1 00:00:35,228 --> 00:00:38,018 W. Curtis Preston: Hi, and welcome to Backup Central's Restore it All podcast. 2 00:00:38,018 --> 00:00:38,738 I'm your host W. 3 00:00:38,738 --> 00:00:40,538 Curtis Preston, AKA Mr. 4 00:00:40,538 --> 00:00:45,038 Backup, and I have with me my pick and pull analyst, Prasanna Malaiyandi. 5 00:00:45,038 --> 00:00:46,078 How's it going, Prasanna? 6 00:00:46,838 --> 00:00:47,918 Prasanna Malaiyandi: I'm good Curtis. 7 00:00:47,918 --> 00:00:51,278 Unfortunately, I'm not doing a great job in helping you, right. 8 00:00:51,488 --> 00:00:53,948 Just given the number of times that you sort of struck out 9 00:00:54,773 --> 00:00:59,633 W. Curtis Preston: Yeah, I, I, um, I still want a part or two that I can 10 00:00:59,633 --> 00:01:09,563 only get from a about 2012 to 2014 Prius and, uh, the silver Prius. 11 00:01:09,593 --> 00:01:09,893 Right. 12 00:01:09,893 --> 00:01:14,303 I it's a very specific, uh, subset. 13 00:01:14,633 --> 00:01:16,973 And the thing is, you know, with the pick and pulls, as you know, 14 00:01:16,978 --> 00:01:18,633 it's like super cheap, right? 15 00:01:18,683 --> 00:01:23,163 The, if you, if you have the wherewithal to go and pull a part 16 00:01:23,183 --> 00:01:26,033 off the car yourself, you save tons. 17 00:01:26,213 --> 00:01:26,543 Right? 18 00:01:26,543 --> 00:01:30,833 I mean, besides the fact that it's a used part, it's like, I don't know a fourth 19 00:01:30,833 --> 00:01:33,263 of the price than, than a normal part. 20 00:01:34,023 --> 00:01:39,353 Um, and, um, so, but I have to 21 00:01:39,398 --> 00:01:40,898 Prasanna Malaiyandi: everyone has the same thought as you 22 00:01:41,663 --> 00:01:42,233 W. Curtis Preston: what's that 23 00:01:42,818 --> 00:01:44,438 Prasanna Malaiyandi: that you go to the pick and pull. 24 00:01:44,648 --> 00:01:47,978 It's cheap, but the supply is limited. 25 00:01:48,308 --> 00:01:48,578 W. Curtis Preston: Yeah. 26 00:01:48,578 --> 00:01:52,408 And, and the, and I have, and the, the company that I use L KQ 27 00:01:52,478 --> 00:01:54,158 pick your part, not a sponsor. 28 00:01:54,338 --> 00:01:58,598 Um, that they, they have a really good system for notifying me with 29 00:01:58,658 --> 00:02:03,968 when any, you know, uh, pick and pull yard within whatever radius I specify 30 00:02:03,973 --> 00:02:05,498 has the car that I'm looking for. 31 00:02:05,648 --> 00:02:08,048 And then I have to go like right away, because. 32 00:02:08,648 --> 00:02:13,028 The Prius is popular and it's either get there like that day or you get 33 00:02:13,028 --> 00:02:17,588 there and, you know, there's not gonna be much left, but so I, I had another, 34 00:02:17,798 --> 00:02:23,018 I had another, uh, failed run, uh, at the pick and pull down in Chula Vista, 35 00:02:24,338 --> 00:02:25,718 which is about a 45 minute drive. 36 00:02:25,718 --> 00:02:26,918 But yeah, not a big deal, 37 00:02:27,293 --> 00:02:27,533 Prasanna Malaiyandi: Yeah. 38 00:02:27,683 --> 00:02:28,163 Now I'm 39 00:02:28,268 --> 00:02:29,498 W. Curtis Preston: here to counsel me on that. 40 00:02:30,263 --> 00:02:32,723 Prasanna Malaiyandi: well, and I'm sure some listeners are like, oh, why don't 41 00:02:32,723 --> 00:02:35,303 you just get like a red color Prius door? 42 00:02:35,783 --> 00:02:38,213 They don't understand how expensive it is 43 00:02:38,213 --> 00:02:41,663 W. Curtis Preston: Yeah, they, yeah, they don't, they just don't clearly. 44 00:02:41,743 --> 00:02:41,903 Yeah. 45 00:02:41,903 --> 00:02:41,973 Uh, 46 00:02:42,353 --> 00:02:45,448 Prasanna Malaiyandi: Body work and paint is insanely expensive to do it right 47 00:02:45,448 --> 00:02:46,228 W. Curtis Preston: it's ridiculous. 48 00:02:46,253 --> 00:02:46,403 Yeah. 49 00:02:46,408 --> 00:02:50,813 This all started because I had a minor scratch on the door, a minor dent on the 50 00:02:50,813 --> 00:02:53,663 door and they wanted $2,500 to fix that. 51 00:02:54,383 --> 00:02:59,693 So I'm like I can get a used door from a pick and pull place for like 70 bucks. 52 00:03:02,773 --> 00:03:08,163 Anyway, anyway, we could talk about that for a while, but, I am once again, excited 53 00:03:08,163 --> 00:03:11,143 to have a long time friend on the podcast. 54 00:03:11,823 --> 00:03:16,208 She has been in the data protection space for a long time as well. 55 00:03:16,698 --> 00:03:22,238 In fact, I got to know her in one of her previous lives at Spectra 56 00:03:22,238 --> 00:03:25,428 Logic, They've been on the podcast a couple of times as you guys know. 57 00:03:25,828 --> 00:03:29,398 She's a fascinating person, both from a technical standpoint and 58 00:03:29,398 --> 00:03:33,988 also this other, very interesting, aspect of her personality that 59 00:03:33,988 --> 00:03:36,898 she really likes large animals. 60 00:03:36,898 --> 00:03:40,858 We're gonna, we're gonna talk about that because it's a fascinating part of her. 61 00:03:41,158 --> 00:03:45,238 Uh, she is now the Senior Vice President of Marketing at Hammerspace, 62 00:03:45,478 --> 00:03:47,188 a global file system provider. 63 00:03:47,488 --> 00:03:50,158 Welcome to the podcast, Molly Presley. 64 00:03:50,923 --> 00:03:52,873 Molly Presley: Well, thank you so much for having me, Curtis. 65 00:03:52,873 --> 00:03:56,263 I have to say, um, Curtis is also a colorful personality. 66 00:03:56,263 --> 00:03:59,533 I've known him long enough to know that to be very true. 67 00:03:59,533 --> 00:04:02,773 And without him knowing I'm gonna bring this up, I will mention that 68 00:04:02,893 --> 00:04:06,553 I remember the very first time I met Curtis was at Storage Networking World. 69 00:04:06,583 --> 00:04:08,053 So those of you've been around for a while. 70 00:04:08,173 --> 00:04:14,203 SNW was a thing, um, both from a work as well as a personal perspective. 71 00:04:15,193 --> 00:04:19,723 So when I first met Curtis at SNW he was dressed like a clown, and 72 00:04:19,723 --> 00:04:23,053 we were sitting in the after hours, and it wasn't just like a clown. 73 00:04:23,293 --> 00:04:26,413 It was, I believe, a net backup, or it was actually maybe a backup 74 00:04:26,413 --> 00:04:28,333 exec seven launch or something. 75 00:04:28,333 --> 00:04:31,573 And they actually offered to do Halloween costumes for everyone. 76 00:04:31,573 --> 00:04:34,453 And Curtis chose to be a very flamboyant clown. 77 00:04:34,843 --> 00:04:36,163 So it absolutely stuck 78 00:04:36,163 --> 00:04:37,078 W. Curtis Preston: I do remember that. 79 00:04:37,108 --> 00:04:38,698 Yeah, that was, 80 00:04:40,398 --> 00:04:41,418 Molly Presley: There must be somewhere 81 00:04:42,178 --> 00:04:42,658 W. Curtis Preston: yeah. 82 00:04:42,748 --> 00:04:43,288 Yeah. 83 00:04:43,378 --> 00:04:44,098 Maybe. 84 00:04:44,938 --> 00:04:50,233 Yeah, that was, yeah, that was a Symantec dinner event, like we were, 85 00:04:50,263 --> 00:04:52,383 we, I guess we were both guests, uh, 86 00:04:52,383 --> 00:04:54,678 Molly Presley: I think we were guests and it was on Halloween 87 00:04:54,678 --> 00:04:56,058 because it was the end of October. 88 00:04:56,898 --> 00:05:00,078 They tried to make right on keeping us away from home on Halloween 89 00:05:00,078 --> 00:05:02,238 by making a dress up event. 90 00:05:02,323 --> 00:05:02,324 Prasanna Malaiyandi: event? 