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Jack Norris - Hadoop on the Hudson - theCUBE


 

>>Live from New York city. It's cute. here's your host? Jeff Frick. >>Hi, Jeff Frick here with the Q we're on the ground at the USS Intrepid at the Hadoop on the Hudson party put on by Matt BARR. It's uh, I think it's the party of the night tonight here in big data week, New York city with strata cough, a dupe world, big data NYC. So Jack a great >>Venue. Yeah, it's excellent. Here. >>The place is filled. I'm just struck by the technology. There's a Gemini capsule over there, about 50 years old. It's about the size of a Volkswagen, I think would be much bigger. And to think that those guys went up into space with probably less technology than is on your four year old flip phone. Amazing. Yeah. >>Not, not much data at all. No. If >>You look at it, just kind of get that bounce on the gravity thing, which I never quite understood. So talk about you guys had some big news today. Once you give us a rundown on some of the announcements, >>We had two big announcements. One was incorporating the map RDB and our community edition that came out. We also reported results from our customers where the majority of customers reported less than a 12 month payback, uh, 65% of five X or greater return and 40%, 10 X or greater. And that included a subset of those customers that had experienced with other distributions. So kind of a Testament to when you get serious about Hadoop, you get serious with Mapbox >>And when they're getting those return on investments, we're always trying to explore where's the big, the big ROI, because it's really in value that's released for the customer. It's not necessarily because it's a cheaper way to do it, >>Right? So, so there are some costs that 63% was cost reduction that was driving it about 41% were top-line revenue projects. And about 23% were related to risk reduction and risk mitigation. And if you add those up, it's greater than a hundred percent because of many customers that are doing multiple applications. >>Great. So you've been coming to Hadoop world for longer than you would admit to me before we came on camera and, and the baseball playoffs are going on right now. I mean, we like to talk in sports analogy. So kind of where are we in, in kind of what inning are we in this adoption of big data and the duke specifically >>Early, early innings. Um, but, uh, what we've seen is the bases are loaded and we're up >>And it's it. And it seems to be we're way past now the POC stage. Now we're really getting in there for that. >>And the, the customer announcement, we did kind of shows how people are hitting it out of the park with Hadoop. And a lot of that is by impacting the operations, impacting the business as it happens. And that's coupling analytics plus this higher arrival rate data from a variety of sources and making adjustments so that you can impact revenue as businesses happening. You can mitigate risk as it's happening. It's not just reporting, looking back >>Function. Right, right. It's being able to react in real time, which is defined by, in time to do something about it. Right. Exactly. All right. Well, thanks for hosting a great party, Jack Norris. Here we are on the ground, uh, at the USS Intrepid at the Hadoop on the Hudson. Uh, uh, if you take a nice picture, tweet that in. I think they got some prizes. Hadoop Hudson is a hashtag Jeff Frick on the ground. You're watching the cube. Thanks. Big ship.

Published Date : Oct 22 2014

SUMMARY :

It's cute. It's uh, I think it's the party of the night tonight here And to think that those guys went up into space with probably less technology than is on your four Not, not much data at all. You look at it, just kind of get that bounce on the gravity thing, which I never quite understood. So kind of a Testament to when you get serious about Hadoop, And when they're getting those return on investments, we're always trying to explore where's the big, And if you add those up, it's greater than a hundred percent because of many customers that are doing multiple applications. So kind of where are we in, Um, but, uh, what we've seen is the bases are loaded and we're up And it seems to be we're way past now the POC stage. And a lot of that is by impacting the operations, It's being able to react in real time, which is defined by,

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Breaking Analysis: ChatGPT Won't Give OpenAI First Mover Advantage


 

>> From theCUBE Studios in Palo Alto in Boston, bringing you data-driven insights from theCUBE and ETR. This is Breaking Analysis with Dave Vellante. >> OpenAI The company, and ChatGPT have taken the world by storm. Microsoft reportedly is investing an additional 10 billion dollars into the company. But in our view, while the hype around ChatGPT is justified, we don't believe OpenAI will lock up the market with its first mover advantage. Rather, we believe that success in this market will be directly proportional to the quality and quantity of data that a technology company has at its disposal, and the compute power that it could deploy to run its system. Hello and welcome to this week's Wikibon CUBE insights, powered by ETR. In this Breaking Analysis, we unpack the excitement around ChatGPT, and debate the premise that the company's early entry into the space may not confer winner take all advantage to OpenAI. And to do so, we welcome CUBE collaborator, alum, Sarbjeet Johal, (chuckles) and John Furrier, co-host of the Cube. Great to see you Sarbjeet, John. Really appreciate you guys coming to the program. >> Great to be on. >> Okay, so what is ChatGPT? Well, actually we asked ChatGPT, what is ChatGPT? So here's what it said. ChatGPT is a state-of-the-art language model developed by OpenAI that can generate human-like text. It could be fine tuned for a variety of language tasks, such as conversation, summarization, and language translation. So I asked it, give it to me in 50 words or less. How did it do? Anything to add? >> Yeah, think it did good. It's large language model, like previous models, but it started applying the transformers sort of mechanism to focus on what prompt you have given it to itself. And then also the what answer it gave you in the first, sort of, one sentence or two sentences, and then introspect on itself, like what I have already said to you. And so just work on that. So it it's self sort of focus if you will. It does, the transformers help the large language models to do that. >> So to your point, it's a large language model, and GPT stands for generative pre-trained transformer. >> And if you put the definition back up there again, if you put it back up on the screen, let's see it back up. Okay, it actually missed the large, word large. So one of the problems with ChatGPT, it's not always accurate. It's actually a large language model, and it says state of the art language model. And if you look at Google, Google has dominated AI for many times and they're well known as being the best at this. And apparently Google has their own large language model, LLM, in play and have been holding it back to release because of backlash on the accuracy. Like just in that example you showed is a great point. They got almost right, but they missed the key word. >> You know what's funny about that John, is I had previously asked it in my prompt to give me it in less than a hundred words, and it was too long, I said I was too long for Breaking Analysis, and there it went into the fact that it's a large language model. So it largely, it gave me a really different answer the, for both times. So, but it's still pretty amazing for those of you who haven't played with it yet. And one of the best examples that I saw was Ben Charrington from This Week In ML AI podcast. And I stumbled on this thanks to Brian Gracely, who was listening to one of his Cloudcasts. Basically what Ben did is he took, he prompted ChatGPT to interview ChatGPT, and he simply gave the system the prompts, and then he ran the questions and answers into this avatar builder and sped it up 2X so it didn't sound like a machine. And voila, it was amazing. So John is ChatGPT going to take over as a cube host? >> Well, I was thinking, we get the questions in advance sometimes from PR people. We should actually just plug it in ChatGPT, add it to our notes, and saying, "Is this good enough for you? Let's ask the real question." So I think, you know, I think there's a lot of heavy lifting that gets done. I think the ChatGPT is a phenomenal revolution. I think it highlights the use case. Like that example we showed earlier. It gets most of it right. So it's directionally correct and it feels like it's an answer, but it's not a hundred percent accurate. And I think that's where people are seeing value in it. Writing marketing, copy, brainstorming, guest list, gift list for somebody. Write me some lyrics to a song. Give me a thesis about healthcare policy in the United States. It'll do a bang up job, and then you got to go in and you can massage it. So we're going to do three quarters of the work. That's why plagiarism and schools are kind of freaking out. And that's why Microsoft put 10 billion in, because why wouldn't this be a feature of Word, or the OS to help it do stuff on behalf of the user. So linguistically it's a beautiful thing. You can input a string and get a good answer. It's not a search result. >> And we're going to get your take on on Microsoft and, but it kind of levels the playing- but ChatGPT writes better than I do, Sarbjeet, and I know you have some good examples too. You mentioned the Reed Hastings example. >> Yeah, I was listening to Reed Hastings fireside chat with ChatGPT, and the answers were coming as sort of voice, in the voice format. And it was amazing what, he was having very sort of philosophy kind of talk with the ChatGPT, the longer sentences, like he was going on, like, just like we are talking, he was talking for like almost two minutes and then ChatGPT was answering. It was not one sentence question, and then a lot of answers from ChatGPT and yeah, you're right. I, this is our ability. I've been thinking deep about this since yesterday, we talked about, like, we want to do this segment. The data is fed into the data model. It can be the current data as well, but I think that, like, models like ChatGPT, other companies will have those too. They can, they're democratizing the intelligence, but they're not creating intelligence yet, definitely yet I can say that. They will give you all the finite answers. Like, okay, how do you do this for loop in Java, versus, you know, C sharp, and as a programmer you can do that, in, but they can't tell you that, how to write a new algorithm or write a new search algorithm for you. They cannot create a secretive code for you to- >> Not yet. >> Have competitive advantage. >> Not yet, not yet. >> but you- >> Can Google do that today? >> No one really can. The reasoning side of the data is, we talked about at our Supercloud event, with Zhamak Dehghani who's was CEO of, now of Nextdata. This next wave of data intelligence is going to come from entrepreneurs that are probably cross discipline, computer science and some other discipline. But they're going to be new things, for example, data, metadata, and data. It's hard to do reasoning like a human being, so that needs more data to train itself. So I think the first gen of this training module for the large language model they have is a corpus of text. Lot of that's why blog posts are, but the facts are wrong and sometimes out of context, because that contextual reasoning takes time, it takes intelligence. So machines need to become intelligent, and so therefore they need to be trained. So you're going to start to see, I think, a lot of acceleration on training the data sets. And again, it's only as good as the data you can get. And again, proprietary data sets will be a huge winner. Anyone who's got a large corpus of content, proprietary content like theCUBE or SiliconANGLE as a publisher will benefit from this. Large FinTech companies, anyone with large proprietary data will probably be a big winner on this generative AI wave, because it just, it will eat that up, and turn that back into something better. So I think there's going to be a lot of interesting things to look at here. And certainly productivity's going to be off the charts for vanilla and the internet is going to get swarmed with vanilla content. So if you're in the content business, and you're an original content producer of any kind, you're going to be not vanilla, so you're going to be better. So I think there's so much at play Dave (indistinct). >> I think the playing field has been risen, so we- >> Risen and leveled? >> Yeah, and leveled to certain extent. So it's now like that few people as consumers, as consumers of AI, we will have a advantage and others cannot have that advantage. So it will be democratized. That's, I'm sure about that. But if you take the example of calculator, when the calculator came in, and a lot of people are, "Oh, people can't do math anymore because calculator is there." right? So it's a similar sort of moment, just like a calculator for the next level. But, again- >> I see it more like open source, Sarbjeet, because like if you think about what ChatGPT's doing, you do a query and it comes from somewhere the value of a post from ChatGPT is just a reuse of AI. The original content accent will be come from a human. So if I lay out a paragraph from ChatGPT, did some heavy lifting on some facts, I check the facts, save me about maybe- >> Yeah, it's productive. >> An hour writing, and then I write a killer two, three sentences of, like, sharp original thinking or critical analysis. I then took that body of work, open source content, and then laid something on top of it. >> And Sarbjeet's example is a good one, because like if the calculator kids don't do math as well anymore, the slide rule, remember we had slide rules as kids, remember we first started using Waze, you know, we were this minority and you had an advantage over other drivers. Now Waze is like, you know, social traffic, you know, navigation, everybody had, you know- >> All the back roads are crowded. >> They're car crowded. (group laughs) Exactly. All right, let's, let's move on. What about this notion that futurist Ray Amara put forth and really Amara's Law that we're showing here, it's, the law is we, you know, "We tend to overestimate the effect of technology in the short run and underestimate it in the long run." Is that the case, do you think, with ChatGPT? What do you think Sarbjeet? >> I think that's true actually. There's a lot of, >> We don't debate this. >> There's a lot of awe, like when people see the results from ChatGPT, they say what, what the heck? Like, it can do this? But then if you use it more and more and more, and I ask the set of similar question, not the same question, and it gives you like same answer. It's like reading from the same bucket of text in, the interior read (indistinct) where the ChatGPT, you will see that in some couple of segments. It's very, it sounds so boring that the ChatGPT is coming out the same two sentences every time. So it is kind of good, but it's not as good as people think it is right now. But we will have, go through this, you know, hype sort of cycle and get realistic with it. And then in the long term, I think it's a great thing in the short term, it's not something which will (indistinct) >> What's your counter point? You're saying it's not. >> I, no I think the question was, it's hyped up in the short term and not it's underestimated long term. That's what I think what he said, quote. >> Yes, yeah. That's what he said. >> Okay, I think that's wrong with this, because this is a unique, ChatGPT is a unique kind of impact and it's very generational. People have been comparing it, I have been comparing to the internet, like the web, web browser Mosaic and Netscape, right, Navigator. I mean, I clearly still remember the days seeing Navigator for the first time, wow. And there weren't not many sites you could go to, everyone typed in, you know, cars.com, you know. >> That (indistinct) wasn't that overestimated, the overhyped at the beginning and underestimated. >> No, it was, it was underestimated long run, people thought. >> But that Amara's law. >> That's what is. >> No, they said overestimated? >> Overestimated near term underestimated- overhyped near term, underestimated long term. I got, right I mean? >> Well, I, yeah okay, so I would then agree, okay then- >> We were off the charts about the internet in the early days, and it actually exceeded our expectations. >> Well there were people who were, like, poo-pooing it early on. So when the browser came out, people were like, "Oh, the web's a toy for kids." I mean, in 1995 the web was a joke, right? So '96, you had online populations growing, so you had structural changes going on around the browser, internet population. And then that replaced other things, direct mail, other business activities that were once analog then went to the web, kind of read only as you, as we always talk about. So I think that's a moment where the hype long term, the smart money, and the smart industry experts all get the long term. And in this case, there's more poo-pooing in the short term. "Ah, it's not a big deal, it's just AI." I've heard many people poo-pooing ChatGPT, and a lot of smart people saying, "No this is next gen, this is different and it's only going to get better." So I think people are estimating a big long game on this one. >> So you're saying it's bifurcated. There's those who say- >> Yes. >> Okay, all right, let's get to the heart of the premise, and possibly the debate for today's episode. Will OpenAI's early entry into the market confer sustainable competitive advantage for the company. And if you look at the history of tech, the technology industry, it's kind of littered with first mover failures. Altair, IBM, Tandy, Commodore, they and Apple even, they were really early in the PC game. They took a backseat to Dell who came in the scene years later with a better business model. Netscape, you were just talking about, was all the rage in Silicon Valley, with the first browser, drove up all the housing prices out here. AltaVista was the first search engine to really, you know, index full text. >> Owned by Dell, I mean DEC. >> Owned by Digital. >> Yeah, Digital Equipment >> Compaq bought it. And of course as an aside, Digital, they wanted to showcase their hardware, right? Their super computer stuff. And then so Friendster and MySpace, they came before Facebook. The iPhone certainly wasn't the first mobile device. So lots of failed examples, but there are some recent successes like AWS and cloud. >> You could say smartphone. So I mean. >> Well I know, and you can, we can parse this so we'll debate it. Now Twitter, you could argue, had first mover advantage. You kind of gave me that one John. Bitcoin and crypto clearly had first mover advantage, and sustaining that. Guys, will OpenAI make it to the list on the right with ChatGPT, what do you think? >> I think categorically as a company, it probably won't, but as a category, I think what they're doing will, so OpenAI as a company, they get funding, there's power dynamics involved. Microsoft put a billion dollars in early on, then they just pony it up. Now they're reporting 10 billion more. So, like, if the browsers, Microsoft had competitive advantage over Netscape, and used monopoly power, and convicted by the Department of Justice for killing Netscape with their monopoly, Netscape should have had won that battle, but Microsoft killed it. In this case, Microsoft's not killing it, they're buying into it. So I think the embrace extend Microsoft power here makes OpenAI vulnerable for that one vendor solution. So the AI as a company might not make the list, but the category of what this is, large language model AI, is probably will be on the right hand side. >> Okay, we're going to come back to the government intervention and maybe do some comparisons, but what are your thoughts on this premise here? That, it will basically set- put forth the premise that it, that ChatGPT, its early entry into the market will not confer competitive advantage to >> For OpenAI. >> To Open- Yeah, do you agree with that? >> I agree with that actually. It, because Google has been at it, and they have been holding back, as John said because of the scrutiny from the Fed, right, so- >> And privacy too. >> And the privacy and the accuracy as well. But I think Sam Altman and the company on those guys, right? They have put this in a hasty way out there, you know, because it makes mistakes, and there are a lot of questions around the, sort of, where the content is coming from. You saw that as your example, it just stole the content, and without your permission, you know? >> Yeah. So as quick this aside- >> And it codes on people's behalf and the, those codes are wrong. So there's a lot of, sort of, false information it's putting out there. So it's a very vulnerable thing to do what Sam Altman- >> So even though it'll get better, others will compete. >> So look, just side note, a term which Reid Hoffman used a little bit. Like he said, it's experimental launch, like, you know, it's- >> It's pretty damn good. >> It is clever because according to Sam- >> It's more than clever. It's good. >> It's awesome, if you haven't used it. I mean you write- you read what it writes and you go, "This thing writes so well, it writes so much better than you." >> The human emotion drives that too. I think that's a big thing. But- >> I Want to add one more- >> Make your last point. >> Last one. Okay. So, but he's still holding back. He's conducting quite a few interviews. If you want to get the gist of it, there's an interview with StrictlyVC interview from yesterday with Sam Altman. Listen to that one it's an eye opening what they want- where they want to take it. But my last one I want to make it on this point is that Satya Nadella yesterday did an interview with Wall Street Journal. I think he was doing- >> You were not impressed. >> I was not impressed because he was pushing it too much. So Sam Altman's holding back so there's less backlash. >> Got 10 billion reasons to push. >> I think he's almost- >> Microsoft just laid off 10000 people. Hey ChatGPT, find me a job. You know like. (group laughs) >> He's overselling it to an extent that I think it will backfire on Microsoft. And he's over promising a lot of stuff right now, I think. I don't know why he's very jittery about all these things. And he did the same thing during Ignite as well. So he said, "Oh, this AI will write code for you and this and that." Like you called him out- >> The hyperbole- >> During your- >> from Satya Nadella, he's got a lot of hyperbole. (group talks over each other) >> All right, Let's, go ahead. >> Well, can I weigh in on the whole- >> Yeah, sure. >> Microsoft thing on whether OpenAI, here's the take on this. I think it's more like the browser moment to me, because I could relate to that experience with ChatG, personally, emotionally, when I saw that, and I remember vividly- >> You mean that aha moment (indistinct). >> Like this is obviously the future. Anything else in the old world is dead, website's going to be everywhere. It was just instant dot connection for me. And a lot of other smart people who saw this. Lot of people by the way, didn't see it. Someone said the web's a toy. At the company I was worked for at the time, Hewlett Packard, they like, they could have been in, they had invented HTML, and so like all this stuff was, like, they just passed, the web was just being passed over. But at that time, the browser got better, more websites came on board. So the structural advantage there was online web usage was growing, online user population. So that was growing exponentially with the rise of the Netscape browser. So OpenAI could stay on the right side of your list as durable, if they leverage the category that they're creating, can get the scale. And if they can get the scale, just like Twitter, that failed so many times that they still hung around. So it was a product that was always successful, right? So I mean, it should have- >> You're right, it was terrible, we kept coming back. >> The fail whale, but it still grew. So OpenAI has that moment. They could do it if Microsoft doesn't meddle too much with too much power as a vendor. They could be the Netscape Navigator, without the anti-competitive behavior of somebody else. So to me, they have the pole position. So they have an opportunity. So if not, if they don't execute, then there's opportunity. There's not a lot of barriers to entry, vis-a-vis say the CapEx of say a cloud company like AWS. You can't replicate that, Many have tried, but I think you can replicate OpenAI. >> And we're going to talk about that. Okay, so real quick, I want to bring in some ETR data. This isn't an ETR heavy segment, only because this so new, you know, they haven't coverage yet, but they do cover AI. So basically what we're seeing here is a slide on the vertical axis's net score, which is a measure of spending momentum, and in the horizontal axis's is presence in the dataset. Think of it as, like, market presence. And in the insert right there, you can see how the dots are plotted, the two columns. And so, but the key point here that we want to make, there's a bunch of companies on the left, is he like, you know, DataRobot and C3 AI and some others, but the big whales, Google, AWS, Microsoft, are really dominant in this market. So that's really the key takeaway that, can we- >> I notice IBM is way low. >> Yeah, IBM's low, and actually bring that back up and you, but then you see Oracle who actually is injecting. So I guess that's the other point is, you're not necessarily going to go buy AI, and you know, build your own AI, you're going to, it's going to be there and, it, Salesforce is going to embed it into its platform, the SaaS companies, and you're going to purchase AI. You're not necessarily going to build it. But some companies obviously are. >> I mean to quote IBM's general manager Rob Thomas, "You can't have AI with IA." information architecture and David Flynn- >> You can't Have AI without IA >> without, you can't have AI without IA. You can't have, if you have an Information Architecture, you then can power AI. Yesterday David Flynn, with Hammersmith, was on our Supercloud. He was pointing out that the relationship of storage, where you store things, also impacts the data and stressablity, and Zhamak from Nextdata, she was pointing out that same thing. So the data problem factors into all this too, Dave. >> So you got the big cloud and internet giants, they're all poised to go after this opportunity. Microsoft is investing up to 10 billion. Google's code red, which was, you know, the headline in the New York Times. Of course Apple is there and several alternatives in the market today. Guys like Chinchilla, Bloom, and there's a company Jasper and several others, and then Lena Khan looms large and the government's around the world, EU, US, China, all taking notice before the market really is coalesced around a single player. You know, John, you mentioned Netscape, they kind of really, the US government was way late to that game. It was kind of game over. And Netscape, I remember Barksdale was like, "Eh, we're going to be selling software in the enterprise anyway." and then, pshew, the company just dissipated. So, but it looks like the US government, especially with Lena Khan, they're changing the definition of antitrust and what the cause is to go after people, and they're really much more aggressive. It's only what, two years ago that (indistinct). >> Yeah, the problem I have with the federal oversight is this, they're always like late to the game, and they're slow to catch up. So in other words, they're working on stuff that should have been solved a year and a half, two years ago around some of the social networks hiding behind some of the rules around open web back in the days, and I think- >> But they're like 15 years late to that. >> Yeah, and now they got this new thing on top of it. So like, I just worry about them getting their fingers. >> But there's only two years, you know, OpenAI. >> No, but the thing (indistinct). >> No, they're still fighting other battles. But the problem with government is that they're going to label Big Tech as like a evil thing like Pharma, it's like smoke- >> You know Lena Khan wants to kill Big Tech, there's no question. >> So I think Big Tech is getting a very seriously bad rap. And I think anything that the government does that shades darkness on tech, is politically motivated in most cases. You can almost look at everything, and my 80 20 rule is in play here. 80% of the government activity around tech is bullshit, it's politically motivated, and the 20% is probably relevant, but off the mark and not organized. >> Well market forces have always been the determining factor of success. The governments, you know, have been pretty much failed. I mean you look at IBM's antitrust, that, what did that do? The market ultimately beat them. You look at Microsoft back in the day, right? Windows 95 was peaking, the government came in. But you know, like you said, they missed the web, right, and >> so they were hanging on- >> There's nobody in government >> to Windows. >> that actually knows- >> And so, you, I think you're right. It's market forces that are going to determine this. But Sarbjeet, what do you make of Microsoft's big bet here, you weren't impressed with with Nadella. How do you think, where are they going to apply it? Is this going to be a Hail Mary for Bing, or is it going to be applied elsewhere? What do you think. >> They are saying that they will, sort of, weave this into their products, office products, productivity and also to write code as well, developer productivity as well. That's a big play for them. But coming back to your antitrust sort of comments, right? I believe the, your comment was like, oh, fed was late 10 years or 15 years earlier, but now they're two years. But things are moving very fast now as compared to they used to move. >> So two years is like 10 Years. >> Yeah, two years is like 10 years. Just want to make that point. (Dave laughs) This thing is going like wildfire. Any new tech which comes in that I think they're going against distribution channels. Lina Khan has commented time and again that the marketplace model is that she wants to have some grip on. Cloud marketplaces are a kind of monopolistic kind of way. >> I don't, I don't see this, I don't see a Chat AI. >> You told me it's not Bing, you had an interesting comment. >> No, no. First of all, this is great from Microsoft. If you're Microsoft- >> Why? >> Because Microsoft doesn't have the AI chops that Google has, right? Google is got so much core competency on how they run their search, how they run their backends, their cloud, even though they don't get a lot of cloud market share in the enterprise, they got a kick ass cloud cause they needed one. >> Totally. >> They've invented SRE. I mean Google's development and engineering chops are off the scales, right? Amazon's got some good chops, but Google's got like 10 times more chops than AWS in my opinion. Cloud's a whole different story. Microsoft gets AI, they get a playbook, they get a product they can render into, the not only Bing, productivity software, helping people write papers, PowerPoint, also don't forget the cloud AI can super help. We had this conversation on our Supercloud event, where AI's going to do a lot of the heavy lifting around understanding observability and managing service meshes, to managing microservices, to turning on and off applications, and or maybe writing code in real time. So there's a plethora of use cases for Microsoft to deploy this. combined with their R and D budgets, they can then turbocharge more research, build on it. So I think this gives them a car in the game, Google may have pole position with AI, but this puts Microsoft right in the game, and they already have a lot of stuff going on. But this just, I mean everything gets lifted up. Security, cloud, productivity suite, everything. >> What's under the hood at Google, and why aren't they talking about it? I mean they got to be freaked out about this. No? Or do they have kind of a magic bullet? >> I think they have the, they have the chops definitely. Magic bullet, I don't know where they are, as compared to the ChatGPT 3 or 4 models. Like they, but if you look at the online sort of activity and the videos put out there from Google folks, Google technology folks, that's account you should look at if you are looking there, they have put all these distinctions what ChatGPT 3 has used, they have been talking about for a while as well. So it's not like it's a secret thing that you cannot replicate. As you said earlier, like in the beginning of this segment, that anybody who has more data and the capacity to process that data, which Google has both, I think they will win this. >> Obviously living in Palo Alto where the Google founders are, and Google's headquarters next town over we have- >> We're so close to them. We have inside information on some of the thinking and that hasn't been reported by any outlet yet. And that is, is that, from what I'm hearing from my sources, is Google has it, they don't want to release it for many reasons. One is it might screw up their search monopoly, one, two, they're worried about the accuracy, 'cause Google will get sued. 'Cause a lot of people are jamming on this ChatGPT as, "Oh it does everything for me." when it's clearly not a hundred percent accurate all the time. >> So Lina Kahn is looming, and so Google's like be careful. >> Yeah so Google's just like, this is the third, could be a third rail. >> But the first thing you said is a concern. >> Well no. >> The disruptive (indistinct) >> What they will do is do a Waymo kind of thing, where they spin out a separate company. >> They're doing that. >> The discussions happening, they're going to spin out the separate company and put it over there, and saying, "This is AI, got search over there, don't touch that search, 'cause that's where all the revenue is." (chuckles) >> So, okay, so that's how they deal with the Clay Christensen dilemma. What's the business model here? I mean it's not advertising, right? Is it to charge you for a query? What, how do you make money at this? >> It's a good question, I mean my thinking is, first of all, it's cool to type stuff in and see a paper get written, or write a blog post, or gimme a marketing slogan for this or that or write some code. I think the API side of the business will be critical. And I think Howie Xu, I know you're going to reference some of his comments yesterday on Supercloud, I think this brings a whole 'nother user interface into technology consumption. I think the business model, not yet clear, but it will probably be some sort of either API and developer environment or just a straight up free consumer product, with some sort of freemium backend thing for business. >> And he was saying too, it's natural language is the way in which you're going to interact with these systems. >> I think it's APIs, it's APIs, APIs, APIs, because these people who are cooking up these models, and it takes a lot of compute power to train these and to, for inference as well. Somebody did the analysis on the how many cents a Google search costs to Google, and how many cents the ChatGPT query costs. It's, you know, 100x or something on that. You can take a look at that. >> A 100x on which side? >> You're saying two orders of magnitude more expensive for ChatGPT >> Much more, yeah. >> Than for Google. >> It's very expensive. >> So Google's got the data, they got the infrastructure and they got, you're saying they got the cost (indistinct) >> No actually it's a simple query as well, but they are trying to put together the answers, and they're going through a lot more data versus index data already, you know. >> Let me clarify, you're saying that Google's version of ChatGPT is more efficient? >> No, I'm, I'm saying Google search results. >> Ah, search results. >> What are used to today, but cheaper. >> But that, does that, is that going to confer advantage to Google's large language (indistinct)? >> It will, because there were deep science (indistinct). >> Google, I don't think Google search is doing a large language model on their search, it's keyword search. You know, what's the weather in Santa Cruz? Or how, what's the weather going to be? Or you know, how do I find this? Now they have done a smart job of doing some things with those queries, auto complete, re direct navigation. But it's, it's not entity. It's not like, "Hey, what's Dave Vellante thinking this week in Breaking Analysis?" ChatGPT might get that, because it'll get your Breaking Analysis, it'll synthesize it. There'll be some, maybe some clips. It'll be like, you know, I mean. >> Well I got to tell you, I asked ChatGPT to, like, I said, I'm going to enter a transcript of a discussion I had with Nir Zuk, the CTO of Palo Alto Networks, And I want you to write a 750 word blog. I never input the transcript. It wrote a 750 word blog. It attributed quotes to him, and it just pulled a bunch of stuff that, and said, okay, here it is. It talked about Supercloud, it defined Supercloud. >> It's made, it makes you- >> Wow, But it was a big lie. It was fraudulent, but still, blew me away. >> Again, vanilla content and non accurate content. So we are going to see a surge of misinformation on steroids, but I call it the vanilla content. Wow, that's just so boring, (indistinct). >> There's so many dangers. >> Make your point, cause we got to, almost out of time. >> Okay, so the consumption, like how do you consume this thing. As humans, we are consuming it and we are, like, getting a nicely, like, surprisingly shocked, you know, wow, that's cool. It's going to increase productivity and all that stuff, right? And on the danger side as well, the bad actors can take hold of it and create fake content and we have the fake sort of intelligence, if you go out there. So that's one thing. The second thing is, we are as humans are consuming this as language. Like we read that, we listen to it, whatever format we consume that is, but the ultimate usage of that will be when the machines can take that output from likes of ChatGPT, and do actions based on that. The robots can work, the robot can paint your house, we were talking about, right? Right now we can't do that. >> Data apps. >> So the data has to be ingested by the machines. It has to be digestible by the machines. And the machines cannot digest unorganized data right now, we will get better on the ingestion side as well. So we are getting better. >> Data, reasoning, insights, and action. >> I like that mall, paint my house. >> So, okay- >> By the way, that means drones that'll come in. Spray painting your house. >> Hey, it wasn't too long ago that robots couldn't climb stairs, as I like to point out. Okay, and of course it's no surprise the venture capitalists are lining up to eat at the trough, as I'd like to say. Let's hear, you'd referenced this earlier, John, let's hear what AI expert Howie Xu said at the Supercloud event, about what it takes to clone ChatGPT. Please, play the clip. >> So one of the VCs actually asked me the other day, right? "Hey, how much money do I need to spend, invest to get a, you know, another shot to the openAI sort of the level." You know, I did a (indistinct) >> Line up. >> A hundred million dollar is the order of magnitude that I came up with, right? You know, not a billion, not 10 million, right? So a hundred- >> Guys a hundred million dollars, that's an astoundingly low figure. What do you make of it? >> I was in an interview with, I was interviewing, I think he said hundred million or so, but in the hundreds of millions, not a billion right? >> You were trying to get him up, you were like "Hundreds of millions." >> Well I think, I- >> He's like, eh, not 10, not a billion. >> Well first of all, Howie Xu's an expert machine learning. He's at Zscaler, he's a machine learning AI guy. But he comes from VMware, he's got his technology pedigrees really off the chart. Great friend of theCUBE and kind of like a CUBE analyst for us. And he's smart. He's right. I think the barriers to entry from a dollar standpoint are lower than say the CapEx required to compete with AWS. Clearly, the CapEx spending to build all the tech for the run a cloud. >> And you don't need a huge sales force. >> And in some case apps too, it's the same thing. But I think it's not that hard. >> But am I right about that? You don't need a huge sales force either. It's, what, you know >> If the product's good, it will sell, this is a new era. The better mouse trap will win. This is the new economics in software, right? So- >> Because you look at the amount of money Lacework, and Snyk, Snowflake, Databrooks. Look at the amount of money they've raised. I mean it's like a billion dollars before they get to IPO or more. 'Cause they need promotion, they need go to market. You don't need (indistinct) >> OpenAI's been working on this for multiple five years plus it's, hasn't, wasn't born yesterday. Took a lot of years to get going. And Sam is depositioning all the success, because he's trying to manage expectations, To your point Sarbjeet, earlier. It's like, yeah, he's trying to "Whoa, whoa, settle down everybody, (Dave laughs) it's not that great." because he doesn't want to fall into that, you know, hero and then get taken down, so. >> It may take a 100 million or 150 or 200 million to train the model. But to, for the inference to, yeah to for the inference machine, It will take a lot more, I believe. >> Give it, so imagine, >> Because- >> Go ahead, sorry. >> Go ahead. But because it consumes a lot more compute cycles and it's certain level of storage and everything, right, which they already have. So I think to compute is different. To frame the model is a different cost. But to run the business is different, because I think 100 million can go into just fighting the Fed. >> Well there's a flywheel too. >> Oh that's (indistinct) >> (indistinct) >> We are running the business, right? >> It's an interesting number, but it's also kind of, like, context to it. So here, a hundred million spend it, you get there, but you got to factor in the fact that the ways companies win these days is critical mass scale, hitting a flywheel. If they can keep that flywheel of the value that they got going on and get better, you can almost imagine a marketplace where, hey, we have proprietary data, we're SiliconANGLE in theCUBE. We have proprietary content, CUBE videos, transcripts. Well wouldn't it be great if someone in a marketplace could sell a module for us, right? We buy that, Amazon's thing and things like that. So if they can get a marketplace going where you can apply to data sets that may be proprietary, you can start to see this become bigger. And so I think the key barriers to entry is going to be success. I'll give you an example, Reddit. Reddit is successful and it's hard to copy, not because of the software. >> They built the moat. >> Because you can, buy Reddit open source software and try To compete. >> They built the moat with their community. >> Their community, their scale, their user expectation. Twitter, we referenced earlier, that thing should have gone under the first two years, but there was such a great emotional product. People would tolerate the fail whale. And then, you know, well that was a whole 'nother thing. >> Then a plane landed in (John laughs) the Hudson and it was over. >> I think verticals, a lot of verticals will build applications using these models like for lawyers, for doctors, for scientists, for content creators, for- >> So you'll have many hundreds of millions of dollars investments that are going to be seeping out. If, all right, we got to wrap, if you had to put odds on it that that OpenAI is going to be the leader, maybe not a winner take all leader, but like you look at like Amazon and cloud, they're not winner take all, these aren't necessarily winner take all markets. It's not necessarily a zero sum game, but let's call it winner take most. What odds would you give that open AI 10 years from now will be in that position. >> If I'm 0 to 10 kind of thing? >> Yeah, it's like horse race, 3 to 1, 2 to 1, even money, 10 to 1, 50 to 1. >> Maybe 2 to 1, >> 2 to 1, that's pretty low odds. That's basically saying they're the favorite, they're the front runner. Would you agree with that? >> I'd say 4 to 1. >> Yeah, I was going to say I'm like a 5 to 1, 7 to 1 type of person, 'cause I'm a skeptic with, you know, there's so much competition, but- >> I think they're definitely the leader. I mean you got to say, I mean. >> Oh there's no question. There's no question about it. >> The question is can they execute? >> They're not Friendster, is what you're saying. >> They're not Friendster and they're more like Twitter and Reddit where they have momentum. If they can execute on the product side, and if they don't stumble on that, they will continue to have the lead. >> If they say stay neutral, as Sam is, has been saying, that, hey, Microsoft is one of our partners, if you look at their company model, how they have structured the company, then they're going to pay back to the investors, like Microsoft is the biggest one, up to certain, like by certain number of years, they're going to pay back from all the money they make, and after that, they're going to give the money back to the public, to the, I don't know who they give it to, like non-profit or something. (indistinct) >> Okay, the odds are dropping. (group talks over each other) That's a good point though >> Actually they might have done that to fend off the criticism of this. But it's really interesting to see the model they have adopted. >> The wildcard in all this, My last word on this is that, if there's a developer shift in how developers and data can come together again, we have conferences around the future of data, Supercloud and meshs versus, you know, how the data world, coding with data, how that evolves will also dictate, 'cause a wild card could be a shift in the landscape around how developers are using either machine learning or AI like techniques to code into their apps, so. >> That's fantastic insight. I can't thank you enough for your time, on the heels of Supercloud 2, really appreciate it. All right, thanks to John and Sarbjeet for the outstanding conversation today. Special thanks to the Palo Alto studio team. My goodness, Anderson, this great backdrop. You guys got it all out here, I'm jealous. And Noah, really appreciate it, Chuck, Andrew Frick and Cameron, Andrew Frick switching, Cameron on the video lake, great job. And Alex Myerson, he's on production, manages the podcast for us, Ken Schiffman as well. Kristen Martin and Cheryl Knight help get the word out on social media and our newsletters. Rob Hof is our editor-in-chief over at SiliconANGLE, does some great editing, thanks to all. Remember, all these episodes are available as podcasts. All you got to do is search Breaking Analysis podcast, wherever you listen. Publish each week on wikibon.com and siliconangle.com. Want to get in touch, email me directly, david.vellante@siliconangle.com or DM me at dvellante, or comment on our LinkedIn post. And by all means, check out etr.ai. They got really great survey data in the enterprise tech business. This is Dave Vellante for theCUBE Insights powered by ETR. Thanks for watching, We'll see you next time on Breaking Analysis. (electronic music)

