,799 So I got in touch with the founder and managing director of Insight 199 00:09:38,799 --> 00:09:42,719 Venture Partners, one of the most successful software, but investors, 200 00:09:44,049 --> 00:09:46,899 gentleman named Jeff Warren, I got to meet him through a friend. 201 00:09:47,589 --> 00:09:52,059 And he said to me that there was this unknown unheard of company down 202 00:09:52,064 --> 00:09:53,659 in Fort Lauderdale called Datacore. 203 00:09:53,679 --> 00:09:55,839 That actually had some pretty cool things. 204 00:09:55,854 --> 00:09:58,974 And, and they were on the, the, the hot side of the spectrum. 205 00:09:58,974 --> 00:10:01,344 They were doing very high performance computing, as I mentioned, 206 00:10:01,344 --> 00:10:02,604 that was the foundation of it. 207 00:10:03,294 --> 00:10:07,614 Um, and the hypothesis was that there was an opportunity to actually 208 00:10:07,944 --> 00:10:13,854 develop a broader offering, uh, based upon a virtualized approach 209 00:10:13,854 --> 00:10:18,104 that would cut across the spectrum from hot to warm, to cool, to cold. 210 00:10:18,104 --> 00:10:18,944 And so that's what we did. 211 00:10:18,944 --> 00:10:20,534 That's what became Datacore one. 212 00:10:21,074 --> 00:10:23,924 We actually organically released a few products. 213 00:10:24,014 --> 00:10:27,674 Uh, we actually had some acquisitions that have been quite successful 214 00:10:27,764 --> 00:10:32,954 in the, uh, object unstructured side, as well as on the container, 215 00:10:33,044 --> 00:10:34,634 uh, native attached storage side. 216 00:10:35,174 --> 00:10:38,144 Um, and then as it relates to ransomware, which was specific to 217 00:10:38,149 --> 00:10:43,634 your question, you know, that evolved over the last several years where, 218 00:10:44,354 --> 00:10:48,374 you know, ransomware was kind of, it was almost one of these random things. 219 00:10:48,374 --> 00:10:52,079 And if you were a CEO of a company, you didn't think much about it a few years 220 00:10:52,079 --> 00:10:56,339 ago and you'd hear, you know, one of your buddies got, got hit with it and you kind 221 00:10:56,339 --> 00:11:00,629 of commiserated with them, but then it got to the point where it wasn't, uh, it 222 00:11:00,629 --> 00:11:02,999 wasn't an, if it was when and how bad. 223 00:11:03,689 --> 00:11:05,729 Um, and that's really, that really changed. 224 00:11:05,729 --> 00:11:07,709 It reminded me a lot in the server business. 225 00:11:07,769 --> 00:11:11,969 Uh, when I was running the server business at HP, uh, it was, you know, 226 00:11:11,969 --> 00:11:14,729 where it was all about nines, how many number of nines you could get. 227 00:11:14,729 --> 00:11:17,309 And so you're trying to get that server not to fail. 228 00:11:18,119 --> 00:11:21,359 Facebook and Google came along and they basically said let's design an 229 00:11:21,359 --> 00:11:23,609 architecture that plans on it failing. 230 00:11:24,299 --> 00:11:27,869 And so that's what ended up with the, the next generation of servers. 231 00:11:27,869 --> 00:11:31,469 And so that's really the, the reality is ransomware is going to hit you. 232 00:11:31,979 --> 00:11:36,419 And it's just a question of, you know, how bad and, and when, um, so we ended 233 00:11:36,419 --> 00:11:40,289 up the, one of the acquisitions we made was a company called Caringo, which 234 00:11:40,289 --> 00:11:45,729 was also a relatively under the radar company based out of Austin, Texas. 235 00:11:45,729 --> 00:11:50,699 Happened to have one of the best hybrid object stores, uh, in the industry. 236 00:11:51,059 --> 00:11:52,619 Just not a lot of people knew about it. 237 00:11:52,649 --> 00:11:55,919 And we were, uh, fortunate enough to partner with them. 238 00:11:55,919 --> 00:11:58,349 We acquired the company about a year and a half ago. 239 00:11:58,709 --> 00:12:01,379 And since then they had some real good architecture for 240 00:12:01,599 --> 00:12:03,959 immutability and, and ransomware. 241 00:12:03,979 --> 00:12:06,009 And since then we've built that out even further. 242 00:12:07,094 --> 00:12:11,594 We actually have partnered with a lot of the backup vendors, you know, Veeam 243 00:12:11,594 --> 00:12:17,084 and CommVault and Cohesity and others to bring an offering that basically, you 244 00:12:17,084 --> 00:12:19,334 know, we just call it a time machine. 245 00:12:19,754 --> 00:12:23,564 It's basically you just, you just know whenever it happens, 246 00:12:23,684 --> 00:12:25,184 we don't do the actual detection. 247 00:12:25,184 --> 00:12:28,424 Obviously we partner with other people that does the actual detection of it. 248 00:12:28,514 --> 00:12:30,704 And, but what, when it is detected. 249 00:12:31,379 --> 00:12:35,189 Basically just reset the clock to the, you know, the nanosecond or 250 00:12:35,189 --> 00:12:39,539 whatever of, of, uh, right before it was attacked and then you re restore. 251 00:12:40,109 --> 00:12:41,999 Um, so that's what our, that's what our solution does. 252 00:12:41,999 --> 00:12:47,609 And it's, it's been very popular because of the, the dynamics in the market where, 253 00:12:47,669 --> 00:12:52,079 you know, everybody's budget now in the it world has this budgeted and it's 254 00:12:52,079 --> 00:12:55,139 been, it's been very successful for us. 255 00:12:56,199 --> 00:12:59,474 Prasanna Malaiyandi: So I just wanted to go back to kind of 256 00:12:59,474 --> 00:13:01,154 Datacore the foundation of it. 257 00:13:01,604 --> 00:13:04,784 So I am sure a lot of our listeners are like, why would I even 258 00:13:04,789 --> 00:13:06,374 need software defined storage? 259 00:13:06,644 --> 00:13:06,944 Right. 260 00:13:06,944 --> 00:13:10,844 Could you sort of go into the benefits, the reasons why you 261 00:13:10,844 --> 00:13:13,994 would want that versus some of the traditional offerings out there. 