91 00:05:02,325 --> 00:05:02,326 I. 92 00:05:03,283 --> 00:05:03,823 W. Curtis Preston: yeah. 93 00:05:04,003 --> 00:05:05,863 And, uh, I do, I do now. 94 00:05:06,013 --> 00:05:09,283 I was, when you first said me dressed up as a clown, I'm like, I think 95 00:05:09,283 --> 00:05:10,483 she's sticking about somebody else. 96 00:05:10,483 --> 00:05:11,423 And then all of a sudden I'm like, oh, 97 00:05:12,238 --> 00:05:12,748 Molly Presley: No, it was 98 00:05:13,633 --> 00:05:14,263 W. Curtis Preston: Yes. 99 00:05:14,353 --> 00:05:18,313 The Symantec dinner, uh, where I was dressed up as a clown. 100 00:05:18,523 --> 00:05:21,973 Um, yeah, that was, that was something. 101 00:05:22,653 --> 00:05:26,073 Talk to us about the animals in your life, Molly. 102 00:05:26,298 --> 00:05:26,538 Molly Presley: Wow. 103 00:05:27,378 --> 00:05:32,058 Well, yeah, as Curtis has mentioned, I have a particular interest in large 104 00:05:32,058 --> 00:05:37,308 animals, and this is both from having them cruising around my home with a few, um, 105 00:05:37,398 --> 00:05:41,488 very large 200 pound great Danes that have at one point I had three of them at once. 106 00:05:42,248 --> 00:05:44,678 In a very small Seattle apartment downtown. 107 00:05:44,678 --> 00:05:45,368 So we were, 108 00:05:45,418 --> 00:05:45,778 Prasanna Malaiyandi: Oh, my. 109 00:05:46,388 --> 00:05:48,788 Molly Presley: tasked through COVID with walking 600 pounds of 110 00:05:48,788 --> 00:05:53,018 dog, um, without the apartment dog walk areas in downtown Seattle. 111 00:05:53,018 --> 00:05:55,448 So everyone of course knew who we were in the area. 112 00:05:55,958 --> 00:06:00,398 Um, we have Clydesdales and then a particular interest in elephant 113 00:06:00,398 --> 00:06:01,838 protection and conservation. 114 00:06:01,838 --> 00:06:07,178 So in Asia and Africa, a lot of holidays spent, um, tending to cleaning 115 00:06:07,178 --> 00:06:09,328 up after large elephants as well. 116 00:06:10,338 --> 00:06:12,103 W. Curtis Preston: I like how you just casually mentioned 117 00:06:12,103 --> 00:06:13,223 that you have Clydesdales. 118 00:06:14,133 --> 00:06:15,663 It was like a parenthetical. 119 00:06:15,783 --> 00:06:15,903 Oh. 120 00:06:15,903 --> 00:06:17,293 And we have a couple of Clydesdales. 121 00:06:17,293 --> 00:06:17,628 Molly Presley: course, of 122 00:06:17,793 --> 00:06:18,513 W. Curtis Preston: Um, 123 00:06:18,648 --> 00:06:20,298 Prasanna Malaiyandi: like small to large, to larger. 124 00:06:20,448 --> 00:06:23,028 So you 125 00:06:23,238 --> 00:06:27,318 W. Curtis Preston: Um, and, and you live and you live in, uh, Colorado, right? 126 00:06:27,318 --> 00:06:27,588 Or you? 127 00:06:27,588 --> 00:06:27,948 No. 128 00:06:28,578 --> 00:06:28,998 Molly Presley: Yeah, I do. 129 00:06:29,358 --> 00:06:30,678 moved back Seattle a bit ago. 130 00:06:31,638 --> 00:06:32,058 W. Curtis Preston: Right. 131 00:06:32,238 --> 00:06:38,208 And, um, what's, what's it like, um, caring for, you know, animals 132 00:06:38,208 --> 00:06:39,768 like that, that are just that large. 133 00:06:41,403 --> 00:06:44,103 Molly Presley: You know, they tend to be the classic saying the 134 00:06:44,103 --> 00:06:45,873 gentle giant tends to be true. 135 00:06:45,903 --> 00:06:49,263 They're very kind loving, good animals, but everything's just harder. 136 00:06:49,323 --> 00:06:55,158 You have to have bigger vehicles, bigger bags of food, bigger bags of cleanup gear. 137 00:06:55,208 --> 00:06:58,148 There tends to be just, everything is a little bit more complicated as far 138 00:06:58,148 --> 00:07:02,672 as how, I mean, just think about how do you get arounda 600 pounds of dog. 139 00:07:02,672 --> 00:07:06,438 We ended up dedicating a minivan with no seats in the back to that. 140 00:07:06,438 --> 00:07:06,788 W. Curtis Preston: Wow. 141 00:07:06,988 --> 00:07:08,258 Yeah, I guess that makes sense, right? 142 00:07:08,348 --> 00:07:08,648 Yeah. 143 00:07:08,993 --> 00:07:11,063 Molly Presley: And you have to be the type of person who doesn't mind 144 00:07:11,063 --> 00:07:14,963 being asked, in fact, enjoys being asked about them and people wanna pet 145 00:07:14,963 --> 00:07:16,883 them and know about caring for them. 146 00:07:16,883 --> 00:07:21,123 Or do you have a saddle for your dog or do you really ride your Clydesdales? 147 00:07:21,143 --> 00:07:25,133 Those kinds of questions, because people are, you know, truly interested, 148 00:07:25,193 --> 00:07:27,553 even though you may have heard the question before a few times. 149 00:07:27,553 --> 00:07:27,743 W. Curtis Preston: Yeah. 150 00:07:28,323 --> 00:07:28,483 Yeah. 151 00:07:28,508 --> 00:07:31,058 Cuz I mean, I mean there are horse people. 152 00:07:32,153 --> 00:07:37,433 But as just Clydesdales, you just don't, you don't see Clydesdales very often 153 00:07:37,433 --> 00:07:39,563 unless you're watching beer commercials. 154 00:07:39,713 --> 00:07:40,073 Right? 155 00:07:40,223 --> 00:07:40,673 I mean, you, 156 00:07:40,838 --> 00:07:41,258 Molly Presley: So people 157 00:07:41,303 --> 00:07:42,443 W. Curtis Preston: see them quite often. 158 00:07:43,058 --> 00:07:43,448 Molly Presley: Yeah. 159 00:07:43,453 --> 00:07:43,658 Yeah. 160 00:07:43,658 --> 00:07:47,288 I mean, people are surprised to see you on a trail ride with the low quarter 161 00:07:47,288 --> 00:07:49,908 horse and the little arabian goes by, and then the big old Clydesdale 162 00:07:49,928 --> 00:07:51,608 comes clumping along with his crew. 163 00:07:51,608 --> 00:07:55,478 And you know, his back stands over six feet tall and people like what? 164 00:07:55,478 --> 00:07:57,328 I didn't ride a Clydesdale. 165 00:07:57,348 --> 00:07:58,058 I had no idea. 166 00:07:58,058 --> 00:07:58,898 This is incredible. 167 00:07:59,663 --> 00:08:01,013 Prasanna Malaiyandi: how do you get up on 168 00:08:02,288 --> 00:08:03,038 Molly Presley: Steps. 169 00:08:03,578 --> 00:08:08,048 they, they actually make steps, mounting blocks, type things to get onto. 170 00:08:08,108 --> 00:08:11,708 But even then when you use the ones that you would use for your traditional 171 00:08:11,708 --> 00:08:16,198 sized horses, quite a jump to go from the steps to the back of the horse. 172 00:08:17,078 --> 00:08:19,898 W. Curtis Preston: I, I don't know if I've ever told you this, uh, Molly, but 173 00:08:19,898 --> 00:08:23,828 I, when, when my kids were little, they, they wanted to do some horseback riding. 174 00:08:23,888 --> 00:08:24,248 Right. 175 00:08:24,278 --> 00:08:27,818 And so we went to one of these places it's actually on base I'm, I'm just 176 00:08:27,818 --> 00:08:30,158 south of camp Pendleton in San Diego. 177 00:08:31,043 --> 00:08:35,393 They had a trail ride, you know, uh, set up where you 178 00:08:35,393 --> 00:08:36,713 could go and ride these horses. 179 00:08:36,763 --> 00:08:40,943 And, and we, we came up, me, my wife had zero interest in getting up. 180 00:08:40,943 --> 00:08:45,203 She, she actually has, she has a, a thing that happened to her when she 181 00:08:45,203 --> 00:08:48,563 was a teenager that like a horse ran away with her and she's like, I'm 182 00:08:48,568 --> 00:08:50,213 not ever getting on a horse again. 183 00:08:50,213 --> 00:08:54,413 But anyway, so, so it was just me and the two kids and one of 184 00:08:54,413 --> 00:08:55,613 which was like really little. 