Published Date : Jan 20 2023

SUMMARY :

bringing you data-driven and ChatGPT have taken the world by storm. So I asked it, give it to the large language models to do that. So to your point, it's So one of the problems with ChatGPT, and he simply gave the system the prompts, or the OS to help it do but it kind of levels the playing- and the answers were coming as the data you can get. Yeah, and leveled to certain extent. I check the facts, save me about maybe- and then I write a killer because like if the it's, the law is we, you know, I think that's true and I ask the set of similar question, What's your counter point? and not it's underestimated long term. That's what he said. for the first time, wow. the overhyped at the No, it was, it was I got, right I mean? the internet in the early days, and it's only going to get better." So you're saying it's bifurcated. and possibly the debate the first mobile device. So I mean. on the right with ChatGPT, and convicted by the Department of Justice the scrutiny from the Fed, right, so- And the privacy and thing to do what Sam Altman- So even though it'll get like, you know, it's- It's more than clever. I mean you write- I think that's a big thing. I think he was doing- I was not impressed because You know like. And he did the same thing he's got a lot of hyperbole. the browser moment to me, So OpenAI could stay on the right side You're right, it was terrible, They could be the Netscape Navigator, and in the horizontal axis's So I guess that's the other point is, I mean to quote IBM's So the data problem factors and the government's around the world, and they're slow to catch up. Yeah, and now they got years, you know, OpenAI. But the problem with government to kill Big Tech, and the 20% is probably relevant, back in the day, right? are they going to apply it? and also to write code as well, that the marketplace I don't, I don't see you had an interesting comment. No, no. First of all, the AI chops that Google has, right? are off the scales, right? I mean they got to be and the capacity to process that data, on some of the thinking So Lina Kahn is looming, and this is the third, could be a third rail. But the first thing What they will do out the separate company Is it to charge you for a query? it's cool to type stuff in natural language is the way and how many cents the and they're going through Google search results. It will, because there were It'll be like, you know, I mean. I never input the transcript. Wow, But it was a big lie. but I call it the vanilla content. Make your point, cause we And on the danger side as well, So the data By the way, that means at the Supercloud event, So one of the VCs actually What do you make of it? you were like "Hundreds of millions." not 10, not a billion. Clearly, the CapEx spending to build all But I think it's not that hard. It's, what, you know This is the new economics Look at the amount of And Sam is depositioning all the success, or 150 or 200 million to train the model. So I think to compute is different. not because of the software. Because you can, buy They built the moat And then, you know, well that the Hudson and it was over. that are going to be seeping out. Yeah, it's like horse race, 3 to 1, 2 to 1, that's pretty low odds. I mean you got to say, I mean. Oh there's no question. is what you're saying. and if they don't stumble on that, the money back to the public, to the, Okay, the odds are dropping. the model they have adopted. Supercloud and meshs versus, you know, on the heels of Supercloud

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Nir Zuk, Palo Alto Networks | Palo Alto Networks Ignite22


 

>> Presenter: theCUBE presents Ignite '22, brought to you by Palo Alto Networks. >> Hey guys and girls. Welcome back to theCube's live coverage at Palo Alto Ignite '22. We're live at the MGM Grand Hotel in beautiful Las Vegas. Lisa Martin here with Dave Vellante. This is day one of our coverage. We've been talking with execs from Palo Alto, Partners, but one of our most exciting things is talking with Founders day. We get to do that next. >> The thing is, it's like I wrote this weekend in my breaking analysis. Understanding the problem in cybersecurity is really easy, but figuring out how to fix it ain't so much. >> It definitely isn't. >> So I'm excited to have Nir here. >> Very excited. Nir Zuk joins us, the founder and CTO of Palo Alto Networks. Welcome, Nir. Great to have you on the program. >> Thank you. >> So Palo Alto Networks, you founded it back in 2005. It's hard to believe that's been 18 years, almost. You did something different, which I want to get into. But tell us, what was it back then? Why did you found this company? >> I thought the world needed another cybersecurity company. I thought it's because there were so many cybersecurity vendors in the world, and just didn't make any sense. This industry has evolved in a very weird way, where every time there was a new challenge, rather than existing vendors dealing with a challenge, you had new vendors dealing with it, and I thought I could put a stop to it, and I think I did. >> You did something differently back in 2005, looking at where you are now, the leader, what was different in your mind back then? >> Yeah. When you found a new company, you have really two good options. There's also a bad option, but we'll skip that. You can either disrupt an existing market, or you can create a new market. So first, I decided to disrupt an existing market, go into an existing market first, network security, then cyber security, and change it. Change the way it works. And like I said, the challenges that every problem had a new vendor, and nobody just stepped back and said, "I think I can solve it with the platform." Meaning, I think I can spend some time not solving a specific problem, but building a platform that then can be used to solve many different problems. And that's what I've done, and that's what Palo Alto Networks has done, and that's where we are today. >> So you look back, you call it now, I think you call it a next gen firewall, but nothing in 2005, can it be next gen? Do you know the Silicon Valley Show? Do you know the show Silicon Valley? >> Oh! Yeah. >> Yeah, of course. >> You got to have a box. But it was a different kind of box- >> Actually. >> Explain that. >> Actually, it's exactly the same thing. You got to have a box. So I actually wanted to call it a necessary evil. Marketing wouldn't go for that. >> No. >> And the reason I wanted to call it a necessary evil, because one of the things that we've done in order to platform our cyber security, again, first network security now, also cloud security, and security operations, is to turn it into a SaaS delivered industry. Today every cyber security professional knows that, when they buy cyber security, they buy usually a SaaS delivered service. Back then, people thought I was crazy to think that customers are going to send their data to their vendor in order to process, and they wanted everything on premise and so on, but I said, "No, customers are going to send information to us for processing, because we have much more processing power than they have." And we needed something in the infrastructure to send us the information. So that's why I wanted to call it the necessary evil. We ended up calling it next generation firewall, which was probably a better term. >> Well, even Veritas. Remember Veritas? They had the no hardware agenda. Even they have a box. So it is like you say, you got to have it. >> It's necessary. >> Okay. You did this, you started this on your own cloud, kind of like Salesforce, ServiceNow. >> Correct. >> Similar now- >> Build your own data centers. >> Build your own data center. Okay, I call it a cloud, but no. >> No, it's the same. There's no cloud, it's just someone else's computer. >> According to Larry Ellison, he was actually probably right about that. But over time, you've had this closer partnership with the public clouds. >> Correct. >> What does that bring you and your customers, and how hard was that to navigate? >> It wasn't that hard for us, because we didn't have that many services. Usually it's harder. Of course, we didn't do a lift and shift, which is their own thing to do with the cloud. We rebuild things for the cloud, and the benefits, of course, are time to market, scale, agility, and in some cases also, cost. >> Yeah, some cases. >> In some cases. >> So you have a sort of a hybrid model today. You still run your own data centers, do you not? >> Very few. >> Really? >> There are very, very few things that we have to do on hardware, like simulating malware and things that cannot be done in a virtual machine, which is pretty much the only option you have in the cloud. They provide bare metal, but doesn't serve our needs. I think that we don't view cloud, and your viewers should not be viewing cloud, as a place where they're going to save money. It's a place where they're going to make money. >> I like that. >> You make much more money, because you're more agile. >> And that's why this conversation is all about, your cost of goods sold they're going to be so high, you're going to have to come back to your own data centers. That's not on your mind right now. What's on your mind is advancing the unit, right? >> Look, my own data center would limit me in scale, would limit my agility. If you want to build something new, you don't have all the PaaS services, the platform as a service, services like database, and AI, and so on. I have to build them myself. It takes time. So yeah, it's going to be cheaper, but I'm not going to be delivering the same thing. So my revenues will be much lower. >> Less top line. What can humans do better than machines? You were talking about your keynote... I'm just going to chat a little bit. You were talking about your keynote. Basically, if you guys didn't see the keynote, that AI is going to run every soc within five years, that was a great prediction that you made. >> Correct. >> And they're going to do things that you can't do today, and then in the future, they're going to do things that you can't... Better than you can do. >> And you just have to be comfortable with that. >> So what do you think humans can do today and in the future better than machines? >> Look, humans can always do better than machines. The human mind can do things that machines cannot do. We are conscious, I don't think machines will be conscious. And you can do things... My point was not that machines can do things that humans cannot do. They can just do it better. The things that humans do today, machines can do better, once machines do that, humans will be free to do things that they don't do today, that machines cannot do. >> Like what? >> Like finding the most difficult, most covert attacks, dealing with the most difficult incidents, things that machines just can't do. Just that today, humans are consumed by finding attacks that machines can find, by dealing with incidents that machines can deal with. It's a waste of time. We leave it to the machines and go and focus on the most difficult problems, and then have the machines learn from you, so that next time or a hundred or a thousand times from now, they can do it themselves, and you focus on the even more difficult. >> Yeah, just like after 9/11, they said that we lack the creativity. That's what humans have, that machines don't, at least today. >> Machines don't. Yeah, look, every airplane has two pilots, even though airplanes have been flying themselves for 30 years now, why do you have two pilots, to do the things that machines cannot do? Like land on the Hudson, right? You always need humans to do the things that machines cannot do. But to leave the things that machines can do to the machines, they'll do it better. >> And autonomous vehicles need breaks. (indistinct) >> In your customer conversations, are customers really grappling with that, are they going, "Yeah, you're right?" >> It depends. It's hard for customers to let go of old habits. First, the habit of buying a hundred different solutions from a hundred different vendors, and you know what? Why would I trust one vendor to do everything, put all my eggs in the same basket? They have all kind of slogans as to why not to do that, even though it's been proven again and again that, doing everything in one system with one brain, versus a hundred systems with a hundred brains, work much better. So that's one thing. The second thing is, we always have the same issue that we've had, I think, since the industrial revolution, of what machines are going to take away my job. No, they're just going to make your job better. So I think that some of our customers are also grappling with that, like, "What do I do if the machines take over?" And of course, like we've said, the machines aren't taking over. They're going to do the benign work, you're going to do the interesting work. You should embrace it. >> When I think about your history as a technology pro, from Check Point, a couple of startups, one of the things that always frustrated you, is when when a larger company bought you out, you ended up getting sucked into the bureaucratic vortex. How do you avoid that at Palo Alto Networks? >> So first, you mean when we acquire company? >> Yes. >> The first thing is that, when we acquire companies, we always acquire for integration. Meaning, we don't just buy something and then leave it on the side, and try to sell it here and there. We integrate it into the core of our products. So that's very important, so that the technology lives, thrives and continues to grow as part of our bigger platform. And I think that the second thing that is very important, from past experience what we've learned, is to put the people that we acquire in key positions. Meaning, you don't buy a company and then put the leader of that company five levels below the CEO. You always put them in very senior positions. Almost always, we have the leaders of the companies that we acquire, be two levels below the CEO, so very senior in the company, so they can influence and make changes. >> So two questions related to that. One is, as you grow your team, can you be both integrated? And second part of the question, can you be both integrated and best of breed? Second part of the question is, do you even have to be? >> So I'll answer it in the third way, which is, I don't think you can be best of breed without being integrated in cybersecurity. And the reason is, again, this split brain that I've mentioned twice. When you have different products do a part of cybersecurity and they don't talk to each other, and they don't share a single brain, you always compromise. You start looking for things the wrong way. I can be a little bit technical here, but please. Take the example of, traditionally you would buy an IDS/IPS, separately from your filtering, separately from DNS security. One of the most important things we do in network security is to find combining control connections. Combining control connections where the adversaries controlling something behind your firewall and is now going around your network, is usually the key heel of the attack. That's why attacks like ransomware, that don't have a commanding control connection, are so difficult to deal with, by the way. So commanding control connections are a key seal of the attacks, and there are three different technologies that deal with it. Neural filtering for neural based commanding control, DNS security for DNS based commanding control, and IDS/IPS for general commanding control. If those are three different products, they'll be doing the wrong things. The oral filter will try to find things that it's not really good at, that the IPS really need to find, and the DN... It doesn't work. It works much better when it's one product doing everything. So I think the choice is not between best of breed and integrated. I think the only choice is integrated, because that's the only way to be best of breed. >> And behind that technology is some kind of realtime data store, I'll call it data lake, database. >> Yeah. >> Whatever. >> It's all driven by the same data. All the URLs, all the domain graph. Everything goes to one big data lake. We collect about... I think we collect about, a few petabytes per day. I don't write the exact number of data. It's all going to the same data lake, and all the intelligence is driven by that. >> So you mentioned in a cheeky comment about, why you founded the company, there weren't enough cybersecurity companies. >> Yeah. >> Clearly the term expansion strategy that Palo Alto Networks has done has been very successful. You've been, as you talked about, very focused on integration, not just from the technology perspective, but from the people perspective as well. >> Correct. >> So why are there still so many cybersecurity companies, and what are you thinking Palo Alto Networks can do to change that? >> So first, I think that there are a lot of cybersecurity companies out there, because there's a lot of money going into cybersecurity. If you look at the number of companies that have been really successful, it's a very small percentage of those cybersecurity companies. And also look, we're not going to be responsible for all the innovation in cybersecurity. We need other people to innovate. It's also... Look, always the question is, "Do you buy something or do you build it yourself?" Now we think we're the smartest people in the world. Of course, we can build everything, but it's not always true that we can build everything. Know that we're the smartest people in the world, for sure. You see, when you are a startup, you live and die by the thing that you build. Meaning if it's good, it works. If it's not good, you die. You run out of money, you shut down, and you just lost four years of your life to this, at least. >> At least. >> When you're a large company, yeah, I can go and find a hundred engineers and hire them. And especially nowadays, it becomes easier, as it became easier, and give them money, and have them go and build the same thing that the startup is building, but they're part of a bigger company, and they'll have more coffee breaks, and they'll be less incentive to go and do that, because the company will survive with or without them. So that's why startups can do things much better, sometimes than larger companies. We can do things better than startups, when it comes to being data driven because we have the data, and nobody can compete against the amount of data that we have. So we have a good combination of finding the right startups that have already built something, already proven that it works with some customers, and of course, building a lot of things internally that we cannot do outside. >> I heard you say in one of the, I dunno, dozens of videos I've listened to you talked to. The industry doesn't need or doesn't want another IoT stovepipe. Okay, I agree. So you got on-prem, AWS, Azure, Google, maybe Alibaba, IoT is going to be all over the place. So can you build, I call it the security super cloud, in other words, a consistent experience with the same policies and edicts across all my estates, irrespective of physical location? Is that technically feasible? Is it what you are trying to do? >> Certainly, what we're trying to do with Prisma Cloud, with our cloud security product, it works across all the clouds that you mentioned, and Oracle as well. It's almost entirely possible. >> Almost. >> Almost. Well, the things that... What you do is you normalize the language that the different cloud scale providers use, into one language. This cloud calls it a S3, and so, AWS calls it S3, and (indistinct) calls it GCS, and so on. So you normalize their terminology, and then build policy using a common terminology that your customers have to get used to. Of course, there are things that are different between the different cloud providers that cannot be normalized, and there, it has to be cloud specific. >> In that instance. So is that, in part, your strategy, is to actually build that? >> Of course. >> And does that necessitate running on all the major clouds? >> Of course. It's not just part of our strategy, it's a major part of our strategy. >> Compulsory. >> Look, as a standalone vendor that is not a cloud provider, we have two advantages. The first one is we're security product, security focused. So we can do much better than them when it comes to security. If you are a AWS, GCP, Azure, and so on, you're not going to put your best people on security, you're going to put them on the core business that you have. So we can do much better. Hey, that's interesting. >> Well, that's not how they talk. >> I don't care how they talk. >> Now that's interesting. >> When something is 4% of your business, you're not going to put it... You're not going to put your best people there. It's just, why would you? You put your best people on 96%. >> That's not driving their revenue. >> Look, it's simple. It's not what we- >> With all due respect. With all due respect. >> So I think we do security much better than them, and they become the good enough, and we become the premium. But certainly, the second thing that give us an advantage and the right to be a standalone security provider, is that we're multicloud, private cloud and all the major cloud providers. >> But they also have a different role. I mean, your role is not the security, the Nitro card or the Graviton chip, or is it? >> They are responsible for securing up to the operating system. We secure everything. >> They do a pretty good job of that. >> No, they do, certainly they have to. If they get bridged at that level, it's not just that one customer is going to suffer, the entire customer base. They have to spend a lot of time and money on it, and frankly, that's where they put their best security people. Securing the infrastructure, not building some cloud security feature. >> Absolutely. >> So Palo Alto Networks is, as we wrap here, on track to nearly double its revenues to nearly seven billion in FY '23, just compared to 2020, you were quoted in the press by saying, "We will be the first $100 billion cyber company." What is next for Palo Alto to achieve that? >> Yeah, so it was Nikesh, our CEO and chairman, that was quoted saying that, "We will double to a hundred billion." I don't think he gave it a timeframe, but what it takes is to double the sales, right? We're at 50 billion market cap right now, so we need to double sales. But in reality, you mentioned that we're growing the turn by doing more and more cybersecurity functions, and taking away pieces. Still, we have a relatively small, even though we're the largest cybersecurity vendor in the world, we have a very low market share that shows you how fragmented the market is. I would also like to point out something that is less known. Part of what we do with AI, is really take the part of the cybersecurity industry, which are service oriented, and that's about 50% of the cybersecurity industry services, and turn it into products. I mean, not all of it. But a good portion of what's provided today by people, and tens of billions of dollars are spent on that, can be done with products. And being one of the very, very few vendors that do that, I think we have a huge opportunity at turning those tens of billions of dollars in human services to AI. >> It's always been a good business taking human labor and translating into R and D, vendor R and D. >> Especially- >> It never fails if you do it well. >> Especially in difficult times, difficult economical times like we are probably experiencing right now around the world. We, not we, but we the world. >> Right, right. Well, congratulations. Coming up on the 18th anniversary. Tremendous amount of success. >> Thank you. >> Great vision, clear vision, STEM expansion strategy, really well underway. We are definitely going to continue to keep our eyes. >> Big company, a hundred billion, that's market capital, so that's a big company. You said you didn't want to work for a big company unless you founded it, is that... >> Unless it acts like a small company. >> There's the caveat. We'll keep our eye on that. >> Thank you very much. >> It's such a pleasure having you on. >> Thank you. >> Same here, thank you. >> All right, for our guests and for Dave Vellante, I'm Lisa Martin. You're watching theCUBE, the leader in live emerging and enterprise tech coverage. (upbeat music)

Published Date : Dec 14 2022

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brought to you by Palo Alto Networks. We get to do that next. but figuring out how to Great to have you on the program. It's hard to believe that's and I thought I could put a stop to it, So first, I decided to Yeah. You got to have a box. You got to have a box. because one of the things that we've done So it is like you say, you got to have it. You did this, you started Build your own data center. No, it's the same. According to Larry Ellison, and the benefits, of So you have a sort option you have in the cloud. You make much more money, back to your own data centers. but I'm not going to be that was a great prediction that you made. things that you can't do today, And you just have to And you can do things... and you focus on the even more difficult. they said that we lack the creativity. to do the things that machines cannot do? And autonomous vehicles need breaks. to make your job better. one of the things that of the companies that we acquire, One is, as you grow your team, and they don't talk to each other, And behind that technology is some kind and all the intelligence So you mentioned in not just from the technology perspective, and you just lost four years that the startup is building, listened to you talked to. clouds that you mentioned, and there, it has to be cloud specific. is to actually build that? It's not just part of our strategy, core business that you have. You're not going to put It's not what we- With all due respect. and the right to be a the Nitro card or the They are responsible for securing customer is going to suffer, just compared to 2020, and that's about 50% of the and D, vendor R and D. experiencing right now around the world. Tremendous amount of success. We are definitely going to You said you didn't want There's the caveat. the leader in live emerging

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LIVE Panel: "Easy CI With Docker"


 