262 00:13:14,564 --> 00:13:14,984 Dave Zabrowski: Sure. 263 00:13:14,984 --> 00:13:15,284 Sure. 264 00:13:15,284 --> 00:13:19,094 So if you look at all the infrastructure in the data center, every single 265 00:13:19,094 --> 00:13:25,064 technology used to be proprietary hardware with a very, very thin software stack, 266 00:13:25,124 --> 00:13:28,304 oftentimes proprietary as well on top it. 267 00:13:28,304 --> 00:13:32,834 And then all those industries actually migrated into a commodity based hardware 268 00:13:32,834 --> 00:13:35,024 with software stack on top of it. 269 00:13:35,029 --> 00:13:38,564 So the value pushed up from hardware into software. 270 00:13:38,684 --> 00:13:41,744 Well, storage has not done that, and it's hard to believe we're sitting here 271 00:13:41,744 --> 00:13:46,304 in 2022 and, and the majority of the storage industry still is proprietary. 272 00:13:47,954 --> 00:13:51,344 And the benefits basically are no different than benefits you 273 00:13:51,344 --> 00:13:52,544 get in the other infrastructure. 274 00:13:52,544 --> 00:13:55,994 Basically, you, you get on, you get on cheaper hardware, you basically 275 00:13:55,999 --> 00:13:57,794 have investment protection backwards. 276 00:13:57,794 --> 00:14:03,014 So you now can move and optimize existing infrastructure, which was extremely 277 00:14:03,019 --> 00:14:04,904 helpful during the COVID recession. 278 00:14:04,904 --> 00:14:08,024 We had a lot of business where we were able to go in with our 279 00:14:08,024 --> 00:14:11,444 software defined approach and leverage existing infrastructure 280 00:14:11,444 --> 00:14:12,854 that had been underutilized. 281 00:14:13,034 --> 00:14:14,204 And then it's future proof. 282 00:14:14,354 --> 00:14:16,294 So you don't have vendor lockin. 283 00:14:16,664 --> 00:14:19,184 Basically can move from, from vendor to vendor. 284 00:14:19,184 --> 00:14:23,084 And then when new, when new technologies come like NVMe over fabric, for 285 00:14:23,084 --> 00:14:26,504 example, you know, you're basically just Futureproof cuz, cuz that's, that's 286 00:14:26,504 --> 00:14:28,094 the beauty of software defined storage. 287 00:14:28,094 --> 00:14:31,304 So it's kinda like a, you know, think of it as a storage virtualization 288 00:14:31,304 --> 00:14:34,034 layer and it's very flexible. 289 00:14:34,284 --> 00:14:38,604 When we do, um, surveys of our customer, We always ask them 290 00:14:38,604 --> 00:14:40,164 why we win and why we lose. 291 00:14:40,169 --> 00:14:44,514 And one of the main reasons why we win is just that you can support 292 00:14:44,544 --> 00:14:47,814 heterogeneous environments, backward looking and forward looking. 293 00:14:49,584 --> 00:14:52,284 W. Curtis Preston: And so what we're talking about is when, when they 294 00:14:52,284 --> 00:14:55,344 buy Datacore, what are they buying? 295 00:14:55,344 --> 00:14:56,604 Are they buying just software? 296 00:14:56,604 --> 00:14:57,684 Do you do an appliance? 297 00:14:57,684 --> 00:14:59,394 And then you put stuff behind the appliance. 298 00:14:59,394 --> 00:15:01,094 Um, you know, how's work. 299 00:15:01,584 --> 00:15:01,854 Dave Zabrowski: Yeah. 300 00:15:01,854 --> 00:15:04,674 So they, so specifically they buy from us the software. 301 00:15:05,379 --> 00:15:07,539 They can have their own hardware installed. 302 00:15:07,749 --> 00:15:12,939 Um, oftentimes we are part of a new project, either a, a new deployment or 303 00:15:12,939 --> 00:15:14,799 an expansion of an existing deployment. 304 00:15:15,099 --> 00:15:18,369 In which case we are put on new hardware that hardware can be bought 305 00:15:18,369 --> 00:15:23,199 by the customer, or oftentimes they go through a partner, a resell it partner. 306 00:15:23,589 --> 00:15:26,859 Uh, we tend to be focused on mid-market is where our sweet spot is. 307 00:15:26,859 --> 00:15:31,539 And a lot of the mid-market customers have, uh, partners, integrator 308 00:15:31,539 --> 00:15:33,369 partners or managed service partner. 309 00:15:34,269 --> 00:15:36,639 That they, that they actually provide that, that bundled 310 00:15:36,639 --> 00:15:38,289 service, but it's very simple. 311 00:15:38,289 --> 00:15:39,339 It's a very simple install. 312 00:15:39,339 --> 00:15:40,569 It's not complex at all. 313 00:15:41,319 --> 00:15:44,229 You basically just load up the hardware, get the hardware running, 314 00:15:44,229 --> 00:15:47,409 and then you install the software in a matter of, you know, an hour 315 00:15:47,409 --> 00:15:48,449 or two you're up and running. 316 00:15:48,804 --> 00:15:52,164 Prasanna Malaiyandi: And because you're sort of decoupled from the hardware. 317 00:15:52,734 --> 00:15:57,294 Is there a lot of tuning the customer has to do or that the partner has 318 00:15:57,299 --> 00:16:00,864 to do in order to sort of optimize the performance or is that all 319 00:16:00,864 --> 00:16:04,554 sort of smarts that you guys have built into your software offering? 320 00:16:04,704 --> 00:16:05,484 Dave Zabrowski: It's both. 321 00:16:05,484 --> 00:16:08,934 I mean, we have configurations, we have best practices. 322 00:16:08,964 --> 00:16:10,764 You know, we do industry benchmarks. 323 00:16:10,764 --> 00:16:13,164 We offer those to our customers, but it depends. 324 00:16:13,164 --> 00:16:17,514 I mean, a lot of applications are very, very specific to, uh, internal 325 00:16:17,514 --> 00:16:20,484 requirements, in which case they would actually tune those, uh, 326 00:16:20,484 --> 00:16:21,744 to those internal requirements. 327 00:16:21,744 --> 00:16:26,844 We do have a solution architect, uh, function in all of our major geos 328 00:16:26,844 --> 00:16:29,694 that helps customers with this type of thing, best practice sharing. 