185 00:08:55,973 --> 00:09:02,228 And, um, we walked up and, and I hear the lady that's leading 186 00:09:02,228 --> 00:09:06,428 this thing, say something along, you know, better get Bessy. 187 00:09:06,848 --> 00:09:08,918 And, uh, and I was like, oh, it's cute. 188 00:09:08,918 --> 00:09:12,098 They got like a small horse for, for my little one. 189 00:09:12,488 --> 00:09:14,678 No, they weren't talking about my little one. 190 00:09:16,538 --> 00:09:22,748 There was a special horse just for me, was basically like Clydesdale sized. 191 00:09:22,808 --> 00:09:23,708 They're like, yeah. 192 00:09:23,888 --> 00:09:24,188 Yeah. 193 00:09:24,218 --> 00:09:25,388 That's your horse over there? 194 00:09:25,388 --> 00:09:27,308 That, that gentle giant. 195 00:09:29,378 --> 00:09:33,728 I was like, That's just, that's just harsh, yeah. 196 00:09:34,148 --> 00:09:34,508 Anyway. 197 00:09:35,123 --> 00:09:37,913 So I just, you know, whenever I talk about you, it's just one of the 198 00:09:37,913 --> 00:09:41,873 things I find most fascinating about you, even though you, you are, you 199 00:09:41,873 --> 00:09:44,303 are like, you know, a nerds nerd. 200 00:09:44,303 --> 00:09:48,833 I mean, you, you, I love how much you're into the technology and you 201 00:09:48,833 --> 00:09:50,333 know how good you are at your job. 202 00:09:50,333 --> 00:09:55,003 I mean, we've, we've talked for years about, you know, multiple 203 00:09:55,193 --> 00:09:56,723 of your previous employers. 204 00:09:57,173 --> 00:10:01,883 Um, obviously, you know, I spent so many years talking to you about Spectra, 205 00:10:02,543 --> 00:10:03,113 Molly Presley: Absolutely. 206 00:10:03,113 --> 00:10:03,413 Yeah. 207 00:10:03,473 --> 00:10:04,043 W. Curtis Preston: Yeah. 208 00:10:04,193 --> 00:10:07,103 And you know, yeah, great company. 209 00:10:07,163 --> 00:10:09,713 And, uh, now you're, you're close to them again. 210 00:10:09,863 --> 00:10:11,813 Um, uh, what do you call it? 211 00:10:11,813 --> 00:10:13,313 Uh, geographically speaking, 212 00:10:13,583 --> 00:10:14,513 Molly Presley: am just up the road. 213 00:10:14,518 --> 00:10:17,123 Now I can see all my old friends and probably the folks you've 214 00:10:17,123 --> 00:10:18,233 had is guests on the show 215 00:10:18,593 --> 00:10:19,613 W. Curtis Preston: Yeah, absolutely. 216 00:10:19,673 --> 00:10:23,693 Um, yeah, we actually had them on, they had a, as I'm sure you're 217 00:10:23,698 --> 00:10:25,013 aware they had a ransomware attack. 218 00:10:25,793 --> 00:10:26,363 Molly Presley: I did. 219 00:10:26,663 --> 00:10:28,283 And they recovered successfully. 220 00:10:28,763 --> 00:10:29,183 W. Curtis Preston: yeah. 221 00:10:29,678 --> 00:10:30,038 Prasanna Malaiyandi: So we had 222 00:10:30,083 --> 00:10:30,413 W. Curtis Preston: that was. 223 00:10:31,133 --> 00:10:31,943 Yeah to, yeah. 224 00:10:31,943 --> 00:10:32,963 Tony talked about that. 225 00:10:32,968 --> 00:10:33,563 That was great. 226 00:10:33,863 --> 00:10:34,493 Molly Presley: Good on them. 227 00:10:34,943 --> 00:10:38,903 Nathan's always been good about using his own company as an example of technology. 228 00:10:40,163 --> 00:10:43,598 W. Curtis Preston: And I will insert our standard disclaimer, uh, Prasanna 229 00:10:43,618 --> 00:10:45,118 and I work for different companies. 230 00:10:45,358 --> 00:10:46,468 He works for Zoom. 231 00:10:46,468 --> 00:10:50,128 I work for Druva and this is not a podcast of either company. 232 00:10:50,518 --> 00:10:53,248 And the opinions that you hear are all Prasana's. 233 00:10:53,913 --> 00:10:57,453 , if you like what you hear or are, you know, watching us by the way, if you, if 234 00:10:57,453 --> 00:11:00,583 you, if you're listening and you wanna watch, you can go to backupcentral.com. 235 00:11:00,963 --> 00:11:03,003 We have the video version of it over there. 236 00:11:03,483 --> 00:11:08,593 And, um, if you, if you like what you see or hear, then, you know, go rate 237 00:11:08,593 --> 00:11:10,973 us, at ratethispodcast.com/restore. 238 00:11:11,583 --> 00:11:14,313 And if you wanna join the conversation, just, you know, gimme 239 00:11:14,313 --> 00:11:20,433 a holler @wcpreston on Twitter, or wcurtispreston@gmail and we cover all 240 00:11:20,433 --> 00:11:26,613 manner of topics, uh, backup, you know, storage, archive, protection storage. 241 00:11:27,268 --> 00:11:27,928 Prasanna Malaiyandi: You said that 242 00:11:28,593 --> 00:11:29,013 W. Curtis Preston: what, 243 00:11:29,218 --> 00:11:30,088 Prasanna Malaiyandi: you already said that? 244 00:11:30,723 --> 00:11:32,553 W. Curtis Preston: oh, did I say, did I say storage twice? 245 00:11:33,363 --> 00:11:35,343 Yeah, well that I made a copy. 246 00:11:35,583 --> 00:11:41,013 I made a copy I just, I just can't help, but make a copy. 247 00:11:41,473 --> 00:11:44,393 The company that you work at now, how long have you been at Hammerspace 248 00:11:45,223 --> 00:11:45,943 Molly Presley: about six months 249 00:11:46,873 --> 00:11:47,293 W. Curtis Preston: Okay. 250 00:11:47,363 --> 00:11:50,363 I referred to them as a global file system provider, but I 251 00:11:50,453 --> 00:11:53,273 don't think that it does justice. 252 00:11:53,273 --> 00:11:59,393 So why don't we before we sort of say what, what, what it is, how about you 253 00:11:59,393 --> 00:12:04,793 tell us, what problem do you think Hammerspace was designed to, to solve. 254 00:12:05,723 --> 00:12:08,063 Molly Presley: Yeah, that's always a better place to start. 255 00:12:08,153 --> 00:12:14,183 And we were designed to solve the problem of kind of using industry 256 00:12:14,183 --> 00:12:15,893 terms, decentralized environments. 257 00:12:15,893 --> 00:12:17,573 So you think about what's happened with. 258 00:12:18,713 --> 00:12:21,833 Our industry from first, just a infrastructure perspective. 259 00:12:21,833 --> 00:12:24,263 It used to be all the data sat in one server. 260 00:12:24,653 --> 00:12:28,463 Then we started to maybe have multiple, multiple clusters in the lab. 261 00:12:28,463 --> 00:12:32,843 Then we started to have some clouds and your data became decentralized 262 00:12:32,843 --> 00:12:34,523 or dispersed into many places. 263 00:12:35,053 --> 00:12:41,143 And that idea of now I have an application or a data scientist or somebody who wants 264 00:12:41,143 --> 00:12:44,773 to take advantage of my data and it's all decentralized in multiple places. 265 00:12:45,193 --> 00:12:50,083 We make it easy for that computer or human or application who wants to use 266 00:12:50,083 --> 00:12:55,183 data that's spread in many locations to work with it as a single data set. 267 00:12:55,898 --> 00:12:59,318 . And then along with that, you think about the other decentralization, which has 268 00:12:59,318 --> 00:13:01,388 occurred is where human beings are living. 269 00:13:01,448 --> 00:13:03,848 And so people are working remotely. 270 00:13:04,328 --> 00:13:07,088 Applications may be sitting in multiple clouds. 271 00:13:07,508 --> 00:13:11,198 And so where the things are that need to use the data are also distributed. 272 00:13:11,378 --> 00:13:13,388 And so Hammerspace makes it very easy. 273 00:13:13,448 --> 00:13:18,698 Even if your data is geographically far from you over networking and latency, 274 00:13:18,698 --> 00:13:19,968 that would make it difficult to access. 