>>Hey, welcome to the live panel. My name is Brett. I am your host, and indeed we are live. In fact, if you're curious about that, if you don't believe us, um, let's just show a little bit of the browser real quick to see. Yup. There you go. We're live. So, all right. So how this is going to work is I'm going to bring in some guests and, uh, in one second, and we're going to basically take your questions on the topic designer of the day, that continuous integration testing. Uh, thank you so much to my guests welcoming into the panel. I've got Carlos, Nico and Mandy. Hello everyone. >>Hello? All right, >>Let's go. Let's go around the room and all pretend we don't know each other and that the internet didn't read below the video who we are. Uh, hi, my name is Brett. I am a Docker captain, which means I'm supposed to know something about Docker. I'm coming from Virginia Beach. I'm streaming here from Virginia Beach, Virginia, and, uh, I make videos on the internet and courses on you to me, Carlos. Hey, >>Hey, what's up? I'm Carlos Nunez. I am a solutions architect, VMware. I do solution things with computers. It's fun. I live in Dallas when I'm moving to Houston in a month, which is where I'm currently streaming. I've been all over the Northeast this whole week. So, um, it's been fun and I'm excited to meet with all of you and talk about CIA and Docker. Sure. >>Yeah. Hey everyone. Uh, Nico, Khobar here. I'm a solution engineer at HashiCorp. Uh, I am streaming to you from, uh, the beautiful Austin, Texas. Uh, ignore, ignore the golden gate bridge here. This is from my old apartment in San Francisco. Uh, just, uh, you know, keeping that, to remember all the good days, um, that that lived at. But, uh, anyway, I work at Patrick Corp and I work on all things, automation, um, and cloud and dev ops. Um, and I'm excited to be here and Mandy, >>Hi. Yeah, Mandy Hubbard. I am streaming from Austin, Texas. I am, uh, currently a DX engineer at ship engine. Um, I've worked in QA and that's kind of where I got my, uh, my Docker experience and, um, uh, moving into DX to try and help developers better understand and use our products and be an advocate for them. >>Nice. Well, thank you all for joining me. Uh, I really appreciate you taking the time out of your busy schedule to be here. And so for those of you in chat, the reason we're doing this live, because it's always harder to do things live. The reason we're here is to answer a question. So we didn't come with a bunch of slides and demos or anything like that. We're here to talk amongst ourselves about ideas and really here for you. So we've, we obviously, this is about easy CII, so we're, we're going to try to keep the conversation around testing and continuous integration and all the things that that entails with containers. But we may, we may go down rabbit holes. We may go veer off and start talking about other things, and that's totally fine if it's in the realm of dev ops and containers and developer and ops workflows, like, Hey, it's, it's kinda game. >>And, uh, these people have a wide variety of expertise. They haven't done just testing, right? We, we live in a world where you all kind of have to wear many hats. So feel free to, um, ask what you think is on the top of your mind. And we'll do our best to answer. It may, might not be the best answer or the correct answer, but we're going to do our best. Um, well, let's get it start off. Uh, let's, let's get a couple of topics to start off with. Uh, th the, the easy CGI was my, one of my three ideas. Cause he's the, one of the things that I'm most excited about is the innovation we're seeing around easier testing, faster testing, automated testing, uh, because as much as we've all been doing this stuff for, you know, 15 years, since 20 years since the sort of Jenkins early days, um, it it's, it seems like it's still really hard and it's still a lot of work. >>So, um, let's go around the room real quick, and everybody can just kind of talk for a minute about like your experience with testing and maybe some of your pain points, like what you don't like about our testing world. Um, and we can talk about some pains, cause I think that will lead us to kind of talk about what, what are the things we're seeing now that might be better, uh, ideas about how to do this. I know for me, uh, testing, obviously there's the code part, but just getting it automated, but mostly getting it in the hands of developers so that they can control their own testing. And don't have to go talk to a person to run that test again, or the mysterious Jenkins platform somewhere. I keep mentioning Jenkins cause it's, it is still the dominant player out there. Um, so for me, I'm, I'm, I, I don't like it when I'm walking into a room and there's, there's only one or two people that know how the testing works or know how to make the new tests go into the testing platform and stuff like that. So I'm always trying to free those things so that any of the developers are enabled and empowered to do that stuff. So someone else, Carlos, anybody, um, >>Oh, I have a lot of opinions on that. Having been a QA engineer for most of my career. Um, the shift that we're saying is everyone is dev ops and everyone is QA. Th the issue I see is no one asked developers if they wanted to be QA. Um, and so being the former QA on the team, when there's a problem, even though I'm a developer and we're all running QA, they always tend to come to the one of the former QA engineers. And they're not really owning that responsibility and, um, and digging in. So that's kind of what I'm saying is that we're all expected to test now. And some people, well, some people don't know how it's, uh, for me it was kind of an intuitive skill. It just kind of fit with my personality, but not knowing what to look for, not knowing what to automate, not even understanding how your API end points are used by your front end to know what to test when a change is made. It's really overwhelming for developers. And, um, we're going to need to streamline that and, and hold their hands a little bit until they get their feet wet with also being QA. >>Right. Right. So, um, uh, Carlos, >>Yeah, uh, testing is like, Tesla is one of my favorite subjects to talk about when I'm baring with developers. And a lot of it is because of what Mandy said, right? Like a lot of developers now who used to write a test and say, Hey, QA, go. Um, I wrote my unit tests. Now write the rest of the test. Essentially. Now developers are expected to be able to understand how testing, uh, testing methodologies work, um, in their local environments, right? Like they're supposed to understand how to write an integration tasks federate into and tasks, a component test. And of course, how to write unit tests that aren't just, you know, assert true is true, right? Like more comprehensive, more comprehensive, um, more high touch unit tests, which include things like mocking and stubbing and spine and all that stuff. And, you know, it's not so much getting those tests. Well, I've had a lot of challenges with developers getting those tests to run in Docker because of usually because of dependency hell, but, um, getting developers to understand how to write tests that matter and mean something. Um, it's, it's, it can be difficult, but it's also where I find a lot of the enjoyment of my work comes into play. So yeah. I mean, that's the difficulty I've seen around testing. Um, big subject though. Lots to talk about there. >>Yeah. We've got, we've already got so many questions coming in. You already got an hour's worth of stuff. So, uh, Nico 81st thoughts on that? >>Yeah, I think I definitely agree with, with other folks here on the panel, I think from a, um, the shift from a skillset perspective that's needed to adopt the new technologies, but I think from even from, uh, aside from the organizational, um, and kind of key responsibilities that, that the new developers have to kinda adapt to and, and kind of inherit now, um, there's also from a technical perspective as there's, you know, um, more developers are owning the full stack, including the infrastructure piece. So that adds a lot more to the plate in Tim's oaf, also testing that component that they were not even, uh, responsible for before. Um, and, um, also the second challenge that, you know, I'm seeing is that on, you know, the long list of added, um, uh, tooling and, you know, there's new tool every other day. Um, and, um, that kind of requires more customization to the testing, uh, that each individual team, um, any individual developer Y by extension has to learn. Uh, so the customization, uh, as well as the, kind of the scope that had, uh, you know, now in conferences, the infrastructure piece, um, uh, both of act to the, to the challenges that we're seeing right now for, um, for CGI and overall testing, um, uh, the developers are saying, uh, in, in the market today. >>Yeah. We've got a lot of questions, um, about all the, all the different parts of this. So, uh, let me just go straight to them. Cause that's why we're here is for the people, uh, a lot of people asking about your favorite tools and in one of this is one of the challenges with integration, right? Is, um, there is no, there are dominant players, but there, there is such a variety. I mean, every one of my customers seems like they're using a different workflow and a different set of tools. So, and Hey, we're all here to just talk about what we're, what we're using, uh, you know, whether your favorite tools. So like a lot of the repeated questions are, what are your favorite tools? Like if you could create it from scratch, uh, what would you use? Pierre's asking, you know, GitHub actions sounds like they're a fan of GitHub actions, uh, w you know, mentioning, pushing the ECR and Docker hub and, uh, using vs code pipeline, I guess there may be talking about Azure pipelines. Um, what, what's your preferred way? So, does anyone have any, uh, thoughts on that anyone want to throw out there? Their preferred pipeline of tooling? >>Well, I have to throw out mine. I might as Jenkins, um, like kind of a honorary cloud be at this point, having spoken a couple of times there, um, all of the plugins just make the functionality. I don't love the UI, but I love that it's been around so long. It has so much community support, and there are so many plugins so that if you want to do something, you don't have to write the code it's already been tested. Um, unfortunately I haven't been able to use Jenkins in, uh, since I joined ship engine, we, most of our, um, our, our monolithic core application is, is team city. It's a dotnet application and TeamCity plays really well with.net. Um, didn't love it, uh, Ms. Jenkins. And I'm just, we're just starting some new initiatives that are using GitHub actions, and I'm really excited to learn, to learn those. I think they have a lot of the same functionality that you're looking for, but, um, much more simplified in is right there and get hubs. So, um, the integration is a lot more seamless, but I do have to go on record that my favorite CICT tools Jenkins. >>All right. You heard it here first people. All right. Anyone else? You're muted? I'm muted. Carlin says muted. Oh, Carla says, guest has muted themselves to Carlos. You got to unmute. >>Yes. I did mute myself because I was typing a lot, trying to, you know, try to answer stuff in the chat. And there's a lot of really dark stuff in there. That's okay. Two more times today. So yeah, it's fine. Yeah, no problem. So totally. And it's the best way to start a play more. So I'm just going to go ahead and light it up. Um, for enterprise environments, I actually am a huge fan of Jenkins. Um, it's a tool that people really understand. Um, it has stood the test of time, right? I mean, people were using Hudson, but 15 years ago, maybe longer. And, you know, the way it works, hasn't really changed very much. I mean, Jenkins X is a little different, but, um, the UI and the way it works internally is pretty familiar to a lot of enterprise environments, which is great. >>And also in me, the plugin ecosystem is amazing. There's so many plugins for everything, and you can make your own if you know, Java groovy. I'm sure there's a perfect Kotlin in there, but I haven't tried myself, but it's really great. It's also really easy to write, um, CIS code, which is something I'm a big fan of. So Jenkins files have been, have worked really well for me. I, I know that I can get a little bit more complex as you start to build your own models and such, but, you know, for enterprise enterprise CIO CD, if you want, especially if you want to roll your own or own it yourself, um, Jenkins is the bellwether and for very good reason now for my personal projects. And I see a lot on the chat here, I think y'all, y'all been agreed with me get hub actions 100%, my favorite tool right now. >>Um, I love GitHub actions. It's, it's customizable, it's modular. There's a lot of plugins already. I started using getting that back maybe a week after when GA and there was no documentation or anything. And I still, it was still my favorite CIA tool even then. Um, and you know, the API is really great. There's a lot to love about GitHub actions and, um, and I, and I use it as much as I can from my personal project. So I still have a soft spot for Travis CAI. Um, you know, they got acquired and they're a little different now trying to see, I, I can't, I can't let it go. I just love it. But, um, yeah, I mean, when it comes to Seattle, those are my tools. So light me up in the comments I will respond. Yeah. >>I mean, I, I feel with you on the Travis, the, I think, cause I think that was my first time experiencing, you know, early days get hub open source and like a free CIA tool that I could describe. I think it was the ammo back then. I don't actually remember, but yeah, it was kind of an exciting time from my experience. There was like, oh, this is, this is just there as a service. And I could just use it. It doesn't, it's like get hub it's free from my open source stuff. And so it does have a soft spot in my heart too. So yeah. >>All right. We've got questions around, um, cam, so I'm going to ask some questions. We don't have to have these answers because sometimes they're going to be specific, but I want to call them out because people in chat may have missed that question. And there's probably, you know, that we have smart people in chat too. So there's probably someone that knows the answer to these things. If, if it's not us, um, they're asking about building Docker images in Kubernetes, which to me is always a sore spot because it's Kubernetes does not build images by default. It's not meant for that out of the gate. And, uh, what is the best way to do this without having to use privileged containers, which privileged containers just implying that yeah, you, you, it probably has more privileges than by default as a container in Kubernetes. And that is a hard thing because, uh, I don't, I think Docker doesn't lie to do that out of the gate. So I don't know if anyone has an immediate answer to that. That's a pretty technical one, but if you, if you know the answer to that in chat, call it out. >>Um, >>I had done this, uh, but I'm pretty sure I had to use a privileged, um, container and install the Docker Damon on the Kubernetes cluster. And I CA I can't give you a better solution. Um, I've done the same. So, >>Yeah, uh, Chavonne asks, um, back to the Jenkins thing, what's the easiest way to integrate Docker into a Jenkins CICB pipeline. And that's one of the challenges I find with Jenkins because I don't claim to be the expert on Jenkins. Is there are so many plugins because of this, of this such a huge ecosystem. Um, when you go searching for Docker, there's a lot that comes back, right. So I, I don't actually have a preferred way because every team I find uses it differently. Um, I don't know, is there a, do you know if there's a Jenkins preferred, a default plugin? I don't even know for Docker. Oh, go ahead. Yeah. Sorry for Docker. And jacon sorry, Docker plugins for Jenkins. Uh, as someone's asking like the preferred or easy way to do that. Um, and I don't, I don't know the back into Jenkins that well, so, >>Well, th the new, the new way that they're doing, uh, Docker builds with the pipeline, which is more declarative versus the groovy. It's really simple, and their documentation is really good. They, um, they make it really easy to say, run this in this image. So you can pull down, you know, public images and add your own layers. Um, so I don't know the name of that plugin, uh, but I can certainly take a minute after this session and going and get that. Um, but if you really are overwhelmed by the plugins, you can just write your, you know, your shell command in Jenkins. You could just by, you know, doing everything in bash, calling the Docker, um, Damon directly, and then getting it working just to see that end to end, and then start browsing for plugins to see if you even want to use those. >>The plugins will allow more integration from end to end. Some of the things that you input might be available later on in the process for having to manage that yourself. But, you know, you don't have to use any of the plugins. You can literally just, you know, do a block where you write your shell command and get it working, and then decide if, for plugins for you. Um, I think it's always under important to understand what is going on under the hood before you, before you adopt the magic of a plugin, because, um, once you have a problem, if you're, if it's all a lockbox to you, it's going to be more difficult to troubleshoot. It's kind of like learning, get command line versus like get cracking or something. Once, once you get in a bind, if you don't understand the underlying steps, it's really hard to get yourself out of a bind, versus if you understand what the plugin or the app is doing, then, um, you can get out of situations a lot easier. That's a good place. That's, that's where I'd start. >>Yeah. Thank you. Um, Camden asks better to build test environment images, every commit in CII. So this is like one of those opinions of we're all gonna have some different, uh, or build on build images on every commit, leveraging the cash, or build them once outside the test pile pipeline. Um, what say you people? >>Uh, well, I I've seen both and generally speaking, my preference is, um, I guess the ant, the it's a consultant answer, right? I think it depends on what you're trying to do, right. So if you have a lot of small changes that are being made and you're creating images for each of those commits, you're going to have a lot of images in your, in your registry, right? And on top of that, if you're building those images, uh, through CAI frequently, if you're using Docker hub or something like that, you might run into rate limiting issues because of Docker's new rate, limiting, uh, rate limits that they put in place. Um, but that might be beneficial if the, if being able to roll back between those small changes while you're testing is important to you. Uh, however, if all you care about is being able to use Docker images, um, or being able to correlate versions to your Docker images, or if you're the type of team that doesn't even use him, uh, does he even use, uh, virgins in your image tags? Then I would think that that might be a little, much you might want to just have in your CIO. You might want to have a stage that builds your Docker images and Docker image and pushes it into your registry, being done first particular branches instead of having to be done on every commit regardless of branch. But again, it really depends on the team. It really depends on what you're building. It really depends on your workflow. It can depend on a number of things like a curse sometimes too. Yeah. Yeah. >>Once had two points here, you know, I've seen, you know, the pattern has been at every, with every, uh, uh, commit, assuming that you have the right set of tests that would kind of, uh, you would benefit from actually seeing, um, the, the, the, the testing workflow go through and can detect any issue within, within the build or whatever you're trying to test against. But if you're just a building without the appropriate set of tests, then you're just basically consuming almond, adding time, as well as all the, the image, uh, stories associated with it without treaty reaping the benefit of, of, of this pattern. Uh, and the second point is, again, I think if you're, if you're going to end up doing a per commit, uh, definitely recommend having some type of, uh, uh, image purging, um, uh, and, and, and garbage collection process to ensure that you're not just wasting, um, all the stories needed and also, um, uh, optimizing your, your bill process, because that will end up being the most time-consuming, um, um, you know, within, within your pipeline. So this is my 2 cents on this. >>Yeah, that's good stuff. I mean, those are both of those are conversations that could lead us into the rabbit hole for the rest of the day on storage management, uh, you know, CP CPU minutes for, uh, you know, your build stuff. I mean, if you're in any size team, more than one or two people, you immediately run into headaches with cost of CIA, because we have now the problem of tools, right? We have so many tools. We can have the CIS system burning CPU cycles all day, every day, if we really wanted to. And so you re very quickly, I think, especially if you're on every commit on every branch, like that gets you into a world of cost mitigation, and you probably are going to have to settle somewhere in the middle on, uh, between the budget, people that are saying you're spending way too much money on the CII platform, uh, because of all these CPU cycles, and then the developers who would love to have everything now, you know, as fast as possible and the biggest, biggest CPU's, and the biggest servers, and have the bills, because the bills can never go fast enough, right. >>There's no end to optimizing your build workflow. Um, we have another question on that. This is another topic that we'll all probably have different takes on is, uh, basically, uh, version tags, right? So on images, we, we have a very established workflow in get for how we make commits. We have commit shots. We have, uh, you know, we know get tags and there's all these things there. And then we go into images and it's just this whole new world that's opened up. Like there's no real consensus. Um, so what, what are your thoughts on the strategy for teams in their image tag? Again, another, another culture thing. Um, commander, >>I mean, I'm a fan of silver when we have no other option. Um, it's just clean and I like the timestamp, you know, exactly when it was built. Um, I don't really see any reason to use another, uh, there's just normal, incremental, um, you know, numbering, but I love the fact that you can pull any tag and know exactly when it was created. So I'm a big fan of bar, if you can make that work for your organization. >>Yep. People are mentioned that in chat, >>So I like as well. Uh, I'm a big fan of it. I think it's easy to be able to just be as easy to be able to signify what a major changes versus a minor change versus just a hot fix or, you know, some or some kind of a bad fix. The problem that I've found with having teams adopt San Bernardo becomes answering these questions and being able to really define what is a major change, what is a minor change? What is a patch, right? And this becomes a bit of an overhead or not so much of an overhead, but, uh, uh, uh, a large concern for teams who have never done versioning before, or they never been responsible for their own versioning. Um, in fact, you know, I'm running into that right now, uh, with, with a client that I'm working with, where a lot, I'm working with a lot of teams, helping them move their applications from a legacy production environment into a new one. >>And in doing so, uh, versioning comes up because Docker images, uh, have tags and usually the tax correlate to versions, but some teams over there, some teams that I'm working with are only maintaining a script and others are maintaining a fully fledged JAK, three tier application, you know, with lots of dependencies. So telling the script, telling the team that maintains a script, Hey, you know, you should use somber and you should start thinking about, you know, what's major, what's my number what's patch. That might be a lot for them. And for someone or a team like that, I might just suggest using commit shots as your versions until you figure that out, or maybe using, um, dates as your version, but for the more for the team, with the larger application, they probably already know the answers to those questions. In which case they're either already using Sember or they, um, or they may be using some other version of the strategy and might be in December, might suit them better. So, um, you're going to hear me say, it depends a lot, and I'm just going to say here, it depends. Cause it really does. Carlos. >>I think you hit on something interesting beyond just how to version, but, um, when to consider it a major release and who makes those decisions, and if you leave it to engineers to version, you're kind of pushing business decisions down the pipe. Um, I think when it's a minor or a major should be a business decision and someone else needs to make that call someone closer to the business should be making that call as to when we want to call it major. >>That's a really good point. And I add some, I actually agree. Um, I absolutely agree with that. And again, it really depends on the team that on the team and the scope of it, it depends on the scope that they're maintaining, right? And so it's a business application. Of course, you're going to have a product manager and you're going to have, you're going to have a product manager who's going to want to make that call because that version is going to be out in marketing. People are going to use it. They're going to refer to and support calls. They're going to need to make those decisions. Sember again, works really, really well for that. Um, but for a team that's maintaining the scripts, you know, I don't know, having them say, okay, you must tell me what a major version is. It's >>A lot, but >>If they want it to use some birds great too, which is why I think going back to what you originally said, Sember in the absence of other options. I think that's a good strategy. >>Yeah. There's a, there's a, um, catching up on chat. I'm not sure if I'm ever going to catch up, but there's a lot of people commenting on their favorite CII systems and it's, and it, it just goes to show for the, the testing and deployment community. Like how many tools there are out there, how many tools there are to support the tools that you're using. Like, uh, it can be a crazy wilderness. And I think that's, that's part of the art of it, uh, is that these things are allowing us to build our workflows to the team's culture. Um, and, uh, but I do think that, you know, getting into like maybe what we hope to be at what's next is I do hope that we get to, to try to figure out some of these harder problems of consistency. Uh, one of the things that led me to Docker at the beginning to begin with was the fact that it wa it created a consistent packaging solution for me to get my code, you know, off of, off of my site of my local system, really, and into the server. >>And that whole workflow would at least the thing that I was making at each step was going to be the same thing used. Right. And that, that was huge. Uh, it was also, it also took us a long time to get there. Right. We all had to, like Docker was one of those ones that decade kind of ideas of let's solidify the, enter, get the consensus of the community around this idea. And we, and it's not perfect. Uh, you know, the Docker Docker file is not the most perfect way to describe how to make your app, but it is there and we're all using it. And now I'm looking for that next piece, right. Then hopefully the next step in that, um, that where we can all arrive at a consensus so that once you hop teams, you know, okay. We all knew Docker. We now, now we're all starting to get to know the manifests, but then there's this big gap in the middle where it's like, it might be one of a dozen things. Um, you know, so >>Yeah, yeah. To that, to that, Brett, um, you know, uh, just maybe more of a shameless plug here and wanting to kind of talk about one of the things that I'm on. So excited, but I work, I work at Tasha Corp. I don't know anyone, or I don't know if many people have heard of, um, you know, we tend to focus a lot on workflows versus technologies, right. Because, you know, as you can see, even just looking at the chat, there's, you know, ton of opinions on the different tooling, right. And, uh, imagine having, you know, I'm working with clients that have 10,000 developers. So imagine taking the folks in the chat and being partnered with one organization or one company and having to make decisions on how to build software. Um, but there's no way you can conversion one or, or one way or one tool, uh, and that's where we're facing in the industry. >>So one of the things that, uh, I'm pretty excited about, and I don't know if it's getting as much traction as you know, we've been focused on it. This is way point, which is a project, an open source project. I believe we got at least, uh, last year, um, which is, it's more of, uh, it's, it is aim to address that really, uh, uh, Brad set on, you know, to come to tool to, uh, make it extremely easy and simple. And, you know, to describe how you want to build, uh, deploy or release your application, uh, in, in a consistent way, regardless of the tools. So similar to how you can think of Terraform and having that pluggability to say Terraform apply or plan against any cloud infrastructure, uh, without really having to know exactly the details of how to do it, uh, this is what wave one is doing. Um, and it can be applied with, you know, for the CIA, uh, framework. So, you know, task plugability into, uh, you know, circle CEI tests to Docker helm, uh, Kubernetes. So that's the, you know, it's, it's a hard problem to solve, but, um, I'm hopeful that that's the path that we're, you know, we'll, we'll eventually get to. So, um, hope, you know, you can, you can, uh, see some of the, you know, information, data on it, on, on HashiCorp site, but I mean, I'm personally excited about it. >>Yeah. Uh I'm to gonna have to check that out. And, um, I told you on my live show, man, we'll talk about it, but talk about it for a whole hour. Uh, so there's another question here around, uh, this, this is actually a little bit more detailed, but it is one that I think a lot of people deal with and I deal with a lot too, is essentially the question is from Cameron, uh, D essentially, do you use compose in your CIO or not Docker compose? Uh, because yes I do. Yeah. Cause it, it, it, it solves so many problems am and not every CGI can, I don't know, there's some problems with a CIO is trying to do it for me. So there are pros and cons and I feel like I'm still on the fence about it because I use it all the time, but also it's not perfect. It's not always meant for CIA. And CIA sometimes tries to do things for you, like starting things up before you start other parts and having that whole order, uh, ordering problem of things anyway. W thoughts and when have thoughts. >>Yes. I love compose. It's one of my favorite tools of all time. Um, and the reason why it's, because what I often find I'm working with teams trying to actually let me walk that back, because Jack on the chat asked a really interesting question about what, what, what the hardest thing about CIS for a lot of teams. And in my experience, the hardest thing is getting teams to build an app that is the same app as what's built in production. A lot of CGI does things that are totally different than what you would do in your local, in your local dev. And as a result of that, you get, you got this application that either doesn't work locally, or it does work, but it's a completely different animal than what you would get in production. Right? So what I've found in trying to get teams to bridge that gap by basically taking their CGI, shifting the CII left, I hate the shift left turn, but I'll use it. >>I'm shifting the CIO left to your local development is trying to say, okay, how do we build an app? How do we, how do we build mot dependencies of that app so that we can build so that we can test our app? How do we run tests, right? How do we build, how do we get test data? And what I found is that trying to get teams to do all this in Docker, which is normally a first for a lot of teams that I'm working with, trying to get them all to do all of this. And Docker means you're running Docker, build a lot running Docker, run a lot. You're running Docker, RM a lot. You ran a lot of Docker, disparate Docker commands. And then on top of that, trying to bridge all of those containers together into a single network can be challenging without compose. >>So I like using a, to be able to really easily categorize and compartmentalize a lot of the things that are going to be done in CII, like building a Docker image, running tests, which is you're, you're going to do it in CII anyway. So running tests, building the image, pushing it to the registry. Well, I wouldn't say pushing it to the registry, but doing all the things that you would do in local dev, but in the same network that you might have a mock database or a mock S3 instance or some of something else. Um, so it's just easy to take all those Docker compose commands and move them into your Yammel file using the hub actions or your dankest Bob using Jenkins, or what have you. Right. It's really, it's really portable that way, but it doesn't work for every team. You know, for example, if you're just a team that, you know, going back to my script example, if it's a really simple script that does one thing on a somewhat routine basis, then that might be a lot of overhead. Um, in that case, you know, you can get away with just Docker commands. It's not a big deal, but the way I looked at it is if I'm, if I'm building, if I build something that's similar to a make bile or rate file, or what have you, then I'm probably gonna want to use Docker compose. If I'm working with Docker, that's, that's a philosophy of values, right? >>So I'm also a fan of Docker compose. And, um, you know, to your point, Carlos, the whole, I mean, I'm also a fan of shifting CEI lift and testing lift, but if you put all that logic in your CTI, um, it changes the L the local development experience from the CGI experience. Versus if you put everything in a compose file so that what you build locally is the same as what you build in CGI. Um, you're going to have a better experience because you're going to be testing something more, that's closer to what you're going to be releasing. And it's also very easy to look at a compose file and kind of, um, understand what the dependencies are and what's happening is very readable. And once you move that stuff to CGI, I think a lot of developers, you know, they're going to be intimidated by the CGI, um, whatever the scripting language is, it's going to be something they're going to have to wrap their head around. >>Um, but they're not gonna be able to use it locally. You're going to have to have another local solution. So I love the idea of a composed file use locally, um, especially if he can Mount the local workspace so that they can do real time development and see their changes in the exact same way as it's going to be built and tested in CGI. It gives developers a high level of confidence. And then, you know, you're less likely to have issues because of discrepancies between how it was built in your local test environment versus how it's built in NCI. And so Docker compose really lets you do all of that in a way that makes your solution more portable, portable between local dev and CGI and reduces the number of CGI cycles to get, you know, the test, the test data that you need. So that's why I like it for really, for local dev. >>It'll be interesting. Um, I don't know if you all were able to see the keynote, but there was a, there was a little bit, not a whole lot, but a little bit talk of the Docker, compose V two, which has now built into the Docker command line. And so now we're shifting from the Python built compose, which was a separate package. You could that one of the challenges was getting it into your CA solution because if you don't have PIP and you got down on the binary and the binary wasn't available for every platform and, uh, it was a PI installer. It gets a little nerdy into how that works, but, uh, and the team is now getting, be able to get unified with it. Now that it's in Golang and it's, and it's plugged right into the Docker command line, it hopefully will be easier to distribute, easier to, to use. >>And you won't have to necessarily have dependencies inside of where you're running it because there'll be a statically compiled binary. Um, so I've been playing with that, uh, this year. And so like training myself to do Docker going from Docker dash compose to Docker space, compose. It is a thing I I'm almost to the point of having to write a shell replacement. Yeah. Alias that thing. Um, but, um, I'm excited to see what that's going, cause there's already new features in it. And it, these built kit by default, like there's all these things. And I, I love build kit. We could make a whole session on build kit. Um, in fact there's actually, um, maybe going on right now, or right around this time, there is a session on, uh, from Solomon hikes, the seat, uh, co-founder of Docker, former CTO, uh, on build kit using, uh, using some other tool on top of build kit or whatever. >>So that, that would be interesting for those of you that are not watching that one. Cause you're here, uh, to do a check that one out later. Um, all right. So another good question was caching. So another one, another area where there is no wrong answers probably, and everyone has a different story. So the question is, what are your thoughts on CII build caching? There's often a debate between security. This is from Quentin. Thank you for this great question. There's often a debate between security reproducibility and build speeds. I haven't found a good answer so far. I will just throw my hat in the ring and say that the more times you want to build, like if you're trying to build every commit or every commit, if you're building many times a day, the more caching you need. So like the more times you're building, the more caching you're gonna likely want. And in most cases caching doesn't bite you in the butt, but that could be, yeah, we, can we get the bit about that? So, yeah. Yeah. >>I'm going to quote Carlos again and say, it depends on, on, you know, how you're talking, you know, what you're trying to build and I'm quoting your colors. Um, yeah, it's, it's got, it's gonna depend because, you know, there are some instances where you definitely want to use, you know, depends on the frequency that you're building and how you're building. Um, it's you would want to actually take advantage of cashing functionalities, um, for the build, uh, itself. Um, but if, um, you know, as you mentioned, there could be some instances where you would want to disable, um, any caching because you actually want to either pull a new packages or, um, you know, there could be some security, um, uh, disadvantages related to security aspects that would, you know, you know, using a cache version of, uh, image layer, for example, could be a problem. And you, you know, if you have a fleet of build, uh, engines, you don't have a good grasp of where they're being cashed. We would have to, um, disable caching in that, in that, um, in those instances. So it, it would depend. >>Yeah, it's, it's funny you have that problem on both sides of cashing. Like there are things that, especially in Docker world, they will cash automatically. And, and then, and then you maybe don't realize that some of that caching could be bad. It's, it's actually using old, uh, old assets, old artifacts, and then there's times where you would expect it to cash, that it doesn't cash. And then you have to do something extra to enable that caching, especially when you're dealing with that cluster of, of CIS servers. Right. And the cloud, the whole clustering problem with caching is even more complex, but yeah, >>But that's, that's when, >>Uh, you know, ever since I asked you to start using build kits and able to build kit, you know, between it's it's it's reader of Boston in, in detecting word, you know, where in, in the bill process needs to cash, as well as, uh, the, the, um, you know, the process. I don't think I've seen any other, uh, approach there that comes close to how efficient, uh, that process can become how much time it can actually save. Uh, but again, I think, I think that's, for me that had been my default approach, unless I actually need something that I would intentionally to disable caching for that purpose, but the benefits, at least for me, the benefits of, um, how bill kit actually been processing my bills, um, from the builds as well as, you know, using the cash up until, you know, how it detects the, the difference in, in, in the assets within the Docker file had been, um, you know, uh, pretty, you know, outweigh the disadvantages that it brings in. So it, you know, take it each case by case. And based on that, determine if you want to use it, but definitely recommend those enabling >>In the absence of a reason not to, um, I definitely think that it's a good approach in terms of speed. Um, yeah, I say you cash until you have a good reason not to personally >>Catch by default. There you go. I think you catch by default. Yeah. Yeah. And, uh, the trick is, well, one, it's not always enabled by default, especially when you're talking about cross server. So that's a, that's a complexity for your SIS admins, or if you're on the cloud, you know, it's usually just an option. Um, I think it also is this, this veers into a little bit of, uh, the more you cash the in a lot of cases with Docker, like the, from like, if you're from images and checked every single time, if you're not pinning every single thing, if you're not painting your app version, you're at your MPN versions to the exact lock file definition. Like there's a lot of these things where I'm I get, I get sort of, I get very grouchy with teams that sort of let it, just let it all be like, yeah, we'll just build two images and they're totally going to have different dependencies because someone happened to update that thing and after whatever or MPM or, or, and so I get grouchy about that, cause I want to lock it all down, but I also know that that's going to create administrative burden. >>Like the team is now going to have to manage versions in a very much more granular way. Like, do we need to version two? Do we need to care about curl? You know, all that stuff. Um, so that's, that's kind of tricky, but when you get to, when you get to certain version problems, uh, sorry, uh, cashing problems, you, you, you don't want those set those caches to happen because it, if you're from image changes and you're not constantly checking for a new image, and if you're not pinning that V that version, then now you, you don't know whether you're getting the latest version of Davion or whatever. Um, so I think that there's, there's an art form to the more you pen, the less you have, the less, you have to be worried about things changing, but the more you pen, the, uh, all your versions of everything all the way down the stack, the more administrative stuff, because you're gonna have to manually change every one of those. >>So I think it's a balancing act for teams. And as you mature, I to find teams, they tend to pin more until they get to a point of being more comfortable with their testing. So the other side of this argument is if you trust your testing, then you, and you have better testing to me, the less likely to the subtle little differences in versions have to be penned because you can get away with those minor or patch level version changes. If you're thoroughly testing your app, because you're trusting your testing. And this gets us into a whole nother rant, but, uh, yeah, but talking >>About penny versions, if you've got a lot of dependencies isn't that when you would want to use the cash the most and not have to rebuild all those layers. Yeah. >>But if you're not, but if you're not painting to the exact patch version and you are caching, then you're not technically getting the latest versions because it's not checking for all the time. It's a weird, there's a lot of this subtle nuance that people don't realize until it's a problem. And that's part of the, the tricky part of allow this stuff, is it, sometimes the Docker can be almost so much magic out of the box that you, you, you get this all and it all works. And then day two happens and you built it a second time and you've got a new version of open SSL in there and suddenly it doesn't work. Um, so anyway, uh, that was a great question. I've done the question on this, on, uh, from heavy. What do you put, where do you put testing in your pipeline? Like, so testing the code cause there's lots of types of testing, uh, because this pipeline gets longer and longer and Docker building images as part of it. And so he says, um, before staging or after staging, but before production, where do you put it? >>Oh man. Okay. So, um, my, my main thought on this is, and of course this is kind of religious flame bait, so sure. You know, people are going to go into the compensation wrong. Carlos, the boy is how I like to think about it. So pretty much in every stage or every environment that you're going to be deploying your app into, or that your application is going to touch. My idea is that there should be a build of a Docker image that has all your applications coded in, along with its dependencies, there's testing that tests your application, and then there's a deployment that happens into whatever infrastructure there is. Right. So the testing, they can get tricky though. And the type of testing you do, I think depends on the environment that you're in. So if you're, let's say for example, your team and you have, you have a main branch and then you have feature branches that merged into the main branch. >>You don't have like a pre-production branch or anything like that. So in those feature branches, whenever I'm doing CGI that way, I know when I freak, when I cut my poll request, that I'm going to merge into main and everything's going to work in my feature branches, I'm going to want to probably just run unit tests and maybe some component tests, which really, which are just, you know, testing that your app can talk to another component or another part, another dependency, like maybe a database doing tests like that, that don't take a lot of time that are fascinating and right. A lot of would be done at the beach branch level and in my opinion, but when you're going to merge that beach branch into main, as part of a release in that activity, you're going to want to be able to do an integration tasks, to make sure that your app can actually talk to all the other dependencies that it talked to. >>You're going to want to do an end to end test or a smoke test, just to make sure that, you know, someone that actually touches the application, if it's like a website can actually use the website as intended and it meets the business cases and all that, and you might even have testing like performance testing, low performance load testing, or security testing, compliance testing that would want to happen in my opinion, when you're about to go into production with a release, because those are gonna take a long time. Those are very expensive. You're going to have to cut new infrastructure, run those tests, and it can become quite arduous. And you're not going to want to run those all the time. You'll have the resources, uh, builds will be slower. Uh, release will be slower. It will just become a mess. So I would want to save those for when I'm about to go into production. Instead of doing those every time I make a commit or every time I'm merging a feature ranch into a non main branch, that's the way I look at it, but everything does a different, um, there's other philosophies around it. Yeah. >>Well, I don't disagree with your build test deploy. I think if you're going to deploy the code, it needs to be tested. Um, at some level, I mean less the same. You've got, I hate the term smoke tests, cause it gives a false sense of security, but you have some mental minimum minimal amount of tests. And I would expect the developer on the feature branch to add new tests that tested that feature. And that would be part of the PR why those tests would need to pass before you can merge it, merge it to master. So I agree that there are tests that you, you want to run at different stages, but the earlier you can run the test before going to production. Um, the fewer issues you have, the easier it is to troubleshoot it. And I kind of agree with what you said, Carlos, about the longer running tests like performance tests and things like that, waiting to the end. >>The only problem is when you wait until the end to run those performance tests, you kind of end up deploying with whatever performance you have. It's, it's almost just an information gathering. So if you don't run your performance test early on, um, and I don't want to go down a rabbit hole, but performance tests can be really useless if you don't have a goal where it's just information gap, uh, this is, this is the performance. Well, what did you expect it to be? Is it good? Is it bad? They can get really nebulous. So if performance is really important, um, you you're gonna need to come up with some expectations, preferably, you know, set up the business level, like what our SLA is, what our response times and have something to shoot for. And then before you're getting to production. If you have targets, you can test before staging and you can tweak the code before staging and move that performance initiative. Sorry, Carlos, a little to the left. Um, but if you don't have a performance targets, then it's just a check box. So those are my thoughts. I like to test before every deployment. Right? >>Yeah. And you know what, I'm glad that you, I'm glad that you brought, I'm glad that you brought up Escalades and performance because, and you know, the definition of performance says to me, because one of the things that I've seen when I work with teams is that oftentimes another team runs a P and L tests and they ended, and the development team doesn't really have too much insight into what's going on there. And usually when I go to the performance team and say, Hey, how do you run your performance test? It's usually just a generic solution for every single application that they support, which may or may not be applicable to the application team that I'm working with specifically. So I think it's a good, I'm not going to dig into it. I'm not going to dig into the rabbit hole SRE, but it is a good bridge into SRE when you start trying to define what does reliability mean, right? >>Because the reason why you test performance, it's test reliability to make sure that when you cut that release, that customers would go to your site or use your application. Aren't going to see regressions in performance and are not going to either go to another website or, you know, lodge in SLA violation or something like that. Um, it does, it does bridge really well with defining reliability and what SRE means. And when you have, when you start talking about that, that's when you started talking about how often do I run? How often do I test my reliability, the reliability of my application, right? Like, do I have nightly tasks in CGI that ensure that my main branch or, you know, some important branch I does not mean is meeting SLA is meeting SLR. So service level objectives, um, or, you know, do I run tasks that ensure that my SLA is being met in production? >>Like whenever, like do I use, do I do things like game days where I test, Hey, if I turn something off or, you know, if I deploy this small broken code to production and like what happens to my performance? What happens to my security and compliance? Um, you can, that you can go really deep into and take creating, um, into creating really robust tests that cover a lot of different domains. But I liked just using build test deploy is the overall answer to that because I find that you're going to have to build your application first. You're going to have to test it out there and build it, and then you're going to want to deploy it after you test it. And that order generally ensures that you're releasing software. That works. >>Right. Right. Um, I was going to ask one last question. Um, it's going to have to be like a sentence answer though, for each one of you. Uh, this is, uh, do you lint? And if you lint, do you lent all the things, if you do, do you fail the linters during your testing? Yes or no? I think it's going to depend on the culture. I really do. Sorry about it. If we >>Have a, you know, a hook, uh, you know, on the get commit, then theoretically the developer can't get code there without running Melinta anyway, >>So, right, right. True. Anyone else? Anyone thoughts on that? Linting >>Nice. I saw an additional question online thing. And in the chat, if you would introduce it in a multi-stage build, um, you know, I was wondering also what others think about that, like typically I've seen, you know, with multi-stage it's the most common use case is just to produce the final, like to minimize the, the, the, the, the, the image size and produce a final, you know, thin, uh, layout or thin, uh, image. Uh, so if it's not for that, like, I, I don't, I haven't seen a lot of, you know, um, teams or individuals who are actually within a multi-stage build. There's nothing really against that, but they think the number one purpose of doing multi-stage had been just producing the minimalist image. Um, so just wanted to kind of combine those two answers in one, uh, for sure. >>Yeah, yeah, sure. Um, and with that, um, thank you all for the great questions. We are going to have to wrap this up and we could go for another hour if we all had the time. And if Dr. Khan was a 24 hour long event and it didn't sadly, it's not. So we've got to make room for the next live panel, which will be Peter coming on and talking about security with some developer ex security experts. And I wanted to thank again, thank you all three of you for being here real quick, go around the room. Um, uh, where can people reach out to you? I am, uh, at Bret Fisher on Twitter. You can find me there. Carlos. >>I'm at dev Mandy with a Y D E N D Y that's me, um, >>Easiest name ever on Twitter, Carlos and DFW on LinkedIn. And I also have a LinkedIn learning course. So if you check me out on my LinkedIn learning, >>Yeah. I'm at Nicola Quebec. Um, one word, I'll put it in the chat as well on, on LinkedIn, as well as, uh, uh, as well as Twitter. Thanks for having us, Brett. Yeah. Thanks for being here. >>Um, and, and you all stay around. So if you're in the room with us chatting, you're gonna, you're gonna, if you want to go to see the next live panel, I've got to go back to the beginning and do that whole thing, uh, and find the next, because this one will end, but we'll still be in chat for a few minutes. I think the chat keeps going. I don't actually know. I haven't tried it yet. So we'll find out here in a minute. Um, but thanks you all for being here, I will be back a little bit later, but, uh, coming up next on the live stuff is Peter Wood security. Ciao. Bye.