329 00:16:30,054 --> 00:16:33,444 And if they do need to tune it specifically, uh, we'll help them do that. 330 00:16:35,199 --> 00:16:38,029 W. Curtis Preston: and then what can you put behind a Datacore 331 00:16:38,049 --> 00:16:40,059 engine from a storage perspective? 332 00:16:41,094 --> 00:16:42,204 Dave Zabrowski: And literally anything. 333 00:16:42,354 --> 00:16:45,234 I mean, what, whatever, whatever you want, you stick behind it. 334 00:16:45,294 --> 00:16:49,104 And any, any of the technologies work, um, behind it, any of the 335 00:16:49,104 --> 00:16:51,834 technologies work in front of it, you can put any app on top of it. 336 00:16:52,314 --> 00:16:54,804 Um, and you know, that, that's how, that's how it works. 337 00:16:55,314 --> 00:16:57,174 W. Curtis Preston: and so, you know, we're talking NAS, we're talking 338 00:16:57,179 --> 00:17:01,824 block, we're talking object on the back end and the same on the front end. 339 00:17:02,574 --> 00:17:04,134 Do you translate? 340 00:17:04,554 --> 00:17:07,404 So can I have object on the back end and NAS on the front end? 341 00:17:07,404 --> 00:17:08,154 Vice versa. 342 00:17:09,684 --> 00:17:15,354 Dave Zabrowski: Um, well, it, so I think the, the answer is, it depends, and 343 00:17:15,354 --> 00:17:16,434 I know you don't like those answers. 344 00:17:16,434 --> 00:17:17,664 Nobody likes those answers, 345 00:17:17,694 --> 00:17:19,584 W. Curtis Preston: You know, I was a consultant for, I was 346 00:17:19,584 --> 00:17:20,854 a consultant for 20 years. 347 00:17:21,034 --> 00:17:22,494 I'm fine with that phrase. 348 00:17:22,734 --> 00:17:23,024 Dave Zabrowski: Yeah. 349 00:17:23,029 --> 00:17:25,014 That's the, that's the unfortunate answer. 350 00:17:25,044 --> 00:17:28,614 Um, it kind of depends on the configuration, uh, but generally speaking. 351 00:17:29,529 --> 00:17:33,339 Your object storage is more your second tier. 352 00:17:33,939 --> 00:17:39,429 Uh, sometimes it's active archiving, which is a, which is a kind of a tier two plus, 353 00:17:39,969 --> 00:17:44,649 you know, if you think of, um, if you think of video streaming, for example, um, 354 00:17:44,679 --> 00:17:49,629 if, if someone let's, let's say a famous actor is in the news for whatever reason. 355 00:17:50,874 --> 00:17:54,924 Those videos, those movies that they have been in, that haven't been that popular. 356 00:17:54,924 --> 00:17:58,644 All of a sudden, those need to be presented very quickly. 357 00:17:58,734 --> 00:18:03,204 And oftentimes that comes outta your second tier out of your active archiving. 358 00:18:03,204 --> 00:18:08,544 So it does depend on that generally though, the, the object store is, 359 00:18:08,544 --> 00:18:13,434 is unstructured data, which is focused on, on cost and performance. 360 00:18:13,614 --> 00:18:16,554 Your first tier is performance and then cost generally. 361 00:18:18,744 --> 00:18:22,314 W. Curtis Preston: And I, I saw a really good presentation years ago. 362 00:18:22,734 --> 00:18:25,704 I believe it was with the actual Active Archive folks, right. 363 00:18:25,704 --> 00:18:27,444 The, the Active Archive Alliance. 364 00:18:27,744 --> 00:18:31,404 And it was actually the folks from, uh, Entertainment Tonight. 365 00:18:32,004 --> 00:18:35,904 And they were talking about exactly the scenario that you described of, 366 00:18:36,504 --> 00:18:44,694 of how that basically the moment some famous person starts trending they 367 00:18:44,694 --> 00:18:46,194 start pulling all of that stuff. 368 00:18:46,224 --> 00:18:46,584 Right. 369 00:18:46,584 --> 00:18:50,814 So that they're able to have that readily available and, um, you know, 370 00:18:50,904 --> 00:18:53,334 to, to, to produce other videos from it. 371 00:18:53,334 --> 00:18:53,664 Right. 372 00:18:54,249 --> 00:18:56,979 Dave Zabrowski: Yeah, well, I mean, if the whole, the whole 373 00:18:56,979 --> 00:19:00,279 media entertainment industry is really going through a golden era. 374 00:19:01,404 --> 00:19:04,989 and it's, it's a lot of, it's driven by technology with the high density 375 00:19:04,989 --> 00:19:08,709 cameras, with a lot of the machine learning and artificial intelligence 376 00:19:08,709 --> 00:19:10,719 that's laying on top of the production. 377 00:19:11,319 --> 00:19:13,809 And then that stuff is really an exciting area. 378 00:19:13,809 --> 00:19:18,819 It's going through, uh, a complete change, uh, where the, the production, 379 00:19:18,819 --> 00:19:23,259 I mean, they're producing on average or your typical set per day is producing 380 00:19:23,259 --> 00:19:25,349 between 10 and 20 terabytes of data. 381 00:19:25,989 --> 00:19:30,519 . Um, and at any given time, there's like 10,000, you know, shoots that 382 00:19:30,519 --> 00:19:32,919 are going on, uh, in, in the world. 383 00:19:33,129 --> 00:19:37,749 So there's just massive amounts of data and that data has to be processed. 384 00:19:37,749 --> 00:19:42,459 It has to be rendered, uh, and then all these AI tools that go on top 385 00:19:42,459 --> 00:19:45,929 of it, which is, you know, natural machine learning, you know, facial 386 00:19:45,939 --> 00:19:48,399 recognition, phon recognition, all this. 387 00:19:49,014 --> 00:19:51,204 Is just generating massive amounts of data. 388 00:19:51,474 --> 00:19:54,174 And that data has to be in perpetuity. 389 00:19:54,174 --> 00:19:58,464 It's not like if you think about a security application, massive 390 00:19:58,464 --> 00:20:00,924 amounts of data, but they only keep it for a short period of time. 391 00:20:01,014 --> 00:20:01,224 Right. 392 00:20:01,229 --> 00:20:02,244 And then it falls off. 393 00:20:02,244 --> 00:20:06,384 So from a storage perspective, that use case is important. 