275 00:13:19,988 --> 00:13:21,788 We overcome that, that barrier. 276 00:13:21,788 --> 00:13:27,398 So we solved the problem, making data accessible, that is decentralized, um, 277 00:13:27,398 --> 00:13:29,668 to remote users and remote applications. 278 00:13:31,193 --> 00:13:33,113 Prasanna Malaiyandi: Are you, when you talk about making data 279 00:13:33,113 --> 00:13:37,103 available, is it sort of intended for like the primary use case? 280 00:13:37,103 --> 00:13:41,603 Like someone's building an application and the data for the application might 281 00:13:41,603 --> 00:13:45,563 be stored across like different clouds with Hammerspace being that interface, or 282 00:13:45,563 --> 00:13:50,033 is it more intended for data is already being stored today in various spots and 283 00:13:50,033 --> 00:13:54,353 Hammerspace sort of gives people who need to consume that data visibility 284 00:13:54,383 --> 00:13:57,173 in a sort of centralized way or both. 285 00:13:58,073 --> 00:13:59,633 Molly Presley: It's honestly, a little bit of both. 286 00:13:59,633 --> 00:14:01,013 I'll give you a couple examples. 287 00:14:01,073 --> 00:14:03,983 Um, one of our partners is Snowflake and I think most people 288 00:14:03,988 --> 00:14:06,743 would listen to this show would know who Snowflake is, but snow. 289 00:14:08,003 --> 00:14:11,753 Primarily worse with data that's already stored in the Snowflake cloud. 290 00:14:12,443 --> 00:14:16,463 However, they've built an enormous amount of inter intelligence in the 291 00:14:16,463 --> 00:14:20,123 applications and the processing and analytics which Snowflake can provide. 292 00:14:20,123 --> 00:14:26,573 So let's just say that you wanted to use the Snowflake applications, um, but your 293 00:14:26,573 --> 00:14:28,163 data didn't live in the Snowflake cloud. 294 00:14:28,163 --> 00:14:30,443 We could bridge that gap and make the data. 295 00:14:30,998 --> 00:14:34,898 Still live, live, where it was created, but easily accessible 296 00:14:34,898 --> 00:14:36,308 to the Snowflake application. 297 00:14:36,308 --> 00:14:39,338 So there is that application piece, but there's also the 298 00:14:39,343 --> 00:14:40,838 visibility for the human being. 299 00:14:41,738 --> 00:14:46,058 Whether it's an AI engine or it's actually like a genomics researcher 300 00:14:46,058 --> 00:14:51,428 working on looking at COVID variants, um, easier ability to access data sets 301 00:14:51,428 --> 00:14:53,138 that are dispersed over multiple places. 302 00:14:53,138 --> 00:14:58,478 So one of our customers, um, if you think about, um, the research around COVID. 303 00:14:58,778 --> 00:15:01,658 There's variants coming out and different countries have their 304 00:15:01,658 --> 00:15:05,978 different data around which variants they have, how quickly is it spreading? 305 00:15:06,008 --> 00:15:08,108 Is there a new variant and ideally. 306 00:15:08,338 --> 00:15:12,268 You would look at all that data together instead of Ethiopia looking 307 00:15:12,268 --> 00:15:13,888 at it separately from South Africa. 308 00:15:13,888 --> 00:15:18,568 And this is a African, um, initiative that's underway right now is to bring 309 00:15:18,568 --> 00:15:22,708 all those data sets together with Hammerspace, to make it easier, to look 310 00:15:22,713 --> 00:15:26,338 at larger populations of data together instead of isolating the data sets. 311 00:15:26,338 --> 00:15:27,658 So it can be person too. 312 00:15:27,923 --> 00:15:30,083 Prasanna Malaiyandi: And I guess in the case of the COVID example, you brought 313 00:15:30,083 --> 00:15:34,373 up the research it's I guess another method people could do today is try 314 00:15:34,373 --> 00:15:38,213 transferring and synchronizing data manually across all these various sources, 315 00:15:38,273 --> 00:15:42,473 like moving the data, which is painful and probably not very practical either. 316 00:15:43,703 --> 00:15:45,023 Molly Presley: I mean, that's exactly it. 317 00:15:45,023 --> 00:15:49,253 So of course it's been solved somehow today, but it's been expensive and 318 00:15:49,258 --> 00:15:52,613 inefficient, so maybe you've now got two or three or four copies of 319 00:15:52,613 --> 00:15:54,133 data, which you have to pay for. 320 00:15:54,693 --> 00:15:59,228 Storing two or three or four copies of the same data you have maybe ingest 321 00:15:59,228 --> 00:16:04,298 and egress charges around, moving in between clouds, networking issues. 322 00:16:04,448 --> 00:16:08,048 Um, and then just the human factor of an it person making 323 00:16:08,053 --> 00:16:09,458 scripts to move data around. 324 00:16:09,463 --> 00:16:11,708 And then you try to figure out what's the master copy? 325 00:16:11,708 --> 00:16:12,488 Who has it? 326 00:16:12,518 --> 00:16:14,048 Do I have all the data or not? 327 00:16:14,048 --> 00:16:16,808 So it's being solved, but not very elegantly today. 328 00:16:16,928 --> 00:16:20,308 And this is an elegant, automated software driven solution. 329 00:16:21,838 --> 00:16:24,628 W. Curtis Preston: In our career, we are often fighting the laws of physics. 330 00:16:24,778 --> 00:16:27,598 And once again, that's kind of what you're doing. 331 00:16:27,808 --> 00:16:30,868 , you're trying to, you're trying to defy the laws of physics. 332 00:16:31,490 --> 00:16:33,770 Molly Presley: I think one way you might look at that is, um, 333 00:16:35,665 --> 00:16:37,075 There are lots of different ways. 334 00:16:37,075 --> 00:16:40,945 People have addressed trying to move data around, make data accessible. 335 00:16:40,945 --> 00:16:45,565 And even in our space, what is a global file system or global name space? 336 00:16:46,075 --> 00:16:48,505 It's a bit confusing people, different approaches. 337 00:16:48,955 --> 00:16:53,755 But the thing that I think is super important to think about is, um, you 338 00:16:53,755 --> 00:16:59,035 know, for, to do data, data discovery, the more data you have access to the better. 339 00:16:59,095 --> 00:17:03,235 So Hammerspace really tries to solve the problem of breaking down the 340 00:17:03,235 --> 00:17:07,165 storage silo, making it so you can look at all of your data together. 341 00:17:07,255 --> 00:17:11,005 In one view, that would be a global name space, and then to solve that 342 00:17:11,005 --> 00:17:14,995 latency problem, you, we don't move the data around the data stays put. 343 00:17:14,995 --> 00:17:17,215 And so you were talking about physics and it's funny, it's something 344 00:17:17,215 --> 00:17:20,665 I talk about with our CEO pretty regularly that we actually don't 345 00:17:20,665 --> 00:17:25,070 believe the concept of data gravity is valid anymore with the Hammerspace 346 00:17:25,090 --> 00:17:29,320 technology, because you no longer have to move the compute to the data. 347 00:17:29,740 --> 00:17:33,370 We will let the data stay put, and our everyone interacts 348 00:17:33,370 --> 00:17:35,590 with metadata instead of data. 349 00:17:35,590 --> 00:17:39,670 So metadata is light, you know, for every petabyte of data, you know, 350 00:17:39,670 --> 00:17:42,610 you maybe have a couple hundred megabytes of metadata, whatever it is. 351 00:17:43,200 --> 00:17:45,630 And you can make multiple copies of the metadata. 