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Contact me 57 00:05:04.240 --> 00:05:09.519 at your Personal bank dot com your personal bank dot com or eight six six 58 00:05:09.639 --> 00:05:13.879 two six eight four four two two eight six six two six eight four four 59 00:05:14.000 --> 00:05:16.879 two two for more info or tune in to the Your Personal Bank Show. 60 00:05:17.600 --> 00:05:23.040 Your Personal Bank Show airs Tuesday's at four pm right here on Case a A 61 00:05:23.199 --> 00:05:26.720 ten fifty am and one oh six point five AFM, this station that leaves 62 00:05:26.759 --> 00:05:32.519 no listeners behind. Join me Stephanie Miller every weekday for the antidote to cantankers, 63 00:05:32.600 --> 00:05:36.480 conservatives and if they don't like it, too bad. It's on Caseyan 64 00:05:36.680 --> 00:05:41.879 weekday mornings from seven to eight ten fifty am and one oh six point five 65 00:05:42.199 --> 00:05:47.800 at m. E digits lock them in for more information, recreation and guaranteed 66 00:05:47.839 --> 00:06:21.959 fun k c AA. The information economy as a rod, the world is 67 00:06:23.079 --> 00:06:28.319 teaming with innovation as new business models. We invent every industry industry. Inside 68 00:06:28.319 --> 00:06:32.279 Analysis is your source of information and insight about how to make the most of 69 00:06:32.360 --> 00:06:39.120 this exciting new Eric. Learn more at inside analysis dot Comside analysis dot com. 70 00:06:39.120 --> 00:06:48.800 And now here's your host, Eric Kavanaugh. People. All right, 71 00:06:48.920 --> 00:06:54.680 ladies, gentlemen, welcome to the future in ead your host Eric Kavanaugh here. 72 00:06:55.079 --> 00:06:57.680 What a wonderful show we have lighted up for you today and the only 73 00:06:57.720 --> 00:07:01.319 coast to coast radio show all about the information economy. It's called Inside Analysis. 74 00:07:01.560 --> 00:07:05.240 Got my good buddy Ri Christian from sixth Sense Advisors. We're working on 75 00:07:05.279 --> 00:07:10.040 some cool stuff these days, and we have Chris Gladwin, the CEO of 76 00:07:10.040 --> 00:07:15.639 a company called Oceans that is fundamentally changing the data warehousing landscapes. So we're 77 00:07:15.680 --> 00:07:17.480 going to dive into what that all means for you, how it's going to 78 00:07:17.560 --> 00:07:21.079 help you get your business done in the near future. And really I wanted 79 00:07:21.120 --> 00:07:25.879 to talk about the evolution of data warehousing. So Chris and I are old 80 00:07:26.000 --> 00:07:29.959 enough to remember all the different eras we've gone through. It used to be 81 00:07:30.040 --> 00:07:32.839 that a data warehouse took you two years to stand up, or sometimes a 82 00:07:32.920 --> 00:07:35.839 year if you were very good at it costs you millions of dollars. Don't 83 00:07:35.879 --> 00:07:40.800 even bother trying unless you have that kind of expense. And then things started 84 00:07:40.839 --> 00:07:44.240 to change. We went through this whole had dupe world that we've talked about 85 00:07:44.279 --> 00:07:46.279 on the show many times, where people thought, oh, no sequel, 86 00:07:46.759 --> 00:07:50.279 is that not only sequel? Or is that no sequel? They thought that 87 00:07:50.480 --> 00:07:55.319 map reduce, which is how Yahoo index the web, could be used to 88 00:07:55.399 --> 00:07:57.519 do a lot of fun stuff with data, and they did. There were 89 00:07:57.560 --> 00:08:01.079 interest some interesting use cases that came out of that. But then cloud storage 90 00:08:01.160 --> 00:08:05.000 dropped precipitously in price, and that kind of killed the value proposition of hadoop, 91 00:08:05.240 --> 00:08:07.519 which was already a bit of a cluji environment. And then what happened, 92 00:08:07.560 --> 00:08:11.639 We had this massive resurgence of sequel. So, for those who don't 93 00:08:11.680 --> 00:08:16.079 know, sequel stands for the structured query language. It is the lingua franca, 94 00:08:16.160 --> 00:08:20.399 if you will, of database query languages. It's how you query a 95 00:08:20.480 --> 00:08:24.079 database to understand what's in it. And this whole search came out with a 96 00:08:24.079 --> 00:08:28.639 company called Snowflake, which at the time and I think had the biggest IPO 97 00:08:28.920 --> 00:08:31.360 in this industry. Ever, now that's come back down to earth a bit. 98 00:08:31.559 --> 00:08:35.840 And then you've got this company Data Bricks that comes along. Then you've 99 00:08:35.879 --> 00:08:39.120 got Amazon Redshift. There are all these different ways that you can slice and 100 00:08:39.159 --> 00:08:43.159 dice data and do your data warehousing, so what makes sense for your company? 101 00:08:43.279 --> 00:08:46.840 And then this new company, Ocean comes along, not exactly new. 102 00:08:46.879 --> 00:08:50.279 They're on version nineteen a while ago, I think, then version twenty one 103 00:08:50.799 --> 00:08:54.720 now, which shows you the maturity of the platform. They came up with 104 00:08:54.759 --> 00:08:58.360 something called a hyper scale data warehousing. So what does that mean. Well, 105 00:08:58.360 --> 00:09:01.799 the cloud gives you this hyper scale capability. Right, So people started 106 00:09:01.799 --> 00:09:07.440 to understand that Google and Amazon and Microsoft, they're the hyper scalers for being 107 00:09:07.480 --> 00:09:11.600 able to deliver functionality at tremendous scale that can scale up increasingly, you can 108 00:09:11.600 --> 00:09:15.600 scale down. That was a bit more of a challenge than scaling up. 109 00:09:15.840 --> 00:09:18.879 But what does that mean for data warehousing? Well, we have demand to 110 00:09:18.960 --> 00:09:22.399 explain all of that. Dialing in from my old old heart of Lamont, 111 00:09:22.440 --> 00:09:26.720 Illinois, Chris Glabin of Oceans, tell us about your vision and what is 112 00:09:26.799 --> 00:09:31.159 hyper scale data warehousing? Hey, Eric, great to be here, Chris, 113 00:09:31.200 --> 00:09:35.240 great to talk with you. Yeah. So, hyperscale is a term 114 00:09:35.240 --> 00:09:39.200 that I think has been used at different times because it changes in its meaning. 115 00:09:39.919 --> 00:09:46.080 What we're seeing is what hyper scale means in data analysis is it's the 116 00:09:46.200 --> 00:09:52.480 point at which the scale of the data analysis dictates a different approach. You 117 00:09:52.559 --> 00:09:56.279 can't just take the normal kind of middle of the road and say, well, 118 00:09:56.320 --> 00:09:58.679 let's just make it bigger. There's a point at which the scale of 119 00:09:58.679 --> 00:10:05.799 your analysis benefits from an approach just for hyper scale. So that's one thing 120 00:10:05.840 --> 00:10:11.039 we see and where we're seeing that in the market is generally when the average 121 00:10:11.080 --> 00:10:13.559 amount that you're analyzing is about a petabyteed data, So every time you run 122 00:10:13.559 --> 00:10:16.799 a query, it's got to look at a petabyte, and you do that 123 00:10:16.879 --> 00:10:22.480 a lot, and you're doing kind of complex things. And in terms of 124 00:10:22.559 --> 00:10:26.039 numbers, what we're seeing is we have gone through the process of identifying what 125 00:10:26.080 --> 00:10:30.679 are all the data sets in the world with business requirements that need that, 126 00:10:31.600 --> 00:10:35.679 and we're seeing it's about on a dollar basis, it's about four percent of 127 00:10:35.679 --> 00:10:41.120 the overall two hundred ten billion dollars data analysis market, so it's about an 128 00:10:41.120 --> 00:10:46.879 eight billion dollar market growing really fast. It's growing not only because the data 129 00:10:46.960 --> 00:10:50.679 keeps growing, but the number of use cases that need that approach is growing, 130 00:10:50.080 --> 00:10:54.919 so it's growth on growth. So it's growing about thirty five percent a 131 00:10:54.000 --> 00:10:58.559 year to about thirty five billion dollars in just five years. So that's the 132 00:10:58.639 --> 00:11:01.840 mark. And then you get into like what's in there? You know, 133 00:11:01.879 --> 00:11:07.120 what is the kind of data that needs this, And certainly we're seeing the 134 00:11:07.120 --> 00:11:11.919 biggest source of that is telecoms themselves. So if you're a telecom your network 135 00:11:11.000 --> 00:11:16.039 is giant, and there's all kinds of reasons why you want to analyze what's 136 00:11:16.039 --> 00:11:18.720 going on. It could be compliance requirements you have, it could be capacity 137 00:11:18.759 --> 00:11:22.879 planning, troubleshooting, performance optimization. But to really do that, you've got 138 00:11:22.879 --> 00:11:26.279 to look at all the metadata that flows in your network to see what's happening. 