394 00:20:06,924 --> 00:20:11,124 but it doesn't have, you know, perpetuity, whereas the movies have perpetuity and 395 00:20:11,124 --> 00:20:14,424 it's a, it's a really, it's an exciting area for us and something that, uh, you 396 00:20:14,424 --> 00:20:18,804 know, we're, we're obviously knee deep into, uh, from a Datacore perspective. 397 00:20:19,574 --> 00:20:22,034 Prasanna Malaiyandi: Taking the media and entertainment industry 398 00:20:22,064 --> 00:20:27,254 as an example, do you see then that people tend to have a vast majority 399 00:20:27,254 --> 00:20:30,044 of their data stored in object? 400 00:20:30,044 --> 00:20:33,114 I know previously I think you talked about sort of the cold, 401 00:20:33,114 --> 00:20:35,514 the warm, the hot tiers, right. 402 00:20:35,604 --> 00:20:39,894 Um, do you see a good chunk of your data then on the cold tiers that 403 00:20:39,924 --> 00:20:43,044 when people are using Datacore or is it depending on the application? 404 00:20:43,044 --> 00:20:43,524 It's a huge 405 00:20:43,554 --> 00:20:46,794 Dave Zabrowski: Yeah, I think the way to think about it then Prasannas is, 406 00:20:46,974 --> 00:20:53,994 you know, let's say in, in the 2000 era, you know, 80% of your data was that was 407 00:20:53,994 --> 00:20:55,944 being produced, was structured data. 408 00:20:56,664 --> 00:20:56,934 Right. 409 00:20:56,964 --> 00:20:59,214 Right now it's the exact opposite and getting more. 410 00:20:59,219 --> 00:21:02,214 So, so if you think about like, if you, you know, and you guys 411 00:21:02,214 --> 00:21:04,974 have one of these smart watches, every time you take a step literal. 412 00:21:05,649 --> 00:21:07,269 You're producing unstructured data. 413 00:21:07,269 --> 00:21:10,149 This video that we're using with Zencaster, that's 414 00:21:10,149 --> 00:21:11,559 producing unstructured data. 415 00:21:11,949 --> 00:21:16,179 Everything is producing unstructured data and all of the new apps 416 00:21:16,209 --> 00:21:18,069 are producing unstructured data. 417 00:21:18,069 --> 00:21:22,029 So that's where we see the market, uh, exploding, um, and 418 00:21:22,029 --> 00:21:23,199 the structured data still there. 419 00:21:23,199 --> 00:21:26,349 I mean, you still need, you know, databases and you still need, 420 00:21:26,379 --> 00:21:27,789 you know, think of eCommerce. 421 00:21:27,789 --> 00:21:31,239 I mean, there's a tremendous amount of structured data in the eCommerce space. 422 00:21:31,969 --> 00:21:35,349 But for us, you know, we, we, we have the structured space. 423 00:21:35,349 --> 00:21:37,959 That's, that's the core of, of, of the company. 424 00:21:38,319 --> 00:21:41,829 Uh, but it's really, you know, the growth engine is on the unstructured side. 425 00:21:43,194 --> 00:21:46,764 Prasanna Malaiyandi: I was going to ask Dave, uh, I know you mentioned that you 426 00:21:46,764 --> 00:21:48,804 had like a SaaS analytics company, right. 427 00:21:48,804 --> 00:21:51,834 That you did, that you sold HPE, right? 428 00:21:52,044 --> 00:21:56,484 Um, when it comes to Datacore, are there analytics that are built into the product 429 00:21:56,489 --> 00:22:02,004 to help users and admins understand sort of workloads applications, like 430 00:22:02,214 --> 00:22:04,794 where to place data, things like that. 431 00:22:04,794 --> 00:22:08,964 Because as a software defined layer, right storage layer, right. 432 00:22:08,964 --> 00:22:11,964 You're kind of removed from the underlying hardware and infrastructure. 433 00:22:11,964 --> 00:22:14,814 And so identifying performance issues, understanding what's going 434 00:22:14,814 --> 00:22:19,164 on may sometimes become more complex in these environments versus sort 435 00:22:19,164 --> 00:22:21,224 of a self-contained appliance. 436 00:22:21,754 --> 00:22:22,474 Dave Zabrowski: it it is. 437 00:22:22,494 --> 00:22:26,304 And we do have those offerings, um, and, and that's become, let's call 438 00:22:26,304 --> 00:22:27,594 it more or less industry standard. 439 00:22:27,594 --> 00:22:31,024 Most of the vendors have those, some of the vendors that have a vertical stack. 440 00:22:31,844 --> 00:22:34,569 They can actually go deeper into the hardware cuz they actually have 441 00:22:34,659 --> 00:22:36,969 more specificity into the hardware. 442 00:22:37,269 --> 00:22:39,489 You know, what we do is we jump from hardware to hardware. 443 00:22:39,494 --> 00:22:44,079 So things like capacity analysis, you know, we can do, you know, SLA 444 00:22:44,349 --> 00:22:48,159 forecasting, um, you know, that type of thing we call 'em insights. 445 00:22:48,164 --> 00:22:54,309 It's basically, you know, data mining for purposes of optimizing, 446 00:22:54,399 --> 00:22:55,469 you know, the infrastructure. 447 00:22:58,279 --> 00:23:03,129 W. Curtis Preston: Historically speaking one, objection to software 448 00:23:03,129 --> 00:23:10,159 defined anything has been, well, the reason why I buy, you know, proprietary 449 00:23:10,159 --> 00:23:11,959 appliances is because it's faster. 450 00:23:12,909 --> 00:23:16,029 That they're able to tweak the hardware and make it perfect for that hardware. 451 00:23:16,029 --> 00:23:20,559 Whereas with you, the performance will be all over the place based 452 00:23:20,559 --> 00:23:22,389 on what I decide to put behind it. 453 00:23:22,989 --> 00:23:25,629 So how do, how do you, how do you respond to that? 454 00:23:25,659 --> 00:23:27,519 Dave Zabrowski: No, that that's a, that's a true statement. 455 00:23:27,519 --> 00:23:32,979 I mean, it depends if, if someone's in a, you know, super, super high performance, 456 00:23:33,429 --> 00:23:38,229 you know, application, um, a monolith solution, a vertical monolith solution, 457 00:23:39,099 --> 00:23:40,779 often time, is there better solution? 458 00:23:40,989 --> 00:23:41,649 Is there better? 