352 00:17:45,720 --> 00:17:49,950 You can easily move that to a new location, a new application 353 00:17:50,220 --> 00:17:53,550 to solve the latency issue, but the data can just stay put. 354 00:17:53,700 --> 00:17:57,810 So all of a sudden this idea of are we trying to overcome physics and this 355 00:17:57,810 --> 00:18:01,770 concept of is there data, gravity and all of that, we're trying to make it where 356 00:18:01,775 --> 00:18:03,570 really create your data where you want to. 357 00:18:04,110 --> 00:18:07,180 And then use it where you want to, and we'll use smart software so 358 00:18:07,180 --> 00:18:10,930 you can interact with it without expense and everything else that has 359 00:18:10,930 --> 00:18:13,480 become kinda, almost a truth in our. 360 00:18:14,110 --> 00:18:14,230 That's 361 00:18:14,885 --> 00:18:18,695 Prasanna Malaiyandi: in the end you eventually will be pulling 362 00:18:18,695 --> 00:18:22,655 the data of some sort when you're reading or accessing, if that's 363 00:18:22,655 --> 00:18:23,675 what your application needs. 364 00:18:23,975 --> 00:18:25,145 It's just because. 365 00:18:25,630 --> 00:18:29,380 Correct me if I'm wrong, because you're sort of processed things by the metadata, 366 00:18:29,380 --> 00:18:31,930 which resides close to you, right? 367 00:18:32,170 --> 00:18:33,730 So you deal with the latency issue. 368 00:18:33,820 --> 00:18:36,700 It helps you filter down what, in the end you need to access. 369 00:18:37,060 --> 00:18:40,100 So you don't need to necessarily pull all the data, just the select data you need. 370 00:18:40,195 --> 00:18:40,255 Yeah. 371 00:18:41,050 --> 00:18:41,650 Molly Presley: That's right. 372 00:18:41,650 --> 00:18:45,490 And then we of course take care of interesting technologies that exist today 373 00:18:45,490 --> 00:18:50,650 and, you know, using object storage in the cloud to move things around efficiently 374 00:18:50,740 --> 00:18:53,200 and low cost object, stored object store. 375 00:18:53,200 --> 00:18:57,490 Even though what we present is a file system, but we use the backend of 376 00:18:57,820 --> 00:19:02,050 smart object stores to move things around efficiently when it's needed, 377 00:19:02,050 --> 00:19:03,400 but we only move what we need to. 378 00:19:05,050 --> 00:19:10,180 W. Curtis Preston: Yeah, I guess I'm, I'm, I'm trying to fathom how that works. 379 00:19:10,180 --> 00:19:12,520 Obviously I get the difference between metadata and data. 380 00:19:13,630 --> 00:19:18,070 I'm trying to understand, like, if you could gimme an example of an 381 00:19:18,070 --> 00:19:23,730 application that's first just using the metadata to make a decision. 382 00:19:24,475 --> 00:19:28,225 Then later accessing the data, I guess maybe because I spend so much of my 383 00:19:28,225 --> 00:19:32,575 time in the, in the backup space and we're, you know, I mean, yeah, obviously 384 00:19:32,575 --> 00:19:36,355 metadata is important, but the data is like, we're all about the data. 385 00:19:36,595 --> 00:19:37,105 So 386 00:19:37,195 --> 00:19:39,745 Prasanna Malaiyandi: we, yeah, maybe we can expand on that COVID 387 00:19:39,745 --> 00:19:43,135 example from the beginning, if, and show that if that works. 388 00:19:44,485 --> 00:19:45,755 Molly Presley: we can definitely talk about that. 389 00:19:46,570 --> 00:19:50,140 So, if you go back to the COVID example that we were talking about, and you're 390 00:19:50,140 --> 00:19:54,160 looking at variants of different, um, you know, generations and whatnot, 391 00:19:54,160 --> 00:19:57,670 that's occurring within COVID a great example would be in a lot of cases. 392 00:19:58,525 --> 00:20:03,515 What organizations need to do is keep their data set in country for maybe 393 00:20:03,515 --> 00:20:06,125 compliance patient care regulations. 394 00:20:06,395 --> 00:20:10,565 And they need to leave it in place, but they wanna be able to have a view to an 395 00:20:10,565 --> 00:20:16,845 analytics application through metadata of the, the amount of test results that 396 00:20:16,845 --> 00:20:21,105 have occurred maybe specific results, but they don't need the entire data set. 397 00:20:21,465 --> 00:20:24,555 So we use something called objective based policies. 398 00:20:24,555 --> 00:20:27,855 And so this is getting super, like nerding out and I'm not gonna nerd 399 00:20:27,855 --> 00:20:31,695 out on you, but, um, you would set an objective saying really my objective, 400 00:20:32,025 --> 00:20:33,365 W. Curtis Preston: We love people that nerd out, Molly. 401 00:20:33,365 --> 00:20:33,815 It's fine. 402 00:20:34,905 --> 00:20:39,075 Molly Presley: as the, you know, so my objective as the data administrator of 403 00:20:39,075 --> 00:20:44,390 this in the organization is, to be able to look at the number of tests and the 404 00:20:44,390 --> 00:20:49,340 number of variants that are occurring and all the rest of the data around that's 405 00:20:49,340 --> 00:20:55,010 being collected, which could be location, um, you know, whatever ethnicity, 406 00:20:55,040 --> 00:20:56,930 gender, that stuff doesn't matter to me. 407 00:20:57,230 --> 00:21:00,750 So they would only interact with the metadata that's associated 408 00:21:00,750 --> 00:21:03,120 with their objectives and pull in. 409 00:21:03,125 --> 00:21:05,700 If they need to move data, they would only move the parts that's 410 00:21:05,700 --> 00:21:07,260 relevant to that objective. 411 00:21:07,260 --> 00:21:11,265 So there's, and this is all automated and set through the Hammerspace 412 00:21:11,285 --> 00:21:14,765 interfaces so that you can say, these are the bits I care about. 413 00:21:15,035 --> 00:21:16,865 I'm not gonna get a human involved with it. 414 00:21:16,865 --> 00:21:20,735 And then you can also set rules about, you can move my data, but only to 415 00:21:20,735 --> 00:21:22,565 this country and not that country. 416 00:21:22,805 --> 00:21:24,245 So you can manage your compliance. 417 00:21:24,245 --> 00:21:27,515 There's a lot of different things that occur within an objective 418 00:21:27,695 --> 00:21:29,685 that helps to automate all of this. 419 00:21:31,685 --> 00:21:32,475 W. Curtis Preston: Interesting. 420 00:21:32,530 --> 00:21:36,400 And, and so like when you, when you say those, the data rules, if you 421 00:21:36,400 --> 00:21:38,620 can move my data, but only here. 422 00:21:39,615 --> 00:21:46,305 So when someone is accessing the data, are you a portal through 423 00:21:46,305 --> 00:21:49,995 which they're accessing their data or is it just sort of giving 424 00:21:50,115 --> 00:21:52,545 Molly Presley: We're yeah, we're actually the name space. 425 00:21:52,545 --> 00:21:57,735 We're a network share an NFS Mount point, an SMB Mount point that all the users. 426 00:21:57,735 --> 00:22:01,335 So if the three of us were using Hammerspace technology, we would 427 00:22:01,335 --> 00:22:04,605 all see the exact same folder structure, directory structure. 428 00:22:05,115 --> 00:22:06,285 As each of us made changes. 429 00:22:06,285 --> 00:22:10,065 We'd see each other's changes, but that would be done on a single. 