139 00:11:28.000 --> 00:11:31.240 And the amount of metadata that it's telecom makes is crazy. It's not 140 00:11:31.519 --> 00:11:37.759 trillion scale, it's hundreds of trillions, and quadrillions is the next number. 141 00:11:37.840 --> 00:11:39.519 You know, that's actually what they want to analyze. So that's just a 142 00:11:39.519 --> 00:11:43.919 little flavor of kind of what hyper scale means. Yeah, you big up 143 00:11:43.919 --> 00:11:48.879 an excellent point, because there are these inflection points in the industry, right 144 00:11:48.960 --> 00:11:52.679 and so historically you'll see things like that's where appliances would come into play. 145 00:11:52.799 --> 00:11:56.039 Right when the software can no longer handle it by itself, you do an 146 00:11:56.039 --> 00:12:00.440 appliance, and then you're leveraging the power of the hardware and the software. 147 00:12:00.720 --> 00:12:03.320 And then of course that kind of changes again, and so you innovate on 148 00:12:03.360 --> 00:12:05.759 the software. And as my business partner, who's mostly retired now, Robin 149 00:12:05.799 --> 00:12:11.080 Blore would once pointed out, all the innovation starts at the hardware level. 150 00:12:11.200 --> 00:12:15.120 At some point, some new piece of hardware changes things. And we were 151 00:12:15.159 --> 00:12:18.799 talking before the show about solid state memory, and if you look at there's 152 00:12:18.799 --> 00:12:22.279 a lot of interesting threads we could pull here. But you look at Osso 153 00:12:22.360 --> 00:12:24.639 Platner and this is a bit off topic, but he saw, like twenty 154 00:12:24.679 --> 00:12:26.759 odd years ago, twenty five years ago, he said, you know what, 155 00:12:26.879 --> 00:12:31.240 memory like solid state is going to come down in price over time, 156 00:12:31.279 --> 00:12:35.200 and at a certain point it's going to cross with data stored on disk. 157 00:12:35.679 --> 00:12:39.879 And so they pushed SAP into this whole in memory world. Well it's before 158 00:12:39.960 --> 00:12:41.679 kubernetties came out, and so now they're you know, kind of sitting on 159 00:12:41.720 --> 00:12:46.440 this monolith in a new distributed world. It's a bit of a different challenge. 160 00:12:46.720 --> 00:12:48.279 But he saw that coming in. You've kind of seen that coming too, 161 00:12:48.399 --> 00:12:52.919 right, because the capacity of these flash and these solid state drives to 162 00:12:52.960 --> 00:12:58.960 deliver data much much faster with less trouble is a big deal. So but 163 00:12:58.360 --> 00:13:01.960 figuring out how to build the infrastructure to leverage all that, well, that's 164 00:13:01.960 --> 00:13:05.240 what you guys been working on for twenty odd versions, right, Yeah, 165 00:13:05.240 --> 00:13:11.960 And we saw that coming almost exactly ten years ago. The revolution that's happening 166 00:13:13.000 --> 00:13:16.960 in solid state is not is not wasn't. It wasn't something you could hide 167 00:13:18.240 --> 00:13:24.480 because it literally was close to fifty billion dollar investment made by semiconductor manufacturers like 168 00:13:24.559 --> 00:13:28.480 Mintel and Samsung and Tashiba, like that doesn't hide in a corner. You 169 00:13:28.519 --> 00:13:33.759 know, that's like ten billion dollars fabs and thousands of engineers and you know, 170 00:13:33.840 --> 00:13:37.120 lots of academic papers and it took a long time. And so one 171 00:13:37.120 --> 00:13:41.120 of the things that it's different though about this what I would argue, it's 172 00:13:41.159 --> 00:13:45.480 the first new building block and computing ever. You know, yeah, your 173 00:13:45.639 --> 00:13:48.519 hard drives and they got bigger and DRAM got bigger, but solid state it's 174 00:13:48.600 --> 00:13:54.600 not a faster spinning disc. It's not cheaper DRAM. It's different, and 175 00:13:54.879 --> 00:13:58.879 you know, you could see it coming from from you know, a decade 176 00:13:58.919 --> 00:14:05.320 away, and unlike most of the time or I think pretty much all we 177 00:14:05.399 --> 00:14:07.879 have, I'm curious what you all think, but I would assert that this 178 00:14:07.960 --> 00:14:15.480 is the first new semiconductor change that didn't start like in supercomputers and work its 179 00:14:15.519 --> 00:14:20.480 way down with the opposite. Apple as a phone manufacturer, device manufacturer, 180 00:14:20.519 --> 00:14:26.120 and Samsung also saw this coming and they got in the production. You know, 181 00:14:26.200 --> 00:14:30.279 they were investors in the fabs and the fab companies, and part of 182 00:14:30.279 --> 00:14:33.480 that was like I get first crack at it. So what we saw as 183 00:14:33.600 --> 00:14:41.200 you know a company completely dedicated to building data analysis platform on solid state and 184 00:14:41.240 --> 00:14:43.639 we have friends. I mean, we've been doing this for a million years. 185 00:14:45.200 --> 00:14:48.639 And there was a point where it was in your phone. It was 186 00:14:48.759 --> 00:14:56.519 in like billion unit production scale in consumer product the phone, the laptop, 187 00:14:56.960 --> 00:15:03.639 and we as the leading technology creator for hyper scale analysis platforms, we could 188 00:15:03.639 --> 00:15:07.840 barely get our hands on samples. It just wasn't in production like consumers were 189 00:15:07.919 --> 00:15:11.919 first. And then what happened and then you had the supply chain issues with 190 00:15:11.000 --> 00:15:13.519 COVID where you know, it just made it really hard to get something unless 191 00:15:13.519 --> 00:15:18.559 you had a guarantee contract. And then about twelve months ago it changed. 192 00:15:18.919 --> 00:15:24.679 Now you want a million, you know, no problem. You know those 193 00:15:24.720 --> 00:15:30.360 fabs are running, they've got the production issues. And the profound change that 194 00:15:30.399 --> 00:15:35.919 you were referencing is what's happening now is it's gone from really hard to get 195 00:15:35.919 --> 00:15:39.120 your hands on so you can get as much as you want. And there's 196 00:15:39.120 --> 00:15:43.080 two metrics that matter. One is the cost per storage and the other is 197 00:15:43.120 --> 00:15:46.960 cost per performance. And what is happening right now is if you look at 198 00:15:48.000 --> 00:15:50.679 the life cycle costs and include only the cost of the drive, but the 199 00:15:50.759 --> 00:15:56.879 cost of power because they're more efficient on power than spending disks the space which 200 00:15:56.879 --> 00:15:58.600 costs money to put in a data center, and they're more efficient on space 201 00:15:58.679 --> 00:16:03.720 than disc. And reliability. When something fails less often, it costs less 202 00:16:04.240 --> 00:16:07.960 in solid state fails less often than spinning disks. If you look at that 203 00:16:08.039 --> 00:16:14.279 life cycle cost solid state, you know, particularly MBM E solid state, 204 00:16:14.360 --> 00:16:18.759 which is the parallel interface that everyone uses, it's a great standard. Is 205 00:16:18.840 --> 00:16:26.000 crossing below spinning disk right now on a cost per terabyte basis with currently two 206 00:16:26.000 --> 00:16:30.519 thousand times the performance, and the next generation will have four thousand times the 207 00:16:30.519 --> 00:16:34.440 performance, then eight thousand times the performance, and spinning disc will never get 208 00:16:34.480 --> 00:16:38.879 faster. So that's happening right now. And what that will cause, and 209 00:16:38.960 --> 00:16:44.519 it will first happen at hyper scale, is the collapse of computing storage tiers 210 00:16:44.559 --> 00:16:48.000 into one. And the only reason they were separate because there was a big 211 00:16:48.000 --> 00:16:52.279 price difference. And then what we can talk about maybe later is around twenty 212 00:16:52.440 --> 00:16:56.399 thirty the cost per performance, which you know, we're using the cost per 213 00:16:56.480 --> 00:17:03.320 million random four k reads per second per dollar solid state will drop under d 214 00:17:03.519 --> 00:17:08.279 ram under the cost for performance, you know, and so and salid state 215 00:17:08.319 --> 00:17:11.680 is so early and it's Moore's law curve that it is in Moore's Law. 216 00:17:11.839 --> 00:17:17.079 Every eighteen months it gets