459 00:23:41,649 --> 00:23:45,909 I answer, um, But with that, of course, they're gonna spend more money on it 460 00:23:46,419 --> 00:23:47,769 and they're gonna get vendor lock in. 461 00:23:47,769 --> 00:23:52,119 So, you know, sometimes customers make that decision to do that, um, 462 00:23:52,124 --> 00:23:54,129 and go and go that vertical monolith. 463 00:23:54,129 --> 00:23:58,689 Now history has shown, um, that, that over time that's not a good solution 464 00:23:58,689 --> 00:24:00,399 for infrastructure, generally speaking. 465 00:24:00,399 --> 00:24:05,019 I mean, that's, if you look back at and pick any technology, But, you know, for 466 00:24:05,019 --> 00:24:08,829 a product cycle or too, you know, for the right applications, that's fine. 467 00:24:08,979 --> 00:24:12,729 You know, and the way I look at it, I'm, you know, I've been a CEO for 21 468 00:24:12,729 --> 00:24:15,939 years now and, you know, I mean, nobody has a hundred percent market share. 469 00:24:15,944 --> 00:24:20,019 So, you know, my, my, my focus with my team is let's, let's go to 470 00:24:20,019 --> 00:24:24,759 those areas where we do have a high value that we bring to the company. 471 00:24:24,759 --> 00:24:28,509 And if somebody a actually really values that monolith vertical stack. 472 00:24:29,189 --> 00:24:29,989 That's great. 473 00:24:30,129 --> 00:24:33,399 Let 'em, you know, let 'em have that and we'll go on and serve other customers. 474 00:24:33,399 --> 00:24:35,199 We have plenty of customers to serve. 475 00:24:36,069 --> 00:24:38,409 W. Curtis Preston: You know, your approach reminds me very much of 476 00:24:38,409 --> 00:24:41,979 sort of the way we think the way we see things at Druva as well, right. 477 00:24:41,979 --> 00:24:47,439 Where, you know, we're doing SaaS based backup of large environments. 478 00:24:47,439 --> 00:24:47,799 Right? 479 00:24:48,249 --> 00:24:50,659 You can't do that for everyone. 480 00:24:51,164 --> 00:24:51,514 Dave Zabrowski: Right, 481 00:24:51,734 --> 00:24:54,654 W. Curtis Preston: You know, if you've got my usual phrase is if you've got 482 00:24:54,894 --> 00:24:58,974 30 petabytes of data in a T1 line, we're probably not your, you know, 483 00:24:59,184 --> 00:25:01,224 the company you need to be talking to. 484 00:25:01,229 --> 00:25:01,554 Right. 485 00:25:02,064 --> 00:25:05,454 Um, but it's the same kind of approach, like you said, nobody 486 00:25:05,454 --> 00:25:06,744 has a hundred percent market share. 487 00:25:07,599 --> 00:25:07,959 Dave Zabrowski: Yeah. 488 00:25:08,649 --> 00:25:12,669 And even if you look at best in class statistics on close rates, the best 489 00:25:12,669 --> 00:25:14,889 companies in the world are closing 30%, 490 00:25:15,594 --> 00:25:16,074 W. Curtis Preston: right. 491 00:25:16,134 --> 00:25:16,524 Yeah. 492 00:25:16,719 --> 00:25:19,059 Dave Zabrowski: and, and, and, and the companies that report numbers higher than 493 00:25:19,059 --> 00:25:20,589 that are probably fudging the numbers. 494 00:25:20,594 --> 00:25:20,949 Right. 495 00:25:20,949 --> 00:25:23,699 So, I mean, that's just the reality of our business, you know, 496 00:25:24,454 --> 00:25:26,524 Prasanna Malaiyandi: Before I know Curtis, you wanna talk about data 497 00:25:26,524 --> 00:25:30,664 protection and backup and all the rest before we switch to that, can you talk 498 00:25:30,664 --> 00:25:32,314 a little bit about container storage? 499 00:25:32,314 --> 00:25:34,744 I know you guys recently did an acquisition. 500 00:25:35,014 --> 00:25:38,404 Just kind of curious about that and what you guys see there. 501 00:25:38,674 --> 00:25:39,064 Dave Zabrowski: sure. 502 00:25:39,064 --> 00:25:42,034 So, so we're actually the leaders in container attached storage. 503 00:25:42,064 --> 00:25:46,354 Um, when I came into the company four years ago, there were a lot of very 504 00:25:46,354 --> 00:25:49,294 smart, uh, engineers and architects. 505 00:25:49,624 --> 00:25:53,374 That came from this high performance computing market had really 506 00:25:53,374 --> 00:25:56,824 pioneered software defined storage in the early days of Datacore. 507 00:25:57,394 --> 00:26:02,974 And we have a ton of patents around this and, and they had this idea that we could 508 00:26:02,974 --> 00:26:08,704 apply some of this high performance into a container, uh, native storage solution. 509 00:26:09,244 --> 00:26:12,844 Um, so we actually created a skunkworks project for about, 510 00:26:12,874 --> 00:26:14,914 about a year and funded. 511 00:26:15,754 --> 00:26:19,774 And gave them the opportunity to prove that, that it could actually 512 00:26:19,774 --> 00:26:23,044 be a better mouse trap in this Kubernetes container environment. 513 00:26:23,374 --> 00:26:24,664 And that turned out to be true. 514 00:26:24,844 --> 00:26:30,814 So in 2019, we actually went out and looked at, uh, partnering with 515 00:26:30,819 --> 00:26:34,384 companies cuz traditional Datacore is not in the Kubernetes space. 516 00:26:34,384 --> 00:26:35,944 We're not in the open source community. 517 00:26:36,424 --> 00:26:40,294 Uh, a lot of our management team have had experiences and that myself 518 00:26:40,299 --> 00:26:43,594 included, but it's not something that was DNA to the company. 519 00:26:44,084 --> 00:26:48,499 So we went out and looked, uh, we actually found, uh, Maya data who were 520 00:26:48,499 --> 00:26:51,259 the pioneers of what's called open EBS. 521 00:26:51,769 --> 00:26:57,109 That project was part of the CNCF, the cloud native compute foundation, which 522 00:26:57,114 --> 00:26:59,479 is the governing body around Kubernetes. 523 00:26:59,959 --> 00:27:04,159 Um, and at that time, Kubernetes had just basically Google had just thrown 524 00:27:04,159 --> 00:27:05,689 some number of hundreds of engineer. 525 00:27:06,514 --> 00:27:08,044 At that, uh, Kubernetes. 