Published Date : May 28 2021

SUMMARY :

Uh, thank you so much to my guests welcoming into the panel. Virginia, and, uh, I make videos on the internet and courses on you to me, So, um, it's been fun and I'm excited to meet with all of you and talk Uh, just, uh, you know, keeping that, to remember all the good days, um, uh, moving into DX to try and help developers better understand and use our products And so for those of you in chat, the reason we're doing this So feel free to, um, ask what you think is on the top of your And don't have to go talk to a person to run that Um, and so being the former QA on the team, So, um, uh, Carlos, And, you know, So, uh, Nico 81st thoughts on that? kind of the scope that had, uh, you know, now in conferences, what we're using, uh, you know, whether your favorite tools. if you want to do something, you don't have to write the code it's already been tested. You got to unmute. And, you know, the way it works, enterprise CIO CD, if you want, especially if you want to roll your own or own it yourself, um, Um, and you know, the API is really great. I mean, I, I feel with you on the Travis, the, I think, cause I think that was my first time experiencing, And there's probably, you know, And I CA I can't give you a better solution. Um, when you go searching for Docker, and then start browsing for plugins to see if you even want to use those. Some of the things that you input might be available later what say you people? So if you have a lot of small changes that are being made and time-consuming, um, um, you know, within, within your pipeline. hole for the rest of the day on storage management, uh, you know, CP CPU We have, uh, you know, we know get tags and there's Um, it's just clean and I like the timestamp, you know, exactly when it was built. Um, in fact, you know, I'm running into that right now, telling the script, telling the team that maintains a script, Hey, you know, you should use somber and you should start thinking I think you hit on something interesting beyond just how to version, but, um, when to you know, I don't know, having them say, okay, you must tell me what a major version is. If they want it to use some birds great too, which is why I think going back to what you originally said, a consistent packaging solution for me to get my code, you know, Uh, you know, the Docker Docker file is not the most perfect way to describe how to make your app, To that, to that, Brett, um, you know, uh, just maybe more of So similar to how you can think of Terraform and having that pluggability to say Terraform uh, D essentially, do you use compose in your CIO or not Docker compose? different than what you would do in your local, in your local dev. I'm shifting the CIO left to your local development is trying to say, you know, you can get away with just Docker commands. And, um, you know, to your point, the number of CGI cycles to get, you know, the test, the test data that you need. Um, I don't know if you all were able to see the keynote, but there was a, there was a little bit, And you won't have to necessarily have dependencies inside of where you're running it because So that, that would be interesting for those of you that are not watching that one. I'm going to quote Carlos again and say, it depends on, on, you know, how you're talking, you know, And then you have to do something extra to enable that caching, in, in the assets within the Docker file had been, um, you know, Um, yeah, I say you cash until you have a good reason not to personally uh, the more you cash the in a lot of cases with Docker, like the, there's an art form to the more you pen, the less you have, So the other side of this argument is if you trust your testing, then you, and you have better testing to the cash the most and not have to rebuild all those layers. And then day two happens and you built it a second And the type of testing you do, which really, which are just, you know, testing that your app can talk to another component or another you know, someone that actually touches the application, if it's like a website can actually Um, the fewer issues you have, the easier it is to troubleshoot it. So if you don't run your performance test early on, um, and you know, the definition of performance says to me, because one of the things that I've seen when I work So service level objectives, um, or, you know, do I run Hey, if I turn something off or, you know, if I deploy this small broken code to production do you lent all the things, if you do, do you fail the linters during your testing? So, right, right. And in the chat, if you would introduce it in a multi-stage build, And I wanted to thank again, thank you all three of you for being here So if you check me out on my LinkedIn Um, one word, I'll put it in the chat as well on, Um, but thanks you all for being here,

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Bill Sharp, EarthCam Inc. | Dell Technologies World 2020


 

>>from around the globe. It's the Cube with digital coverage of Dell Technologies. World Digital Experience Brought to You by Dell Technologies. >>Welcome to the Cubes Coverage of Dell Technologies World 2020. The digital coverage Find Lisa Martin And then we started to be talking with one of Dell Technologies customers. Earth Camp. Joining Me is built sharp, the senior VP of product development and strategy from Earth Camp Phil, Welcome to the Cube. >>Thank you so much. >>So talk to me a little bit. About what Earth Cam does this very interesting Web can technology? You guys have tens of thousands of cameras and sensors all over the globe give her audience and understanding of what you guys are all about. >>Sure thing. The world's leading provider of Webcam technologies and mentioned content services were leaders and live streaming time lapse imaging primary focus in the vertical construction. So a lot of these, the most ambitious, largest construction projects around the world, you see, these amazing time lapse movies were capturing all of that imagery. You know, basically, around the clock of these cameras are are sending all of that image content to us when we're generating these time lapse movies from it. >>You guys, you're headquartered in New Jersey and I was commenting before we went live about your great background. So you're actually getting to be on site today? >>Yes, Yes, that's where lives from our headquarters in Upper Saddle River, New Jersey. >>Excellent. So in terms of the types of information that you're capturing. So I was looking at the website and see from a construction perspective or some of the big projects you guys have done the Hudson Yards, the Panama Canal expansion, the 9 11 Museum. But you talked about one of the biggest focus is that you have is in the construction industry in terms of what type of data you're capturing from all of these thousands of edge devices give us a little bit of insight into how much data you're capturing high per day, how it gets from the edge, presumably back to your court data center for editing. >>Sure, and it's not just construction were also in travel, hospitality, tourism, security, architectural engineering, basically, any any industry that that need high resolution visualization of their their projects or their their performance or of their, you know, product flow. So it's it's high resolution documentation is basically our business. There are billions of files in the isil on system right now. We are ingesting millions of images a month. We are also creating very high resolution panoramic imagery where we're taking hundreds and sometimes multiple hundreds of images, very high resolution images and stitching these together to make panoramas that air up to 30 giga pixel, sometimes typically around 1 to 2 giga pixel. But that composite imagery Eyes represents millions of images per per month coming into the storage system and then being, uh, stitched together to those those composites >>the millions of images coming in every month. You mentioned Isil on talk to me a little bit about before you were working with Delhi, EMC and Power Scale. How are you managing this massive volume of data? >>Sure we had. We've used a number of other enterprise storage systems. It was really nothing was as easy to manage Azazel on really is there was there was a lot of a lot of problems with overhead, the amount of time necessary from a systems administrator resource standpoint, you to manage that, uh, and and it's interesting with the amount of data that we handle. This is being billions of relatively small files there there, you know, half a megabyte to a couple of megabytes each. It's an interesting data profile, which, which isil on really is well suited for. >>So if we think about some of the massive changes that we've all been through the last in 2020 what are some of the changes that that Earth Kemp has seen with respect to the needs for organizations? Or you mentioned other industries, like travel hospitality? Since none of us could get to these great travel destinations, Have you seen a big drive up in the demand and the need to process data more data faster? >>Yeah, that's an injury interesting point with with the Pandemic. Obviously we had to pivot and move a lot of people toe working from home, which we were able to do pretty quickly. But there's also an interesting opportunity that arose from this, where so many of our customers and other people also have to do the same. And there is an increased demand for our our technology so people can remotely collaborate. They can. They can work at a distance. They can stay at home and see what's going on in these projects sites. So we really so kind of an uptick in the in the need for our products and services. And we've also created Cem basically virtual travel applications. We have an application on the Amazon Fire TV, which is the number one app in the travel platform of people can kind of virtually travel when they can't really get out there. So it's, uh, we've been doing kind of giving back Thio to people that are having having some issues with being able to travel around. We've done the fireworks of the Washington Mall around the Statue of Liberty for the July 4th, and this year will be Webcasting and New Year's in Times Square for our 25th year, actually. So again, helping people travel virtually and be, uh, maintain can be collectivity with with each other and with their projects, >>which is so essential during these times, where for the last 67 months everyone is trying to get a sense of community, and most of us just have the Internet. So I also heard you guys were available on Apple TV, someone to fire that up later and maybe virtually travel. Um, but tell me a little bit about how working in conjunction with Delta Technologies and Power Cell How is that enabled you to manage this massive volume change you've experienced this year? Because, as you said, it's also about facilitating collaboration, which is largely online these days. >>Yeah, I mean, the the great things they're working with Dell has been just our confidence in this infrastructure. Like I said, the other systems we worked with in the past we've always found ourselves kind of second guessing. Obviously, resolutions are increasing. The camera performance is increasing. Streaming video is everything is is constantly getting bigger and better, faster. Maurits And we're always innovating. We found ourselves on previous storage platforms having to really kind of go back and look at the second guess we're at with it With with this, this did L infrastructure. That's been it's been fantastic. We don't really have to think about that as much. We just continue innovating everything scales as we needed to dio. It's it's much easier to work with, >>so you've got power scale at your core data center in New Jersey. Tell me a little bit about how data gets from thes tens of thousands of devices at the edge, back to your editors for editing and how power scale facilitates faster editing, for example. >>Basically, you imagine every one of these cameras on It's not just camera. We have mobile applications. We have fixed position of robotic cameras. There's all these different data acquisition systems were integrating with weather sensors and different types of telemetry. All of that data is coming back to us over the Internet, so these are all endpoints in our network. Eso that's that's constantly being ingested into our network and say WTO. I salon the big the big thing that's really been a timesaver Working with the video editors is, instead of having to take that content, move it into an editing environment where we have we have a whole team of award winning video editors. Creating these time lapse is we don't need to keep moving that around. We're working natively on Iselin clusters. They're doing their editing, their subsequent edits. Anytime we have to update or change these movies as a project evolves, that's all it happened right there on that live environment on the retention. Is there if we have to go back later on all of our customers, data is really kept within that 11 area. It's consolidated, its secure. >>I was looking at the Del Tech website. There's a case study that you guys did earth campaign with Deltek saying that the video processing time has been reduced 20%. So that's a pretty significant increase. I could imagine what the volumes changing so much now but on Li not only is huge for your business, but to the demands that your customers have as well, depending on where there's demands are coming from >>absolutely and and just being able to do that a lot faster and be more nimble allows us to scale. We've added actually against speaking on this pandemic, we've actually added person who we've been hiring people. A lot of those people are working remotely, as as we've stated before on it's just with the increase in business. We have to continue to keep building on that on this storage environments been been great. >>Tell me about what you guys really kind of think about with respect to power scale in terms of data management, not storage management and what that difference means to your business. >>Well, again, I mean number number one was was really eliminating the amount of resource is amount of time we have to spend managing it. We've almost eliminated any downtime of any of any kind. We have greater storage density, were able to have better visualization on how our data is being used, how it's being access so as thes as thes things, a revolving. We really have good visibility on how the how the storage system is being used in both our production and our and also in our backup environments. It's really, really easy for us Thio to make our business decisions as we innovate and change processes, having that continual visibility and really knowing where we stand. >>And you mentioned hiring folks during the pandemic, which is fantastic but also being able to do things much in a much more streamlined way with respect to managing all of this data. But I am curious in terms of of innovation and new product development. What have you been able to achieve because you've got more resource is presumably to focus on being more innovative rather than managing storage >>well again? It's were always really pushing the envelope of what the technology can do. As I mentioned before, we're getting things into, you know, 20 and 30 Giga pixel. You know, people are talking about megapixel images were stitching hundreds of these together. We've we're just really changing the way imagery is used, uh, both in the time lapse and also just in archival process. Ah, lot of these things we've done with the interior. You know, we have this virtual reality product where you can you can walk through and see in the 3 60 bubble. We're taking that imagery, and we're combining it with with these been models who are actually taking the three D models of the construction site and combining it with the imagery. And we can start doing things to visualize progress and different things that are happening on the site. Look for clashes or things that aren't built like they're supposed to be built, things that maybe aren't done on the proper schedule or things that are maybe ahead of schedule, doing a lot of things to save people, time and money on these construction sites. We've also introduced a I machine learning applications into directly into the workflow in this in the storage environment. So we're detecting equipment and people and activities in the site where a lot of that would have been difficult with our previous infrastructure, it really is seamless and working with YSL on now. >>Imagine, by being able to infuse AI and machine learning, you're able to get insight faster to be ableto either respond faster to those construction customers, for example, or alert them. If perhaps something isn't going according to plan. >>A lot of it's about schedule. It's about saving money about saving time and again, with not as many people traveling to the sites, they really just have have constant visualization of what's going on. Day to day, we're detecting things like different types of construction equipment and things that are happening on the side. We're partnering with people that are doing safety analytics and things of that nature. So these these are all things that are very important to construction sites. >>What are some of the things as we are rounding out the calendar year 2020? What are some of the things that you're excited about going forward in 2021? That Earth cam is going to be able to get into and to deliver >>it, just MAWR and more people really, finally seeing the value. I mean, I've been doing this for 20 years, and it's just it's it's It's amazing how we're constantly seeing new applications and more people understanding how valuable these visual tools are. That's just a fantastic thing for us because we're really trying to create better lives through visual information. We're really helping people with things they can do with this imagery. That's what we're all about that's really exciting to us in a very challenging environment right now is that people are are recognizing the need for this technology and really starting to put it on a lot more projects. >>Well, it's You can kind of consider an essential service, whether or not it's a construction company that needs to manage and oversee their projects, making sure they're on budget on schedule, as you said, Or maybe even just the essential nous of helping folks from any country in the world connect with a favorite favorite travel location or sending the right to help. From an emotional perspective, I think the essential nous of what you guys are delivering is probably even more impactful now, don't you think? >>Absolutely and again about connecting people and when they're at home. And recently we we webcast the president's speech from the Flight 93 9 11 observation from the memorial. There was something where the only the immediate families were allowed to travel there. We webcast that so people could see that around the world we have documented again some of the biggest construction projects out there. The new rate years greater stadium was one of the recent ones, uh, is delivering this kind of flagship content. Wall Street Journal is to use some of our content recently to really show the things that have happened during the pandemic in Times Square's. We have these cameras around the world. So again, it's really bringing awareness of letting people virtually travel and share and really remain connected during this this challenging time on and again, we're seeing a really increase demand in the traffic in those areas as well. >>I can imagine some of these things that you're doing that you're achieving now are going to become permanent, not necessarily artifacts of Cove in 19 as you now have the opportunity to reach so many more people and probably the opportunity to help industries that might not have seen the value off this type of video to be able to reach consumers that they probably could never reach before. >>Yeah, I think the whole nature of business and communication and travel on everything is really going to be changed from this point forward. It's really people are looking at things very, very differently and again, seeing the technology really can help with so many different areas that, uh, that it's just it's gonna be a different kind of landscape out there we feel on that's really, you know, continuing to be seen on the uptick in our business and how many people are adopting this technology. We're developing a lot more. Partnerships with other companies were expanding into new industries on again. You know, we're confident that the current platform is going to keep up with us and help us, you know, really scale and evolved as thes needs air growing. >>It sounds to me like you have the foundation with Dell Technologies with power scale to be able to facilitate the massive growth that you're saying and the skill in the future like you've got that foundation. You're ready to go? >>Yeah, we've been We've been We've been using the system for five years already. We've already added capacity. We can add capacity on the fly, Really haven't hit any limits. And what we can do, It's It's almost infinitely scalable, highly redundant. Gives everyone a real sense of security on our side. And, you know, we could just keep innovating, which is what we do without hitting any any technological limits with with our partnership. >>Excellent. Well, Bill, I'm gonna let you get back to innovating for Earth camp. It's been a pleasure talking to you. Thank you so much for your time today. >>Thank you so much. It's been a pleasure >>for Bill Sharp and Lisa Martin. You're watching the cubes. Digital coverage of Dell Technologies World 2020. Thanks for watching. Yeah,

Published Date : Oct 22 2020

SUMMARY :

It's the Cube with digital coverage of Dell The digital coverage Find Lisa Martin And then we started to be talking with one of Dell Technologies So talk to me a little bit. You know, basically, around the clock of these cameras are are sending all of that image content to us when we're generating So you're actually getting to be on site today? have is in the construction industry in terms of what type of data you're capturing There are billions of files in the isil on system right You mentioned Isil on talk to me a little bit about before lot of problems with overhead, the amount of time necessary from a systems administrator resource We have an application on the Amazon Fire TV, which is the number one app in the travel platform of people So I also heard you guys were available on Apple TV, having to really kind of go back and look at the second guess we're at with it With with this, thes tens of thousands of devices at the edge, back to your editors for editing and how All of that data is coming back to us There's a case study that you guys did earth campaign with Deltek saying that absolutely and and just being able to do that a lot faster and be more nimble allows us Tell me about what you guys really kind of think about with respect to power scale in to make our business decisions as we innovate and change processes, having that continual visibility and really being able to do things much in a much more streamlined way with respect to managing all of this data. of the construction site and combining it with the imagery. Imagine, by being able to infuse AI and machine learning, you're able to get insight faster So these these are all things that are very important to construction sites. right now is that people are are recognizing the need for this technology and really starting to put it on a lot or sending the right to help. the things that have happened during the pandemic in Times Square's. many more people and probably the opportunity to help industries that might not have seen the value seeing the technology really can help with so many different areas that, It sounds to me like you have the foundation with Dell Technologies with power scale to We can add capacity on the fly, Really haven't hit any limits. It's been a pleasure talking to you. Thank you so much. Digital coverage of Dell Technologies World

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Bill Sharp V1


 

>> Announcer: From around the globe, it's theCUBE! With digital coverage of Dell Technologies World, digital experience. Brought to you by Dell Technologies. >> Welcome to theCUBE's coverage of Dell Technologies World 2020, the digital coverage. I'm Lisa Martin, and I'm excited to be talking with one of Dell Technologies' customers EarthCam. Joining me is Bill Sharp, the senior VP of product development and strategy from EarthCam. Bill, welcome to theCUBE. >> Thank you so much. >> So talk to me a little bit about what EarthCam does. This is very interesting webcam technology. You guys have tens of thousands of cameras and sensors all over the globe. Give our audience an understanding of what you guys are all about. >> Sure thing. The world's leading provider of webcam technologies, you mentioned content and services, we're leaders in live streaming, time-lapse imaging, primary focus in the vertical construction. So with a lot of these, the most ambitious, largest construction projects around the world that you see these amazing time-lapse movies, we're capturing all of that imagery basically around the clock, these cameras are sending all of that image content to us and we're generating these time-lapse movies from it. >> You guys are headquartered in New Jersey. I was commenting before we went live about your great background. So you're actually getting to be onsite today? >> Yes, yes. We're live from our headquarters in upper Saddle River, New Jersey. >> Excellent, so in terms of the types of information that you're capturing, so I was looking at the website, and see from a construction perspective, some of the big projects you guys have done, the Hudson Yards, the Panama Canal expansion, the 9/11 museum. But you talked about one of the biggest focuses that you have is in the construction industry. In terms of what type of data you're capturing from all of these thousands of edge devices, give us a little bit of an insight into how much data you're capturing per day, how it gets from the edge, presumably, back to your core data center for editing. >> Sure, and it's not just construction. We're also in travel, hospitality, tourism, security, architecture, engineering, basically any industry that need high resolution visualization of their projects or their performance or their product flow. So it's high resolution documentation is basically our business. There are billions of files in the Isilon system right now. We are ingesting millions of images a month. We are also creating very high resolution panoramic imagery where we're taking hundreds and sometimes multiple hundreds of images, very high resolution images and stitching these together to make panoramas that are up to 30 gigapixel sometimes. Typically around one to two gigapixel but that composite imagery represents millions of images per month coming into the storage system and then being stitched together to those composites. >> So millions of images coming in every month, you mentioned Isilon. Talk to me a little bit about before you were working with Dell EMC and PowerScale, how were you managing this massive volume of data? >> Sure, we've used a number of other enterprise storage systems. It was really nothing was as easy to manage as Isilon really is. There was a lot of problems with overhead, the amount of time necessary from a systems administrator resource standpoint, to manage that. And it's interesting with the amount of data that we handle, being billions of relatively small files. They're, you know, a half a megabyte to a couple of megabytes each. It's an interesting data profile which Isilon really is well suited for. >> So if we think about some of the massive changes that we've all been through in the last, in 2020, what are some of the changes that EarthCam hasn't seen with respect to the needs for organizations, or you mentioned other industries like travel, hospitality, since none of us can get to these great travel destinations, have you seen a big drive up in the demand and the need to process more data faster? >> Yeah, that's an interesting point with the pandemic. I mean, obviously we had to pivot and move a lot of people to working from home, which we were able to do pretty quickly, but there's also an interesting opportunity that arose from this where so many of our customers and other people also have to do the same. And there is an increased demand for our technology. So people can remotely collaborate. They can work at a distance, they can stay at home and see what's going on in these project sites. So we really saw kind of an uptick in the need for our products and services. And we've also created some basically virtual travel applications. We have an application on the Amazon Fire TV which is the number one app in the travel platform, and people can kind of virtually travel when they can't really get out there. So it's, we've been doing kind of giving back to people that are having some issues with being able to travel around. We've done the fireworks at the Washington Mall around the Statue of Liberty for July 4th. And this year we'll be webcasting New Years in Times Square for our 25th year, actually. So again, helping people travel virtually and maintain connectivity with each other, and with their projects. >> Which is so essential during these times where for the last six, seven months, everyone is trying to get a sense of community and most of us just have the internet. So I also heard you guys were available on the Apple TV, someone should fire that up later and maybe virtually travel. But tell me a little bit about how working in conjunction with Dell Technologies and PowerScale. How has that enabled you to manage this massive volume change that you've experienced this year? Because as you said, it's also about facilitating collaboration which is largely online these days. >> Yeah, and I mean, the great things of working with Dell has been just our confidence in this infrastructure. Like I said, the other systems we've worked with in the past we've always found ourselves kind of second guessing. We're constantly innovating. Obviously resolutions are increasing. The camera performance is increasing, streaming video is, everything is constantly getting bigger and better, faster, more, and we're always innovating. We found ourselves on previous storage platforms having to really kind of go back and look at them, second guess where we're at with it. With the Dell infrastructure it's been fantastic. We don't really have to think about that as much. We just continue innovating, everything scales as we need it to do. It's much easier to work with. >> So you've got PowerScale at your core data center in New Jersey. Tell me a little bit about how data gets from these tens of thousands of devices at the edge, back to your editors for editing, and how PowerScale facilitates faster editing, for example. >> Well, basically you can imagine every one of these cameras, and it's not just cameras. It's also, you know, we have 360 virtual reality kind of bubble cameras. We have mobile applications, we have fixed position and robotic cameras. There's all these different data acquisition systems we're integrating with weather sensors and different types of telemetry. All of that data is coming back to us over the internet. So these are all endpoints in our network. So that's constantly being ingested into our network and saved to Isilon. The big thing that's really been a time saver working with the video editors is instead of having to take that content, move it into an editing environment where we have a whole team of award-winning video editors creating these time lapses. We don't need to keep moving that around. We're working natively on Isilon clusters. They're doing their editing there, and subsequent edits. Anytime we have to update or change these movies as a project evolves, that's all, can happen right there on that live environment. And the retention is there. If we have to go back later on, all of our customers' data is really kept within that one area, it's consolidated and it's secure. >> I was looking at the Dell Tech website, and there's a case study that you guys did, EarthCam did with Dell Tech saying that the video processing time has been reduced 20%. So that's a pretty significant increase. I can imagine with the volumes changing so much now, not only is huge to your business but to the demands that your customers have as well, depending on where those demands are coming from. >> Absolutely. And just being able to do that a lot faster and be more nimble allows us to scale. We've added actually, again, speaking of during this pandemic, we've actually added personnel, we've been hiring people. A lot of those people are working remotely as we've stated before. And it's just with the increase in business, we have to continue to keep building on that, and this storage environment's been great. >> Tell me about what you guys really kind of think about with respect to PowerScale in terms of data management, not storage management, and what that difference means to your business. >> Well, again, I mean, number one was really eliminating the amount of resources. The amount of time we have to spend managing it. We've almost eliminated any downtime of any kind. We have greater storage density, we're able to have better visualization on how our data is being used, how it's being accessed. So as these things are evolving, we really have good visibility on how the storage system is being used in both our production and also in our backup environments. It's really, really easy for us to make our business decisions as we innovate and change processes, having that continual visibility and really knowing where we stand. >> And you mentioned hiring folks during the pandemic, which is fantastic, but also being able to do things in a much more streamlined way with respect to managing all of this data. But I am curious in terms of innovation and new product development, what have you been able to achieve? Because you've got more resources presumably to focus on being more innovative rather than managing storage. >> Well, again, it's, we're always really pushing the envelope of what the technology can do. As I mentioned before, we're getting things into, you know, 20 and 30 gigapixels, people are talking about megapixel images, we're stitching hundreds of these together. We're just really changing the way imagery is used both in the time lapse and also just in archival process. A lot of these things we've done with the interior, we have this virtual reality product where you can walk through and see in a 360 bubble, we're taking that imagery and we're combining it with these BIM models. So we're actually taking the 3D models of the construction site and combining it with the imagery. And we can start doing things to visualize progress, and different things that are happening on the site, look for clashes or things that aren't built like they're supposed to be built, things that maybe aren't done on the proper schedule or things that are maybe ahead of schedule, doing a lot of things to save people time and money on these construction sites. We've also introduced AI and machine learning applications directly into the workflow in the storage environment. So we're detecting equipment and people and activities in the site where a lot of that would have been difficult with our previous infrastructure. It really is seamless and working with Isilon now. >> I imagine by being able to infuse AI and machine learning, you're able to get insights faster, to be able to either respond faster to those construction customers, for example, or alert them if perhaps something isn't going according to plan. >> Yeah, a lot of it's about schedule, it's about saving money, about saving time. And again, with not as many people traveling to these sites, they really just have to have constant visualization of what's going on day to day. We're detecting things like different types of construction equipment and things that are happening on the site. We're partnering with people that are doing safety analytics and things of that nature. So these are all things that are very important to construction sites. >> What are some of the things as we are rounding out the calendar year 2020, what are some of the things that you're excited about going forward in 2021, that EarthCam is going to be able to get into and to deliver? >> Just more and more people really finally seeing the value. I mean I've been doing this for 20 years and it's just, it's amazing how we're constantly seeing new applications and more people understanding how valuable these visual tools are. That's just a fantastic thing for us because we're really trying to create better lives through visual information. We're really helping people with the things they can do with this imagery. That's what we're all about. And that's really exciting to us in a very challenging environment right now is that people are recognizing the need for this technology and really starting to put it on a lot more projects. >> Well, you can kind of consider it an essential service whether or not it's a construction company that needs to manage and oversee their projects, making sure they're on budget, on schedule, as you said, or maybe even just the essentialness of helping folks from any country in the world connect with a favorite travel location, or (indistinct) to help from an emotional perspective. I think the essentialness of what you guys are delivering is probably even more impactful now, don't you think? >> Absolutely. And again about connecting people when they're at home, and recently we webcast the president's speech from the Flight 93 9/11 observation from the memorial, there was something where only the immediate families were allowed to travel there. We webcast that so people could see that around the world. We've documented, again, some of the biggest construction projects out there, the new Raiders stadium was one of the recent ones, just delivering this kind of flagship content. Wall Street Journal has used some of our content recently to really show the things that have happened during the pandemic in Times Square. We have these cameras around the world. So again, it's really bringing awareness. So letting people virtually travel and share and really remain connected during this challenging time. And again, we're seeing a real increased demand in the traffic in those areas as well. >> I can imagine some of these things that you're doing that you're achieving now are going to become permanent not necessarily artifacts of COVID-19, as you now have the opportunity to reach so many more people and probably the opportunity to help industries that might not have seen the value of this type of video to be able to reach consumers that they probably could never reach before. >> Yeah, I think the whole nature of business and communication and travel and everything is really going to be changed from this point forward. It's really, people are looking at things very, very differently. And again, seeing that the technology really can help with so many different areas that it's just, it's going to be a different kind of landscape out there we feel. And that's really continuing to be seen as on the uptick in our business and how many people are adopting this technology. We're developing a lot more partnerships with other companies, we're expanding into new industries. And again, you know, we're confident that the current platform is going to keep up with us and help us really scale and evolve as these needs are growing. >> It sounds to me like you have the foundation with Dell Technologies, with PowerScale, to be able to facilitate the massive growth that you were saying and the scale in the future, you've got that foundation, you're ready to go. >> Yeah, we've been using the system for five years already. We've already added capacity. We can add capacity on the fly, really haven't hit any limits in what we can do. It's almost infinitely scalable, highly redundant. It gives everyone a real sense of security on our side. And you know, we can just keep innovating, which is what we do, without hitting any technological limits with our partnership. >> Excellent, well, Bill, I'm going to let you get back to innovating for EarthCam. It's been a pleasure talking to you. Thank you so much for your time today. >> Thank you so much. It's been a pleasure. >> For Bill Sharp, I'm Lisa Martin, you're watching theCUBE's digital coverage of Dell Technologies World 2020. Thanks for watching. (calm music)

Published Date : Oct 6 2020

SUMMARY :

Brought to you by Dell Technologies. excited to be talking of what you guys are all about. of that image content to us to be onsite today? in upper Saddle River, New Jersey. one of the biggest focuses that you have coming into the storage system Talk to me a little bit about before the amount of time necessary and move a lot of people and most of us just have the internet. Yeah, and I mean, the great of devices at the edge, is instead of having to take that content, not only is huge to your business And just being able to means to your business. on how the storage system is being used also being able to do things and activities in the site to be able to either respond faster and things that are happening on the site. and really starting to put any country in the world see that around the world. and probably the opportunity And again, seeing that the to be able to facilitate We can add capacity on the fly, I'm going to let you get back Thank you so much. of Dell Technologies World 2020.