twice as good. DRAM is not, spinning, 217 00:17:17.079 --> 00:17:18.839 disc is not. They're kind of stuck. And so this is where we 218 00:17:18.839 --> 00:17:23.440 are today and where we're headed as solid state is going to completely change, 219 00:17:25.079 --> 00:17:30.000 you know, how computing is built and how data is analyzed. Yeah, 220 00:17:30.039 --> 00:17:33.559 that is absolutely fascinating. I'm going to bring in Chris Christian to comment on 221 00:17:33.599 --> 00:17:37.319 that. I mean, these are the kinds of things that engineers need to 222 00:17:37.359 --> 00:17:41.920 be focused on, and frankly, CDOs and CTOs and CIOs need to have 223 00:17:42.599 --> 00:17:48.200 in their perspective because this is going to have tremendous impact on what you're able 224 00:17:48.240 --> 00:17:52.119 to do if you're not embracing this new way of doing things. But Christian, 225 00:17:52.119 --> 00:17:56.680 what do you think? Oh? Absolutely agree? In fact, oh 226 00:17:56.200 --> 00:18:03.319 highlight where Chris was taking the thought process too. If you want to think 227 00:18:03.359 --> 00:18:08.000 of in the next seven years spectrum twenty thirty, right, what's going to 228 00:18:08.200 --> 00:18:14.039 change? The question is what's going to change? That's what bothers a lot 229 00:18:14.079 --> 00:18:19.279 of CIOs and cd aos, not just the digital officers, but the data 230 00:18:19.319 --> 00:18:26.119 and what's going to change. We are becoming drawn driven Everything is getting automated. 231 00:18:26.200 --> 00:18:32.480 Everything is becoming its own landloans and its own points where data is going 232 00:18:32.519 --> 00:18:37.759 to intercept and criss cross, and what boundary is safe versus what is okay? 233 00:18:37.920 --> 00:18:45.000 Verse public versus what's private? When you're looking at those boundaries exploding. 234 00:18:45.079 --> 00:18:48.880 In the next seven years, we're going to look at volumes of data, 235 00:18:49.200 --> 00:18:53.160 which is like, yeah, I built a five terabyte data whereas people will 236 00:18:53.279 --> 00:18:59.480 laugh, five terabyte terabyte would be like something that happens on a daily basis. 237 00:19:00.000 --> 00:19:02.319 I built a five beta byte data ware Else, now you're going to 238 00:19:02.400 --> 00:19:04.839 be talking, yeah, that's good, But then we're going to be talking 239 00:19:04.880 --> 00:19:11.440 about fifty beta bye data warehouses. But we'll still say data warehouse, right, 240 00:19:11.480 --> 00:19:17.640 I mean some tween. While the spectrum of storage is fantastic, the 241 00:19:17.799 --> 00:19:22.160 problem solving still has to be put on top of it so that you can 242 00:19:22.319 --> 00:19:27.799 tether and then get the solution stack built on top of that foundation. And 243 00:19:27.839 --> 00:19:33.480 to me, that's where Eric, I think we should take our next set 244 00:19:33.559 --> 00:19:40.759 of discussion points with Chriss. Why datawareousing? Yeah, well that's a good 245 00:19:40.839 --> 00:19:42.240 question, So go throw it over to you, Chris. Now you have 246 00:19:42.720 --> 00:19:48.799 these data lakes people are talking about these large language models doing very interesting things, 247 00:19:48.880 --> 00:19:52.319 but it's not data warehousing for sure. And you're really focused intently on 248 00:19:52.359 --> 00:19:59.640 the data warehousing use cases at hyper scale to enable organizations to really slice and 249 00:19:59.640 --> 00:20:03.079 dice, right, because the thing is the factor you can cut through the 250 00:20:03.119 --> 00:20:06.519 data, the factor you can do the analysis and get the answers. The 251 00:20:06.559 --> 00:20:11.200 more questions you can ask, the more chance you have of finding the signal 252 00:20:11.240 --> 00:20:14.400 you wanted to change your business, right, And I think a lot of 253 00:20:14.440 --> 00:20:21.119 the older solutions now are frankly encumbered not just by bureaucracy, but by technical 254 00:20:21.160 --> 00:20:25.119 debt and sort of traditional hurdles that they're just not getting over. They're kind 255 00:20:25.119 --> 00:20:29.559 of running into walls. That changes behavior inside the organization. You ask less 256 00:20:29.640 --> 00:20:33.559 questions, you're less risky. If you will, you take a fewer chances, 257 00:20:33.720 --> 00:20:37.599 and then you don't do as well. I mean that's my take on 258 00:20:37.640 --> 00:20:41.359 it. But what do you see, Chris. What we're seeing in customers 259 00:20:41.519 --> 00:20:47.039 is the compromises they've had to make at hyper scale often involve things like, 260 00:20:48.160 --> 00:20:49.680 you know, letting most of the data fall off the table. I mean, 261 00:20:49.720 --> 00:20:55.039 they just they can't effectively analyze it, so they say they don't even 262 00:20:55.039 --> 00:20:57.200 collect it. You know, automobile manufacturers, if you have one hundred million 263 00:20:57.240 --> 00:21:03.039 cars. You know, I talked about tell for a telco prior to oceent. 264 00:21:03.160 --> 00:21:07.480 In this next generation of data warehouse capability that we're talking about, they 265 00:21:07.559 --> 00:21:12.640 really couldn't cost effectively collect and analyze all network metata on the network. It's 266 00:21:12.960 --> 00:21:17.920 quadrillion rows on a table. It just doesn't make sense. They're not going 267 00:21:17.960 --> 00:21:22.200 to spend half a billion dollars on a supercomputer, and so therefore then they 268 00:21:22.279 --> 00:21:27.079 end up making compromises. So if you want to do like performance analysis, 269 00:21:27.119 --> 00:21:30.759 you don't care about averages. All you care about is the worst. You 270 00:21:30.799 --> 00:21:34.200 know, what's the worst performance on my network and what were the conditions. 271 00:21:34.200 --> 00:21:37.920 But that means you actually have to have all of it to see the worst 272 00:21:37.400 --> 00:21:41.519 and identify the things that correlate with the worst. So what we've seen where 273 00:21:41.759 --> 00:21:45.440 you know, companies would then either just not have all the data and they 274 00:21:45.559 --> 00:21:48.000 make the best of it. In some cases like an ad tech, some 275 00:21:48.079 --> 00:21:52.279 of our customers they would down. You know, there's there's you know, 276 00:21:52.559 --> 00:21:56.920 tens of millions of digital ads that go up for auction every second, and 277 00:21:56.920 --> 00:22:02.240 there's whole industries of people that either supply those inventory of ad placements or buy 278 00:22:02.279 --> 00:22:06.279 those and analyze those. And because you know that's such a volume, if 279 00:22:06.279 --> 00:22:10.200 they want to back test an algorithm for some kind of campaign they're going to 280 00:22:10.319 --> 00:22:12.680 run on the last three months, well that's just too much data, So 281 00:22:12.720 --> 00:22:18.440 then they would downsample. It's something like point one percent of that ad exchange. 282 00:22:18.480 --> 00:22:19.960 But the net of the problem with that is then if you had a 283 00:22:19.960 --> 00:22:26.319 really large customer that wanted to test a campaign, the results become inaccurate. 284 00:22:26.079 --> 00:22:32.319 So what we're seeing is because attociate, we're using this solid state technology, 285 00:22:32.440 --> 00:22:36.279 which is like game changing. It's you know, and going back to your 286 00:22:36.319 --> 00:22:38.440 time in earlier, the best you can do as a software designer is to 287 00:22:38.440 --> 00:22:42.160 go as fast as the hardware. And so what we're able to do is 288 00:22:42.160 --> 00:22:47.400 to address these problems where you can do full resolution data results can be accurate 289 00:22:47.559 --> 00:22:52.000 even at hyper scale. Yeah, that's fascinating stuff. I mean really, 290 00:22:52.079 --> 00:22:56.880 and I'm sure there are some mindset issues that companies have to kind of get 291 00:22:56.920 --> 00:23:00.720 over. But the person in the room who understands the knowledge is the person 292 00:23:00.720 --> 00:23:03.640 who is run into these barriers and to your point, has had to say, 293 00:23:03.720 --> 00:23:07.640 well, let's just use point one percent of the data. I mean, 294 00:23:07.759 --> 00:23:11.359 down sampling always gets you that lossy characteristic right where it's like, well, 295 00:23:11.720 --> 00:23:15.240 am I now going to lose visibility into the signal I was trying to 296 00:23:15.279 --> 00:23:19.279 find in the first place. Well, that's not a very effective solution. 