526 00:27:08,044 --> 00:27:10,534 And it was pretty clear that Kubernetes was gonna be the, 527 00:27:10,714 --> 00:27:14,554 the container orchestration, uh, framework, uh, of the future. 528 00:27:15,484 --> 00:27:17,224 So those things all kind of converged. 529 00:27:17,224 --> 00:27:20,464 And then we ended up actually putting an investment into Maya data. 530 00:27:21,304 --> 00:27:22,264 We put money into them. 531 00:27:22,264 --> 00:27:24,004 We actually merged our two teams. 532 00:27:24,514 --> 00:27:28,474 We, uh, had cross license rights, cross technology rights. 533 00:27:28,654 --> 00:27:30,334 We created a separate board of directors. 534 00:27:30,334 --> 00:27:33,304 I was on that as was Insight venture Partners. 535 00:27:33,379 --> 00:27:36,829 Um, and we worked with them collaboratively for about a year and 536 00:27:36,829 --> 00:27:41,239 a half, and then just acquired them in November, uh, of this past year. 537 00:27:41,239 --> 00:27:43,299 And now they're a hundred percent part of Datacore. 538 00:27:43,999 --> 00:27:49,669 Um, so what we've seen is the open EBS that, that open source product 539 00:27:49,669 --> 00:27:51,289 has really, really taken off. 540 00:27:51,289 --> 00:27:56,959 I mean, we're, we're now over a million downloads per month, uh, for that product. 541 00:27:57,049 --> 00:28:03,009 Um, so it's one of the fastest growing parts of the Kubernetes ecosystem. 542 00:28:03,060 --> 00:28:08,989 Datacore has released our enterprise grade version of that, uh, this past quarter. 543 00:28:09,049 --> 00:28:13,669 And then we'll continue to evolve that, but that market is very, very exciting. 544 00:28:14,149 --> 00:28:20,179 It's a market that if you look at core, you look at edge, you look at cloud, you 545 00:28:20,209 --> 00:28:23,389 know, for most workloads going forward. 546 00:28:23,629 --> 00:28:25,819 Um, that solution is the best solution. 547 00:28:25,819 --> 00:28:26,479 It's the lightest. 548 00:28:27,499 --> 00:28:29,929 It's the most agile and it's the cheapest. 549 00:28:30,079 --> 00:28:35,029 And I'm a firm believer that that container native storage 550 00:28:35,149 --> 00:28:38,839 position of ours is gonna do great things over the coming years. 551 00:28:38,839 --> 00:28:42,049 We actually already have an installed base of customers. 552 00:28:42,199 --> 00:28:49,999 And so what we see is new applications in, uh, core cloud and edge, uh, And they will 553 00:28:49,999 --> 00:28:55,399 be, those applications will be done based upon the container native storage stack. 554 00:28:55,849 --> 00:28:58,729 And one of the most surprising things to me, if I look back on 555 00:28:58,729 --> 00:29:02,869 our hypothesis in 2019, we kind of thought it would be the top of the 556 00:29:02,874 --> 00:29:05,239 pyramid, the companies that had scale. 557 00:29:05,749 --> 00:29:09,439 That could afford to bring on the Kubernetes trained engineers and 558 00:29:09,439 --> 00:29:11,179 then born in the cloud companies. 559 00:29:11,179 --> 00:29:12,769 So that was our business plan at the time. 560 00:29:12,769 --> 00:29:16,639 Those two markets, well what's happened is it's, it's all markets. 561 00:29:16,699 --> 00:29:18,619 I mean, everyone, it's, it's crazy. 562 00:29:18,619 --> 00:29:23,839 Like if you look at the CNCF stats, uh, all geos, so Europe is 563 00:29:23,839 --> 00:29:27,589 actually leading the us, believe it or not in CNCF, uh, deployments. 564 00:29:28,039 --> 00:29:32,199 Uh, Asia's right there with them, but all, all regions, all verticals. 565 00:29:33,604 --> 00:29:37,624 And, and basically all use cases are, are being consumed with Kubernetes. 566 00:29:37,764 --> 00:29:40,084 I, I was traveling, I just came back from Europe. 567 00:29:40,084 --> 00:29:44,224 I was traveling in the Rhine valley and, you know, Southwest Germany, 568 00:29:44,229 --> 00:29:50,074 like manufacturing, you know, Mecca of Germany, you know, very, very established 569 00:29:50,074 --> 00:29:54,724 companies producing, you know, kind of like not cutting edge stuff, but 570 00:29:54,754 --> 00:29:56,554 you know, good manufacturing stuff. 571 00:29:56,884 --> 00:29:58,084 And I talked to the CIO there. 572 00:29:58,084 --> 00:29:59,794 One of our customers who's been a customer for. 573 00:30:00,784 --> 00:30:03,574 I was talking about the future of containers. 574 00:30:03,574 --> 00:30:08,944 So have you thought about, will you, when will you, and he's like, well, we got half 575 00:30:08,944 --> 00:30:13,954 of our applications already ported over to, to Kubernetes and, and we're using 576 00:30:13,954 --> 00:30:16,084 open EBS, you know, it was hilarious. 577 00:30:16,474 --> 00:30:19,384 Um, so that's what we found on the container native storage 578 00:30:19,934 --> 00:30:22,384 side is that it's, it's coming. 579 00:30:23,284 --> 00:30:28,294 Most of the let's call them the, you know, the easier applications have 580 00:30:28,294 --> 00:30:33,329 already been ported to kubernetes, the harder applications, which 581 00:30:33,329 --> 00:30:34,979 require the persistent state. 582 00:30:35,879 --> 00:30:38,549 Those have been ramping up over this past year. 583 00:30:38,549 --> 00:30:39,839 We'll see that accelerate. 584 00:30:39,839 --> 00:30:45,329 And, you know, I think in two to three years, it will be the exception that 585 00:30:45,479 --> 00:30:50,129 new applications will be written that won't be leveraging the Kubernetes. 586 00:30:50,129 --> 00:30:52,289 And I don't know, I've seen numbers as high as 80%. 587 00:30:52,289 --> 00:30:55,739 I mean, who knows, but I, I just think it's, you know, anytime you 588 00:30:55,739 --> 00:30:58,829 have something that is the cheapest. 589 00:30:59,159 --> 00:31:01,229 The lightest weight, the most agile. 590 00:31:01,289 --> 00:31:05,549 And it's based on an open framework that doesn't have lock in, feels 591 00:31:05,549 --> 00:31:07,169 like that's a formula for success 592 00:31:07,814 --> 00:31:08,174 W. Curtis Preston: Right. 