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Sanjay Poonen, VMware | AWS Summit Online 2020


 

>> Announcer: From theCUBE Studios in Palo Alto and Boston, connecting with thought leaders all around the world, this is a CUBE conversation. >> Hello, welcome back to theCUBE's coverage, CUBE Virtual's coverage, CUBE digital coverage, of AWS Summit, virtual online, Amazon Summit's normally in face-to-face all around the world, it's happening now online, follow the sun. Of course, we want to bring theCUBE coverage like we do at the events digitally, and we've got a great guest that usually comes on face-to-face, he's coming on virtual, Sanjay Poonen, the chief operating officer of VMware. Sanjay great to see you, thanks for coming in virtually, you look great. >> Hey, John thank you very much. Always a pleasure to talk to you. This is the new reality. We both happen to live very close to each other, me in Los Altos, you in Palo Alto, but here we are in this new mode of communication. But the good news is I think you guys at theCUBE were pioneering a lot of digital innovation, the AI platform, so hopefully it's not much of an adjustment for you guys to move digital. >> It's not really a pivot, just move the boat, put the sails up and sail into the next generation, which brings up really the conversation that we're seeing, which is this digital challenge, the virtual world, it's virtualization, Sanjay, it sounds like VMware. Virtualization spawned so much opportunity, it created Amazon, some say, I'd say. Virtualizing our world, life is now integrated, we're immersed into each other, physical and digital, you got edge computing, you got cloud native, this is now a clear path to customers that recognize with the pandemic challenges of at-scale, that they have to operate their business, reset, reinvent, and grow coming out of this pandemic. This has been a big story that we've been talking about and a lot of smart managers looking at projects saying, I'm doubling down on that, and I'm going to move the resources from this, the people and budget, to this new reality. This is a tailwind for the folks who were prepared, the ones that have the experience, the ones that did the work. theCUBE, thanks for the props, but VMware as well. Your thoughts and reaction to this new reality, because it has to be cloud native, otherwise it doesn't work, your thoughts. >> Yeah, I think, John, you're right on. We were very fortunate as a company to invent the term virtualization for an x86 architecture and the category 20 years ago when Diane founded this great company. And I would say you're right, the public cloud is the instantiation of virtualization at its sort of scale format and we're excited about this Amazon partnership, we'll talk more about that. This new world of doing everything virtual has taken the same concepts to whole new levels. We are partnering very closely with companies like Zoom, because a good part of this is being able to deliver video experiences in there, we'll talk about that if needed. Cloud native security, we announced an acquisition today in container security that's very important because we're making big moves in security, security's become very important. I would just say, John, the first thing that was very important to us as we began to shelter in place was the health of our employees. Ironically, if I go back to, in January I was in Davos, in fact some of your other folks who were on the show earlier, Matt Garman, Andy, we were all there in January. The crisis already started in China, but it wasn't on the world scene as much of a topic of discussion. Little did we know, three, four weeks later, fast forward to February things were moving so quickly. I remember a Friday late in February where we were just about to go the next week to Las Vegas for our in-person sales kickoffs. Thousands of people, we were going to do, I think, five or 6,000 people in Las Vegas and then another 3,000 in Barcelona, and then finally in Singapore. And it had not yet been categorized a pandemic. It was still under this early form of some worriable virus. We decided for the health and safety of our employees to turn the entire event that was going to happen on Monday to something virtual, and I was so proud of the VMware team to just basically pivot just over the weekend. To change our entire event, we'd been thinking about video snippets. We have to become in this sort of virtual, digital age a little bit like TV producers like yourself, turn something that's going to be one day sitting in front of an audience to something that's a lot shorter, quicker snippets, so we began that, and the next thing we began doing over the next several weeks while the shelter in place order started, was systematically, first off, tell our employees, listen, focus on your health, but if you're healthy, turn your attention to serving your customers. And we began to see, which we'll talk about hopefully in the context of the discussion, parts of our portfolio experience a tremendous amount of interest for a COVID-centered world. Our digital workplace solutions, endpoint security, SD-WAN, and that trifecta began to be something that we began to see story after story of customers, hospitals, schools, governments, retailers, pharmacies telling us, thank you, VMware, for helping us when we needed those solutions to better enable our people on the front lines. And all VMware's role, John, was to be a digital first responder to the first responder, and that gave tremendous amount of motivation to all of our employees into it. >> Yeah, and I think that's a great point. One of the things we've been talking about, and you guys have been aligned with this, you mentioned some of those points, is that as we work at home, it points out that digital and technology is now part of lifestyle. So we used to talk about consumerization of IT, or immersion with augmented reality and virtual reality, and then talk about the edge of the network as an endpoint, we are at the edge of the network, we're at home, so this highlights some of the things that are in demand, workspaces, VPN provisioning, these new tools, that some cases we've been hearing people that no one ever thought of having a forecast of 100% VPN penetration. Okay, you did the AirWatch deal way back when you first started, these are now fruits of those labors. So I got to ask you, as managers of your customer base are out there thinking, okay, I got to double down on the right growth strategy for this post-pandemic world, the smart managers are going to look at the technologies enabled for business outcome, so I have to ask you, innovation strategies are one thing, saying it, putting it place, but now more than ever, putting them in action is the mandate that we're hearing from customers. Okay I need an innovation strategy, and I got to put it into action fast. What do you say to those customers? What is VMware doing with AWS, with cloud, to make those innovation strategies not only plausible but actionable? >> That's a great question, John. We focused our energy, before even COVID started, as we prepared for this year, going into sales kickoffs and our fiscal year, around five priorities. Number one was enabling the world to be multicloud, private cloud and public cloud, and clearly our partnership here with Amazon is the best example of that and they are our preferred cloud partner. Secondly, building modern apps with microservices and cloud native, what we call app modernization. Thirdly, which is a key part to the multicloud, is building out the entire network stack, data center networking, the firewalls, the load bouncing in SD-WAN, so I'd call that cloud network. Number four, the modernization of workplace with an additional workspace solution, Workspace ONE. And five, intrinsic security from all aspects of security, network, endpoint, and cloud. So those five priorities were what we began to think through, organize our portfolio, we call them solution pillars, and for any of your viewers who're interested, there's a five-minute version of the VMware story around those five pillars that you can watch on YouTube that I did, you just search for Sanjay Poonen and five-minute story. But then COVID hit us, and we said, okay we got to take these strategies now and make them more actionable. Exactly your question, right? So a subset of that portfolio of five began to become more actionable, because it's pointless going and talking about stuff and it's like, hey, listen, guys, I'm a house on fire, I don't care about the curtains and all the wonderful art. You got to help me through this crisis. So a subset of that portfolio became kind of what was those, think about now your laptop at home, or your endpoint at home. People wanted, on top of their Zoom call, or surrounding their Zoom call, a virtual desktop managed easily, so we began to see Workspace ONE getting a lot of interest from our customers, especially the VDI part of that portfolio. Secondly, that laptop at home needed to be secured. Traditional, old, legacy AV solutions that've worked, enter Carbon Black, so Workspace ONE plus Carbon Black, one and two. Third, that laptop at home needs network acceleration, because we're dialoguing and, John, we don't want any latency. Enter SD-WAN. So the trifecta of Workspace ONE, Carbon Black and VeloCloud, that began to see even more interest and we began to hone in our portfolio around those three. So that's an example of where you have a general strategy, but then you apply it to take action in the midst of a crisis, and then I say, listen, that trifecta, let's just go and present what we can do, we call that the business continuity or business resilience part of our portfolio. We began to start talking to customers, and saying, here's our business continuity solution, here's what we could do to help you, and we targeted hospitals, schools, governments, pharmacies, retailers, the ones who're on the front line of this and said again, that line I said earlier, we want to be a digital first responder to you, you are the real first responder. Right before this call I got off a CIO call with the CIO of a major hospital in the northeast area. What gives me great joy, John, is the fact that we are serving them. Their beds are busting at the seam, in serving patients-- >> And ransomware's a huge problem you guys-- >> We're serving them. >> And great stuff there, Sanjay, I was just on a call this morning with a bunch of folks in the security industry, thought leaders, was in DC, some generals were there, some real thought leaders, trying to figure out security policy around biosecurity, COVID-19, and this invisible disruption, and they were equating it to like the World Wars. Big inflection point, and one of the generals said, in those times of crisis you need alliances. So I got to ask you, COVID-19 is impactful, it's going to have serious impact on the critical nature of it, like you said, the house is on fire, don't worry about the curtains. Alliances matter more than ever when you need to come together. You guys have an ecosystem, Amazon's got an ecosystem, this is going to be a really important test to the alliances out there. How do you view that as you look forward? You need the alliances to be successful, to compete and win in the new world as this invisible enemy, if you will, or disruptor happens, what's your thoughts? >> Yeah, I'll answer in a second, just for your viewers, I sneezed, okay? I've been on your show dozens of time, John, but in your live show, if I sneezed, you'd hear the loud noise. The good news in digital is I can mute myself when a sneeze is about to happen, and we're able to continue the conversation, so these are some side benefits of the digital part of it. But coming to your question on alliance, super important. Ecosystems are how the world run around, united we stand, divided we fall. We have made ecosystems, I've always used this phrase internally at VMware, sort of like Isaac Newton, we see clearly because we stand on the shoulders of giants. So VMware is always able to be bigger of a company if we stand on the shoulders of bigger giants. Who were those companies 20 years ago when Diane started the company? It was the hardware economy of Intel and then HP and Dell, at the time IBM, now Lenovo, Cisco, NetApp, DMC. Today, the new hardware companies Amazon, Azure, Google, whoever have you, we were very, I think, prescient, if you would, to think about that and build a strategic partnership with Amazon three or four years ago. I've mentioned on your show before, Andy's a close friend, he was a classmate over at Harvard Business School, Pat, myself, Ragoo, really got close to Andy and Matt Garman and Mike Clayville and several members of their teams, Teresa Carlson, and began to build a partnership that I think is one of the most incredible success stories of a partnership. And Dell's kind of been a really strong partner with us on private cloud, having now Amazon with public cloud has been seminal, we do regular meetings and build deep integration of, VMware Cloud and AWS is not some announcement two or three years ago. It's deep engineering between, Bask's now in a different role, but in his previous role, that and people like Mark Lohmeyer in our team. And that deep engineering allows us to know and tell customers this simple statement, which both VMware and Amazon reps tell their customers today, if you have a workload running on vSphere, and you want to move that to Amazon, the best place, the preferred place for that is VMware Cloud and Amazon. If you try to refactor that onto a native VC 2, it's a waste of time and money. So to have the entire army of VMware and Amazon telling customers that statement is a huge step, because it tells customers, we have 70 million virtual machines running on-prem. If customers are looking to move those workloads to Amazon, the best place for that VMware Cloud and AWS, and we have some credible customer case studies. Freddie Mac was at VMworld last year. IHS Markit was at VMworld last year talking about it. Those are two examples and many more started it, so we would like to have every VMware and Amazon customer that's thinking about VMware to look at this partnership as one of the best in the industry and say very similar to what Andy I think said on stage at the time of this announcement, it doesn't have to be now a trade-off between public and private cloud, you can get the best of both worlds. That's what we're trying to do here-- >> That's a great point, I want to get your thoughts on leadership, as you look at COVID-19, one of our tracks we're going to be promoting heavily on theCUBE.net and our sites, around how to manage through this crisis. Andy Jassy was quoted on the fireside chat, which is coming up here in North America, but I saw it yesterday in New Zealand time as I time shifted over there, it's a two-sided door versus a one-sided door. That was kind of his theme is you got to be able to go both ways. And I want to get your thoughts, because you might know what you're doing in certain contexts, but if you don't know where you're going, you got to adjust your tactics and strategies to match that, and there's and old expression, if you don't know where you're going, every road will take you there, okay? And so a lot of enterprise CXOs or CEOs have to start thinking about where they want to go with their business, this is the growth strategy. Then you got to understand which roads to take. Your thoughts on this? Obviously we've been thinking it's cloud native, but if I'm a decision maker, I want to make sure I have an architecture that's going to carry me forward to the future. I need to make sure that I know where I'm going, so I know what road I'm on. Versus not knowing where I'm going, and every road looks good. So your thoughts on leadership and what people should be thinking around knowing what their destination is, and then the roads to take? >> John, I think it's the most important question in this time. Great leaders are born through crisis, whether it's Winston Churchill, Charles de Gaulle, Roosevelt, any of the leaders since then, in any country, Mahatma Gandhi in India, the country I grew up, Nelson Mandela, MLK, all of these folks were born through crisis, sometimes severe crisis, they had to go to jail, they were born through wars. I would say, listen, similar to the people you talked about, yeah, there's elements of this crisis that similar to a World War, I was talking to my 80 year old father, he's doing well. I asked him, "When was the world like this?" He said, "Second World War." I don't think this crisis is going to last six years. It might be six or 12 months, but I really don't think it'll be six years. Even the health care professionals aren't. So what do we learn through this crisis? It's a test of our leadership, and leaders are made or broken during this time. I would just give a few guides to leaders, this is something tha, Andy's a great leader, Pat, myself, we all are thinking through ways by which we can exercise this. Think of Sully Sullenberger who landed that plane on the Hudson. Did he know when he flew that airbus, US Airways airbus, that few flock of birds were going to get in his engine, and that he was going to have to land this plane in the Hudson? No, but he was making decisions quickly, and what did he exude to his co-pilot and to the rest of staff, calmness and confidence and appropriate communication. And I think it's really important as leaders, first off, that we communicate, communicate, communicate, communicate to our employees. First, our obligation is first to our employees, our family first, and then of course to our company employees, all 30,000 at VMware, and I'm sure similarly Andy does it to his, whatever, 60, 70,000 at AWS. And then you want to be able to communicate to them authentically and with clarity. People are going to be reading between the lines of everything you say, so one of the things I've sought to do with my team, all the front office functions report to me, is do half an hour Zoom video conferences, in the time zone that's convenient to them, so Japan, China, India, Europe, in their time zone, so it's 10 o'clock my time because it's convenient to Japan, and it's just 10 minutes of me speaking of what I'm seeing in the world, empathizing with them but listening to them for 20 minutes. That is communication. Authentically and with clarity, and then turn your attention to your employees, because we're going stir crazy sitting at home, I get it. And we've got to abide by the ordinances with whatever country we're in, turn your attention to your customers. I've gotten to be actually more productive during this time in having more customer conference calls, video conference calls on Zoom or whatever platform with them, and I'm looking at this now as an opportunity to engage in a new way. I have to be better prepared, like I said, these are shorter conversations, they're not as long. Good news I don't have to all over the place, that's better for my family, better for the carbon emission of the world, and also probably for my life long term. And then the third thing I would say is pick one area that you can learn and improve. For me, the last few years, two, three years, it's been security. I wanted to get the company into security, as you saw today we've announced mobile, so I helped architect the acquisition of Carbon Black, very similar to kind of the moves I've made six years ago around AirWatch, very key part to all of our focus to getting more into security, and I made it a personal goal that this year, at the start of the year, before COVID, I was going to meet 1,000 CISOs, in the Fortune 1000 Global 2000. Okay, guess what, COVID happens, and quite frankly that goal's gotten a little easier, because it's much easier for me to meet a lot more people on Zoom video conferences. I could probably do five, 10 per day, and if there's 200 working days in a day, I can easily get there, if I average about five per day, and sometimes I'm meeting them in groups of 10, 20. >> So maybe we can get you on theCUBE more often too, 'cause you have access to a video camera. >> That is my growth mindset for this year. So pick a growth mindset area. Satya Nadella puts this pretty well, "Move from being a know-it-all to a learn-it-all." And that's the mindset, great company. Andy has that same philosophy for Amazon, I think the great leaders right now who are running these cloud companies have that growth mindset. Pick an area that you can grow in this time, and you will find ways to do it. You'll be able to learn online and then be able to teach in some fashion. So I think communicate effectively, authentically, turn your attention to serving your customers, and then pick some growth area that you can learn yourself, and then we will come out of this crisis collectively, individuals and as partners, like VMware and Amazon, and then collectively as a society, I believe we'll come out stronger. >> Awesome great stuff, great insight there, Sanjay. Really appreciate you sharing that leadership. Back to the more of technical questions around leadership is cloud native. It's clear that there's going to be a line in the sand, if you will, there's going to be a right side of history, people are going to have to be on the right side of history, and I believe it's cloud native. You're starting to see this emersion. You guys have some news, you just announced today, you acquired a Kubernetes security startup, around Kubernetes, obviously Kubernetes needs security, it's one of those key new enablers, disruptive enablers out there. Cloud native is a path that is a destination opportunity for people to think about, why that acquisition? Why that company? Why is VMware making this move? >> Yeah, we felt as we talked about our plans in security, backing up to things I talked about in my last few appearances on your show at VMworld, when we announced Carbon Black, was we felt the security industry was broken because there was too many point benders, and we figured there'd be three to five control points, network, endpoint, cloud, where we could play a much more pronounced role at moving a lot of these point benders, I describe this as not having to force our customers to go to a doctor and say I've got to eat 5,000 tablets to get healthy, you make it part of your diet, you make it part of the infrastructure. So how do we do that? With network security, we're off to the races, we're doing a lot more data center networking, firewall, load bouncing, SD-WAN. Really, reality is we can eat into a lot of the point benders there that I've just been, and quite frankly what's happened to us very gratifying in the network security area, you've seen the last few months, some firewall vendors are buying SD-WAN players, kind of following our strategy. That's a tremendous validation of the fact that the network security space is being disrupted. Okay, move to endpoint security, part of the reason we acquired Carbon Black was to unify the client side, Workspace ONE and Carbon Black should come together, and we're well under way in doing that, make Carbon Black agentless on the server side with vSphere, we're well on the way to that, you'll see that very soon. By the way both those things are something that the traditional endpoint players can't do. And then bring out new forms of workload. Servers that are virtualized by VMware is just one form of work. What are other workloads? AWS, the public clouds, and containers. Container's just another workload. And we've been looking at container security for a long time. What we didn't want to do was buy another static analysis player, another platform and replatform it. We felt that we could get great technology, we have incredible grandeur on container cell. It's sort of Red Hat and us, they're the only two companies who are doing Kubernetes scales. It's not any of these endpoint players who understand containers. So Kubernetes, VMware's got an incredible brand and relevance and knowledge there. The networking part of it, service mesh, which is kind of a key component also to this. We've been working with Google and others like Istio in service mesh, we got a lot of IP there that the traditional endpoint players, Symantec, McAfee, Trend, CrowdStrike, don't know either Kubernetes or service mesh well. We add now container security into this, we really distinguish ourselves further from the traditional endpoint players with bringing together, not just the endpoint platform that can do containers, but also Kubernetes service mesh. So why is that important? As people think about their future in containers, they'll want to do this at the runtime level, not at the static level. They'll want to do it at build time And they'll want to have it integrated with some of their networking capabilities like service mesh. Who better to think about that IP and that evolution than VMware, and now we bring, I think it's 12 to 14 people we're bringing in from this acquisition. Several of them in Israel, some of them here in Palo Alto, and they will build that platform into the tech that VMware has onto the Carbon Black cloud and we will deliver that this year. It's not going to be years from now. >> Did you guys talk about the-- >> Our capability, and then we can bring the best of Carbon Black, with Tanzu, service mesh, and even future innovation, like, for example, there's a big movement going around, this thing call open policy agent OPA, which is an open source effort around policy management. You should expect us to embrace that, there could be aspects of OPA that also play into the future of this container security movement, so I think this is a really great move for Patrick and his team, I'm very excited. Patrick is the CEO of Carbon Black and the leader of that security business unit, and he came to me and said, "Listen, one of the areas "we need to move in is container security "because it's the number one request I'm hearing "from our CESOs and customers." I said, "Go ahead Patrick. "Find out who are the best player you could acquire, "but you have to triangulate that strategy "with the Tanzu team and the NSX team, "and when you have a unified strategy what we should go, "we'll go an make the right acquisition." And I'm proud of what he was able to announce today. >> And I noticed you guys on the release didn't talk about the acquisition amount. Was it not material, was it a small amount? >> No, we don't disclose small, it's a tuck-in acquisition. You should think of this as really bringing us some tech and some talent, and being able to build that into the core of the platform of Carbon Black. Carbon Black was the real big move we made. Usually what we do, you saw this with AirWatch, right, anchor on a fairly big move. We paid I think 2.1 billion for Carbon Black, and then build and build and build on top of that, partner very heavily, we didn't talk about that. If there's time we could talk about it. We announced today a security alliance with top SIEM players, in what's called a sock alliance. Who's announced in there? Splunk, IBM QRadar, Google Chronicle, Sumo Logic, and Exabeam, five of the biggest SIEM players are embracing VMware in endpoint security, saying, Carbon Black is who we want to work with. Nobody else has that type of partnership, so build, partner, and then buy. But buy is always very carefully thought through, we're not one of these companies like CA of the past that just bought every company and then it becomes a graveyard of dead acquisition. Our view is we're very disciplined about how we think about acquisition. Acquisitions for us are often the last resort, because we'd prefer to build and partner. But sometimes for time-to-market reasons, we acquire, and when we acquire, it's thoughtful, it's well-organized within VMware, and we take care of our people, 'cause we want, I mean listen, why do acquisitions fail? Because the good people leave. So we're excited about this team, the team in Israel, and the team in Palo Alto, they come from Octarine. We're going to integrate them rapidly into the platform, and this is a good evidence of VMware investing more in security, and our Q3 earnings pulled, John, I said, sorry, we said that the security business was a billion dollar business at VMware already, primarily from network, but some from endpoint. This is evidence of us putting more fuel behind that fire. It's only been six, seven months and Patrick's made his first acquisition inside Carbon Black, so you're going to see us investing more in security, it's an important priority for the company, and I expect us to be a very prominent player in these three pillars, network security, endpoint security, endpoint is both client and the workload, and cloud. Network, endpoint, cloud, they are the three areas where we think there's lots of room for innovation in security. >> Well, we'll be watching, we'll be reporting and analyzing the moves. Great playbook, by the way. Love that organic partnering and then key acquisitions which you build around, it's a great playbook, I think it's very relevant for this time. The most important question I have to ask you, Sanjay, and this is a personal question, because you're the leader of VMware, I noticed that, we all know you're into music, you've been putting music online, kind of a virtual band. You've also hired a CUBE alumni, Victoria Verango from McAfee who also puts up music, you've got some musicians, but you kind of know how to do the digital moves there, so the question is, will the music at VMworld this year be virtual? >> Oh, man. Victoria is actually an even better musician than me. I'm excited about his marketing gifts, but I'm also excited to watch him. But yeah, you've heard him sing, he's got a voice that's somewhat similar to Sting, so we, just for fun, in our Diwali, which is an Indian celebration last year, Tom Corn, myself, and a wonderful lady named Divya, who's got a beautiful voice, had sung a song, which was off the soundtrack of the Bollywood movie, "Secret Superstar," and we just for fun decided to record that in our three separate homes, and put that out on YouTube. You can listen, it's just a two or three-minute run, and it kind of went a little bit viral. And I was thinking to myself, hey, if this is one way by which we can let the VMware community know that, hey, you know what, art conquers COVID-19, you can do music even socially distant, and bring out the spirit of VMware, which is community. So we might build on that idea, Victoria and I were talking about that last night and saying, hey, maybe we do a virtual music kind of concert of maybe 10 or 15 or 20 voices in the various different countries. Record piece of a song and music and put it out there. I think these are just ways by which we're having fun in a virtual setting where people get to see a different side of VMware where, and the intent here, we're all amateurs, John, we're not like great. There are going to be mistakes in this music. If you listen to that audio, it sounds a little tinny, 'cause we're recording it off our iPhone and our iPad microphone. But we'll do the best we can, the point is just to show the human spirit and to show that we care, and at the end of the day, see, the COVID-19 virus has no prejudice on color of skin, or nationality, or ethnicity. It's affecting the whole world. We all went into the tunnel at different times, we will come out of this tunnel together and we will be a stronger human fabric when we're done with this, We shall absolutely overcome. >> Sanjay, give us a quick update to end the segment on your thoughts around VMworld. It's one of the biggest events, we look forward to it. It's the only even left standing that theCUBE's been to every year of theCUBE's existence, we're looking forward to being part of theCUBE virtual. It's been announced it's virtual. What are some of the thinking going on at the highest levels within the VMware community around how you're going to handle VMworld this year? >> Listen, when we began to think about it, we had to obviously give our customers and folks enough notice, so we didn't want to just spring that sometime this summer. So we decided to think through it carefully. I asked Robin, our CMO, to talk to many of the other CMOs in the industry. Good news is all of these are friends of ours, Amazon, Microsoft, Google, Salesforce, Adobe, and even some smaller companies, IBM did theirs. And if they were in the first half of the year, they had to go virtual 'cause we're sheltered in place, and IBM did theirs, Okta did theirs, and we began to watch how they were doing this. We're kind of in the second half, because we were August, September, and we just sensed a lot of hesitancy from our customers that wanted to get on a plane to come here, and even if we got just 500, 1,000, a few thousand, it wasn't going to be the same and there would always be that sort of, even if we were getting back to that, some worry, so we figured we'd do something that might be semi-digital, and we may have some people that roam, but the bulk of it is going to be digital, and we changed the dates to be a little later. I think it's September 20th to 29th. Right now it's all public now, we announced that, and we're going to make it a great program. In some senses like we're becoming TV producer. I told our team we got to be like Disney or ESPN or whoever your favorite show is, YouTube, and produce a really good several-hour program that has got a different way in which digital content is provided, smaller snippets, very interesting speakers, great brand names, make the content clear, crisp and compelling. And if we do that, this will be, I don't know, maybe it's the new norm for some period of time, or it might be forever, I don't know. >> John: We're all learning. >> In the past we had huge conferences that were busting 50, 70, 100,000 and then after the dot-com era, those all shrunk, they're like smaller conferences, and now with advent of companies like Amazon and Salesforce, we have huge events that, like VMworld, are big events. We may move to a environment that's a lot more digital, I don't know what the future of in-presence physical conferences are, but we, like others, we're working with AWS in terms of their future with Reinvent, what Microsoft's doing with Ignite, what Google's doing with Next, what Salesforce's going to do with Dreamforce, all those four companies are good partners of ours. We'll study theirs, we'll work together as a community, the CMOs of all those companies, and we'll come together with something that's a very good digital experience for our customers, that's really what counts. Today I did a webinar with a partner. Typically when we did a briefing in our briefing center, 20 people came. There're 100 people attending this, I got a lot more participation in this QBR that I did with this SI partner, one of the top SIs in the world, in an online session with them, than would I have gotten if they'd all come to Palo Alto. That's goodness. Should we take the best of that world and some physical presence? Maybe in the future, we'll see how it goes. >> Content quality. You know, you know content. Content quality drives everything online, good engagement creates community, that's a nice flywheel. I think you guys will figure it out, you've got a lot of great minds there, and of course, theCUBE virtual will be helping out as we can, and we're rethinking things too-- >> We count on that, John-- >> We're going to be open minded to new ideas, and, hey, whatever's the best content we can deliver, whether it's CUBE, or with you guys, or whoever, we're looking forward to it. Sanjay, thanks for spending the time on this CUBE Keynote coverage of AWS Summit. Since it's digital we can do longer programs, we can do more diverse content. We got great customer practitioners coming up, talking about their journey, their innovation strategies. Sanjay Poonen, COO of VMware, thank you for taking your precious time out of your day today. >> Thank you, John, always a pleasure. >> Thank you. Okay, more CUBE, virtual CUBE digital coverage of AWS Summit 2020, theCUBE.net is we're streaming, and of course, tons of videos on innovation, DevOps, and more, scaling cloud, scaling on-premise hybrid cloud, and more. We got great interviews coming up, stay with us our all-day coverage. I'm John Furrier, thanks for watching. (upbeat music)

Published Date : May 13 2020

SUMMARY :

leaders all around the world, all around the world, This is the new reality. and I'm going to move and the next thing we began doing and I got to put it into action fast. and all the wonderful art. You need the alliances to be successful, and began to build a and then the roads to take? and then of course to So maybe we can get you and then be able to teach in some fashion. to be a line in the sand, part of the reason we and the leader of that didn't talk about the acquisition amount. and the team in Palo Alto, I have to ask you, Sanjay, and to show that we care, standing that theCUBE's been to but the bulk of it is going to be digital, In the past we had huge conferences and we're rethinking things too-- We're going to be and of course, tons of

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Prem Jain, Pensando Systems | Welcome to the New Edge 2019


 

>>From New York city. It's the cube covering. Welcome to the new edge brought to you by systems. >>Okay, we'll come back. You're ready. Jeff Frick here with the cube. We're in downtown Manhattan at the top of Goldman Sachs, like 43 stories above the Hudson. It was a really beautiful view a couple hours ago, but the cloud has moved in and that's only appropriate cause it's cloud is a big theme of why we're here today. We're here for the Penn Zando event. It's called welcome to the new edge. They just come out of stealth mode after two and a half years, almost three years, raised a ton of money, got a really rockstar team and we're excited to have the CEO with us today to tell us a little bit about more what's going on. And that's prem Jane and again, the CEO of Penn Sandow prem. Great to see you. Nice to see you too. So everything we did running up to this event before we could get any of the news, we, we, we tried to figure out what was going on and all it kept coming up was NPLS, NPLS, NPLS, which I thought was a technology, which it is, but it's really about the team. Tell us a little bit about the team in which you guys have built prior and, and why you're such a, a well functioning and kind of forward thinking group of people. >>So I think the team is working together. Mario Luca, myself and Sony were working together since 1983 except for Sony. Sony joined us after the first company, which has crescendo, got acquired by Cisco in 1993 and since then four of us are working together. Uh, we have done many, uh, spinnings inside the Cisco and demo was the first one. Then we did, uh, uh, Nova systems, which was the second, then we did recently in CMA. Uh, and then after we left we thought we are going to retire, but we talked about it and we says, you know, there is still transitions happening in the industry and maybe we have few more years to go back to the, you know, industry and, and do something which is very challenging and, and uh, impacting. I think everything which we have done in the past is to create a impact in the industry and make that transition which is occurring very successful, >>which is really hard to do. And, and John Chambers who, who's on the board and spoke earlier today, you know, kind of talked about these 10 year cycles of significant change in our industry and you know, Clayton Christianson innovator's dilemma, it's really easy when you are successful at one of those to kind of sit on your laurels. In fact, it's really, really hard to kill yourself and go on to the next thing you guys have done this time and time and time again. Is there a unique chemistry in the way you guys look forward or you just, you just get bored with what you built and you want to build something new. I mean, what is some of the magic, because even John said, as soon as he heard that you were the team behind it, he was like, sign me up. I don't know what they're building but I don't really care cause I know these people can deliver. >>I think it's very good the, whenever you look at any startup, the most important thing which comes up as the team and you're seeing a lot of startup fails because the team didn't work together or they got their egos into this one. Since we are working for so long, they compliment each other. That's the one thing which is very important. Mario, Luca, myself, they come from engineering backgrounds. Sony comes from marketing, sales, uh, type of background and we all lady in terms of the brain, if you think about is the Mario behind the scene, Luca is really the execution machine and I'm, you can think like as a heart, okay. Putting this thing together. Uh, as a team, we work very complimentary with each other. It does not mean that we agree on everything, right? We disagree. We argue. We basically challenge each other. But one thing good about this particular team is that once we come to a conclusion, we just focus and execute. And team is also known to work with customers all the time. I mean, even when we started Penn Sando, we talked to many customers in the very beginning. They shape up our ideas, they shape up the directions, which is we are going and what transitions are occurring in the industries and all that. That's another thing which is we take customer very seriously in our thought process of building a product. >>So when you were thinking around sitting around the table, deciding whether you guys wanted to do it again, what were the challenges that you saw? What was the kind of the feedback loop that came in that, that started this? The, uh, the gym of the idea >>thing is also is that, uh, we had, we had developed so many different products as you saw today in the launch, eight or nine, uh, billion dollar product line and stuff like that. So we all have a very good system experience what is really needed, what transitions are occurring and stuff like that. When we started this one, we were not really sure what we wanted to do it, but in the last one when we did the, uh, NCMA, we realize that the enterprise thing, which we deliver the ACI solution for the enterprise, the realize that these services was the most complex way of incorporating into that particular architectures. So right from the beginning of interview realized that the, this particular thing is nobody has touched it, nobody thought about it out of the box thinking that how can you make it into a distributed fashion, which has also realized that cloud is going, everything distributed. >>They got away from the centralized appliances. So as the enterprise is now thinking of doing it cloud-like architectures and stuff like that. And the third thing which was really triggered us also, there was a company which is a new Poona which got acquired by Amazon in 2016 and we were looking at it what kinds of things they are doing and we said we can do much better architecturally and next generation, uh, architecture, which can really enable all the other cloud vendors. Some of them are our partners to make sure they can leverage that particular technologies and build the next generation cloud. And that's where this idea of new edge came in because we also saw that the new applications like IOT is five G's and artificial intelligence, machine learning, robotics or drones, you just name it intelligent devices, which is going to get connected. What is the best place to process them is at the edge or also at the backend with the application where the server is running these and that is another edge compute edge, right? >>In that particular sense. So our idea was to develop a product so that it can cover wide segment of the market, enterprise cloud providers, service borders, but focus very narrowly delivering these services into existing architectures. Also people who are building, building the next generation architectures. Right, so it's the distributed services platform or the distributed services architecture. So at its core for people that didn't make it today, what is it? It's basically is a distributed service platforms. The foundation of that is really our custom processor, which is we have designed is highly programmable. It's software defined so that all the protocols, which is typically people hardwired in our case is programmable. It's all programs which is we are writing the language which you selected as before and before extensions. The software stack is the major differentiated thing which is running on the top of this particular processor, which is we have designed in such a way that is hardware agnostics. >>The the, the capabilities which we have built is easily integrated into the existing environment. So if people already have cloud and they want to leverage our technologies, they can really deploy it in the enterprise. We are basically replacing lot of appliances, simplifying the architectures, making sure they can enable the service as they grow model, which is really amazing because right now they had to say firewall goes here, load balancer goes here, these a VPN devices goes there. In our case it's very simple. You put in every server of our technologies and our software stack and our Venice, which is our policy manager, which is sitting outside and it's based upon Kubernete X a architectures is basically a microservices, which is we are running and managing the life cycle of this particular product family and also providing the visibility and uh, uh, accountability in terms of exactly what is going on in that particular network. >>And it's all driven by intent-based architecture, which is policy driven, right? So software defined sitting on software defined Silicon. So you get the benefits of the Silicon, but it's also programmable Silicon, but it's still, you're sitting, you've got a software stack on top of that that manages that cloud and then the form factors as small as a Nick. Yes. So he can stick it in the HP HP server. Yeah. It specifically goes into any PCI slot in any server, uh, in the industry. Yes. It's amazing. Well, first incarnation, but, but, but, but, but that's a really simple implementation, right? Just to get radiation and easy to deploy. Right. And you guys are, you're yourself where involved in security that's involved in managing the storage. It's simple low power, which I thought was a pretty interesting attribute that you defined early on. Clearly thinking about edge and these distributed, uh, things all over the place. >>They're metal programmable. And then the other thing that was talked about a lot today was the observability. Yes. Um, why observability why was that so important? What were you hearing from customers that were really leading you down that path? Yeah, it's important. Uh, you know, surprisingly enough, uh, the visibility is one of the biggest challenge. Most of the data center faces today. A lot of people tried to do multiple different things, but they're never able to do it, uh, in, in the way we are doing it. One is that we don't run anything on the host. Some people have done it right on the train running the agent on the host. Some people have tried to run virtual machines on the those particular environment. In our case there's nothing which is running on the host site. It runs on our card and having end to end that visibility we can provide latency, very accurate latency to the, to the applications which is very important for these customers. >>Also, what is really going on there is the problem in the network. Isolation is another big thing. When something get lost they don't know where it got lost. We can provide that thing. Another important thing that you're doing, which is not being done in the industries. Everything which is we are doing is flow based means if I'm talking to you, there is a flow being set up between you and me and we are monitoring every flow and one of the advantages of our processor is we have four to eight gigabytes of memory, so we can keep these States, have these flows inside, and that gives a tremendous advantage for us to do lots of things, which as you can imagine going forward, we will be delivering it such as, for example, behavior of these flows and things from this point of view, once you understand the behavior of the flow, you can also provide lot of security features because if I'm not talking to you and suddenly I start talking to you and I know that there's something went wrong, right, right. >>And they should be able to look at the behavior analysis and should be able to tell exactly what's going on. You mean we want a real time snapshot of what's really happening instead of a instead of a sample of something that happened a little. No, absolutely. You're absolutely connected. Yeah. Yeah. Um, that's terrific. So you put together to accompany and you immediately went out and talked to a whole bunch of customers. I was amazed at the number of customers and partners that you had here at the launch. Um, was that for validation? Were you testing hypotheses or, or were there some things that the customers were telling you about that maybe you weren't aware of or maybe didn't get the right priority? I think it's all of the above. What you mentioned our, it's in our DNA by the way. You know, we don't design products, we don't design things without talking to customers. >>Validation is very important that we are on the right track because you may try to solve the customer problem, which is not today's problem. Maybe future's problem. Our idea was that then you can develop the product it was set on the shelf. We don't want to do that. We wanted to make sure that, that this is the hard problem customer is facing today. At the same time looking at it, what futuristic in their architecture is understanding the customers, how, what are they doing today, how they're deploying it. The use cases are understanding those very well and making sure that we are designing. Because when we design a seeker, when your designer processor, you know, you cannot design for one year, it has to be a longterm, right? And you need to make sure that we understand the current problems, we understand the future problems and design that in pretty much your spark and you've been in this space forever. >>You're at Cisco before. And so just love to get your take on exponential growth. You know, such an interesting concept that people have a really hard time grasping exponential growth and we're seeing it clearly with data and data flows and ultimately everything's got to go through the network. I mean, when you, when you think back with a little bit of perspective at the incredible increase in the data flow and the amount of data is being stored and the distribution of these, um, applications now out to the edge and store and compute and take action at the edge, you know, what do you think about, how do you, how do you kind of stay on top of that as somebody who kind of sees the feature relatively effectively, how do you try to stay on top of exponential curves? As you know, very valuable data is very important for anybody in any business. >>Whether it's financial, whether it's healthcare, whether it's, and it's becoming even more and more important because of machine learning, artificial intelligence, which is coming in to really process this particular data and predict certain things which is going to happen, right? We wanted to be close to the data and the closest place to be data is where the application is running. That's one place clears closest to the data at the edge is where data is coming in from the IOT devices, from the 5g devices, from the, you know, you know all kinds of appliances which is being classified under IOT devices. We wanted to be, make sure that we are close to the data, doesn't matter where you deploy and we want to be agnostic. Actually our technologies and architectures designed that this boundary is between North, South, East, West is going to go away in future cloud. >>A lot of things which is being done in the backend will be become at the edge like we talked about before. So we are really a journey which is just starting in this particular detectors and you're going to see a lot more innovations coming from us continuously in this particular directions. And again, based upon the feedback which you're going to get from cloud customers with enterprise customers, but they were partners and other system ecosystem partners, which is going to give us a lot of feedback. Great. Well again, thanks for uh, for having us out and congratulations to uh, to you and the team. It must be really fun to pull the covers off. absolutely. It is very historical day for us. This is something we were waiting for two years and nine months to see this particular date, to have our customers come on the stage and talk about our technologies and why they think it's very important. Thank you very much for giving me this opportunity to talk to you. Thank you. Alright, thanks prem. Thanks. He's prem. I'm Jeff. You're watching the cube where it depends. Sandow launch at the top of Goldman Sachs in downtown Manhattan. Thanks for watching. We'll see you next time.