297 00:23:19.519 --> 00:23:22.880 And again to be able to do it quickly. And what you mentioned the 298 00:23:23.200 --> 00:23:29.319 stat like two thousand times fast or some of these these new solid state drives 299 00:23:29.319 --> 00:23:33.720 are compared to their counterparts, Well, you know that's more than an order 300 00:23:33.759 --> 00:23:37.240 of magnitude. That's more. That's that's two orders of magnitude, right, 301 00:23:37.319 --> 00:23:42.799 It's like just off the charts kind of and it fundamentally changes the conversations that 302 00:23:42.880 --> 00:23:48.119 you have and how your team operates. And so that's what excites me is 303 00:23:48.160 --> 00:23:52.039 you're opening this new portal for companies that go down the hyper scale highway with 304 00:23:52.119 --> 00:23:56.799 data warehousing to where you don't have to limit yourself to small views of the 305 00:23:56.839 --> 00:24:00.960 world. And that is going to fundamentally change and it saves tremendous amounts of 306 00:24:00.960 --> 00:24:06.160 money. 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If you or someone 354 00:27:49.920 --> 00:28:15.759 in your family has developed a serious illness, including various two five, four, 355 00:28:15.359 --> 00:28:25.119 thirty two eighteen. Welcome back to Inside Analysis. Here's your host, 356 00:28:25.680 --> 00:28:32.200 Eric Tavanaugh show. All right, folks, welcome back to Inside Analysis. 357 00:28:32.519 --> 00:28:37.839 You're host here, Eric Kavanaugh talking with Chris Christian and Chris Gladwin of Ocean's. 358 00:28:37.000 --> 00:28:41.319 Chris's my consultant friend from many, many years ago, also in the 359 00:28:41.400 --> 00:28:42.920 Chicagoland area. I grew up in the Chicagoland area. And you know before 360 00:28:42.960 --> 00:28:47.160 the show, we were talking about three fifty five and how I three fifty 361 00:28:47.160 --> 00:28:49.720 five opened up. And I used to be the editor for the Lamont Metropolitan 362 00:28:49.720 --> 00:28:53.799 newspaper and I actually interviewed a guy from the Illinois Department of Transportation about the 363 00:28:53.839 --> 00:28:56.880 project at the time, and he told me something very interesting. He said 364 00:28:56.920 --> 00:29:02.559 that the laws do not allow you to go and build a highway according to 365 00:29:02.759 --> 00:29:06.799 future traffic speculation, like how much traffic you think is going to be in 366 00:29:06.839 --> 00:29:08.759 the future. They say, you can only use the data of traffic that 367 00:29:08.880 --> 00:29:12.359 is currently in this region, and that's how you justify where you build these 368 00:29:12.440 --> 00:29:17.079 highways. Well, once they built three fifty five. I promise you there 369 00:29:17.119 --> 00:29:19.480 are all kinds of people that change their driving habits because it was so much 370 00:29:19.480 --> 00:29:22.160 easier to go up to the North suburbs and it was like night and day 371 00:29:22.200 --> 00:29:26.000 basically. And you can kind of make this comparison, this loose comparison of 372 00:29:26.000 --> 00:29:30.480 traditional versus hyper scale, and traditional is going the back roads and hitting stop 373 00:29:30.559 --> 00:29:33.119 lights wherever you go. It takes you an hour and ten minutes to get 374 00:29:33.160 --> 00:29:37.680 from Bowlingberg to Schaumberg, and then the hyper skill shows up. It's like, 375 00:29:37.759 --> 00:29:40.720 oh nope, you just get in the highway. There's no turn offs. 376 00:29:40.759 --> 00:29:42.400 You can just keep rocket and rolling and get there and no time. 377 00:29:42.799 --> 00:29:48.079 It changes behavior and new things happen, and you start to change how you 378 00:29:48.119 --> 00:29:49.799 planned your day because I don't have to spend an hour in ten minutes. 379 00:29:49.839 --> 00:29:53.799 I can only spend twenty seven minutes. So these new technologies, Chris, 380 00:29:53.799 --> 00:29:57.519 when they come along, they open up whole new possibilities. So it's not 381 00:29:57.680 --> 00:30:02.000 just that you can do the old stuff ten times faster, or it's that 382 00:30:02.119 --> 00:30:03.960 yeah, you can do that, and you could do all this other interesting 383 00:30:04.000 --> 00:30:07.400 stuff, and that's where the business value tends to accrue. What do you 384 00:30:07.440 --> 00:30:15.039 think, Yeah. Generally, what we're seeing is by designing a new software 385 00:30:15.640 --> 00:30:21.799 data analysis engine for hyper scale analysis using this amazing new building block, the 386 00:30:21.880 --> 00:30:26.799 NVMME solid state drive, as well as some other pretty choice building blocks like 387 00:30:26.880 --> 00:30:30.599 high corkunt CPUs and hundred GIGNIT connections, everywhere, you really can't get to 388 00:30:30.640 --> 00:30:37.000 ten times the price performance than what was previously available. In some cases you 389 00:30:37.119 --> 00:30:41.039 get to a hundred times. And every time, you know, and this 390 00:30:41.079 --> 00:30:42.880 is not the only time there's been ten times the price performance for some kind 391 00:30:42.880 --> 00:30:48.079 of IT capability, It's happened many, many times. And every time it 392 00:30:48.160 --> 00:30:55.519 happens, two interesting things occur. One is industry or consumers never keep doing 393 00:30:55.519 --> 00:31:00.400 what they're doing before and just save the money right never. They always find 394 00:31:00.480 --> 00:31:03.000 if it's ten times the price performance, they will find more than ten times 395 00:31:03.039 --> 00:31:07.000 the things to do with it, and the pie grows bigger. I challenge 396 00:31:07.039 --> 00:31:11.079 you to, you know, give me a counter example, because I'll name 397 00:31:11.079 --> 00:31:15.160 everything else. So that that always happens. And the other thing that's interesting, 398 00:31:15.200 --> 00:31:18.720 and part of why that happens is when you look back ten years, 399 00:31:18.720 --> 00:31:26.160 twenty years later at what are the uses now of that disruptive new technology typically 400 00:31:26.200 --> 00:31:32.519 at least eighty percent are new things that could only exist once the disruptive technology 401 00:31:32.640 --> 00:31:37.240 change occurred. You know, twenty percent or less are what was there before 402 00:31:37.799 --> 00:31:42.160 becomes bigger, faster, cheaper, and more than eighty percent becomes only now 403 00:31:42.160 --> 00:31:48.200 that I have broadband? Can I have a giant system that distributes videos of 404 00:31:48.359 --> 00:31:56.440 pets known as YouTube and other silly things like that didn't that couldn't happen before 405 00:31:56.480 --> 00:31:59.759 broadband. You know, broadband happens boom, here comes YouTube, and there's 406 00:32:00.200 --> 00:32:02.000 you know, many many examples of things like that. Like we in my 407 00:32:02.079 --> 00:32:06.880 last company, clever Safe, we dominated the market for photo sharing, for 408 00:32:07.039 --> 00:32:10.759 software for photo sharing systems. Photo sharing was kind of the first incarnation of 409 00:32:10.759 --> 00:32:15.720 cloud storage. It could you know, there was you know, this idea 410 00:32:15.720 --> 00:32:17.559 of like take as many pictures as you want, will storm forever and every 411 00:32:17.599 --> 00:32:23.079 once, so I'll sell your mug like that business cannot exist and that's a 412 00:32:23.200 --> 00:32:28.240 very great business for a lot of companies. It couldn't exist until the price 413 00:32:28.319 --> 00:32:32.599 performance of you know, super reliable photo storage systems at hyperscale went down about 414 00:32:32.640 --> 00:32:37.000 one hundred times and you just you know, you just see example after example 415 00:32:37.000 --> 00:32:43.359 where when these big disruptions occur, all kinds of new innovation happens. Yeah, 416 00:32:43.720 --> 00:32:46.279 now that's exactly right. And I love the comments you made of you 417 00:32:46.279 --> 00:32:50.160 know, when you do get this increase in performance, never did they just 418 00:32:50.240 --> 00:32:53.039 save the money, especially in analytics, Chris, because in analytics, again 419 00:32:53.079 --> 00:32:57.799 you're trying to understand your business. And so now when you have these new 420 00:32:57.839 --> 00:33:00.759 tools to dig deeper into your own data to understand, like you take the 421 00:33:00.759 --> 00:33:06.480 Telco example, that's a big deal. If you can problem solve faster and 422 00:33:06.599 --> 00:33:09.440 figure out how to fix this issue, you're not going to lose a thousand 423 00:33:09.480 --> 00:33:13.759 or two thousand customers to your competition, for example. And just little things 424 00:33:13.799 --> 00:33:17.240 like that really add up quickly. But it's the case across the analytical spectrum, 425 00:33:17.279 --> 00:33:21.480 the more you can analyze, the better off you're going to be. 426 00:33:21.720 --> 00:33:24.000 And you know the fact is that some of these old solutions, even though 427 00:33:24.079 --> 00:33:28.079 Territata, for example, I'm told, just spend two hundred and fifty million 428 00:33:28.119 --> 00:33:32.640 dollars to refactor their entire engine to run in the cloud and Amazon Web Services 429 00:33:32.839 --> 00:33:37.559 now they just rolled out for Microsoft recent it's a lot of money to spend 430 00:33:37.200 --> 00:33:43.440 refactoring. But Chris has some interesting thoughts about Kubernetes as well, because with 431 00:33:43.519 --> 00:33:47.680 kubernetties, you're federating all this compute, which you know, I've always joked 432 00:33:47.720 --> 00:33:52.480 we sacrifice state at the altar of scale to be able to do these things. 