593 00:31:09,134 --> 00:31:14,234 Well, let let's, um, and by the way, just, uh, uh, CNCF, that's the 594 00:31:14,264 --> 00:31:16,544 cloud native computing foundation. 595 00:31:16,904 --> 00:31:17,504 Um, 596 00:31:17,789 --> 00:31:21,359 Dave Zabrowski: That's the governing body of, of, you know, Kubernetes let's 597 00:31:21,359 --> 00:31:23,609 call it and the community around it. 598 00:31:24,239 --> 00:31:26,639 W. Curtis Preston: Just in case, uh, any of our listeners weren't 599 00:31:26,639 --> 00:31:28,199 familiar with that particular acronym. 600 00:31:28,679 --> 00:31:31,379 Uh, let's just round out here talking about data protection. 601 00:31:31,379 --> 00:31:36,209 Now it looks like this, uh, anti ransomware piece. 602 00:31:36,209 --> 00:31:40,879 It looks like it's, it's powered by your object storage, formerly known as Caringo. 603 00:31:41,099 --> 00:31:41,669 It's funny. 604 00:31:41,669 --> 00:31:45,509 I was, I was browsing it not knowing about the Caringo acquisition. 605 00:31:45,569 --> 00:31:47,129 And the first thing I saw was Swarm. 606 00:31:47,129 --> 00:31:47,369 I was. 607 00:31:48,134 --> 00:31:49,544 That's a branded term. 608 00:31:49,544 --> 00:31:52,424 And then I realized, oh, that's that's Caringo's term. 609 00:31:52,744 --> 00:32:00,604 So this is a sort of on demand, disc based backup for the primary. 610 00:32:01,369 --> 00:32:01,789 Right. 611 00:32:01,789 --> 00:32:04,699 That that's sort of being managed by your whole thing, but apparently powered 612 00:32:04,699 --> 00:32:06,199 by object storage in the back end. 613 00:32:06,589 --> 00:32:10,189 You wanna just, and this is what you were referring to in the front, 614 00:32:10,519 --> 00:32:13,459 uh, the first few minutes where you were talking about the time machine, 615 00:32:13,819 --> 00:32:16,789 which by the way, I'm pretty sure is another branded term, but, um, you 616 00:32:16,869 --> 00:32:17,449 Dave Zabrowski: It probably is. 617 00:32:17,499 --> 00:32:22,179 Well, basically it just, we work in partnership with the backup vendors. 618 00:32:22,179 --> 00:32:23,649 So we are not a backup vendor. 619 00:32:23,709 --> 00:32:24,369 Just to be clear. 620 00:32:24,374 --> 00:32:27,489 We sit, we sit aside of the backup vendors. 621 00:32:27,494 --> 00:32:30,579 As I mentioned, you could actually, you know, U utilize Datacore 622 00:32:30,699 --> 00:32:32,659 right out of the Veeam, UI. 623 00:32:32,659 --> 00:32:35,419 But we, we have partnerships with Commvault and Cohesity 624 00:32:35,439 --> 00:32:37,209 and, and others that are coming. 625 00:32:37,829 --> 00:32:41,334 um, but basically, you know, they're, we're, we're basically 626 00:32:41,334 --> 00:32:42,864 doing what they want us to do. 627 00:32:42,954 --> 00:32:48,054 So if they want us to back up, um, from the unstructured data, uh, 628 00:32:48,054 --> 00:32:53,124 that's what we do and, uh, we'll time stamp it and, and protect it 629 00:32:53,334 --> 00:32:54,714 and make it available when they want. 630 00:32:55,014 --> 00:32:55,734 And that's it. 631 00:32:55,824 --> 00:32:57,264 So it's, uh, 632 00:32:58,014 --> 00:33:01,194 W. Curtis Preston: really just basically backup storage or 633 00:33:01,194 --> 00:33:03,054 storage for backup and recovery. 634 00:33:03,784 --> 00:33:04,074 Dave Zabrowski: Yeah. 635 00:33:04,284 --> 00:33:06,794 W. Curtis Preston: I, I guess earlier I, I, I got. 636 00:33:07,674 --> 00:33:08,214 Idea. 637 00:33:08,214 --> 00:33:11,484 And maybe we're talking about a different part of the product that if I was attacked 638 00:33:11,484 --> 00:33:16,884 by a ransomware, that you basically had this ability to just easily put me back 639 00:33:17,454 --> 00:33:21,894 to before the ransomware attack, without involving a third party backup product. 640 00:33:22,074 --> 00:33:23,154 Am I misunderstanding? 641 00:33:23,709 --> 00:33:27,069 Dave Zabrowski: no, we, we work with the backup vendors, but the immutability 642 00:33:27,069 --> 00:33:31,469 basically will, will actually protect the, the, the data itself. 643 00:33:31,469 --> 00:33:31,479 And. 644 00:33:32,004 --> 00:33:32,274 Yeah. 645 00:33:32,274 --> 00:33:32,424 Yeah. 646 00:33:32,429 --> 00:33:36,294 And if you think of the active archive example I gave you before, you know, on 647 00:33:36,294 --> 00:33:42,234 the one hand, it's actually, it's tee up data for people that it's not deep 648 00:33:42,234 --> 00:33:46,554 glacier, you know, it's something that people want on a, on an as needed basis. 649 00:33:47,304 --> 00:33:50,454 So we have to have performance it's, you know, so it's not like, you know, 650 00:33:50,454 --> 00:33:52,344 you call in and get it three days later. 651 00:33:52,914 --> 00:33:57,624 Um, and then when it comes to, to backup, basically you wanna just roll back. 652 00:33:57,714 --> 00:34:01,614 Um, there's a, there's a concept of concept that we talk about here called 653 00:34:01,614 --> 00:34:06,234 continuous data protection, is essentially the same, you know, the same idea. 654 00:34:06,654 --> 00:34:09,564 You know, that again, that was some of the patents from some of 655 00:34:09,564 --> 00:34:13,134 the earlier, you know, Datacore expertise, but it's the same idea. 656 00:34:13,194 --> 00:34:16,524 You basically keep, keep track of things on a timestamp basis. 657 00:34:17,844 --> 00:34:22,224 When you detect, um, some sort of violation, uh, you just go back to 658 00:34:22,224 --> 00:34:26,064 T whatever, uh, from that violation and just, just restore the data. 659 00:34:26,214 --> 00:34:28,794 It's, it's, it's very, very simple in concept. 