Published Date : Oct 18 2019

SUMMARY :

brought to you by systems. Tell us a little bit about the team in which you guys have built prior and, in the industry and make that transition which is occurring very successful, and go on to the next thing you guys have done this time and time and time again. That's the one thing which is very important. thing is also is that, uh, we had, we had developed so many different products as you saw today And the third thing which was really triggered us also, It's all programs which is we are writing the language which you the service as they grow model, which is really amazing because right now they had to say It's simple low power, which I thought was a pretty interesting attribute that you defined to the applications which is very important for these customers. advantage for us to do lots of things, which as you can imagine I was amazed at the number of customers and partners that you had here Validation is very important that we are on the right track because you may try to solve the customer and take action at the edge, you know, what do you think about, We wanted to be, make sure that we are close to the data, doesn't matter where you deploy and we want to be agnostic. So we are really a journey which is just starting in this particular detectors

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Soni Jiandani, Pensando Systems & Joshua Matheus, Goldman Sachs | Welcome to the New Edge 2019


 

>>From New York city. It's the cube covering. Welcome to the new edge brought to you by systems. >>Hey, welcome back everybody. Jeff, Rick here with the cube. We are in Manhattan at the top of Goldman Sachs. It is a great view if you ever get an opportunity to come up here, I think 43 floors over the Hudson you could see forever. But this is the cloud events. So the clouds are here and we're excited to be here is the Penn Penn Sandow launch in the name of the event is welcome to the new edge, which is a pretty interesting play. We hear a lot about edge but we haven't really heard of that company really focusing on the edge as their primary go to market activity and really thinking about the edge first. So we're excited to have the cofounder cube Olam and many time guests a Sony Gian Deni. She's the co founder and chief business officer. So many great to see you. Good to see you too. >>And our hosts here at Goldman Sachs is uh, Josh Matthews. He's a managing director of technology at Goldman. Josh. Great to see you. You too. And thank you and thanks for hosting us. Nice. A nice place to come to work every day. So great conversation today. Congratulations on the launch of the company over two years in stealth mode. Talk a little bit about that. What is it like to be in stealth mode for so long and you guys raised big money, you've got a big team, you're doing heavy duty technology. What's it been like to finally open up the curtains and tell everybody what you've been? >>It's clearly very interesting and exciting. Normally it's taken me nine months to deliver a baby this time it's been two and a half years of being instilled while we have been getting ready for this baby to come out. So it's phenomenally exciting that too to be sharing the stage with our customers and our investors and our strategic partners. >>Yeah, I thought it was pretty interesting that you're launching with customers and when you really told the story on stage of how early you engaged with Josh and his team, um, first I want to get your kinda your perspective. Why were you doing that so early and what did that ultimately do with some of the design decisions that you guys made? And then we'll come back to Josh as to, you know, his participation. >>So I think whenever you conduct technology transitions, having a sense from customers that have the ability to look out two to three years is very important because when you're capturing market transitions, doing it with customer inputs is far more relevant than going about it alone. Uh, the other key thing about this architectural shift is that it allows the flexibility for every customer to go take pieces of how they want to bring the cloud architectures and bring it into their environment. So understanding that use case and understanding the compelling reasons of what problems both technological and business can be solving and having that perspective into the product definition and the design and the influence that customers like Josh you've had is why we are sitting here and talking about them in production. Uh, as opposed to, yeah, we're thinking about where we are. We are looking at it from a proof of concept perspective. Right. >>And Josh, your, your perspective, you said earlier today that, you know, as long as a sign is involved, you're, you're, uh, you're happy to jump in and see what she's been working on. So how, >>you know, how did you get involved, how did they reach out to you and, and what is it like working on, you know, technology so early in its development that you get to actually have some serious influence? Well, it's an amazing opportunity, um, to get exactly what you want, um, exactly what you know is going to solve problems for the business here. Um, you know, and the other thing is, you know, we've worked with this team, uh, through almost every spinning. Uh, I think it was a little young for the, maybe the first one. Um, but, uh, otherwise this team has worked with them through at least 15 years or more. So we knew the track record for execution and then for us on this product, I mean, it was an opportunity because it's truly a startup. Um, you know, Sony and the team brought us in. >>Uh, we kind of just put out problems on the table that we were trying to solve and then, you know, they came up with the product and the idea and we were able to put together, you know, yeah, these are our priority one, two, three that we want to go for. And you know, we've just been developing alongside them. So both software and, you know, driving what the feature set is. Right. So what were some of those problems guys? Price seemed like forever ago when you started this conversation, but as you kind of looked forward a couple of years back that you could see that were coming, that you needed addressed. You know, it's funny, we started with kind of like, well we think containerization is going to be explosive and, and you know, really everything's on virtual machines or bare metal, mostly virtual machines. So one, you know, as containers come out, how do we track them, secure them, um, how do we even secure, uh, you know, the virtual machines and our environment cause they're, you know, over almost a quarter million of them. >>The idea of being able to put, um, network policy, that's I would say incorruptible, not actually on the server, but at, you know, that's why we use firewalls, right? So solving that security problem was number one. The other one was being able to have the telemetry to see what's happening, what's changing, um, and troubleshoot at, you know, at the network layer from every single server. Again, it's all about scale. Like things were just scaling and the throughput's going up, traditional methods of being able to see what's on your network. You can't look in the middle, it just can't keep up. It's just speeds and feeds. So being able to push those things to the edge. And then lastly, it really happened more, um, through the process here. But about a year and a half ago, um, we began segmenting our network the same way a 5g provider does with a technology called segment routing. >>And we just said, that's kind of our follow on technologies to, you know, put the network in the server and put this segment routing capability all the way out at the edge. So, you know, some things we foresaw and other things we've just developed. You know, it's been, it's been two and a half years. So, um, it's been a great partnership and you know, I think more, more features will come. Well Sony, you and the team, but it's been talked about all day long, have have a history of multiple times that you've kind of brought these big transformational technologies. Um, head what, what did you guys see a couple of years back and kind of this progression, you saw this opportunity >>to do something a little bit different than you've done in the past, which is actually go out, raise, raise around and uh, and do a real startup. What was the opportunity that you saw this? >>So we saw a number of challenges and opportunities. At the same time, we, we clearly saw that, uh, the cloud architectures that have been built by the leaders, like the incumbents like AWS today have a lot of the intelligence that is being pushed into their, their respective compute platforms. Uh, and we also noticed that at the same time, while that was what was needed to build the first generation of the cloud, the new age applications, and even as gardener has predicted that 75% of all enterprise data and applications will be processed at the edge by 2025. If that happens, then you need that intelligence at the edge. You need the ability to go do it where the action is, which is at the edge. And very consistently we found that the architectures, including scale out storage, we're also driving the need for this intelligence to be on in a scale-out manner. >>So if you're going to scale out computing, you need the services to be going hand in hand with that scale. Our computer architecture for the enterprises so they can simplify their architectures and bring the cloud models that have only existed in the cloud world, into their own data centers and their own private clouds. So there were these technology transitions we saw were coming down the pike. It's easier said now in 2019 it wasn't so simple in 2017 because we had to look at these multiple technology transitions. And surprisingly, when we call those things out, as we were shaping the company's strategy, getting validation of the use cases from customers like Josh was pivotally important because it was for the validating that this would be the direction that the enterprises and the cloud customers would be taking. So the reason you start with a vision, you start with looking at where the technology transitions are going to be occurring and getting the customers that are looking farther out validated plays a very important role so that you can go and focus on the biggest problems that you need to go and solve. Right, right. >>It just seems like the, the, the big problem, um, for most layman's is, is the old one, which, why networking exists in the first place, which is do you bring the data to the compute or do you bring the compute to the data? And now as you said, in kind of this hyper distributed world, um, that's not really a viable answer either one, right? Because the two are blended and have to be together so that you don't necessarily have to move one to the other or the other back the other direction. So, and then the second piece that you talked about over and over in your, in your presentation with security and you know, everybody talks about security all the time. Everybody gets hacked every day. Um, and there's this constant theme that security has to be baked in, you know, kind of throughout the process as opposed to kind of bolted on at the end. You guys took that approach from day, just speak >>it into the architecture. Yes. That was crucially important because when you are trying to address the needs of the enterprise, particularly in regulated markets like financial services, you want to be in a position where you have thought about it and baked it into the platform ground up. Uh, and so when we are building the program of a process, so we had the opportunity to go put the right elements on it. In order to make it tamper proof, we had to go think about encrypting all the traffic and communication between our policy manager and the distributed services platforms at the edge. We also then took it a step further to say, now if there were to be a bad actor that were to attack from an operating system vulnerability perspective, how do we ensure that we can contain that bad actor as opposed to being propagated over the infrastructure? So those elements are things you cannot bolt on at design time, or when you need to go put those into the design day one, right. Only on top of that foundation, then can you build a very secure set of services, whether it's encryption, whether it's distributed via services, so on and so forth. >>Uh, and Josh, I'm curious on your take as we've seen kind of software defined everything, uh, slowly take over as opposed to, you know, kind of single purpose machines or single purpose appliances, et cetera. Yep. Really a different opportunity for you to control. Um, but also to see a lot of talk today about, about policy management. A lot of talk about, um, observability and as you said now even segmentation of the networks, like you segment the nodes and you segment everything else. You know, how, how do you see this kind of software defined everything continuing to evolve and what does it enable you to do that you can't do with just a static device? I mean, the approach we took, um, we started like, you know, years ago, about six years ago was saying we can get computers, uh, deployed for our applications. No problem. Uh, and you know, at, at on demand and in our internal cloud, now we can do it as a hybrid cloud solution. >>One of the biggest problems we had in software defined was how do you put security policy, firewall policy, um, with that compute and in, you know, our industry, there's lots of segmentation for material nonpublic information. Um, compliance, you know, it could be internet facing, B2B facing. Uh, we do that today. We program various firewall vendors automatically. Uh, we allow our application developers to create, um, these policies and push them through as code and then program the firewall. What we were really looking to do here is distribute that. So we F day one in getting pen Sandow into production was to use our uh, our firewall system. It's called pinnacle. We, um, we programmed from pinnacle directly into the Penn Songdo Venice manager via API and then it, you know, uses its inventory systems to push those things out. So for us, software defined has been around, I like to call it the store front, but for the developer it's network policy, it's load balancing. >>Um, and, and that's really what they see. Those are the big products on the net. Everything else is just packet forwarding to them. So we wanted with pen Sandow at least starting with security to have that bar set day one and then get, you know, all the benefits of scale, throughput and having the policies close to the, on the edge. You know, we're back to talking about the edge. We want to right there with the, with the deployment, with the workload or the application. And that's, that's what we're doing right off the bat. Yeah. What are the things you mentioned in your talk was w is, you know, kind of in the theme of atomic computing, right? You want to get smaller and smaller units so that you can apply and redeploy based on wherever the workload is and in the change. And you said you've now been able to, you know, basically take things out of dedicated, you know, kind of a dedicated space, dedicated line and dedicated job so that you can now put them in a more virtualized situation. >>Exactly. Grab more resources as you need them. Well, you'd think the architecture, I mean even just theater of the mind is just, you're saying, I'm going to put this specific thing that I have to secure behind these firewalls. So it's one cabinet of computers or a hundred it's still behind a set of firewalls. It's a very North, South, you know, get in and get out here. You're talking about having that same level of security and I think that's novel, right? There hasn't been, if you look at virtual firewalls or you know, IP tables on Linux, I mean it's corruptible. It's, it's, it can be attacked on the computer. And once it's, you know, once you've been attacked in that, that that attack vector has been, you know, hit your, your compromised. This is a separate management plane. Um, you know, separate control plane. The server doesn't see it. >>That security is provided. It's at scale, it's East, West. The more computers that have the pen Sandow, you know, architecture inside of them, the, you know, the wider you can go, right. And then the North South goes away. I'm just curious to get your perspective. Um, as you know, everyone is a technology company. At the same time, technology budgets are going down, people are hard to hire. Uh, your data is growing exponentially and everything's a security threat. Yes. So as you get up in the morning, get ready to drive to work and you're drinking your coffee, I mean, how do you, you know, kind of communicate to make sure to senior management knows kind of what your objectives are in this, this kind of ongoing challenge to do more with less. And it, even though it's an increasingly strategic place or is it actually is what the company does now, it just happens to wrap it around your plane services or financial services or travel or whatever. >>Uh, I think your eye, and I had said it to John before, um, it has to come from that budget has to come from somewhere. So I think a combination of, of one that's less, well, I'll say the one that's easier to quantify is you're going to take budget from say appliance manufacturer and move it to a distributed edge and you're going to hopefully save some money while you do it. Um, you're going to do it at scale. You're gonna do it at, you know, high throughput and the security is the same or better. So that's, that's one, that's one place to take capital from. The other one is to say, can I use the next computer? Yes. Because I don't have to deploy these other new computers behind this stack of firewalls. Is there agility there? Is there efficiency, um, on my buying less servers and using, you know, more of what I have and doing it, you know, able to deploy faster. >>And it's harder to quantify. I think if you could, you know, over time, see I bought 20% less server, uh, capacity or, you know, x86 capacity, that's a savings. And the other one that's very hard to quantify, but it's always nice to have the development community. And we've had it recently where they say, Hey, this took me a month to deploy instead of a year. Um, and you know, the purchase cycles, uh, you know, for procurement and deployment, they're long, you know, in enterprise you want them to be quick, but they're really not. So all of those things add up. And that's the story. You know, I would tell, you know, any manager, right? Yeah, >>yeah. I think, you know, the old historic way that utilization rates were just so, so, so, so low between CPU and memory, everything else. Cause if nothing else, because to get another box, you know, could take a long time. Yeah. Well, final, final question for you, Tony. You talked about architectures and being locked into architectures and you and you talked about you guys are already looking forward, you know, to kind of your next rev, your next release, kind of your next step forwards. What, where do you see kind of the direction, don't give away any secrets, but um, you know, kind of where you guys going. What are your priorities now that you've launched? You got a little bit more money in the bank. >>Well, our biggest priorities will be to focus on customer success is to make sure that the customer journey is indeed replicable at scale, is to enable the partner's success. Uh, so in addition to Goldman Sachs, the ability to go and replicate it across the federated markets, whether it's global financial services, healthcare, federal, and partnering with each B enterprise so that they can on their platform, amplify the value of this architecture, not just on the compute platforms but on, in other areas. And the third one clearly is for our cloud customers is to make sure that they are in a position to build a world class cloud architecture on top of which then they can build their own, deliver their own services, their own secret sauces, uh, so that they can Excel at whatever that cloud is. Whether it's to become the leading edge platform as a service customer, whether it is to be the leading edge of software's a service platform customer. So it's all about the execution as a, as you heard in that room. And that's fundamentally what we're going to strive to be, is to be a great execution machine and keep our heads down and focused on making our customers and our partners very successful. >>Well, certainly, congratulations again to you and the team on the launch today. And Josh, thank you for hosting this terrific event and being an early customer. Yeah. Yeah. Happy to be. Alright. I'm Jetta. Sone. Josh, we're the topic. Goldman Sachs at the Penn Sandow the new welcome to the new edge. Thanks for watching. We'll see you next time.

Published Date : Oct 18 2019

SUMMARY :

brought to you by systems. Good to see you too. And thank you and thanks for hosting us. So it's phenomenally exciting that too to be sharing the stage with our customers And then we'll come back to Josh as to, you know, his participation. So I think whenever you conduct technology transitions, having a sense from customers that And Josh, your, your perspective, you said earlier today that, you know, as long as a sign is involved, you know, and the other thing is, you know, we've worked with this team, uh, through almost every spinning. is going to be explosive and, and you know, really everything's on virtual machines or bare metal, not actually on the server, but at, you know, that's why we use firewalls, right? And we just said, that's kind of our follow on technologies to, you know, put the network in the server What was the opportunity that you saw this? If that happens, then you need that intelligence at the edge. and focus on the biggest problems that you need to go and solve. Um, and there's this constant theme that security has to be baked in, you know, kind of throughout the process as So those elements are things you I mean, the approach we took, um, we started like, you know, One of the biggest problems we had in software defined was how do you put security policy, you know, kind of a dedicated space, dedicated line and dedicated job so that you can now put It's a very North, South, you know, get in and get out here. the pen Sandow, you know, architecture inside of them, the, you know, the wider you can go, more of what I have and doing it, you know, able to deploy faster. Um, and you know, the purchase cycles, uh, you know, for procurement and deployment, because to get another box, you know, could take a long time. as you heard in that room. Well, certainly, congratulations again to you and the team on the launch today.

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Jay Krone & Alyson Langon, Dell EMC | Dell Technologies World 2019


 

live from Las Vegas it's the queue covering del technology's world 2019 brought to you by Dell technologies and it's ecosystem partners welcome back everyone to the cubes live coverage of Dell technologies world here live in Las Vegas I'm your host Rebecca night along with my co-host Stu minimun we have Jay Crone he is the senior consultant portfolio marketing at Dell EMC and Allison Langham consultant product marketing Dell EMC thank you so much for coming on the cube thanks for returning on so ricktum are we gonna talk today about cloud storage and data protection but I want to start with cloud and I'm gonna start with you Jay talk a little bit about what your customers what they want to do with the cloud well and so what one of the things we found is as cloud has been out there for a while and people have learned about what they can do with it it's not not the panacea that people thought there are about it about four or five use cases the big one is disaster recovery so a lot of people who can't won't don't care two don't have the money to set up a second data center will rent the cloud you know rent rent both capacity and computer in the cloud so disaster recovery is the big one we'll talk about that more in terms of some specific announcements the other ones make make sense it's really sort of the rent instead of buy test and development you know you want you want to spit up a test environment and run it for three hours find out what it what it tells you and then tear it down and not have to pay for it back up an archive is kind of a related to the disaster recovery but it's a little bit different use case because often people want to put the clout you two put the data some place to store it regular regulatory requirements that's an example which is different than disaster recovery analytics a big again it's this like what we used to call high performance computing where you need a lot of compute and a lot of storage for a short period of time and you don't want to you have a data center full of stuff that you're paying for or not using and then the last one there's lots of words for this the polite marketing term is workload migration also known as lift and shift which is these are the people that actually do want to take a workload from on-premises and pick it up and move it to the cloud wholesale so those are those are the ones disaster recovery is still far and away the most popular so Allison you know our observation coming in this week is there's a lot of discussion about that hybrid and multi cloud a lot of that focus gets put on you know the public clouds I mean you bring Satya Nadella to the show we're gonna talk a lot about Microsoft Azure and even when we get into the data center you know we we've seen the ascendancy of VX rail and that's an underlying component for many of the solutions that were all doubt but I know you're gonna help bring to us is help fill out some of the rest of the portfolio is you know from the EMC side and as Dell EMC comes in there's a large storage portfolio does that get left behind when we talk about cloud or pulled into the entire discussion yeah and a great question so you know when we think about our you know cloud strategy as a whole for Dell Dell technologies you know there's really there's two there's two pieces to that and so a lot of what you heard about yesterday and the big announcements around the Dell technologies cloud that's really helping customers really just completely transform to a cloud operating model and a lot of like the people processes technology implications of doing so the other piece of that is around our cloud enabled infrastructure which is really complementary to a lot of what we talked about yesterday and our cloud enabled infrastructure you know that's more of what we heard about today and what we're doing across both our storage and data protection portfolios to help customers modernize their existing infrastructure to be able to extend their data centers to the cloud so and it's you know these are there's our complementary pieces it's not really um it's not an aura conversation it's really an an conversation both pieces are really important when thinking about your cloud strategy just depending on you know workload and transformational readiness and where you're at to be able to do that so that's where a lot of our storage cloud capabilities come come into play all right okay maybe we could bring us down a little a little bit of level as to you know how I explain how cloud cloud enabled isn't cloud watching you know something like power Mac's you know well and that is that's interesting in fact I had that we just walked out of the booth was talking one of the product managers who had presiding to a customer that had one of some gear from a distinguished competitor shall we say was interested in PowerMax partly because of the cloud story so and PowerMax is is is just joining the cloud family and one of the things that we are have announced here that was talked about in the keynote is cloud storage services which is an offering that we have through a cloud service provider that allows you for example is an existing PowerMax customer to use s rdf to use native replication to replicate into the cloud and then in a VMware environment here's that disaster recovery use case coming in a VMware environment use Site Recovery Manager to perform a failover and then this service provider well you will read will spin up those VMs and VM works out on AWS so what you basically get is an automatic failover for VMware environments with power max so it's an extreme and unity by the way so both are a nice launch so we get we get that disaster recovery use case enabling you know our bread-and-butter our industry-leading storage platform so that's that's that's a big piece of the news and that wasn't that was announced here the other thing I do want to point out with that announcement is there's a multi cloud capability the the one I just discussed is that the automatic VMware use case but there's also the ability through our service provider to connect to regular AWS in addition to vm worked on an AWS Google and Azure which we might have heard a little about yesterday and we're excited about that as well Alison this is a very competitive market and customers really expect a lot they want new capabilities they want the latest and greatest what is the strategy and the messaging behind why Dell is the is the choice right so no I talked a little bit about they are speaking specifically around our cloud enable the infrastructure you know we had a lot of great announcements today but really we've been having we've been incorporating these cloud capabilities and functionality with on our storage and data protection portfolio for a long time and it's been around and we just we haven't really been talking about it but we have a lot of you know comprehensive cloud features and you know we sort of look at that in you know there's three specific areas where we really look for it to innovate with the cloud in our in our storage and data protection portfolio so that's areas like our cloud connected system so that's like data mobility our ability to tear data from on-prem to the cloud then we also have as Jay was just talking about Platte Lake we have our Cloud Data Services which includes our new cloud storage services offerings but it's also things like being able to deploy in the cloud and as opposed to extending to the cloud so things like cloud edition or data domain virtualization where you're deploying a software-defined version in the cloud and then spanning across the top of that from on-prem in the core to the cloud we have our cloud data insights so that's things like cloud IQ or clarity now that really enable you to proactively monitor and manage not just your infrastructure but also your data and really use that the artificial intelligence built into those to you know get you know good insights to manage manage and monitor your data from on-prem to the cloud so really that but those three areas we really bring together you know a comprehensive set of features to cloud enable your your infrastructure ok wondering if you can bring us inside some of the conversations you're having with customers you're wearing the shirt I see around a lot of the booths yeah you know you know what what are some of the you know top kind of business challenges and you know how are things different now than they might have been back when we called this EMC world well so the there's the disaster recovery use case which is which as I said is new the other thing that's happening is there's that five years of learning that people have had around the public cloud I was talking with a reseller yesterday and one of the value propositions that we have for this particular offering especially the cloud storage services is because the storage is at a service provider that is not the cloud provider shall we say they can offer a different economic model so what we're finding is people are finding new ways to go to the cloud for less money so and that's and that works out really really well because it makes the cloud more affordable for everybody it makes it gives them it gives us some additional business opportunity and most importantly it gives customers the ability to use the cloud consumption model the effects model and the outsourcing of the resources that they couldn't do before so that's the big thing is we were basically enabling the public cloud in ways that we couldn't have done to your point five years ago in addition to the cost benefits of that just building on the multi cloud piece with our cloud storage service is offering it's also about you know some concerns that like big concerns around public clouds like security and having control of your data their cloud storage services offering your data is actually sitting on external storage so it's directly connected to the cloud you have like a high-speed connection into the public cloud to be able to run your applications but and you can connect to multiple clouds move data between clouds you know as as it suits the business needs there's different workloads but at the same time you're still maintaining control of that data on you know durable persistent Dell EMC storage right it's on the gear you know and love and as I said all of our native replication this is this is wonderful because if you're a customer with gear on site you don't perceive any change your your s RDF pipe if you will it leaves the building like it used to it just goes to a cloud provider instead of a data center across the Hudson River so to speak well data protection and data security are it's a big theme this year for good and for good reason where do you think cuz the customer mindset is right now our customers appropriately concerned about the the threats that they face and the requirements that are that are bearing down or are they are they head in the sand I mean how would you describe where customers are right now in terms of thinking through these things everybody is concerned about security so the answer is it's right up there you know and we look at you know the the security is some of it is off site but it's it's things like Allison said we offer a model where your data is it it's in the lockbox that you know of as a unity or a nice loan or a Max and it's not in some amorphous place you know up there in the in the cloud as it were and that that gives people a lot of a lot of a warm warm fuzzy feeling and things like data at rest encryption at work on the storage arrays still work on the storage arrays when it's in the cloud so those features are still available to customers that they already know and love all right Alison one of the other things we've been talking a lot about this week is the VMware and Dell EMC pieces have come together more than ever before you know I think back you know when we used to rank how does EMC storage do with VMware well how many integrations does it have now many of the solutions you know VX rail it's got VCF sitting on it can you talk about how they did the VMware and the LMC storage pieces have been coming together even more yeah absolutely so specifically what one of the solutions that Jay was talking about earlier that automated disaster recovery feature from for our cloud storage services that's all about that's all about VMs it's all about VMware integration and it it really offers that if you get this disaster recovery as a service model for VMware environments who are running VMware cloud on AWS and they get you get that complete operational consistency so it's that's a huge benefit to our customers so there's that where it's you're leveraging for the automated disaster recovery it's either power max or unity including the new unity XT which was recently announced being able to completely have operational consistency within your VMware environment from on-prem to the cloud addition to that in addition to that we talked a lot about yesterday about the Dell tech cloud which VX rail is a key component of that we also have our storage our key storage platforms are also validated with VMware Cloud foundation for you know some more like high-performance workloads so we really have so things like power max and unity are also validated with VMware Cloud foundation to be able to get that Best of Breed storage as part of that stack as well it's something that you asked what's changed something that's kind of interesting so we're in the storage division we're in the storage business unit and we have weekly meetings bi-weekly meetings with the VMware cloud folks so that just tells you what's important there's VMware and cloud you know in that word and here we are as you know some of your prime your primary and unstructured storage people working on a regular basis with it with the VMware folks and that is an example of how the companies are coming together and doing doing things differently than we did before how are you finding this show this is the 10th year that the cube has been at at Dell Dell technologies world but back then Dell EMC world what are you how are you finding the vibe this year what's what is the tone of things very cloud focused on which has been a huge huge tip this year that's everything that we're hearing about is very cloud centered which is well it's nice to see you I wouldn't that wouldn't say it's so much of a victory lap bike but there's a lot of excitement certainly in our area on the floor there's a lot of work that has been done over the last couple of years to to get things aligned and put some new processes in place and get some new products out so you let you listen to the you know the Jeff Clark portions of the keynote in particularly yesterday and today he just goes this this this this and this and that's where customers want to see you know we have folks coming 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Published Date : Apr 30 2019

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Lenovo Transform 2.0 Keynote | Lenovo Transform 2018


 