433 00:33:52.920 --> 00:33:57.559 But still there are inefficiencies baked into that system as well. So if 434 00:33:57.599 --> 00:34:00.519 you do your job right on the ocean's side, there's really not a lot 435 00:34:00.559 --> 00:34:05.680 of benefits to get from going into a cuban, righties environment. All you're 436 00:34:05.720 --> 00:34:08.320 trying to do is enable your analysts to crunch data. Chris will throw whatever 437 00:34:08.360 --> 00:34:13.239 do you first, and then we'll get Chris. Yeah, And the point 438 00:34:13.280 --> 00:34:20.639 you are bringing about the availability of valid data is very essential, right. 439 00:34:20.679 --> 00:34:24.719 I mean, for many years we've all gone in the traditional route where we've 440 00:34:24.760 --> 00:34:28.920 said, yeah, this model work, said no, this one doesn't, 441 00:34:29.079 --> 00:34:35.480 or while a bridge table suddenly becomes a point where data just goes Christ all 442 00:34:35.599 --> 00:34:39.800 kinds of issues that have been faced for a number of years where analytics could 443 00:34:39.800 --> 00:34:45.400 not succeed because of the lack of data. The bottom line, right, 444 00:34:45.599 --> 00:34:52.159 that is something that I would love to hear Chris talk about because hyper scaling 445 00:34:52.880 --> 00:34:59.639 would mean now your data fabrication. This is a term that ocean would love 446 00:34:59.679 --> 00:35:04.079 to talk about, but a good definition. So I think if we can 447 00:35:04.119 --> 00:35:08.239 get give us a quick definition on what the data fabrication thought processes and then 448 00:35:08.280 --> 00:35:15.239 we talk more about why, then bringing the analytic suitcase or anything else is 449 00:35:15.280 --> 00:35:19.840 going to be so much more easier Or folks don't understand, but that's the 450 00:35:19.880 --> 00:35:22.199 bottom line, that's the grassholds that we want to get to, Chris or 451 00:35:24.360 --> 00:35:29.920 I think the other big change we're seeing in data analysis kind of from a 452 00:35:29.960 --> 00:35:34.840 traditional even the kind of recent generation of data warehouse life hopefully can data bricks 453 00:35:34.880 --> 00:35:44.199 and redshift is a shift from kind of traditional batch oriented data loading to data 454 00:35:44.440 --> 00:35:50.679 never stops streaming at scale and in the database now has to exist in this 455 00:35:50.800 --> 00:35:53.920 environment where data is constantly growing, and that you know, one of the 456 00:35:54.000 --> 00:36:00.880 challenges is taking you know, terabits per second of input data which is never 457 00:36:00.559 --> 00:36:06.960 in an easy, easy to digest state, in getting that transformed from some 458 00:36:07.119 --> 00:36:14.199 typical semi structured at messy state into relational schema that is index secondary index, 459 00:36:14.280 --> 00:36:19.360 compressed and cryptic and showing up in queries within seconds. Now that traditionally has 460 00:36:19.400 --> 00:36:22.119 not been possible either. And that's another thing that we were able to do 461 00:36:22.159 --> 00:36:27.679 it did Ocean because the big it's also interesting to think about where does data 462 00:36:27.719 --> 00:36:31.840 come from? And at hyper scale, zero percent of the data comes from 463 00:36:31.920 --> 00:36:37.079 typing. People don't type at hyper scale. Just to give you a sense, 464 00:36:37.159 --> 00:36:44.440 a minimum system for Ocean would be, you know, a petobyte a 465 00:36:44.440 --> 00:36:49.199 trillion rows and a table a trillion if you print it out circles of the 466 00:36:49.199 --> 00:36:52.039 Earth seventy three times, and if you were to scroll it, you know, 467 00:36:52.079 --> 00:36:55.800 two pages a second. Assuming you could see data that fast, it's 468 00:36:57.000 --> 00:37:01.880 centuries to just scroll through that table like that is not something that humans can 469 00:37:01.920 --> 00:37:06.639 create, and that is not something that humans can even read, you know. 470 00:37:06.719 --> 00:37:10.559 So a couple changes there, and that all the data being analyzed at 471 00:37:10.599 --> 00:37:15.400 hyper scale is machine generated. It's routers, it's cars, its satellites, 472 00:37:15.480 --> 00:37:22.039 it's instruments, and then the other big changes we've entered this time. I 473 00:37:22.039 --> 00:37:23.119 mean, it used to be like, you know, if you really had 474 00:37:23.119 --> 00:37:25.280 to look into something, you could go look at the locks. You know, 475 00:37:25.320 --> 00:37:29.599 you could you could actually go look at the data. Like you can't 476 00:37:29.639 --> 00:37:32.360 go look at the data anymore. Human beings cannot see data at this scale. 477 00:37:32.400 --> 00:37:38.400 They are now completely dependent on these tools that look at the data for 478 00:37:38.639 --> 00:37:45.440 them, and an increasing amount of what people are using is machine learning artificial 479 00:37:45.480 --> 00:37:49.480 intelligence to characterize that for them. I mean, I can't look at everything, 480 00:37:49.519 --> 00:37:52.880 So just go look at these hundred trillion things and tell me what's forced 481 00:37:52.880 --> 00:37:55.800 standard deviations out of normal? And maybe I can look at that, but 482 00:37:55.880 --> 00:37:59.639 I can't look at the raw data ever. Again, So those are a 483 00:37:59.679 --> 00:38:01.960 couple of giant changes we're seeing. That's funny. You have a couple great 484 00:38:02.000 --> 00:38:04.880 quotes that I can look at. They're on data. Ever. Again, 485 00:38:05.440 --> 00:38:07.400 that's a funny thing. And let's talk about observability too, because you were 486 00:38:07.400 --> 00:38:12.519 just kind of referencing that observe ability is this huge space now that has blown 487 00:38:12.599 --> 00:38:15.559 up for lots of different reasons. But to your point, with observability, 488 00:38:15.639 --> 00:38:21.440 you've got all these signals of data that streaming constantly, and what you're trying 489 00:38:21.440 --> 00:38:25.760 to figure out is how to apply the appropriate filters to get signals when necessary. 490 00:38:25.920 --> 00:38:30.480 So you mentioned like four levels of deviation for example, can you talk 491 00:38:30.559 --> 00:38:37.320 about how you're able to leverage data warehousing technology at hyper scale in order to 492 00:38:37.440 --> 00:38:44.239 enable meaningful observability across these systems. And there's also the issue of cloud environments, 493 00:38:44.239 --> 00:38:46.360 which have lots of different things going on. It's not just your ERP 494 00:38:46.519 --> 00:38:50.800 you have to worry about, or one database. It's lots of different things 495 00:38:50.840 --> 00:38:54.599 interacting in real time to push this stuff forward. So talk about how you're 496 00:38:54.639 --> 00:39:00.280 able to help people understand what's happening in that world and how that actually works. 