430 00:22:11,325 --> 00:22:14,475 We would be interacting with the metadata and that's what we would 431 00:22:14,475 --> 00:22:16,285 be presenting the directory tree. 432 00:22:16,285 --> 00:22:20,485 So it is a NAS, it's just a NAS that has storage environments 433 00:22:20,485 --> 00:22:22,495 that can be in many places. 434 00:22:24,415 --> 00:22:25,075 W. Curtis Preston: interesting. 435 00:22:25,225 --> 00:22:27,745 Molly Presley: I'm gonna give you another example that sometimes a little, so the 436 00:22:27,745 --> 00:22:32,215 fastest adoption that we've had of our environment is in visual effects studios. 437 00:22:32,725 --> 00:22:36,235 And this is environments where again, you think about that, what happened with 438 00:22:36,240 --> 00:22:42,485 COVID and nobody was flying actors to have physical shoots of film, because 439 00:22:42,485 --> 00:22:45,955 they're worried about travel and proximity and all social distancing. 440 00:22:46,665 --> 00:22:51,170 So visual effects and animation was how a lot of the entertainment 441 00:22:51,170 --> 00:22:52,310 and production films were done. 442 00:22:52,310 --> 00:22:57,070 So the need for animation and visual effects went up dramatically. 443 00:22:57,070 --> 00:22:58,450 Like orders of magnitude. 444 00:22:59,080 --> 00:23:03,280 And in the meantime, the artists were scattering to all over where they wanted 445 00:23:03,280 --> 00:23:06,550 to live, where their families lives and they were no longer close to the studios. 446 00:23:07,000 --> 00:23:12,160 And so what they have done is used Hammerspace as the way to, um, make 447 00:23:12,160 --> 00:23:14,020 it easy to spin up a new artist. 448 00:23:14,025 --> 00:23:17,890 So a new artist could be in Africa or India or wherever 449 00:23:17,950 --> 00:23:19,060 they happen to be living. 450 00:23:19,630 --> 00:23:23,380 You give them access to the global name space and they instantly can see. 451 00:23:24,220 --> 00:23:28,150 What is all the content, all the clips, they aren't actually moving them. 452 00:23:28,150 --> 00:23:30,880 They're making copies of the video close and they're just viewing. 453 00:23:31,270 --> 00:23:31,600 Okay. 454 00:23:31,600 --> 00:23:36,880 I have, um, Moana and I have this Netflix show depending on who the studio is and 455 00:23:36,880 --> 00:23:38,470 I can see, okay, here's all the content. 456 00:23:38,470 --> 00:23:45,145 My job is only to edit the motion of the faces in this particular clip of film. 457 00:23:45,385 --> 00:23:47,365 So I'm just gonna move that one clip. 458 00:23:47,365 --> 00:23:51,505 The rest of the film can stay wherever it is, and they can work on that animation. 459 00:23:51,805 --> 00:23:55,975 And then as they do their work, this metadata is being synchronized. 460 00:23:55,975 --> 00:23:58,615 So if another artist says, oh, I thought I was supposed to be working on the 461 00:23:58,615 --> 00:24:02,515 animation of that face, they can see what the other person is doing and not step 462 00:24:02,515 --> 00:24:06,595 on each other or later have to figure out how do I merge changes, things like that. 463 00:24:06,595 --> 00:24:12,200 So it's, it's really helped ramp up remote artists working on content, that's 464 00:24:12,200 --> 00:24:14,720 massive and you can't move around easily. 465 00:24:15,080 --> 00:24:18,080 And so they can just work on the clips and segments that they want to. 466 00:24:18,350 --> 00:24:21,520 And this is all done integrated with their tools. 467 00:24:21,525 --> 00:24:26,380 So with Autodesk shot grid and Tara deci their virtual studio tools. 468 00:24:26,380 --> 00:24:28,900 So there's a lot of tools that's integrated with to make it really easy 469 00:24:28,900 --> 00:24:32,320 that they're using their own tools and this kind of data orchestration. 470 00:24:32,320 --> 00:24:33,640 The background is automated. 471 00:24:34,380 --> 00:24:35,880 Prasanna Malaiyandi: I think that's interesting. 472 00:24:36,180 --> 00:24:41,010 I feel a lot of storage vendors tend to sort of say, Hey, here's an NFS 473 00:24:41,430 --> 00:24:45,720 Mount point or an SMB Mount point, go at it versus kind of what, uh, 474 00:24:45,720 --> 00:24:49,680 Hammerspace is doing is giving you that automation, those policy management, 475 00:24:49,990 --> 00:24:54,575 integration into the consumers or the end users tools, rather than just 476 00:24:54,575 --> 00:24:57,285 saying, Hey, here's a point go for it. 477 00:24:57,345 --> 00:24:57,565 Molly Presley: Yeah. 478 00:24:57,565 --> 00:25:02,075 I, I really think this is just the next generation of how storage and data 479 00:25:02,075 --> 00:25:04,025 management and hybrid cloud will work. 480 00:25:04,025 --> 00:25:06,335 And I've worked in all of these types of companies. 481 00:25:06,335 --> 00:25:09,935 And I know this is a thing that customers and I've been using the term, the 482 00:25:09,935 --> 00:25:14,805 missing link in what customers expect will work when they go to a hybrid 483 00:25:14,805 --> 00:25:16,635 cloud versus how it actually works. 484 00:25:16,635 --> 00:25:20,475 So if you think about the technologies that exist today, sure. 485 00:25:20,565 --> 00:25:23,835 You can run a NAS instance of any of the popular NAS vendors in the 486 00:25:23,840 --> 00:25:28,305 cloud and in the data center, but they're separate silos of data. 487 00:25:28,425 --> 00:25:32,225 So you as user would still have to say, Hmm, okay. 488 00:25:32,305 --> 00:25:33,535 Where am I gonna put my data? 489 00:25:33,535 --> 00:25:35,365 Where is the data I created before? 490 00:25:35,365 --> 00:25:37,585 How do I make that available to someone else? 491 00:25:37,885 --> 00:25:38,035 And. 492 00:25:39,015 --> 00:25:41,955 Then the matter of opening a ticket with it and say, okay, now Curtis 493 00:25:41,955 --> 00:25:45,315 needs access to this share and let's open up a share and set up the 494 00:25:45,315 --> 00:25:46,935 IP networking for him to do that. 495 00:25:46,935 --> 00:25:49,875 And the DNS servers and everything is very manual. 496 00:25:50,145 --> 00:25:53,895 The way Hammerspace handles it is: you just set up both 497 00:25:53,895 --> 00:25:56,115 of you with access to our. 498 00:25:56,520 --> 00:25:57,210 NFS share. 499 00:25:57,210 --> 00:26:00,840 Let's say the Hammerspace share and no matter where the data is stored, if 500 00:26:00,840 --> 00:26:05,950 Curtis says you can have access to it, um, he just says that permission in TaDa! 501 00:26:05,970 --> 00:26:07,140 you have access to it. 502 00:26:07,140 --> 00:26:08,700 There's no it involved. 503 00:26:08,760 --> 00:26:11,940 There's no data silos where you're saying, gosh, I don't 504 00:26:11,940 --> 00:26:13,140 know what's in, Curtis' share. 505 00:26:13,145 --> 00:26:13,950 I only know what's in mine. 506 00:26:14,180 --> 00:26:14,910 How would I ever know? 507 00:26:14,910 --> 00:26:18,810 We, we overcome that so you can see the data that's being created and 508 00:26:18,810 --> 00:26:21,615 collaborated on by many data users. 509 00:26:21,705 --> 00:26:23,715 And you know, most environments need that. 510 00:26:23,745 --> 00:26:26,715 It's not designed for where you want your own personal information. 511 00:26:27,405 --> 00:26:28,065 Well, to yourself. 512 00:26:28,065 --> 00:26:30,530 It's environments that are collaborative and are doing 513 00:26:30,530 --> 00:26:32,180 research or that type of thing. 