(electronic dance music) (Intel Jingle) (ethereal electronic dance music) ♪ Okay ♪ (upbeat techno dance music) ♪ Oh oh oh oh ♪ ♪ Oh oh oh oh ♪ ♪ Oh oh oh oh oh ♪ ♪ Oh oh oh oh ♪ ♪ Oh oh oh oh oh ♪ ♪ Take it back take it back ♪ ♪ Take it back ♪ ♪ Take it back take it back ♪ ♪ Take it back ♪ ♪ Take it back take it back ♪ ♪ Yeah everybody get loose yeah ♪ ♪ Yeah ♪ ♪ Ye-yeah yeah ♪ ♪ Yeah yeah ♪ ♪ Everybody everybody yeah ♪ ♪ Whoo whoo ♪ ♪ Whoo whoo ♪ ♪ Whoo yeah ♪ ♪ Everybody get loose whoo ♪ ♪ Whoo ♪ ♪ Whoo ♪ ♪ Whoo ♪ >> As a courtesy to the presenters and those around you, please silence all mobile devices, thank you. (electronic dance music) ♪ Everybody get loose ♪ ♪ Whoo ♪ ♪ Whoo ♪ ♪ Whoo ♪ ♪ Whoo ♪ ♪ Whoo ♪ ♪ Whoo ♪ ♪ Whoo ♪ ♪ Whoo ♪ (upbeat salsa music) ♪ Ha ha ha ♪ ♪ Ah ♪ ♪ Ha ha ha ♪ ♪ So happy ♪ ♪ Whoo whoo ♪ (female singer scatting) >> Ladies and gentlemen, please take your seats. Our program will begin momentarily. ♪ Hey ♪ (female singer scatting) (male singer scatting) ♪ Hey ♪ ♪ Whoo ♪ (female singer scatting) (electronic dance music) ♪ All hands are in don't go ♪ ♪ Red all hands are in don't go ♪ ♪ Red red red red ♪ ♪ All hands are in don't go ♪ ♪ Red all hands are in don't go ♪ ♪ Red red red red ♪ ♪ All hands are in don't go ♪ ♪ Red all hands are in don't go ♪ ♪ All hands are in don't go ♪ ♪ Red all hands are in don't go ♪ ♪ Red red red red ♪ ♪ Red don't go ♪ ♪ All hands are in don't go ♪ ♪ In don't go ♪ ♪ Oh red go ♪ ♪ All hands are in don't go ♪ ♪ Red all hands are in don't go ♪ ♪ All hands are in don't go ♪ ♪ Red all hands are in don't go ♪ ♪ Red red red red ♪ ♪ All hands are red don't go ♪ ♪ All hands are in red red red red ♪ ♪ All hands are in don't go ♪ ♪ All hands are in red go ♪ >> Ladies and gentlemen, there are available seats. Towards house left, house left there are available seats. If you are please standing, we ask that you please take an available seat. We will begin momentarily, thank you. ♪ Let go ♪ ♪ All hands are in don't go ♪ ♪ Red all hands are in don't go ♪ ♪ All hands are in don't go ♪ ♪ Red all hands are in don't go ♪ (upbeat electronic dance music) ♪ Just make me ♪ ♪ Just make me ♪ ♪ Just make me ♪ ♪ Just make me ♪ ♪ Just make me ♪ ♪ I live ♪ ♪ Just make me ♪ ♪ Just make me ♪ ♪ Hey ♪ ♪ Yeah ♪ ♪ Oh ♪ ♪ Ah ♪ ♪ Ah ah ah ah ah ah ♪ ♪ Just make me ♪ ♪ Just make me ♪ (bouncy techno music) >> Ladies and gentlemen, once again we ask that you please take the available seats to your left, house left, there are many available seats. If you are standing, please make your way there. The program will begin momentarily, thank you. Good morning! This is Lenovo Transform 2.0! (keyboard clicks) >> Progress. Why do we always talk about it in the future? When will it finally get here? We don't progress when it's ready for us. We need it when we're ready, and we're ready now. Our hospitals and their patients need it now, our businesses and their customers need it now, our cities and their citizens need it now. To deliver intelligent transformation, we need to build it into the products and solutions we make every day. At Lenovo, we're designing the systems to fight disease, power businesses, and help you reach more customers, end-to-end security solutions to protect your data and your companies reputation. We're making IT departments more agile and cost efficient. We're revolutionizing how kids learn with VR. We're designing smart devices and software that transform the way you collaborate, because technology shouldn't just power industries, it should power people. While everybody else is talking about tomorrow, we'll keep building today, because the progress we need can't wait for the future. >> Please welcome to the stage Lenovo's Rod Lappen! (electronic dance music) (audience applauding) >> Alright. Good morning everyone! >> Good morning. >> Ooh, that was pretty good actually, I'll give it one more shot. Good morning everyone! >> Good morning! >> Oh, that's much better! Hope everyone's had a great morning. Welcome very much to the second Lenovo Transform event here in New York. I think when I got up just now on the steps I realized there's probably one thing in common all of us have in this room including myself which is, absolutely no one has a clue what I'm going to say today. So, I'm hoping very much that we get through this thing very quickly and crisply. I love this town, love New York, and you're going to hear us talk a little bit about New York as we get through here, but just before we get started I'm going to ask anyone who's standing up the back, there are plenty of seats down here, and down here on the right hand side, I think he called it house left is the professional way of calling it, but these steps to my right, your left, get up here, let's get you all seated down so that you can actually sit down during the keynote session for us. Last year we had our very first Lenovo Transform. We had about 400 people. It was here in New York, fantastic event, today, over 1,000 people. We have over 62 different technology demonstrations and about 15 breakout sessions, which I'll talk you through a little bit later on as well, so it's a much bigger event. Next year we're definitely going to be shooting for over 2,000 people as Lenovo really transforms and starts to address a lot of the technology that our commercial customers are really looking for. We were however hampered last year by a storm, I don't know if those of you who were with us last year will remember, we had a storm on the evening before Transform last year in New York, and obviously the day that it actually occurred, and we had lots of logistics. Our media people from AMIA were coming in. They took the, the plane was circling around New York for a long time, and Kamran Amini, our General Manager of our Data Center Infrastructure Group, probably one of our largest groups in the Lenovo DCG business, took 17 hours to get from Raleigh, North Carolina to New York, 17 hours, I think it takes seven or eight hours to drive. Took him 17 hours by plane to get here. And then of course this year, we have Florence. And so, obviously the hurricane Florence down there in the Carolinas right now, we tried to help, but still Kamran has made it today. Unfortunately, very tragically, we were hoping he wouldn't, but he's here today to do a big presentation a little bit later on as well. However, I do want to say, obviously, Florence is a very serious tragedy and we have to take it very serious. We got, our headquarters is in Raleigh, North Carolina. While it looks like the hurricane is just missing it's heading a little bit southeast, all of our thoughts and prayers and well wishes are obviously with everyone in the Carolinas on behalf of Lenovo, everyone at our headquarters, everyone throughout the Carolinas, we want to make sure everyone stays safe and out of harm's way. We have a great mixture today in the crowd of all customers, partners, industry analysts, media, as well as our financial analysts from all around the world. There's over 30 countries represented here and people who are here to listen to both YY, Kirk, and Christian Teismann speak today. And so, it's going to be a really really exciting day, and I really appreciate everyone coming in from all around the world. So, a big round of applause for everyone whose come in. (audience applauding) We have a great agenda for you today, and it starts obviously a very consistent format which worked very successful for us last year, and that's obviously our keynote. You'll hear from YY, our CEO, talk a little bit about the vision he has in the industry and how he sees Lenovo's turned the corner and really driving some great strategy to address our customer's needs. Kirk Skaugen, our Executive Vice President of DCG, will be up talking about how we've transformed the DCG business and once again are hitting record growth ratios for our DCG business. And then you'll hear from Christian Teismann, our SVP and General Manager for our commercial business, get up and talk about everything that's going on in our IDG business. There's really exciting stuff going on there and obviously ThinkPad being the cornerstone of that I'm sure he's going to talk to us about a couple surprises in that space as well. Then we've got some great breakout sessions, I mentioned before, 15 breakout sessions, so while this keynote section goes until about 11:30, once we get through that, please go over and explore, and have a look at all of the breakout sessions. We have all of our subject matter experts from both our PC, NBG, and our DCG businesses out to showcase what we're doing as an organization to better address your needs. And then obviously we have the technology pieces that I've also spoken about, 62 different technology displays there arranged from everything IoT, 5G, NFV, everything that's really cool and hot in the industry right now is going to be on display up there, and I really encourage all of you to get up there. So, I'm going to have a quick video to show you from some of the setup yesterday on a couple of the 62 technology displays we've got on up on stage. Okay let's go, so we've got a demonstrations to show you today, one of the greats one here is the one we've done with NC State, a high-performance computing artificial intelligence demonstration of fresh produce. It's about modeling the population growth of the planet, and how we're going to supply water and food as we go forward. Whoo. Oh, that is not an apple. Okay. (woman laughs) Second one over here is really, hey Jonas, how are you? Is really around virtual reality, and how we look at one of the most amazing sites we've got, as an install on our high-performance computing practice here globally. And you can see, obviously, that this is the Barcelona supercomputer, and, where else in New York can you get access to being able to see something like that so easily? Only here at Lenovo Transform. Whoo, okay. (audience applauding) So there's two examples of some of the technology. We're really encouraging everyone in the room after the keynote to flow into that space and really get engaged, and interact with a lot of the technology we've got up there. It seems I need to also do something about my fashion, I've just realized I've worn a vest two days in a row, so I've got to work on that as well. Alright so listen, the last thing on the agenda, we've gone through the breakout sessions and the demo, tonight at four o'clock, there's about 400 of you registered to be on the cruise boat with us, the doors will open behind me. the boat is literally at the pier right behind us. You need to make sure you're on the boat for 4:00 p.m. this evening. Outside of that, I want everyone to have a great time today, really enjoy the experience, make it as experiential as you possibly can, get out there and really get in and touch the technology. There's some really cool AI displays up there for us all to get involved in as well. So ladies and gentlemen, without further adieu, it gives me great pleasure to introduce to you a lover of tennis, as some of you would've heard last year at Lenovo Transform, as well as a lover of technology, Lenovo, and of course, New York City. I am obviously very pleasured to introduce to you Yang Yuanqing, our CEO, as we like to call him, YY. (audience applauding) (upbeat funky music) >> Good morning, everyone. >> Good morning. >> Thank you Rod for that introduction. Welcome to New York City. So, this is the second year in a row we host our Transform event here, because New York is indeed one of the most transformative cities in the world. Last year on this stage, I spoke about the Fourth Industrial Revolution, and our vision around the intelligent transformation, how it would fundamentally change the nature of business and the customer relationships. And why preparing for this transformation is the key for the future of our company. And in the last year I can assure you, we were being very busy doing just that, from searching and bringing global talents around the world to the way we think about every product and every investment we make. I was here in New York just a month ago to announce our fiscal year Q1 earnings, which was a good day for us. I think now the world believes it when we say Lenovo has truly turned the corner to a new phase of growth and a new phase of acceleration in executing the transformation strategy. That's clear to me is that the last few years of a purposeful disruption at Lenovo have led us to a point where we can now claim leadership of the coming intelligent transformation. People often asked me, what is the intelligent transformation? I was saying this way. This is the unlimited potential of the Fourth Industrial Revolution driven by artificial intelligence being realized, ordering a pizza through our speaker, and locking the door with a look, letting your car drive itself back to your home. This indeed reflect the power of AI, but it just the surface of it. The true impact of AI will not only make our homes smarter and offices more efficient, but we are also completely transformed every value chip in every industry. However, to realize these amazing possibilities, we will need a structure built around the key components, and one that touches every part of all our lives. First of all, explosions in new technology always lead to new structures. This has happened many times before. In the early 20th century, thousands of companies provided a telephone service. City streets across the US looked like this, and now bundles of a microscopic fiber running from city to city bring the world closer together. Here's what a driving was like in the US, up until 1950s. Good luck finding your way. (audience laughs) And today, millions of vehicles are organized and routed daily, making the world more efficient. Structure is vital, from fiber cables and the interstate highways, to our cells bounded together to create humans. Thankfully the structure for intelligent transformation has emerged, and it is just as revolutionary. What does this new structure look like? We believe there are three key building blocks, data, computing power, and algorithms. Ever wondered what is it behind intelligent transformation? What is fueling this miracle of human possibility? Data. As the Internet becomes ubiquitous, not only PCs, mobile phones, have come online and been generating data. Today it is the cameras in this room, the climate controls in our offices, or the smart displays in our kitchens at home. The number of smart devices worldwide will reach over 20 billion in 2020, more than double the number in 2017. These devices and the sensors are connected and generating massive amount of data. By 2020, the amount of data generated will be 57 times more than all the grains of sand on Earth. This data will not only make devices smarter, but will also fuel the intelligence of our homes, offices, and entire industries. Then we need engines to turn the fuel into power, and the engine is actually the computing power. Last but not least the advanced algorithms combined with Big Data technology and industry know how will form vertical industrial intelligence and produce valuable insights for every value chain in every industry. When these three building blocks all come together, it will change the world. At Lenovo, we have each of these elements of intelligent transformations in a single place. We have built our business around the new structure of intelligent transformation, especially with mobile and the data center now firmly part of our business. I'm often asked why did you acquire these businesses? Why has a Lenovo gone into so many fields? People ask the same questions of the companies that become the leaders of the information technology revolution, or the third industrial transformation. They were the companies that saw the future and what the future required, and I believe Lenovo is the company today. From largest portfolio of devices in the world, leadership in the data center field, to the algorithm-powered intelligent vertical solutions, and not to mention the strong partnership Lenovo has built over decades. We are the only company that can unify all these essential assets and deliver end to end solutions. Let's look at each part. We now understand the important importance data plays as fuel in intelligent transformation. Hundreds of billions of devices and smart IoTs in the world are generating better and powering the intelligence. Who makes these devices in large volume and variety? Who puts these devices into people's home, offices, manufacturing lines, and in their hands? Lenovo definitely has the front row seats here. We are number one in PCs and tablets. We also produces smart phones, smart speakers, smart displays. AR/VR headsets, as well as commercial IoTs. All of these smart devices, or smart IoTs are linked to each other and to the cloud. In fact, we have more than 20 manufacturing facilities in China, US, Brazil, Japan, India, Mexico, Germany, and more, producing various devices around the clock. We actually make four devices every second, and 37 motherboards every minute. So, this factory located in my hometown, Hu-fi, China, is actually the largest laptop factory in the world, with more than three million square feet. So, this is as big as 42 soccer fields. Our scale and the larger portfolio of devices gives us access to massive amount of data, which very few companies can say. So, why is the ability to scale so critical? Let's look again at our example from before. The early days of telephone, dozens of service providers but only a few companies could survive consolidation and become the leader. The same was true for the third Industrial Revolution. Only a few companies could scale, only a few could survive to lead. Now the building blocks of the next revolution are locking into place. The (mumbles) will go to those who can operate at the scale. So, who could foresee the total integration of cloud, network, and the device, need to deliver intelligent transformation. Lenovo is that company. We are ready to scale. Next, our computing power. Computing power is provided in two ways. On one hand, the modern supercomputers are providing the brute force to quickly analyze the massive data like never before. On the other hand the cloud computing data centers with the server storage networking capabilities, and any computing IoT's, gateways, and miniservers are making computing available everywhere. Did you know, Lenovo is number one provider of super computers worldwide? 170 of the top 500 supercomputers, run on Lenovo. We hold 89 World Records in key workloads. We are number one in x86 server reliability for five years running, according to ITIC. a respected provider of industry research. We are also the fastest growing provider of hyperscale public cloud, hyper-converged and aggressively growing in edge computing. cur-ges target, we are expand on this point soon. And finally to run these individual nodes into our symphony, we must transform the data and utilize the computing power with advanced algorithms. Manufactured, industry maintenance, healthcare, education, retail, and more, so many industries are on the edge of intelligent transformation to improve efficiency and provide the better products and services. We are creating advanced algorithms and the big data tools combined with industry know-how to provide intelligent vertical solutions for several industries. In fact, we studied at Lenovo first. Our IT and research teams partnered with our global supply chain to develop an AI that improved our demand forecasting accuracy. Beyond managing our own supply chain we have offered our deep learning supply focused solution to other manufacturing companies to improve their efficiency. In the best case, we have improved the demand, focused the accuracy by 30 points to nearly 90 percent, for Baosteel, the largest of steel manufacturer in China, covering the world as well. Led by Lenovo research, we launched the industry-leading commercial ready AR headset, DaystAR, partnering with companies like the ones in this room. This technology is being used to revolutionize the way companies service utility, and even our jet engines. Using our workstations, servers, and award-winning imaging processing algorithms, we have partnered with hospitals to process complex CT scan data in minutes. So, this enable the doctors to more successfully detect the tumors, and it increases the success rate of cancer diagnosis all around the world. We are also piloting our smart IoT driven warehouse solution with one of the world's largest retail companies to greatly improve the efficiency. So, the opportunities are endless. This is where Lenovo will truly shine. When we combine the industry know-how of our customers with our end-to-end technology offerings, our intelligent vertical solutions like this are growing, which Kirk and Christian will share more. Now, what will drive this transformation even faster? The speed at which our networks operate, specifically 5G. You may know that Lenovo just launched the first-ever 5G smartphone, our Moto Z3, with the new 5G Moto model. We are partnering with multiple major network providers like Verizon, China Mobile. With the 5G model scheduled to ship early next year, we will be the first company to provide a 5G mobile experience to any users, customers. This is amazing innovation. You don't have to buy a new phone, just the 5G clip on. What can I say, except wow. (audience laughs) 5G is 10 times the fast faster than 4G. Its download speed will transform how people engage with the world, driverless car, new types of smart wearables, gaming, home security, industrial intelligence, all will be transformed. Finally, accelerating with partners, as ready as we are at Lenovo, we need partners to unlock our full potential, partners here to create with us the edge of the intelligent transformation. The opportunities of intelligent transformation are too profound, the scale is too vast. No company can drive it alone fully. We are eager to collaborate with all partners that can help bring our vision to life. We are dedicated to open partnerships, dedicated to cross-border collaboration, unify the standards, share the advantage, and market the synergies. We partner with the biggest names in the industry, Intel, Microsoft, AMD, Qualcomm, Google, Amazon, and Disney. We also find and partner with the smaller innovators as well. We're building the ultimate partner experience, open, shared, collaborative, diverse. So, everything is in place for intelligent transformation on a global scale. Smart devices are everywhere, the infrastructure is in place, networks are accelerating, and the industries demand to be more intelligent, and Lenovo is at the center of it all. We are helping to drive change with the hundreds of companies, companies just like yours, every day. We are your partner for intelligent transformation. Transformation never stops. This is what you will hear from Kirk, including details about Lenovo NetApp global partnership we just announced this morning. We've made the investments in every single aspect of the technology. We have the end-to-end resources to meet your end-to-end needs. As you attend the breakout session this afternoon, I hope you see for yourself how much Lenovo has transformed as a company this past year, and how we truly are delivering a future of intelligent transformation. Now, let me invite to the stage Kirk Skaugen, our president of Data Center growth to tell you about the exciting transformation happening in the global Data C enter market. Thank you. (audience applauding) (upbeat music) >> Well, good morning. >> Good morning. >> Good morning! >> Good morning! >> Excellent, well, I'm pleased to be here this morning to talk about how we're transforming the Data Center and taking you as our customers through your own intelligent transformation journey. Last year I stood up here at Transform 1.0, and we were proud to announce the largest Data Center portfolio in Lenovo's history, so I thought I'd start today and talk about the portfolio and the progress that we've made over the last year, and the strategies that we have going forward in phase 2.0 of Lenovo's transformation to be one of the largest data center companies in the world. We had an audacious vision that we talked about last year, and that is to be the most trusted data center provider in the world, empowering customers through the new IT, intelligent transformation. And now as the world's largest supercomputer provider, giving something back to humanity, is very important this week with the hurricanes now hitting North Carolina's coast, but we take this most trusted aspect very seriously, whether it's delivering the highest quality products on time to you as customers with the highest levels of security, or whether it's how we partner with our channel partners and our suppliers each and every day. You know we're in a unique world where we're going from hundreds of millions of PCs, and then over the next 25 years to hundred billions of connected devices, so each and every one of you is going through this intelligent transformation journey, and in many aspects were very early in that cycle. And we're going to talk today about our role as the largest supercomputer provider, and how we're solving humanity's greatest challenges. Last year we talked about two special milestones, the 25th anniversary of ThinkPad, but also the 25th anniversary of Lenovo with our IBM heritage in x86 computing. I joined the workforce in 1992 out of college, and the IBM first personal server was launching at the same time with an OS2 operating system and a free mouse when you bought the server as a marketing campaign. (audience laughing) But what I want to be very clear today, is that the innovation engine is alive and well at Lenovo, and it's really built on the culture that we're building as a company. All of these awards at the bottom are things that we earned over the last year at Lenovo. As a Fortune now 240 company, larger than companies like Nike, or AMEX, or Coca-Cola. The one I'm probably most proud of is Forbes first list of the top 2,000 globally regarded companies. This was something where 15,000 respondents in 60 countries voted based on ethics, trustworthiness, social conduct, company as an employer, and the overall company performance, and Lenovo was ranked number 27 of 2000 companies by our peer group, but we also now one of-- (audience applauding) But we also got a perfect score in the LGBTQ Equality Index, exemplifying the diversity internally. We're number 82 in the top working companies for mothers, top working companies for fathers, top 100 companies for sustainability. If you saw that factory, it's filled with solar panels on the top of that. And now again, one of the top global brands in the world. So, innovation is built on a customer foundation of trust. We also said last year that we'd be crossing an amazing milestone. So we did, over the last 12 months ship our 20 millionth x86 server. So, thank you very much to our customers for this milestone. (audience applauding) So, let me recap some of the transformation elements that have happened over the last year. Last year I talked about a lot of brand confusion, because we had the ThinkServer brand from the legacy Lenovo, the System x, from IBM, we had acquired a number of networking companies, like BLADE Network Technologies, et cetera, et cetera. Over the last year we've been ramping based on two brand structures, ThinkAgile for next generation IT, and all of our software-defined infrastructure products and ThinkSystem as the world's highest performance, highest reliable x86 server brand, but for servers, for storage, and for networking. We have transformed every single aspect of the customer experience. A year and a half ago, we had four different global channel programs around the world. Typically we're about twice the mix to our channel partners of any of our competitors, so this was really important to fix. We now have a single global Channel program, and have technically certified over 11,000 partners to be technical experts on our product line to deliver better solutions to our customer base. Gardner recently recognized Lenovo as the 26th ranked supply chain in the world. And, that's a pretty big honor, when you're up there with Amazon and Walmart and others, but in tech, we now are in the top five supply chains. You saw the factory network from YY, and today we'll be talking about product shipping in more than 160 countries, and I know there's people here that I've met already this morning, from India, from South Africa, from Brazil and China. We announced new Premier Support services, enabling you to go directly to local language support in nine languages in 49 countries in the world, going directly to a native speaker level three support engineer. And today we have more than 10,000 support specialists supporting our products in over 160 countries. We've delivered three times the number of engineered solutions to deliver a solutions orientation, whether it's on HANA, or SQL Server, or Oracle, et cetera, and we've completely reengaged our system integrator channel. Last year we had the CIO of DXE on stage, and here we're talking about more than 175 percent growth through our system integrator channel in the last year alone as we've brought that back and really built strong relationships there. So, thank you very much for amazing work here on the customer experience. (audience applauding) We also transformed our leadership. We thought it was extremely important with a focus on diversity, to have diverse talent from the legacy IBM, the legacy Lenovo, but also outside the industry. We made about 19 executive changes in the DCG group. This is the most senior leadership team within DCG, all which are newly on board, either from our outside competitors mainly over the last year. About 50 percent of our executives were now hired internally, 50 percent externally, and 31 percent of those new executives are diverse, representing the diversity of our global customer base and gender. So welcome, and most of them you're going to be able to meet over here in the breakout sessions later today. (audience applauding) But some things haven't changed, they're just keeping getting better within Lenovo. So, last year I got up and said we were committed with the new ThinkSystem brand to be a world performance leader. You're going to see that we're sponsoring Ducati for MotoGP. You saw the Ferrari out there with Formula One. That's not a surprise. We want the Lenovo ThinkSystem and ThinkAgile brands to be synonymous with world record performance. So in the last year we've gone from 39 to 89 world records, and partners like Intel would tell you, we now have four times the number of world record workloads on Lenovo hardware than any other server company on the planet today, with more than 89 world records across HPC, Java, database, transaction processing, et cetera. And we're proud to have just brought on Doug Fisher from Intel Corporation who had about 10-17,000 people on any given year working for him in workload optimizations across all of our software. It's just another testament to the leadership team we're bringing in to keep focusing on world-class performance software and solutions. We also per ITIC, are the number one now in x86 server reliability five years running. So, this is a survey where CIOs are in a blind survey asked to submit their reliability of their uptime on their x86 server equipment over the last 365 days. And you can see from 2016 to 2017 the downtime, there was over four hours as noted by the 750 CXOs in more than 20 countries is about one percent for the Lenovo products, and is getting worse generation from generation as we went from Broadwell to Pearlie. So we're taking our reliability, which was really paramount in the IBM System X heritage, and ensuring that we don't just recognize high performance but we recognize the highest level of reliability for mission-critical workloads. And what that translates into is that we at once again have been ranked number one in customer satisfaction from you our customers in 19 of 22 attributes, in North America in 18 of 22. This is a survey by TVR across hundreds of customers of us and our top competitors. This is the ninth consecutive study that we've been ranked number one in customer satisfaction, so we're taking this extremely seriously, and in fact YY now has increased the compensation of every single Lenovo employee. Up to 40 percent of their compensation bonus this year is going to be based on customer metrics like quality, order to ship, and things of this nature. So, we're really putting every employee focused on customer centricity this year. So, the summary on Transform 1.0 is that every aspect of what you knew about Lenovo's data center group has transformed, from the culture to the branding to dedicated sales and marketing, supply chain and quality groups, to a worldwide channel program and certifications, to new system integrator relationships, and to the new leadership team. So, rather than me just talk about it, I thought I'd share a quick video about what we've done over the last year, if you could run the video please. Turn around for a second. (epic music) (audience applauds) Okay. So, thank you to all our customers that allowed us to publicly display their logos in that video. So, what that means for you as investors, and for the investor community out there is, that our customers have responded, that this year Gardner just published that we are the fastest growing server company in the top 10, with 39 percent growth quarter-on-quarter, and 49 percent growth year-on-year. If you look at the progress we've made since the transformation the last three quarters publicly, we've grown 17 percent, then 44 percent, then 68 percent year on year in revenue, and I can tell you this quarter I'm as confident as ever in the financials around the DCG group, and it hasn't been in one area. You're going to see breakout sessions from hyperscale, software-defined, and flash, which are all growing more than a 100 percent year-on-year, supercomputing which we'll talk about shortly, now number one, and then ultimately from profitability, delivering five consecutive quarters of pre-tax profit increase, so I think, thank you very much to the customer base who's been working with us through this transformation journey. So, you're here to really hear what's next on 2.0, and that's what I'm excited to talk about today. Last year I came up with an audacious goal that we would become the largest supercomputer company on the planet by 2020, and this graph represents since the acquisition of the IBM System x business how far we were behind being the number one supercomputer. When we started we were 182 positions behind, even with the acquisition for example of SGI from HP, we've now accomplished our goal actually two years ahead of time. We're now the largest supercomputer company in the world. About one in every four supercomputers, 117 on the list, are now Lenovo computers, and you saw in the video where the universities are said, but I think what I'm most proud of is when your customers rank you as the best. So the awards at the bottom here, are actually Readers Choice from the last International Supercomputing Show where the scientific researchers on these computers ranked their vendors, and we were actually rated the number one server technology in supercomputing with our ThinkSystem SD530, and the number one storage technology with our ThinkSystem DSS-G, but more importantly what we're doing with the technology. You're going to see we won best in life sciences, best in data analytics, and best in collaboration as well, so you're going to see all of that in our breakout sessions. As you saw in the video now, 17 of the top 25 research institutions in the world are now running Lenovo supercomputers. And again coming from Raleigh and watching that hurricane come across the Atlantic, there are eight supercomputers crunching all of those models you see from Germany to Malaysia to Canada, and we're happy to have a SciNet from University of Toronto here with us in our breakout session to talk about what they're doing on climate modeling as well. But we're not stopping there. We just announced our new Neptune warm water cooling technology, which won the International Supercomputing Vendor Showdown, the first time we've won that best of show in 25 years, and we've now installed this. We're building out LRZ in Germany, the first ever warm water cooling in Peking University, at the India Space Propulsion Laboratory, at the Malaysian Weather and Meteorological Society, at Uninett, at the largest supercomputer in Norway, T-Systems, University of Birmingham. This is truly amazing technology where we're actually using water to cool the machine to deliver a significantly more energy-efficient computer. Super important, when we're looking at global warming and some of the electric bills can be millions of dollars just for one computer, and could actually power a small city just with the technology from the computer. We've built AI centers now in Morrisville, Stuttgart, Taipei, and Beijing, where customers can bring their AI workloads in with experts from Intel, from Nvidia, from our FPGA partners, to work on their workloads, and how they can best implement artificial intelligence. And we also this year launched LICO which is Lenovo Intelligent Compute Orchestrator software, and it's a software solution that simplifies the management and use of distributed clusters in both HPC and AI model development. So, what it enables you to do is take a single cluster, and run both HPC and AI workloads on it simultaneously, delivering better TCO for your environment, so check out LICO as well. A lot of the customers here and Wall Street are very excited and using it already. And we talked about solving humanity's greatest challenges. In the breakout session, you're going to have a virtual reality experience where you're going to be able to walk through what as was just ranked the world's most beautiful data center, the Barcelona Supercomputer. So, you can actually walk through one of the largest supercomputers in the world from Barcelona. You can see the work we're doing with NC State where we're going to have to grow the food supply of the world by 50 percent, and there's not enough fresh water in the world in the right places to actually make all those crops grow between now and 2055, so you're going to see the progression of how they're mapping the entire globe and the water around the world, how to build out the crop population over time using AI. You're going to see our work with Vestas is this largest supercomputer provider in the wind turbine areas, how they're working on wind energy, and then with University College London, how they're working on some of the toughest particle physics calculations in the world. So again, lots of opportunity here. Take advantage of it in the breakout sessions. Okay, let me transition to hyperscale. So in hyperscale now, we have completely transformed our business model. We are now powering six of the top 10 hyperscalers in the world, which is a significant difference from where we were two years ago. And the reason we're doing that, is we've coined a term called ODM+. We believe that hyperscalers want more procurement power than an ODM, and Lenovo is doing about $18 billion of procurement a year. They want a broader global supply chain that they can get from a local system integrator. We're more than 160 countries around the world, but they want the same world-class quality and reliability like they get from an MNC. So, what we're doing now is instead of just taking off the shelf motherboards from somewhere, we're starting with a blank sheet of paper, we're working with the customer base on customized SKUs and you can see we already are developing 33 custom solutions for the largest hyperscalers in the world. And then we're not just running notebooks through this factory where YY said, we're running 37 notebook boards a minute, we're now putting in tens and tens and tens of thousands of server board capacity per month into this same factory, so absolutely we can compete with the most aggressive ODM's in the world, but it's not just putting these things in in the motherboard side, we're also building out these systems all around the world, India, Brazil, Hungary, Mexico, China. This is an example of a new hyperscale customer we've had this last year, 34,000 servers we delivered in the first six months. The next 34,000 servers we delivered in 68 days. The next 34,000 servers we delivered in 35 days, with more than 99 percent on-time delivery to 35 data centers in 14 countries as diverse as South Africa, India, China, Brazil, et cetera. And I'm really ashamed to say it was 99.3, because we did have a forklift driver who rammed their forklift right through the middle of the one of the server racks. (audience laughing) At JFK Airport that we had to respond to, but I think this gives you a perspective of what it is to be a top five global supply chain and technology. So last year, I said we would invest significantly in IP, in joint ventures, and M and A to compete in software defined, in networking, and in storage, so I wanted to give you an update on that as well. Our newest software-defined partnership is with Cloudistics, enabling a fully composable cloud infrastructure. It's an exclusive agreement, you can see them here. I think Nag, our founder, is going to be here today, with a significant Lenovo investment in the company. So, this new ThinkAgile CP series delivers the simplicity of the public cloud, on-premise with exceptional support and a marketplace of essential enterprise applications all with a single click deployment. So simply put, we're delivering a private cloud with a premium experience. It's simple in that you need no specialists to deploy it. An IT generalist can set it up and manage it. It's agile in that you can provision dozens of workloads in minutes, and it's transformative in that you get all of the goodness of public cloud on-prem in a private cloud to unlock opportunity for use. So, we're extremely excited about the ThinkAgile CP series that's now shipping into the marketplace. Beyond that we're aggressively ramping, and we're either doubling, tripling, or quadrupling our market share as customers move from traditional server technology to software-defined technology. With Nutanix we've been public, growing about more than 150 percent year-on-year, with Nutanix as their fastest growing Nutanix partner, but today I want to set another audacious goal. I believe we cannot just be Nutanix's fastest growing partner but we can become their largest partner within two years. On Microsoft, we are already four times our market share on Azure stack of our traditional business. We were the first to launch our ThinkAgile on Broadwell and on Skylake with the Azure Stack Infrastructure. And on VMware we're about twice our market segment share. We were the first to deliver an Intel-optimized Optane-certified VSAN node. And with Optane technology, we're delivering 50 percent more VM density than any competitive SSD system in the marketplace, about 10 times lower latency, four times the performance of any SSD system out there, and Lenovo's first to market on that. And at VMworld you saw CEO Pat Gelsinger of VMware talked about project dimension, which is Edge as a service, and we're the only OEM beyond the Dell family that is participating today in project dimension. Beyond that you're going to see a number of other partnerships we have. I'm excited that we have the city of Bogota Columbia here, an eight million person city, where we announced a 3,000 camera video surveillance solution last month. With pivot three you're going to see city of Bogota in our breakout sessions. You're going to see a new partnership with Veeam around backup that's launching today. You're going to see partnerships with scale computing in IoT and hyper-converged infrastructure working on some of the largest retailers in the world. So again, everything out in the breakout session. Transitioning to storage and data management, it's been a great year for Lenovo, more than a 100 percent growth year-on-year, 2X market growth in flash arrays. IDC just reported 30 percent growth in storage, number one in price performance in the world and the best HPC storage product in the top 500 with our ThinkSystem DSS G, so strong coverage, but I'm excited today to announce for Transform 2.0 that Lenovo is launching the largest data management and storage portfolio in our 25-year data center history. (audience applauding) So a year ago, the largest server portfolio, becoming the largest fastest growing server OEM, today the largest storage portfolio, but as you saw this morning we're not doing it alone. Today Lenovo and NetApp, two global powerhouses are joining forces to deliver a multi-billion dollar global alliance in data management and storage to help customers through their intelligent transformation. As the fastest growing worldwide server leader and one of the fastest growing flash array and data management companies in the world, we're going to