497 00:39:00.159 --> 00:39:04.800 An increasing amount of what's happening is what's happening right now. You know, 498 00:39:04.960 --> 00:39:07.840 it can't just be oh data it was, you know, fresh from 499 00:39:07.920 --> 00:39:12.400 yesterday. It's got to be fresh from three seconds ago. And so you 500 00:39:12.400 --> 00:39:17.360 know, so much of what you want to understand is involves this fresh data. 501 00:39:17.519 --> 00:39:22.159 So it's and it's not. It's sometimes just looking at that data itself, 502 00:39:22.280 --> 00:39:25.480 and sometimes it's looking at that data comparison to older data, but it's 503 00:39:25.559 --> 00:39:30.840 less and less just looking at older data. So this real time analytics capability 504 00:39:30.880 --> 00:39:36.679 that we've incorporated at hyper Scale and our platform is essential, and all of 505 00:39:36.679 --> 00:39:39.400 our customers are using it now because all of them have, you know, 506 00:39:39.480 --> 00:39:44.519 this need and the big unlock we're seeing in there, just like we're seeing 507 00:39:44.519 --> 00:39:51.079 in the data. Analysis at hyper Scale is using machine learning more and more 508 00:39:51.119 --> 00:39:57.000 to tell you not only what is in the data that's pouring in all the 509 00:39:57.039 --> 00:39:59.920 time. But what but how to deal with it? You know, it's 510 00:40:00.039 --> 00:40:04.519 takes you know, they like there's so much mood coming in, it's changing 511 00:40:04.559 --> 00:40:08.519 so much that we're using AI more and more to help us load and characterize 512 00:40:08.519 --> 00:40:15.360 the data as it streams in classification basically, right, So you're trying to 513 00:40:15.400 --> 00:40:20.400 classify in real time and ascertain what's normal and what's not normal. I mean, 514 00:40:20.400 --> 00:40:22.639 I think that's basically the rule of thumb. Right, this is normal 515 00:40:22.679 --> 00:40:28.920 behavior. Now if something gets out of black, that's abnormal behavior. What's 516 00:40:28.920 --> 00:40:31.199 going on there? And this is how you have to figure out how to 517 00:40:31.239 --> 00:40:36.199 use these different filters to classify and to understand in real time. And you're 518 00:40:36.719 --> 00:40:38.880 you know, so let's think about Larry Ellison, for example, who I 519 00:40:38.920 --> 00:40:44.400 guess five years ago was talking about the healing the self healing database, right, 520 00:40:44.440 --> 00:40:46.360 that was their big thing. It sounds like you're kind of going down 521 00:40:46.360 --> 00:40:52.320 a similar path where you're using machine learning an AI to ascertain, Wait, 522 00:40:52.440 --> 00:40:54.840 something is wrong over here, let's go fix that, Reset this, no, 523 00:40:55.119 --> 00:40:58.039 do this, do that? Whatever? Is that kind of what you're 524 00:40:58.079 --> 00:41:00.719 talking about, is this self healing kick? Yeah, And I would say 525 00:41:00.719 --> 00:41:07.199 that that self filling thought from five years ago kind of the universe there is 526 00:41:07.639 --> 00:41:12.880 that this database is a fixed sometimes fixed data set, and that's where you 527 00:41:12.920 --> 00:41:17.800 have to heal. What I'm reporting is that the world has changed to where 528 00:41:19.360 --> 00:41:22.760 so much of the part of the database isn't just what's there, but what's 529 00:41:22.840 --> 00:41:27.599 arriving, so that the healing that you have to do isn't I mean, 530 00:41:27.639 --> 00:41:30.320 the static part. It's actually pretty easy. It's a lot harder to deal 531 00:41:30.400 --> 00:41:35.199 with, like what's going on now and has that changes? Because the world, 532 00:41:35.920 --> 00:41:37.159 like it doesn't matter what the data set is. If it's you know, 533 00:41:37.400 --> 00:41:40.920 vehicle data, there's road construction, you know, there's traffic, there's 534 00:41:40.960 --> 00:41:45.800 a public safety and so if it's you know, ad tech data, something's 535 00:41:45.840 --> 00:41:47.320 happened in the market, So like how's that change, you know, the 536 00:41:47.360 --> 00:41:51.239 buying and selling of ad placement? If it's interesting, you know, I 537 00:41:51.280 --> 00:41:57.840 mean it just like you can't remove the dynamic nature when you think about anything, 538 00:41:57.880 --> 00:41:59.920 even you know, including just like what does it mean to heal? 539 00:42:00.199 --> 00:42:02.239 What's the kind of harm that you cant fix? Well, it's it's more 540 00:42:02.280 --> 00:42:07.400 in the flow of data as opposed to the batch of data. That's very 541 00:42:07.480 --> 00:42:09.239 interesting, and that's true, right, I mean, data is always moving. 542 00:42:09.280 --> 00:42:12.599 It always has been moving. But you made a good point because the 543 00:42:12.599 --> 00:42:16.599 old mindset was Okay, here's the static date base that I have and something 544 00:42:16.639 --> 00:42:20.000 broke, let me fix this static things like, no, this thing is 545 00:42:20.039 --> 00:42:23.840 moving constantly. It's always moving. So you're trying to really understand the flows 546 00:42:23.920 --> 00:42:28.440 of data. What is the nature of this flow of data in this particular 547 00:42:28.559 --> 00:42:31.960 environment, and point that over to like you say, some events, Well, 548 00:42:32.000 --> 00:42:36.440 what happened, Let's get to the bottom of this. That's really a 549 00:42:36.519 --> 00:42:39.519 magical characteristic of these modern systems. What do you think, Chris, go 550 00:42:39.519 --> 00:42:46.639 ahead, no other piece to what Chris was saying. I was. I 551 00:42:46.639 --> 00:42:52.159 think it was about twenty months ago. I was working with the San Diego 552 00:42:52.239 --> 00:43:00.519 Department of Biological Sciences. They said they were trying to capture shellfish brain movement 553 00:43:04.159 --> 00:43:06.960 a bit. They had an issue. They said, we don't know how 554 00:43:07.000 --> 00:43:10.800 to make this effective. And I spent like almost a week trying to understand 555 00:43:10.920 --> 00:43:15.400 what were they trying to get? To Christ's point, right, I mean, 556 00:43:15.440 --> 00:43:19.880 if you don't know what you're trying to get, everything is going to 557 00:43:19.960 --> 00:43:23.440 be like, oh, it's outside this. Once we ascertain what we were 558 00:43:23.519 --> 00:43:29.119 trying to get, in that constant flow. We could then say where the 559 00:43:29.159 --> 00:43:34.599 spikes were, where the abnormalities were, where there was an interception that needed 560 00:43:34.719 --> 00:43:38.800 to be done. All kinds of pieces could be reacted, but you need 561 00:43:38.800 --> 00:43:45.360 to have somebody who knows the system really well in order to understand what is 562 00:43:45.400 --> 00:43:51.159 it that you're missing so that you can take care of it at that very 563 00:43:51.239 --> 00:43:55.000 minute. I mean, it's a latch on a sap. There is no 564 00:43:55.320 --> 00:43:59.559 Yeah, it can wait for something to come now, it's like, do 565 00:43:59.639 --> 00:44:06.320 it good, dare I'm now really understanding why Chris uses this term hyper scale 566 00:44:06.360 --> 00:44:08.599 and the on a past show we get people to find hyper scale and one 567 00:44:08.639 --> 00:44:12.639 guy said loss of control. And that's kind of what you're tie. 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Call eight hundred seven one oh three 616 00:47:54.760 --> 00:48:00.760 seven three nine, eight hundred seven one oh three seven three nine, eight 617 00:48:00.840 --> 00:48:05.519 hundred seven one h three seven three nine. That's eight hundred seven one h 618 00:48:05.840 --> 00:48:09.880 thirty seven thirty nine nmls sixty six h six wwxs dot org. This is 619 00:48:09.880 --> 00:48:14.239 not an offer or commitment to lend subject to borrow. We're improperty qualifications, 620 00:48:14.360 --> 00:48:20.679 not all of borrow wres. We'll qualify terms and conditions apply equal housing express. 621 00:48:21.039 --> 00:48:30.280 Welcome back to Inside Analysis. Here's your host, Eric Kabanaugh. All 622 00:48:30.360 --> 00:48:35.199 right, folks, back here on Inside Analysis of fascinating conversation with Chris Gladwin 623 00:48:35.280 --> 00:48:38.840 of Oscience and Christian Christman of sixth Sense Advisors. We're talking all about hyper 624 00:48:38.880 --> 00:48:42.880 scale, hyper scale of data warehousing. There's an ocean of data. Now, 625 00:48:43.400 --> 00:48:45.639 it's not a problem if you have the right approach. And we had 626 00:48:45.639 --> 00:48:49.679 a great question from one of our studio audience members who who mused, if 627 00:48:49.679 --> 00:48:52.719 we're never going to be able to see the raw data again, the question 628 00:48:52.760 --> 00:48:58.599 of analyzing behavioral anomalies or lineage and trustworthiness comes up pretty quickly, alike intrusion 629 00:48:58.599 --> 00:49:02.280 detection or regulatory compliance, etc. And the break. You made a really 630 00:49:02.320 --> 00:49:06.519 good point, Chris, about how that the whether it's compliant or not, 631 00:49:06.559 --> 00:49:08.599 it's never going to be in just some field in a database. It's always 632 00:49:08.599 --> 00:49:14.119 an assessment based on multiple pieces of information. And to your point, at 633 00:49:14.119 --> 00:49:17.199 the scale of information flow. These days, it's going to be a complex 634 00:49:19.079 --> 00:49:22.519 layering of decisions based upon the data, right, go ahead without explanation. 