514 00:26:33,200 --> 00:26:36,680 W. Curtis Preston: Yeah, I, I will say I, I understood the second example 515 00:26:36,680 --> 00:26:37,850 a lot better than the first one. 516 00:26:38,090 --> 00:26:39,740 So that's so that's good. 517 00:26:40,520 --> 00:26:44,810 Um, it's something that, you know, I've spent a lot of time in and around 518 00:26:44,810 --> 00:26:48,040 the, the media and entertainment space. 519 00:26:48,090 --> 00:26:51,265 I've worked with companies trying to back up that stuff. 520 00:26:51,325 --> 00:26:51,715 Right. 521 00:26:51,775 --> 00:26:57,190 Um, because one of the problems that I remember I was working with the folks that 522 00:26:57,190 --> 00:27:04,150 were making Shrek 2, back when that was new, and their problem was each animator 523 00:27:04,480 --> 00:27:11,015 needed the entire set of data that at least they needed it to look like they had 524 00:27:11,015 --> 00:27:16,565 the entire set of data in order to select which backgrounds they wanted to to use. 525 00:27:16,955 --> 00:27:20,465 And, um, that was an interesting problem to solve. 526 00:27:20,465 --> 00:27:23,495 And it sounds like that this would, this would help to solve that problem. 527 00:27:23,990 --> 00:27:25,640 Molly Presley: Yeah, it does very much. 528 00:27:25,730 --> 00:27:29,150 Um, and that kind of problem occurs when you think about a studio, 529 00:27:29,150 --> 00:27:30,990 maybe that advertises the same film. 530 00:27:31,730 --> 00:27:35,600 In different countries and they have to localize a look and feel, or the clips 531 00:27:35,600 --> 00:27:39,410 they'll carry for an advertisement in Japan may be different than America. 532 00:27:39,410 --> 00:27:40,100 That type of thing. 533 00:27:40,130 --> 00:27:42,560 It works beautifully for that type of solution. 534 00:27:43,140 --> 00:27:48,720 It's just overall, it's become difficult as we have so many technologies that 535 00:27:48,720 --> 00:27:51,750 come out, one's a little better, a little different, or has a little functionality 536 00:27:51,750 --> 00:27:53,340 than another in the storage space. 537 00:27:53,760 --> 00:27:55,230 And you need those differences. 538 00:27:55,230 --> 00:28:00,735 You need the fast performance of Pure Storage or Vast Data, or you need the 539 00:28:00,765 --> 00:28:05,745 hybrid cloud, um, image that Qumulo has, or, you know, whatever it's, as you go 540 00:28:05,745 --> 00:28:09,585 through the different technologies and you bought something for those reasons, 541 00:28:09,705 --> 00:28:14,730 and yet your users don't have access to all the different storage vendors 542 00:28:14,850 --> 00:28:16,830 and need to know what data exists. 543 00:28:16,830 --> 00:28:21,060 So having the name space that sits above it, that makes it so it can have 544 00:28:21,060 --> 00:28:25,410 the performance or capacity or security that they need in their storage systems. 545 00:28:25,410 --> 00:28:29,730 And that doesn't limit a user from having visibility to all of the data is really 546 00:28:29,730 --> 00:28:31,190 kind of a simple way to think about it. 547 00:28:31,935 --> 00:28:34,815 Prasanna Malaiyandi: And I know you talked about some of the features that 548 00:28:34,815 --> 00:28:36,405 these individual storage vendors have. 549 00:28:37,465 --> 00:28:39,965 I know Hammerspace brings its own innovative features. 550 00:28:41,195 --> 00:28:45,575 For those other storage vendors, do things sort of get least 551 00:28:45,575 --> 00:28:47,135 common denominator, if you will. 552 00:28:47,495 --> 00:28:47,765 Right. 553 00:28:47,765 --> 00:28:51,455 In terms of the features functionality of those underlying storage arrays, 554 00:28:51,460 --> 00:28:56,335 or is Hammerspace still able to allow those storage arrays to bring their 555 00:28:56,335 --> 00:29:00,385 innovative features, functionality and Hammerspace leverages, or 556 00:29:00,385 --> 00:29:02,015 has its own capabilities on top. 557 00:29:02,525 --> 00:29:03,830 Molly Presley: Yeah, it's a really good question. 558 00:29:03,920 --> 00:29:08,165 Um, So when you take, let's say you assimilate the metadata out of your 559 00:29:08,255 --> 00:29:10,415 NetApp and your Vast and whatever it is. 560 00:29:10,775 --> 00:29:13,775 Um, at that point you're using the features in Hammerspace, so they 561 00:29:13,775 --> 00:29:15,395 can be done at a global level. 562 00:29:15,395 --> 00:29:19,715 So if you wanna set that, you know, a specific replication functionality, 563 00:29:19,715 --> 00:29:23,435 or if you wanna be able to have ransomware policies put in place, 564 00:29:23,495 --> 00:29:27,095 um, if you wanna have encryption set, you can do that at a global level. 565 00:29:27,095 --> 00:29:31,495 So you don't have the risk of, oh gosh, I'm encrypting on this environment and 566 00:29:31,495 --> 00:29:34,105 not this one or, um, that type of thing. 567 00:29:34,105 --> 00:29:36,385 So we take over the management at that level. 568 00:29:36,385 --> 00:29:41,395 So really in the end, the other storage systems become capacity and performance. 569 00:29:41,455 --> 00:29:45,185 Um, and the features are handled at a global level within Hammerspace. 570 00:29:46,655 --> 00:29:49,345 W. Curtis Preston: By the way while researching Hammerspace, 571 00:29:49,360 --> 00:29:51,425 the company I come, I came across. 572 00:29:52,555 --> 00:29:57,505 I I, what I'm absolutely sure is the origin of, you know, why you 573 00:29:57,505 --> 00:30:03,055 would name the company that, and this, this idea of a, uh, so I just 574 00:30:03,055 --> 00:30:08,325 found the, the Hammerspace Wikipedia page, and, you know, they say a fan 575 00:30:08,325 --> 00:30:13,995 envisioned, extra dimensional, instantly accessible storage area in fiction. 576 00:30:14,715 --> 00:30:18,765 Uh, and it it's used to describe how, how characters can seemingly 577 00:30:19,005 --> 00:30:21,260 out of thin air make objects appear. 578 00:30:21,338 --> 00:30:25,028 Molly Presley: That's where the name came from is that idea of, you know, bug's 579 00:30:25,028 --> 00:30:29,618 bunny has the appearance of very small pockets and yet can pull a massive hammer 580 00:30:29,618 --> 00:30:35,408 out of his pocket and Bonk his bow on Um, Hammerspace is that extra dimension 581 00:30:35,408 --> 00:30:40,358 of what appears small, you can actually pull this massive amount of data out of. 582 00:30:40,358 --> 00:30:42,698 So it's that metadata kind of analogy. 583 00:30:44,103 --> 00:30:45,843 W. Curtis Preston: I will ask one very obvious question. 584 00:30:45,843 --> 00:30:45,873 Okay. 585 00:30:47,373 --> 00:30:47,793 Prasanna Malaiyandi: Yes, 586 00:30:48,873 --> 00:30:58,713 W. Curtis Preston: So does this beautiful, multidimensional, storage space, impact 587 00:30:58,713 --> 00:31:02,883 how I would back up the data because in the end, that is, you know, one 588 00:31:02,888 --> 00:31:04,023 of the things that we care about. 589 00:31:04,413 --> 00:31:05,283 Molly Presley: It definitely could. 590 00:31:05,283 --> 00:31:08,973 Backup is one of those things that I think most storage vendors have 591 00:31:08,973 --> 00:31:10,833 not tried to take on too much. 592 00:31:10,863 --> 00:31:15,003 Of course we have data protection, we have snapshots and replication 593 00:31:15,003 --> 00:31:16,233 and all those types of things. 