660 00:34:28,799 --> 00:34:31,404 It's, it's obviously more challenging from a technical perspective 661 00:34:31,404 --> 00:34:32,664 than that, but concept is easy. 662 00:34:32,739 --> 00:34:36,699 W. Curtis Preston: it, it would seem like you would do that part without 663 00:34:36,699 --> 00:34:38,379 the third party backup vendors. 664 00:34:38,409 --> 00:34:39,339 I, you understand 665 00:34:39,444 --> 00:34:40,944 Dave Zabrowski: We do we do. 666 00:34:40,949 --> 00:34:41,514 We do. 667 00:34:41,574 --> 00:34:41,934 Yeah. 668 00:34:42,234 --> 00:34:42,504 Yeah. 669 00:34:42,534 --> 00:34:43,404 We, we do do that. 670 00:34:43,404 --> 00:34:46,954 In fact, if you have a, if you just have a, you know, Datacore 671 00:34:46,974 --> 00:34:49,044 on its own, um, absolutely. 672 00:34:49,049 --> 00:34:53,694 With our UI, you set that up and it can do it, but, but more, more often, I mean, I 673 00:34:53,694 --> 00:34:59,004 would say the standard is there's backup vendors in the market that we work with. 674 00:34:59,004 --> 00:35:01,734 That's that's more the, I would say the typical use. 675 00:35:03,024 --> 00:35:05,214 W. Curtis Preston: Is the CDP functionality, is it part of 676 00:35:05,214 --> 00:35:07,734 the core product or is that something extra that you pay for? 677 00:35:07,959 --> 00:35:09,039 Dave Zabrowski: No, it's part of the core. 678 00:35:09,684 --> 00:35:10,074 W. Curtis Preston: Okay. 679 00:35:10,194 --> 00:35:10,644 All right. 680 00:35:11,094 --> 00:35:14,154 So you could, you could have that and it is, and it is CD. 681 00:35:14,214 --> 00:35:15,384 So it is continuous. 682 00:35:15,384 --> 00:35:18,204 I can go back to literally any point in time, not a 683 00:35:18,204 --> 00:35:19,644 particular snapshot that I took. 684 00:35:19,899 --> 00:35:20,109 Dave Zabrowski: Yeah. 685 00:35:20,114 --> 00:35:20,529 Correct. 686 00:35:20,679 --> 00:35:21,169 Correct. 687 00:35:21,819 --> 00:35:24,159 Prasanna Malaiyandi: And I'll throw out a name, Curtis, because I know 688 00:35:24,159 --> 00:35:25,899 we talked about time machine, right. 689 00:35:25,899 --> 00:35:26,979 You and I would love it. 690 00:35:26,979 --> 00:35:27,249 Right. 691 00:35:27,254 --> 00:35:28,419 TiVo and the DVRs. 692 00:35:28,419 --> 00:35:28,749 Right? 693 00:35:29,769 --> 00:35:31,059 Dave Zabrowski: See, on the DVS 694 00:35:31,719 --> 00:35:32,199 W. Curtis Preston: Yeah. 695 00:35:32,379 --> 00:35:36,159 Um, my TiVo rest in peace. 696 00:35:36,489 --> 00:35:37,059 Um, 697 00:35:38,529 --> 00:35:39,249 um, 698 00:35:39,249 --> 00:35:41,859 Dave Zabrowski: That's like, one of those technologies is like, how did that fail? 699 00:35:41,859 --> 00:35:43,059 You know, it's like web van. 700 00:35:43,059 --> 00:35:43,899 It's like, wait a minute. 701 00:35:43,899 --> 00:35:44,919 How did web van fail? 702 00:35:44,919 --> 00:35:45,699 You know, you think, 703 00:35:46,389 --> 00:35:46,989 W. Curtis Preston: yeah, 704 00:35:47,019 --> 00:35:49,959 I'm, I'm not actually familiar with web van, but, uh, definitely 705 00:35:49,959 --> 00:35:54,039 familiar with Tivo longtime Tivo customer and I've recently retired. 706 00:35:54,039 --> 00:35:56,799 My Tivo I'm I've now moved to YouTube TV. 707 00:35:57,379 --> 00:36:04,054 Dave, thanks a lot for, you know, coming on here and, um, you know, I, I, um, I, 708 00:36:04,054 --> 00:36:09,454 I am super jelly of, although I'm, I'm jealous of the weather of the water. 709 00:36:09,754 --> 00:36:13,054 I am not jealous of the weather everywhere else that you have, 710 00:36:13,279 --> 00:36:16,669 Dave Zabrowski: Yeah, but the thing is in Fort Lauderdale, you know, little 711 00:36:16,999 --> 00:36:20,179 known fact, I mean, we're cooler than the rest of the nation in the summer. 712 00:36:20,179 --> 00:36:22,309 Believe it or not, it rarely gets above 90. 713 00:36:23,419 --> 00:36:24,499 Rarely gets a button. 714 00:36:24,499 --> 00:36:25,669 Now you live in Orlando. 715 00:36:25,669 --> 00:36:26,389 That's different. 716 00:36:26,449 --> 00:36:27,079 That's different. 717 00:36:27,079 --> 00:36:27,319 Fort 718 00:36:27,439 --> 00:36:28,819 W. Curtis Preston: Well, I lived in Orlando. 719 00:36:28,819 --> 00:36:30,019 I live in San Diego. 720 00:36:30,019 --> 00:36:30,469 Now. 721 00:36:31,129 --> 00:36:33,529 I think I'll take, I'll take our weather over your 722 00:36:33,589 --> 00:36:34,939 Dave Zabrowski: you're you're spoiled. 723 00:36:34,939 --> 00:36:35,359 No, you're 724 00:36:35,509 --> 00:36:39,409 W. Curtis Preston: If it, if it, if it, if it hits 90, we're shutting down like this, 725 00:36:40,009 --> 00:36:42,409 just cuz nobody here has air conditioning. 726 00:36:42,469 --> 00:36:42,859 Right? 727 00:36:42,904 --> 00:36:45,784 Dave Zabrowski: Yeah, no, I know that I lived, I lived in, uh, Southern Cal 728 00:36:45,784 --> 00:36:50,434 for about 10 years and, uh, I used to used to think like if you had dials 729 00:36:50,439 --> 00:36:53,314 and you could change the weather, you wouldn't touch the dials ever in 730 00:36:53,314 --> 00:36:55,264 Southern California, but it's good. 731 00:36:55,264 --> 00:36:55,804 Thank you. 732 00:36:55,809 --> 00:36:58,744 And, uh, for, for your time gentlemen, and, uh, best of luck, 733 00:36:58,744 --> 00:37:01,264 if there's any follow up I can have, uh, please, please ping me. 734 00:37:01,414 --> 00:37:02,134 W. Curtis Preston: Yeah, absolutely. 735 00:37:02,134 --> 00:37:04,414 Prasanna, thanks again for your great questions 736 00:37:04,564 --> 00:37:05,314 Prasanna Malaiyandi: as always. 737 00:37:05,314 --> 00:37:08,614 I try and nice meet to meet you, Dave, and thanks for putting 738 00:37:08,614 --> 00:37:09,584 up with my questions too. 739 00:37:10,244 --> 00:37:10,824 Dave Zabrowski: That's great 740 00:37:10,824 --> 00:37:12,544 W. Curtis Preston: and, uh, thanks to our listeners. 741 00:37:12,544 --> 00:37:15,374 Make sure to subscribe so that you can restore it all.