635 00:49:23.360 --> 00:49:27.519 Yeah, So if you, for example, you want to do intrusion detection, 636 00:49:28.039 --> 00:49:30.280 it's not like there's a data field somewhere in a database that says intrusion 637 00:49:30.280 --> 00:49:34.639 detection, yes or no. I mean you have to look at like a 638 00:49:34.719 --> 00:49:38.559 whole bunch of raw data and there might be a couple layers of analysis. 639 00:49:38.599 --> 00:49:43.760 You know, there's the analysis, then there's the analyzing. The analysis that 640 00:49:43.880 --> 00:49:47.239 says that is an intrusion and it characterizes that. So that's really what you're 641 00:49:47.239 --> 00:49:52.599 trying to get at. Is this meta meta information, you know, is 642 00:49:52.639 --> 00:49:55.519 it an intrusion detection? Is this a risk? Is this trustworthy? You 643 00:49:55.559 --> 00:49:59.480 know, those are higher level things like you're building a credit score, you 644 00:49:59.480 --> 00:50:04.840 know for trustworthiness or business risk analysis. Well, that credit score might have 645 00:50:05.239 --> 00:50:09.000 a thousand different variables that go into it, and each of those variables might 646 00:50:09.079 --> 00:50:12.960 have you know, a million data points or about of the data points that 647 00:50:13.000 --> 00:50:15.320 go and do it. So that's that's really more and more what you're seeing 648 00:50:15.440 --> 00:50:20.639 is you don't the raw data is not the thing that has business value. 649 00:50:20.719 --> 00:50:24.119 It's the analyzed result. Yeah, no, that that's exactly right. And 650 00:50:24.280 --> 00:50:29.400 you also made a really good point about just this new building block and the 651 00:50:29.519 --> 00:50:35.159 NVMe solid state drives and the interface and how it's it's literally the opposite of 652 00:50:35.239 --> 00:50:37.800 spinning disk. Can you explain that through audience, because that's a very compelling 653 00:50:37.880 --> 00:50:42.400 story. Go ahead. I mean something we've we've asserted at ocient for the 654 00:50:42.519 --> 00:50:45.719 last ten years since we've been hearing about a real solid state drive. The 655 00:50:46.079 --> 00:50:52.719 parallel interface NVMe is the name of that non volatile memory express which is now, 656 00:50:52.760 --> 00:50:55.840 like I said, it's in your phone, it's in your laptop. 657 00:50:55.920 --> 00:51:01.480 It's it went from nowhere just a few years ago too. It's in everyone's 658 00:51:01.519 --> 00:51:07.159 phone now. And that's also different because normally that kind of new technology, 659 00:51:07.159 --> 00:51:10.639 where it's new physics, new materials, starts and supercomputers and works its way 660 00:51:10.679 --> 00:51:15.880 down to consumer products, and here it just started in your phone, which 661 00:51:15.880 --> 00:51:20.440 is pretty amazing. And one of the things we think it's profound about it 662 00:51:20.480 --> 00:51:24.239 is we we assert that it's really the first new building block in computing. 663 00:51:24.440 --> 00:51:29.519 And as we were talking about earlier hardware, at the end of the day 664 00:51:29.719 --> 00:51:34.079 is what dictates price performance and what happens and all you can do as a 665 00:51:34.119 --> 00:51:38.559 software architect and developer is make your software go as fast as the hard work 666 00:51:38.599 --> 00:51:43.480 and go you can go no faster than that. And the way that NVMME 667 00:51:43.639 --> 00:51:49.280 solid state works is basically, as you're just saying, Eric, exactly the 668 00:51:49.320 --> 00:51:52.559 opposite on how a spinning disc work. A spinning disc is like a record 669 00:51:52.559 --> 00:51:58.280 player, and it has a ReadWrite head that sits in exactly one spot, 670 00:51:58.360 --> 00:52:01.960 exactly one time, and it sucks or spits bits onto that you onto or 671 00:52:02.039 --> 00:52:06.880 from that media. But it is in one place at one time, So 672 00:52:06.960 --> 00:52:10.679 physically what it wants to see is a serial stream of things to do, 673 00:52:10.719 --> 00:52:15.880 a serial sequence one after another, because it's only in one place. Databases, 674 00:52:16.000 --> 00:52:19.679 when you're doing a count or a sort, or you know the things 675 00:52:19.679 --> 00:52:23.159 that databases do on the inside shuffle, you know, they actually want you 676 00:52:23.239 --> 00:52:30.320 to do a whole bunch of random parallel operations. But what the whole generation 677 00:52:30.440 --> 00:52:34.840 of multiple generations of databases data warehouses had to do was to figure out how 678 00:52:34.880 --> 00:52:38.800 to take what you want to be multiple parallel things and have them happen one 679 00:52:38.840 --> 00:52:42.360 thing at a time on a spinning disk. Because anything in volume was on 680 00:52:42.400 --> 00:52:46.519 a spinning disk. Solid state is exactly the opposite. It doesn't want one 681 00:52:46.519 --> 00:52:51.239 thing to do at a time. The prior versions wanted two hundred and fifty 682 00:52:51.239 --> 00:52:57.159 six parallel tasks. Now they want five hundred and twelve headed to twenty four 683 00:52:57.239 --> 00:53:01.360 and so on parallel tasks. Her drive, you know, thousands and thousands 684 00:53:01.400 --> 00:53:07.039 of times a second. It is exactly the opposite. So what that means 685 00:53:07.639 --> 00:53:13.159 in terms of your database, your software is what I'm describing is the interface 686 00:53:13.199 --> 00:53:19.239 between like a primitive database operation like a shuffle or sort, and the physical 687 00:53:19.320 --> 00:53:22.840 drive. That's called your IO layer. Okay, and if your IO layer 688 00:53:22.920 --> 00:53:27.679 has to treat your storage media exactly the opposite, what it means is you 689 00:53:27.760 --> 00:53:31.519 got to rewrite your whole IO layers. And that's forty percent of a database. 690 00:53:31.760 --> 00:53:36.559 So if you really yeah, you could take an old architecture and set 691 00:53:36.599 --> 00:53:38.920 it on spinning disk, or sorry, take it off spinning disk, set 692 00:53:38.920 --> 00:53:43.400 it on solid state. It'll run ten times faster, but it should run 693 00:53:43.440 --> 00:53:46.679 ten thousand times faster. You're just leaving orders of magnitude. Now, you 694 00:53:46.719 --> 00:53:51.599 got to write your IO layer. But you'll find if you write your we 695 00:53:51.679 --> 00:53:54.239 write your IO layer, which we did anocent next thing, you know, 696 00:53:54.280 --> 00:53:58.320 you're gonna have to rewrite your memory allocators of Linux because they're not designed to 697 00:53:58.320 --> 00:54:00.760 flow this much data. Next thing, you know, you're mucking down around 698 00:54:00.800 --> 00:54:07.760 with the actual assembler driver for that, you know, that mBMI solid state 699 00:54:07.840 --> 00:54:10.719 drive. And you also have to think called v tune, which is the 700 00:54:10.760 --> 00:54:14.960 thing that you use to tune the Intel CPU just right with the you know, 701 00:54:14.960 --> 00:54:17.159 the cashing layers. Like you're down there, like there's no there's no 702 00:54:17.199 --> 00:54:21.760 further down than where you are in the computing stack, and then you have 703 00:54:21.800 --> 00:54:24.199 to go up from there. So the next thing up from the IO layer 704 00:54:24.880 --> 00:54:28.639 is you know, we call it the virtual machine. You know, it's 705 00:54:28.679 --> 00:54:35.960 the thing that once your parser kind of parses out your your query, you 706 00:54:36.000 --> 00:54:37.239 know, the parser will give you, you know, the parst query. 