594 00:31:16,233 --> 00:31:21,393 But in the end, if you were setting up a Druva backup policy, you would point 595 00:31:21,393 --> 00:31:24,753 it at the Hammerspace name space, and set it just the way you always have. 596 00:31:25,698 --> 00:31:29,868 You know, whatever your requirements are, your retention, you, your network shares. 597 00:31:29,868 --> 00:31:32,748 If you're used to backing up NFS, you present NFS. 598 00:31:32,748 --> 00:31:34,458 So the process is the same. 599 00:31:34,458 --> 00:31:39,618 You just would do it at the Hammerspace level instead, backup for individual 600 00:31:39,948 --> 00:31:42,288 silo within the Hammerspace environment. 601 00:31:43,398 --> 00:31:47,268 It's easier if you think about that, you can only set the policies once 602 00:31:47,268 --> 00:31:48,738 and it covers all of your data. 603 00:31:49,188 --> 00:31:53,688 Um, but it do you know, it does require just integrating with 604 00:31:53,768 --> 00:31:55,803 Hammerspace file shares instead. 605 00:31:56,553 --> 00:31:59,073 Prasanna Malaiyandi: and I'm guessing because backup, you kind 606 00:31:59,073 --> 00:32:00,603 of need access to everything. 607 00:32:00,873 --> 00:32:03,183 Your policies might be slightly different, right. 608 00:32:03,183 --> 00:32:06,573 For someone trying to do a backup or like a tool trying to do a 609 00:32:06,573 --> 00:32:08,073 backup, then like a normal user, 610 00:32:09,003 --> 00:32:09,333 Molly Presley: Yeah. 611 00:32:09,338 --> 00:32:14,313 There's a lot of access optionality built in, you know, super user access 612 00:32:14,313 --> 00:32:18,753 to everything versus you as a user only are allowed to access a certain 613 00:32:18,753 --> 00:32:20,103 thing for a certain amount of time. 614 00:32:20,103 --> 00:32:23,538 And of course, a backup environment would need access at a massive 615 00:32:23,538 --> 00:32:25,798 level, but you know, you can set it just as read, not write. 616 00:32:25,818 --> 00:32:28,548 Those types of things, which often would be a best practice. 617 00:32:29,578 --> 00:32:30,958 Prasanna Malaiyandi: You mentioned ransomware. 618 00:32:30,963 --> 00:32:32,758 I know that's a hot topic these days. 619 00:32:32,758 --> 00:32:38,398 Could you talk about how Hammerspace protects you prevent or how you 620 00:32:38,398 --> 00:32:41,128 handle ransomware situations? 621 00:32:42,028 --> 00:32:42,718 Molly Presley: Yeah, definitely. 622 00:32:42,718 --> 00:32:47,488 I think anybody who's in any sort of data management or data storage 623 00:32:48,208 --> 00:32:50,848 environment needs to be thinking about ransomware as a problem. 624 00:32:51,253 --> 00:32:55,723 Obviously that is top of mind for many companies, um, well for every company. 625 00:32:56,473 --> 00:33:00,253 And so if you think about a global data environment, which is what we call it, so 626 00:33:00,253 --> 00:33:04,963 you create this global data environment, which incorporates all of your data and 627 00:33:05,023 --> 00:33:08,383 you can think kind of ransomware person kinda putting their fingers together, 628 00:33:08,383 --> 00:33:10,083 going, Ooh, I want access that thing. 629 00:33:10,333 --> 00:33:13,903 Um, certainly there's multiple layers of what we have built into 630 00:33:13,903 --> 00:33:20,308 the environment, as far as access protections, um, immutable, snapshots. 631 00:33:20,308 --> 00:33:24,298 Um, we keep, because we have this very intelligent metadata layer. 632 00:33:24,568 --> 00:33:26,818 We actually have the ability to do undelete. 633 00:33:26,818 --> 00:33:32,428 So at a administrative level, even if somebody did maliciously delete data, um, 634 00:33:32,518 --> 00:33:37,078 we can do an undelete, which is housed in a location, you know, a different metadata 635 00:33:37,083 --> 00:33:38,458 environment that they can't touch. 636 00:33:38,458 --> 00:33:40,588 So there's, there's quite a few pieces. 637 00:33:40,588 --> 00:33:45,208 If you think about the different layers of access encryption. 638 00:33:45,643 --> 00:33:50,413 Um, taking of the data, things like that, that are built into the environment. 639 00:33:50,713 --> 00:33:54,913 Um, I wouldn't say that we are a ransomware company. 640 00:33:54,913 --> 00:33:58,603 I think all of our technologies need to do things to help protect against ransomware. 641 00:33:58,843 --> 00:34:01,423 There may be cases where somebody would go partner with a company whose 642 00:34:01,753 --> 00:34:03,493 job is to protect against ransomware. 643 00:34:03,973 --> 00:34:07,303 Um, and of course we would integrate with that, but there's several 644 00:34:07,303 --> 00:34:11,273 levels of controls and we've had customers, you know, who know 645 00:34:11,353 --> 00:34:13,543 they've had are undergoing attacks. 646 00:34:13,543 --> 00:34:14,443 It's very common. 647 00:34:14,593 --> 00:34:17,893 You know, that customers know that they've see, they see 'em having almost daily. 648 00:34:17,893 --> 00:34:18,133 I think 649 00:34:21,448 --> 00:34:26,188 W. Curtis Preston: That is a perfect application for the metadata only access. 650 00:34:26,338 --> 00:34:26,698 Right? 651 00:34:26,703 --> 00:34:30,628 So if you have a SEIM/SOAR tool that's monitoring what's happening with 652 00:34:30,628 --> 00:34:35,818 the metadata changes to the files would react, would, would result 653 00:34:35,818 --> 00:34:37,648 in changes in the metadata, right? 654 00:34:37,648 --> 00:34:41,848 And you being able to provide access to just the data data without all the data. 655 00:34:42,238 --> 00:34:45,638 Um, that sounds like a perfect application for your tool as well. 656 00:34:45,638 --> 00:34:46,208 Molly Presley: Exactly. 657 00:34:46,208 --> 00:34:47,883 Prasanna Malaiyandi: Another example I could think of is, 658 00:34:48,063 --> 00:34:49,953 especially with multi-cloud right. 659 00:34:50,013 --> 00:34:53,133 People might be choosing different clouds for cost reasons or feature 660 00:34:53,313 --> 00:34:57,123 reasons, and they may not be experts at it, but with Hammerspace, right. 661 00:34:57,123 --> 00:35:01,533 You could kind of give them that seamless interface across the clouds as well. 662 00:35:03,603 --> 00:35:04,143 W. Curtis Preston: cool. 663 00:35:04,803 --> 00:35:06,423 Well, Molly, I'm glad we had you on. 664 00:35:06,423 --> 00:35:07,473 Thanks for joining us. 665 00:35:08,028 --> 00:35:09,588 Molly Presley: Really interesting conversation. 666 00:35:10,188 --> 00:35:11,178 I've known you for a long time. 667 00:35:11,178 --> 00:35:11,628 Curtis. 668 00:35:11,628 --> 00:35:15,318 Prasanna's a smart, fun podcaster as well. 669 00:35:15,378 --> 00:35:17,288 W. Curtis Preston: a, he's a great co-host. 670 00:35:17,338 --> 00:35:19,258 I am very lucky to have him, so thanks. 671 00:35:19,258 --> 00:35:20,028 Thanks Prasanna. 672 00:35:20,378 --> 00:35:20,818 Prasanna Malaiyandi: Thank You, Curtis. 673 00:35:20,838 --> 00:35:22,498 Molly, it was a pleasure to meet you. 674 00:35:22,748 --> 00:35:26,528 W. Curtis Preston: And, thanks again to our listeners. 675 00:35:26,528 --> 00:35:29,048 You know, you are why we do this after all. 676 00:35:29,258 --> 00:35:35,318 Well, that, and we're bored, but, uh so we thank you for listening and remember to 677 00:35:35,748 --> 00:35:38,258 subscribe so that you can restore it all.