707 00:54:37.280 --> 00:54:42.280 Down to this virtual machine is optimizer and it's going to have to figure out 708 00:54:42.320 --> 00:54:44.639 how to drop it down to the aisle layer. So that's the thing that 709 00:54:44.679 --> 00:54:47.159 figures out Every query can be done in infinite number of ways, So think 710 00:54:47.199 --> 00:54:51.159 about all the different ways and maybe think about it for half a second and 711 00:54:51.159 --> 00:54:52.960 then decide, all right, this is how we're doing it, and down 712 00:54:52.000 --> 00:54:55.719 you go to the aisle layer. So you know, that's another forty percent 713 00:54:55.719 --> 00:55:00.760 of your database. So you know, like parsers are not exactly rocket science 714 00:55:00.840 --> 00:55:04.119 right now. You can go find a nice open source parser, but you 715 00:55:04.239 --> 00:55:07.280 end up having to rewrite your whole database stack, you know, down to 716 00:55:07.320 --> 00:55:09.960 the memory allocators if you really want this thing to go. And that's what 717 00:55:10.000 --> 00:55:14.239 we had to do with It took us, because it always takes at least 718 00:55:14.280 --> 00:55:15.639 five years. It took us five years. That's why you were saying version 719 00:55:15.719 --> 00:55:20.960 nineteen was our first production version, because it really was nineteen versions. But 720 00:55:20.960 --> 00:55:22.960 that's what you have to do. That's amazing. And you know, I'm 721 00:55:23.000 --> 00:55:27.960 reminded of a couple of things. One I had doctor Michael Stonebreaker on this 722 00:55:28.000 --> 00:55:30.960 show and it was a long time. I was probably like ten twelve years 723 00:55:30.960 --> 00:55:36.400 ago, and at that point in time, he said eighty percent of the 724 00:55:36.440 --> 00:55:40.119 code that has written should just be thrown away. And what he was talking 725 00:55:40.159 --> 00:55:45.679 about is what you're talking about. That as the hardware layers change and as 726 00:55:45.719 --> 00:55:49.880 we get new building blocks to play around with, those old software applications were 727 00:55:49.880 --> 00:55:53.400 designed to work in much different environments. And so how you designed this thing 728 00:55:53.480 --> 00:55:57.280 to you know, to do calls and to pull and do all polling or 729 00:55:57.320 --> 00:56:00.880 whatever it is that you're doing that has to be cognizant of the new stack, 730 00:56:00.960 --> 00:56:05.519 if you will, to your point with these NBMA, because it is 731 00:56:05.519 --> 00:56:08.679 completely different because it wants lots of things in parallel. You have to read 732 00:56:08.880 --> 00:56:13.480 write stuff, and it takes a lot of time to go all the way 733 00:56:13.519 --> 00:56:16.800 down to that level. But unless you do that, you're missing huge chunks 734 00:56:16.800 --> 00:56:21.519 of potential optimization along the way, right Chris, Yeah, And you know 735 00:56:21.559 --> 00:56:25.800 I use the word assembler recently in that profit answer how often does that happen? 736 00:56:27.320 --> 00:56:30.239 In twenty twenty three? And you know that's just a little bit of 737 00:56:30.280 --> 00:56:34.360 code down there, you know, the Linux layer, but the database itself 738 00:56:34.360 --> 00:56:37.000 we had to rewrite and C plus plus and it's not like we don't know 739 00:56:37.079 --> 00:56:42.239 that that takes longer. It does take longer. But let me tell you. 740 00:56:42.280 --> 00:56:46.119 If you have like two seconds of garbage collection and you're running a query 741 00:56:46.239 --> 00:56:50.719 where I don't know, you're gonna look at fifty trillion things and you want 742 00:56:50.760 --> 00:56:52.960 an answer in ten seconds, that means you're looking at five trillion things a 743 00:56:52.960 --> 00:56:57.719 second, your garbage collect for two seconds, and you got ten trillion things 744 00:56:57.760 --> 00:57:01.360 to go put somewhere that's not going to work. So you know, next 745 00:57:01.360 --> 00:57:04.360 thing, you know, you're you know, you're down there and see plus 746 00:57:04.360 --> 00:57:07.039 plus and you're doing all this kind of crazy stuff where you're making everything stateless 747 00:57:07.039 --> 00:57:10.480 so you can just like parallelize the forever. You know, in our in 748 00:57:10.519 --> 00:57:15.440 our system it is. You know, if you've got a five hundred task 749 00:57:15.679 --> 00:57:17.360 per drive, I mean these are little solid state drives. You can put 750 00:57:17.360 --> 00:57:20.440 you know, twenty of them in a U. You can put twenty years 751 00:57:20.480 --> 00:57:22.199 on a cluster. Next thing, you know, to get a million parallel 752 00:57:22.239 --> 00:57:28.480 tasks at every single layer of the stack, and things are flying. I 753 00:57:28.519 --> 00:57:30.320 mean you're doing five trillion things a second, you know, so every you 754 00:57:30.320 --> 00:57:34.360 know, it's flowing up and down that stack. Five trillion things a second, 755 00:57:35.840 --> 00:57:37.480 you know, million parallel tasks and each task there's a lot of things 756 00:57:37.559 --> 00:57:42.280 each second, you know. So it's just nuts. And so building something 757 00:57:42.320 --> 00:57:45.480 like that, it just takes forever. There's just no way around it. 758 00:57:45.920 --> 00:57:51.519 Yeah, well, because you built two hyper scale. That's another interesting topic 759 00:57:51.559 --> 00:57:53.920 in and of itself. Right, if you're building for a particular end state, 760 00:57:54.000 --> 00:57:58.360 you're saying, I want this thing to good on this track, But 761 00:57:58.440 --> 00:58:00.360 no, you're saying I want this car to be able to go faster and 762 00:58:00.400 --> 00:58:04.519 faster on whatever track happens to come around, right, Isn't that the kind 763 00:58:04.519 --> 00:58:07.599 of difference in mindset. Yeah, we have a customer right now who we've 764 00:58:07.599 --> 00:58:09.800 done it on paper, but this is the first time we're doing it. 765 00:58:09.800 --> 00:58:14.760 They want to demo with one hundred trillion row table, So we're building one 766 00:58:14.800 --> 00:58:16.920 of those right now. Yeah, I mean you design it. I mean, 767 00:58:17.159 --> 00:58:21.360 you know, we designed we actually one of the original customers that were 768 00:58:21.360 --> 00:58:23.480 still working with because it takes a long time to build something like this. 769 00:58:23.599 --> 00:58:30.280 They wanted something we're literally it was about a quadrillion quadrillion rows and a table, 770 00:58:30.960 --> 00:58:35.480 and they wanted to run queries that would hit most of that and give 771 00:58:35.519 --> 00:58:38.440 answers in like ten seconds. And yeah, I mean you're going to do 772 00:58:38.440 --> 00:58:43.920 one hundred tillion things a second to answer that. So we've just been on 773 00:58:43.960 --> 00:58:47.320 paper, but designing on paper and actually having it run you know, in 774 00:58:47.400 --> 00:58:52.599 the physical universe or two different things. So we're kind of hitting in terms 775 00:58:52.639 --> 00:58:55.320 of actual scale, like that hundred trillion scale right now. And we've got 776 00:58:55.320 --> 00:58:59.719 a number of customers that are like an exibite scale, you know, which 777 00:58:59.760 --> 00:59:04.039 will you get into the Quadralions. Yeah well, Folks podcast bonus segment is 778 00:59:04.079 --> 00:59:07.679 up next. Look these guys up online Ocean's Chris Gladman, Your Listenings and 779 00:59:07.280 --> 00:59:13.840 Hillistinea KCAA Lomolinda at one oh six point five FMK two ninety three CF Marino 780 00:59:13.920 --> 00:59:20.679 Valley, NBC News Radio, I'm Chris Garagio. Officials in Hawaii put the 781 00:59:20.679 --> 00:59:23.840 confirmed death toll at ninety three from the Lahinah wildfire, and Maui Police Chief 782 00:59:23.920 --> 00:59:29.159 John Pelletier said search crews were using dogs to look for remains in the wreckage 783 00:59:29.159 --> 00:59:31.599 and that only three percent of the Destroit area has been covered so far. 784 00:59:31.960 --> 00:59:37.800 The Lahinah wildfires being called the deadliest US wildfire and over a century. A 785 00:59:37.880 --> 00:59:40.800 plane that crashed during an air show near Detroit was a Soviet era fighter jet. 786 00:59:40.880 --> 00:59:45.800 The privately owned Mid twenty three was participating in the thunder Over Michigan air 787 00:59:45.840 --> 00:59:49.480 Show, one of the largest in the nation. Video posted on social media 788 00:59:49.480 --> 00:59:53.079 shows two people apparently ejecting from the plane before it crashed into an apartment complex 789 00:59:53.119 --> 00:59:57.960 parking lot forty miles east of Detroit. The two pilots were transferred to a 790 00:59:58.000 --> 01:00:01.239 nearby medical facility. They're in stay condition. No other injuries were reported. 791 01:00:01.519 --> 01:00:07.360 Former Vice President Mike Pence says his former boss got bad advice from quote crackpot 792 01:00:07.440 --> 01:00:08.480 lawyers about the vice president's role in