deliver more choice to customers than ever before, global scale that's never been seen, supply chain efficiencies, and rapidly accelerating innovation and solutions. So, let me unwrap this a little bit for you and talk about what we're announcing today. First, it's the largest portfolio in our history. You're going to see not just storage solutions launching today but a set of solution recipes from NetApp that are going to make Lenovo server and NetApp or Lenovo storage work better together. The announcement enables Lenovo to go from covering 15 percent of the global storage market to more than 90 percent of the global storage market and distribute these products in more than 160 countries around the world. So we're launching today, 10 new storage platforms, the ThinkSystem DE and ThinkSystem DM platforms. They're going to be centrally managed, so the same XClarity management that you've been using for server, you can now use across all of your storage platforms as well, and it'll be supported by the same 10,000 plus service personnel that are giving outstanding customer support to you today on the server side. And we didn't come up with this in the last month or the last quarter. We're announcing availability in ordering today and shipments tomorrow of the first products in this portfolio, so we're excited today that it's not just a future announcement but something you as customers can take advantage of immediately. (audience applauding) The second part of the announcement is we are announcing a joint venture in China. Not only will this be a multi-billion dollar global partnership, but Lenovo will be a 51 percent owner, NetApp a 49 percent owner of a new joint venture in China with the goal of becoming in the top three storage companies in the largest data and storage market in the world. We will deliver our R and D in China for China, pooling our IP and resources together, and delivering a single route to market through a complementary channel, not just in China but worldwide. And in the future I just want to tell everyone this is phase one. There is so much exciting stuff. We're going to be on the stage over the next year talking to you about around integrated solutions, next-generation technologies, and further synergies and collaborations. So, rather than just have me talk about it, I'd like to welcome to the stage our new partner NetApp and Brad Anderson who's the senior vice president and general manager of NetApp Cloud Infrastructure. (upbeat music) (audience applauding) >> Thank You Kirk. >> So Brad, we've known each other a long time. It's an exciting day. I'm going to give you the stage and allow you to say NetApp's perspective on this announcement. >> Very good, thank you very much, Kirk. Kirk and I go back to I think 1994, so hey good morning and welcome. My name is Brad Anderson. I manage the Cloud Infrastructure Group at NetApp, and I am honored and privileged to be here at Lenovo Transform, particularly today on today's announcement. Now, you've heard a lot about digital transformation about how companies have to transform their IT to compete in today's global environment. And today's announcement with the partnership between NetApp and Lenovo is what that's all about. This is the joining of two global leaders bringing innovative technology in a simplified solution to help customers modernize their IT and accelerate their global digital transformations. Drawing on the strengths of both companies, Lenovo's high performance compute world-class supply chain, and NetApp's hybrid cloud data management, hybrid flash and all flash storage solutions and products. And both companies providing our customers with the global scale for them to be able to meet their transformation goals. At NetApp, we're very excited. This is a quote from George Kurian our CEO. George spent all day yesterday with YY and Kirk, and would have been here today if it hadn't been also our shareholders meeting in California, but I want to just convey how excited we are for all across NetApp with this partnership. This is a partnership between two companies with tremendous market momentum. Kirk took you through all the amazing results that Lenovo has accomplished, number one in supercomputing, number one in performance, number one in x86 reliability, number one in x86 customers sat, number five in supply chain, really impressive and congratulations. Like Lenovo, NetApp is also on a transformation journey, from a storage company to the data authority in hybrid cloud, and we've seen some pretty impressive momentum as well. Just last week we became number one in all flash arrays worldwide, catching EMC and Dell, and we plan to keep on going by them, as we help customers modernize their their data centers with cloud connected flash. We have strategic partnerships with the largest hyperscalers to provide cloud native data services around the globe and we are having success helping our customers build their own private clouds with just, with a new disruptive hyper-converged technology that allows them to operate just like hyperscalers. These three initiatives has fueled NetApp's transformation, and has enabled our customers to change the world with data. And oh by the way, it has also fueled us to have meet or have beaten Wall Street's expectations for nine quarters in a row. These are two companies with tremendous market momentum. We are also building this partnership for long term success. We think about this as phase one and there are two important components to phase one. Kirk took you through them but let me just review them. Part one, the establishment of a multi-year commitment and a collaboration agreement to offer Lenovo branded flash products globally, and as Kurt said in 160 countries. Part two, the formation of a joint venture in PRC, People's Republic of China, that will provide long term commitment, joint product development, and increase go-to-market investment to meet the unique needs to China. Both companies will put in storage technologies and storage expertise to form an independent JV that establishes a data management company in China for China. And while we can dream about what phase two looks like, our entire focus is on making phase one incredibly successful and I'm pleased to repeat what Kirk, is that the first products are orderable and shippable this week in 160 different countries, and you will see our two companies focusing on the here and now. On our joint go to market strategy, you'll see us working together to drive strategic alignment, focused execution, strong governance, and realistic expectations and milestones. And it starts with the success of our customers and our channel partners is job one. Enabling customers to modernize their legacy IT with complete data center solutions, ensuring that our customers get the best from both companies, new offerings the fuel business success, efficiencies to reinvest in game-changing initiatives, and new solutions for new mission-critical applications like data analytics, IoT, artificial intelligence, and machine learning. Channel partners are also top of mind for both our two companies. We are committed to the success of our existing and our future channel partners. For NetApp channel partners, it is new pathways to new segments and to new customers. For Lenovo's channel partners, it is the competitive weapons that now allows you to compete and more importantly win against Dell, EMC, and HP. And the good news for both companies is that our channel partner ecosystem is highly complementary with minimal overlap. Today is the first day of a very exciting partnership, of a partnership that will better serve our customers today and will provide new opportunities to both our companies and to our partners, new products to our customers globally and in China. I am personally very excited. I will be on the board of the JV. And so, I look forward to working with you, partnering with you and serving you as we go forward, and with that, I'd like to invite Kirk back up. (audience applauding) >> Thank you. >> Thank you. >> Well, thank you, Brad. I think it's an exciting overview, and these products will be manufactured in China, in Mexico, in Hungary, and around the world, enabling this amazing supply chain we talked about to deliver in over 160 countries. So thank you Brad, thank you George, for the amazing partnership. So again, that's not all. In Transform 2.0, last year, we talked about the joint ventures that were coming. I want to give you a sneak peek at what you should expect at future Lenovo events around the world. We have this Transform in Beijing in a couple weeks. We'll then be repeating this in 20 different locations roughly around the world over the next year, and I'm excited probably more than ever about what else is coming. Let's talk about Telco 5G and network function virtualization. Today, Motorola phones are certified on 46 global networks. We launched the world's first 5G upgradable phone here in the United States with Verizon. Lenovo DCG sells to 58 telecommunication providers around the world. At Mobile World Congress in Barcelona and Shanghai, you saw China Telecom and China Mobile in the Lenovo booth, China Telecom showing a video broadband remote access server, a VBRAS, with video streaming demonstrations with 2x less jitter than they had seen before. You saw China Mobile with a virtual remote access network, a VRAN, with greater than 10 times the throughput and 10x lower latency running on Lenovo. And this year, we'll be launching a new NFV company, a software company in China for China to drive the entire NFV stack, delivering not just hardware solutions, but software solutions, and we've recently hired a new CEO. You're going to hear more about that over the next several quarters. Very exciting as we try to drive new economics into the networks to deliver these 20 billion devices. We're going to need new economics that I think Lenovo can uniquely deliver. The second on IoT and edge, we've integrated on the device side into our intelligent devices group. With everything that's going to consume electricity computes and communicates, Lenovo is in a unique position on the device side to take advantage of the communications from Motorola and being one of the largest device companies in the world. But this year, we're also going to roll out a comprehensive set of edge gateways and ruggedized industrial servers and edge servers and ISP appliances for the edge and for IoT. So look for that as well. And then lastly, as a service, you're going to see Lenovo delivering hardware as a service, device as a service, infrastructure as a service, software as a service, and hardware as a service, not just as a glorified leasing contract, but with IP, we've developed true flexible metering capability that enables you to scale up and scale down freely and paying strictly based on usage, and we'll be having those announcements within this fiscal year. So Transform 2.0, lots to talk about, NetApp the big news of the day, but a lot more to come over the next year from the Data Center group. So in summary, I'm excited that we have a lot of customers that are going to be on stage with us that you saw in the video. Lots of testimonials so that you can talk to colleagues of yourself. Alamos Gold from Canada, a Canadian gold producer, Caligo for data optimization and privacy, SciNet, the largest supercomputer we've ever put into North America, and the largest in Canada at the University of Toronto will be here talking about climate change. City of Bogota again with our hyper-converged solutions around smart city putting in 3,000 cameras for criminal detection, license plate detection, et cetera, and then more from a channel mid market perspective, Jerry's Foods, which is from my home state of Wisconsin, and Minnesota which has about 57 stores in the specialty foods market, and how they're leveraging our IoT solutions as well. So again, about five times the number of demos that we had last year. So in summary, first and foremost to the customers, thank you for your business. It's been a great journey and I think we're on a tremendous role. You saw from last year, we're trying to build credibility with you. After the largest server portfolio, we're now the fastest-growing server OEM per Gardner, number one in performance, number one in reliability, number one in customer satisfaction, number one in supercomputing. Today, the largest storage portfolio in our history, with the goal of becoming the fastest growing storage company in the world, top three in China, multibillion-dollar collaboration with NetApp. And the transformation is going to continue with new edge gateways, edge servers, NFV solutions, telecommunications infrastructure, and hardware as a service with dynamic metering. So thank you for your time. I've looked forward to meeting many of you over the next day. We appreciate your business, and with that, I'd like to bring up Rod Lappen to introduce our next speaker. Rod? (audience applauding) >> Thanks, boss, well done. Alright ladies and gentlemen. No real secret there. I think we've heard why I might talk about the fourth Industrial Revolution in data and exactly what's going on with that. You've heard Kirk with some amazing announcements, obviously now with our NetApp partnership, talk about 5G, NFV, cloud, artificial intelligence, I think we've hit just about all the key hot topics. It's with great pleasure that I now bring up on stage Mr. Christian Teismann, our senior vice president and general manager of commercial business for both our PCs and our IoT business, so Christian Teismann. (techno music) Here, take that. >> Thank you. I think I'll need that. >> Okay, Christian, so obviously just before we get down, you and I last year, we had a bit of a chat about being in New York. >> Exports. >> You were an expat in New York for a long time. >> That's true. >> And now, you've moved from New York. You're in Munich? >> Yep. >> How does that feel? >> Well Munich is a wonderful city, and it's a great place to live and raise kids, but you know there's no place in the world like New York. >> Right. >> And I miss it a lot, quite frankly. >> So what exactly do you miss in New York? >> Well there's a lot of things in New York that are unique, but I know you spent some time in Japan, but I still believe the best sushi in the world is still in New York City. (all laughing) >> I will beg to differ. I will beg to differ. I think Mr. Guchi-san from Softbank is here somewhere. He will get up an argue very quickly that Japan definitely has better sushi than New York. But obviously you know, it's a very very special place, and I have had sushi here, it's been fantastic. What about Munich? Anything else that you like in Munich? >> Well I mean in Munich, we have pork knuckles. >> Pork knuckles. (Christian laughing) Very similar sushi. >> What is also very fantastic, but we have the real, the real Oktoberfest in Munich, and it starts next week, mid-September, and I think it's unique in the world. So it's very special as well. >> Oktoberfest. >> Yes. >> Unfortunately, I'm not going this year, 'cause you didn't invite me, but-- (audience chuckling) How about, I think you've got a bit of a secret in relation to Oktoberfest, probably not in Munich, however. >> It's a secret, yes, but-- >> Are you going to share? >> Well I mean-- >> See how I'm putting you on the spot? >> In the 10 years, while living here in New York, I was a regular visitor of the Oktoberfest at the Lower East Side in Avenue C at Zum Schneider, where I actually met my wife, and she's German. >> Very good. So, how about a big round of applause? (audience applauding) Not so much for Christian, but more I think, obviously for his wife, who obviously had been drinking and consequently ended up with you. (all laughing) See you later, mate. >> That's the beauty about Oktoberfest, but yes. So first of all, good morning to everybody, and great to be back here in New York for a second Transform event. New York clearly is the melting pot of the world in terms of culture, nations, but also business professionals from all kind of different industries, and having this event here in New York City I believe is manifesting what we are trying to do here at Lenovo, is transform every aspect of our business and helping our customers on the journey of intelligent transformation. Last year, in our transformation on the device business, I talked about how the PC is transforming to personalized computing, and we've made a lot of progress in that journey over the last 12 months. One major change that we have made is we combined all our device business under one roof. So basically PCs, smart devices, and smart phones are now under the roof and under the intelligent device group. But from my perspective makes a lot of sense, because at the end of the day, all devices connect in the modern world into the cloud and are operating in a seamless way. But we are also moving from a device business what is mainly a hardware focus historically, more and more also into a solutions business, and I will give you during my speech a little bit of a sense of what we are trying to do, as we are trying to bring all these components closer together, and specifically also with our strengths on the data center side really build end-to-end customer solution. Ultimately, what we want to do is make our business, our customer's businesses faster, safer, and ultimately smarter as well. So I want to look a little bit back, because I really believe it's important to understand what's going on today on the device side. Many of us have still grown up with phones with terminals, ultimately getting their first desktop, their first laptop, their first mobile phone, and ultimately smartphone. Emails and internet improved our speed, how we could operate together, but still we were defined by linear technology advances. Today, the world has changed completely. Technology itself is not a limiting factor anymore. It is how we use technology going forward. The Internet is pervasive, and we are not yet there that we are always connected, but we are nearly always connected, and we are moving to the stage, that everything is getting connected all the time. Sharing experiences is the most driving force in our behavior. In our private life, sharing pictures, videos constantly, real-time around the world, with our friends and with our family, and you see the same behavior actually happening in the business life as well. Collaboration is the number-one topic if it comes down to workplace, and video and instant messaging, things that are coming from the consumer side are dominating the way we are operating in the commercial business as well. Most important beside technology, that a new generation of workforce has completely changed the way we are working. As the famous workforce the first generation of Millennials that have now fully entered in the global workforce, and the next generation, it's called Generation Z, is already starting to enter the global workforce. By 2025, 75 percent of the world's workforce will be composed out of two of these generations. Why is this so important? These two generations have been growing up using state-of-the-art IT technology during their private life, during their education, school and study, and are taking these learnings and taking these behaviors in the commercial workspace. And this is the number one force of change that we are seeing in the moment. Diverse workforces are driving this change in the IT spectrum, and for years in many of our customers' focus was their customer focus. Customer experience also in Lenovo is the most important thing, but we've realized that our own human capital is equally valuable in our customer relationships, and employee experience is becoming a very important thing for many of our customers, and equally for Lenovo as well. As you have heard YY, as we heard from YY, Lenovo is focused on intelligent transformation. What that means for us in the intelligent device business is ultimately starting with putting intelligence in all of our devices, smartify every single one of our devices, adding value to our customers, traditionally IT departments, but also focusing on their end users and building products that make their end users more productive. And as a world leader in commercial devices with more than 33 percent market share, we can solve problems been even better than any other company in the world. So, let's talk about transformation of productivity first. We are in a device-led world. Everything we do is connected. There's more interaction with devices than ever, but also with spaces who are increasingly becoming smart and intelligent. YY said it, by 2020 we have more than 20 billion connected devices in the world, and it will grow exponentially from there on. And users have unique personal choices for technology, and that's very important to recognize, and we call this concept a digital wardrobe. And it means that every single end-user in the commercial business is composing his personal wardrobe on an ongoing basis and is reconfiguring it based on the work he's doing and based where he's going and based what task he is doing. I would ask all of you to put out all the devices you're carrying in your pockets and in your bags. You will see a lot of you are using phones, tablets, laptops, but also cameras and even smartwatches. They're all different, but they have one underlying technology that is bringing it all together. Recognizing digital wardrobe dynamics is a core factor for us to put all the devices under one roof in IDG, one business group that is dedicated to end-user solutions across mobile, PC, but also software services and imaging, to emerging technologies like AR, VR, IoT, and ultimately a AI as well. A couple of years back there was a big debate around bring-your-own-device, what was called consumerization. Today consumerization does not exist anymore, because consumerization has happened into every single device we build in our commercial business. End users and commercial customers today do expect superior display performance, superior audio, microphone, voice, and touch quality, and have it all connected and working seamlessly together in an ease of use space. We are already deep in the journey of personalized computing today. But the center point of it has been for the last 25 years, the mobile PC, that we have perfected over the last 25 years, and has been the undisputed leader in mobility computing. We believe in the commercial business, the ThinkPad is still the core device of a digital wardrobe, and we continue to drive the success of the ThinkPad in the marketplace. We've sold more than 140 million over the last 26 years, and even last year we exceeded nearly 11 million units. That is about 21 ThinkPads per minute, or one Thinkpad every three seconds that we are shipping out in the market. It's the number one commercial PC in the world. It has gotten countless awards but we felt last year after Transform we need to build a step further, in really tailoring the ThinkPad towards the need of the future. So, we announced a new line of X1 Carbon and Yoga at CES the Consumer Electronics Show. And the reason is not we want to sell to consumer, but that we do recognize that a lot of CIOs and IT decision makers need to understand what consumers are really doing in terms of technology to make them successful. So, let's take a look at the video. (suspenseful music) >> When you're the number one business laptop of all time, your only competition is yourself. (wall shattering) And, that's different. Different, like resisting heat, ice, dust, and spills. Different, like sharper, brighter OLA display. The trackpoint that reinvented controls, and a carbon fiber roll cage to protect what's inside, built by an engineering and design team, doing the impossible for the last 25 years. This is the number one business laptop of all time, but it's not a laptop. It's a ThinkPad. (audience applauding) >> Thank you very much. And we are very proud that Lenovo ThinkPad has been selected as the best laptop in the world in the second year in a row. I think it's a wonderful tribute to what our engineers have been done on this one. And users do want awesome displays. They want the best possible audio, voice, and touch control, but some users they want more. What they want is super power, and I'm really proud to announce our newest member of the X1 family, and that's the X1 extreme. It's exceptionally featured. It has six core I9 intel chipset, the highest performance you get in the commercial space. It has Nvidia XTX graphic, it is a 4K UHD display with HDR with Dolby vision and Dolby Atmos Audio, two terabyte in SSD, so it is really the absolute Ferrari in terms of building high performance commercial computer. Of course it has touch and voice, but it is one thing. It has so much performance that it serves also a purpose that is not typical for commercial, and I know there's a lot of secret gamers also here in this room. So you see, by really bringing technology together in the commercial space, you're creating productivity solutions of one of a kind. But there's another category of products from a productivity perspective that is incredibly important in our commercial business, and that is the workstation business . Clearly workstations are very specifically designed computers for very advanced high-performance workloads, serving designers, architects, researchers, developers, or data analysts. And power and performance is not just about the performance itself. It has to be tailored towards the specific use case, and traditionally these products have a similar size, like a server. They are running on Intel Xeon technology, and they are equally complex to manufacture. We have now created a new category as the ultra mobile workstation, and I'm very proud that we can announce here the lightest mobile workstation in the industry. It is so powerful that it really can run AI and big data analysis. And with this performance you can go really close where you need this power, to the sensors, into the cars, or into the manufacturing places where you not only wannna read the sensors but get real-time analytics out of these sensors. To build a machine like this one you need customers who are really challenging you to the limit. and we're very happy that we had a customer who went on this journey with us, and ultimately jointly with us created this product. So, let's take a look at the video. (suspenseful music) >> My world involves pathfinding both the hardware needs to the various work sites throughout the company, and then finding an appropriate model of desktop, laptop, or workstation to match those needs. My first impressions when I first seen the ThinkPad P1 was I didn't actually believe that we could get everything that I was asked for inside something as small and light in comparison to other mobile workstations. That was one of the I can't believe this is real sort of moments for me. (engine roars) >> Well, it's better than general when you're going around in the wind tunnel, which isn't alway easy, and going on a track is not necessarily the best bet, so having a lightweight very powerful laptop is extremely useful. It can take a Xeon processor, which can support ECC from when we try to load a full car, and when we're analyzing live simulation results. through and RCFT post processor or example. It needs a pretty powerful machine. >> It's come a long way to be able to deliver this. I hate to use the word game changer, but it is that for us. >> Aston Martin has got a lot of different projects going. There's some pretty exciting projects and a pretty versatile range coming out. Having Lenovo as a partner is certainly going to ensure that future. (engine roars) (audience applauds) >> So, don't you think the Aston Martin design and the ThinkPad design fit very well together? (audience laughs) So if Q, would get a new laptop, I think you would get a ThinkPad X P1. So, I want to switch gears a little bit, and go into something in terms of productivity that is not necessarily on top of the mind or every end user but I believe it's on top of the mind of every C-level executive and of every CEO. Security is the number one threat in terms of potential risk in your business and the cost of cybersecurity is estimated by 2020 around six trillion dollars. That's more than the GDP of Japan and we've seen a significant amount of data breach incidents already this years. Now, they're threatening to take companies out of business and that are threatening companies to lose a huge amount of sensitive customer data or internal data. At Lenovo, we are taking security very, very seriously, and we run a very deep analysis, around our own security capabilities in the products that we are building. And we are announcing today a new brand under the Think umbrella that is called ThinkShield. Our goal is to build the world's most secure PC, and ultimately the most secure devices in the industry. And when we looked at this end-to-end, there is no silver bullet around security. You have to go through every aspect where security breaches can potentially happen. That is why we have changed the whole organization, how we look at security in our device business, and really have it grouped under one complete ecosystem of solutions, Security is always something where you constantly are getting challenged with the next potential breach the next potential technology flaw. As we keep innovating and as we keep integrating, a lot of our partners' software and hardware components into our products. So for us, it's really very important that we partner with companies like Intel, Microsoft, Coronet, Absolute, and many others to really as an example to drive full encryption on all the data seamlessly, to have multi-factor authentication to protect your users' identity, to protect you in unsecured Wi-Fi locations, or even simple things like innovation on the device itself, to and an example protect the camera, against usage with a little thing like a thinkShutter that you can shut off the camera. SO what I want to show you here, is this is the full portfolio of ThinkShield that we are announcing today. This is clearly not something I can even read to you today, but I believe it shows you the breadth of security management that we are announcing today. There are four key pillars in managing security end-to-end. The first one is your data, and this has a lot of aspects around the hardware and the software itself. The second is identity. The third is the security around online, and ultimately the device itself. So, there is a breakout on security and ThinkShield today, available in the afternoon, and encourage you to really take a deeper look at this one. The first pillar around productivity was the device, and around the device. The second major pillar that we are seeing in terms of intelligent transformation is the workspace itself. Employees of a new generation have a very different habit how they work. They split their time between travel, working remotely but if they do come in the office, they expect a very different office environment than what they've seen in the past in cubicles or small offices. They come into the office to collaborate, and they want to create ideas, and they really work in cross-functional teams, and they want to do it instantly. And what we've seen is there is a huge amount of investment that companies are doing today in reconfiguring real estate reconfiguring offices. And most of these kind of things are moving to a digital platform. And what we are doing, is we want to build an entire set of solutions that are just focused on making the workspace more productive for remote workforce, and to create technology that allow people to work anywhere and connect instantly. And the core of this is that we need to be, the productivity of the employee as high as possible, and make it for him as easy as possible to use these kind of technologies. Last year in Transform, I announced that we will enter the smart office space. By the end of last year, we brought the first product into the market. It's called the Hub 500. It's already deployed in thousands of our customers, and it's uniquely focused on Microsoft Skype for Business, and making meeting instantly happen. And the product is very successful in the market. What we are announcing today is the next generation of this product, what is the Hub 700, what has a fantastic audio quality. It has far few microphones, and it is usable in small office environment, as well as in major conference rooms, but the most important part of this new announcement is that we are also announcing a software platform, and this software platform allows you to run multiple video conferencing software solutions on the same platform. Many of you may have standardized for one software solution or for another one, but as you are moving in a world of collaborating instantly with partners, customers, suppliers, you always will face multiple software standards in your company, and Lenovo is uniquely positioned but providing a middleware platform for the device to really enable multiple of these UX interfaces. And there's more to come and we will add additional UX interfaces on an ongoing base, based on our customer requirements. But this software does not only help to create a better experience and a higher productivity in the conference room or the huddle room itself. It really will allow you ultimately to manage all your conference rooms in the company in one instance. And you can run AI technologies around how to increase productivity utilization of your entire conference room ecosystem in your company. You will see a lot more devices coming from the node in this space, around intelligent screens, cameras, and so on, and so on. The idea is really that Lenovo will become a core provider in the whole movement into the smart office space. But it's great if you have hardware and software that is really supporting the approach of modern IT, but one component that Kirk also mentioned is absolutely critical, that we are providing this to you in an as a service approach. Get it what you want, when you need it, and pay it in the amount that you're really using it. And within UIT there is also I think a new philosophy around IT management, where you're much more focused on the value that you are consuming instead of investing into technology. We are launched as a service two years back and we already have a significant number of customers running PC as a service, but we believe as a service will stretch far more than just the PC device. It will go into categories like smart office. It might go even into categories like phone, and it will definitely go also in categories like storage and server in terms of capacity management. I want to highlight three offerings that we are also displaying today that are sort of building blocks in terms of how we really run as a service. The first one is that we collaborated intensively over the last year with Microsoft to be the launch pilot for their Autopilot offering, basically deploying images easily in the same approach like you would deploy a new phone on the network. The purpose really is to make new imaging and enabling new PC as seamless as it's used to be in the phone industry, and we have a complete set of offerings, and already a significant number customers have deployed Autopilot with Lenovo. The second major offering is Premier Support, like in the in the server business, where Premier Support is absolutely critical to run critical infrastructure, we see a lot of our customers do want to have Premier Support for their end users, so they can be back into work basically instantly, and that you have the highest possible instant repair on every single device. And then finally we have a significant amount of time invested into understanding how the software as a service really can get into one philosophy. And many of you already are consuming software as a service in many different contracts from many different vendors, but what we've created is one platform that really can manage this all together. All these things are the foundation for a device as a service offering that really can manage this end-to-end. So, implementing an intelligent workplace can be really a daunting prospect depending on where you're starting from, and how big your company ultimately is. But how do you manage the transformation of technology workspace if you're present in 50 or more countries and you run an infrastructure for more than 100,000 people? Michelin, famous for their tires, infamous for their Michelin star restaurant rating, especially in New York, and instantly recognizable by the Michelin Man, has just doing that. Please welcome with me Damon McIntyre from Michelin to talk to us about the challenges and transforming collaboration and productivity. (audience applauding) (electronic dance music) Thank you, David. >> Thank you, thank you very much. >> We on? >> So, how do you feel here? >> Well good, I want to thank you first of all for your partnership and the devices you create that helped us design, manufacture, and distribute the best tire in the world, okay? I just had to say it and put out there, alright. And I was wondering, were those Michelin tires on that Aston Martin? >> I'm pretty sure there is no other tire that would fit to that. >> Yeah, no, thank you, thank you again, and thank you for the introduction. >> So, when we talk about the transformation happening really in the workplace, the most tangible transformation that you actually see is the drastic change that companies are doing physically. They're breaking down walls. They're removing cubes, and they're moving to flexible layouts, new desks, new huddle rooms, open spaces, but the underlying technology for that is clearly not so visible very often. So, tell us about Michelin's strategy, and the technology you are deploying to really enable this corporation. >> So we, so let me give a little bit a history about the company to understand the daunting tasks that we had before us. So we have over 114,000 people in the company under 170 nationalities, okay? If you go to the corporate office in France, it's Clermont. It's about 3,000 executives and directors, and what have you in the marketing, sales, all the way up to the chain of the global CIO, right? Inside of the Americas, we merged in Americas about three years ago. Now we have the Americas zone. There's about 28,000 employees across the Americas, so it's really, it's really hard in a lot of cases. You start looking at the different areas that you lose time, and you lose you know, your productivity and what have you, so there, it's when we looked at different aspects of how we were going to manage the meeting rooms, right? because we have opened up our areas of workspace, our CIO, CEOs in our zones will no longer have an office. They'll sit out in front of everybody else and mingle with the crowd. So, how do you take those spaces that were originally used by an individual but now turn them into like meeting rooms? So, we went through a large process, and looked at the Hub 500, and that really met our needs, because at the end of the day what we noticed was, it was it was just it just worked, okay? We've just added it to the catalog, so we're going to be deploying it very soon, and I just want to again point that I know everybody struggles with this, and if you look at all the minutes that you lose in starting up a meeting, and we know you know what I'm talking about when I say this, it equates to many many many dollars, okay? And so at the end the day, this product helps us to be more efficient in starting up the meeting, and more productive during the meeting. >> Okay, it's very good to hear. Another major trend we are seeing in IT departments is taking a more hands-off approach to hardware. We're seeing new technologies enable IT to create a more efficient model, how IT gets hardware in the hands of end-users, and how they are ultimately supporting themselves. So what's your strategy around the lifecycle management of the devices? >> So yeah you mentioned, again, we'll go back to the 114,000 employees in the company, right? You imagine looking at all the devices we use. I'm not going to get into the number of devices we have, but we have a set number that we use, and we have to go through a process of deploying these devices, which we right now service our own image. We build our images, we service them through our help desk and all that process, and we go through it. If you imagine deploying 25,000 PCs in a year, okay? The time and the daunting task that's behind all that, you can probably add up to 20 or 30 people just full-time doing that, okay? So, with partnering with Lenovo and their excellent technology, their technical teams, and putting together the whole process of how we do imaging, it now lifts that burden off of our folks, and it shifts it into a more automated process through the cloud, okay? And, it's with the Autopilot on the end of the project, we'll have Autopilot fully engaged, but what I really appreciate is how Lenovo really, really kind of got with us, and partnered with us for the whole process. I mean it wasn't just a partner between Michelin and Lenovo. Microsoft was also partnered during that whole process, and it really was a good project that we put together, and we hope to have something in a full production mode next year for sure. >> So, David thank you very, very much to be here with us on stage. What I really want to say, customers like you, who are always challenging us on every single aspect of our capabilities really do make the big difference for us to get better every single day and we really appreciate the partnership. >> Yeah, and I would like to say this is that I am, I'm doing what he's exactly said he just said. I am challenging Lenovo to show us how we can innovate in our work space with your devices, right? That's a challenge, and it's going to be starting up next year for sure. We've done some in the past, but I'm really going to challenge you, and my whole aspect about how to do that is bring you into our workspace. Show you how we make how we go through the process of making tires and all that process, and how we distribute those tires, so you can brainstorm, come back to the table and say, here's a device that can do exactly what you're doing right now, better, more efficient, and save money, so thank you. >> Thank you very much, David. (audience applauding) Well it's sometimes really refreshing to get a very challenging customers feedback. And you know, we will continue to grow this business together, and I'm very confident that your challenge will ultimately help to make our products even more seamless together. So, as we now covered productivity and how we are really improving our devices itself, and the transformation around the workplace, there is one pillar left I want to talk about, and that's really, how do we make businesses smarter than ever? What that really means is, that we are on a journey on trying to understand our customer's business, deeper than ever, understanding our customer's processes even better than ever, and trying to understand how we can help our customers to become more competitive by injecting state-of-the-art technology in this intelligent transformation process, into core processes. But this cannot be done without talking about a fundamental and that is the journey towards 5G. I really believe that 5G is changing everything the way we are operating devices today, because they will be connected in a way like it has never done before. YY talked about you know, 20 times 10 times the amount of performance. There are other studies that talk about even 200 times the performance, how you can use these devices. What it will lead to ultimately is that we will build devices that will be always connected to the cloud. And, we are preparing for this, and Kirk already talked about, and how many operators in the world we already present with our Moto phones, with how many Telcos we are working already on the backend, and we are working on the device side on integrating 5G basically into every single one of our product in the future. One of the areas that will benefit hugely from always connected is the world of virtual reality and augmented reality. And I'm going to pick here one example, and that is that we have created a commercial VR solution for classrooms and education, and basically using consumer type of product like our Mirage Solo with Daydream and put a solution around this one that enables teachers and schools to use these products in the classroom experience. So, students now can have immersive learning. They can studying sciences. They can look at environmental issues. They can exploring their careers, or they can even taking a tour in the next college they're going to go after this one. And no matter what grade level, this is how people will continue to learn in the future. It's quite a departure from the old world of textbooks. In our area that we are looking is IoT, And as YY already elaborated, we are clearly learning from our own processes around how we improve our supply chain and manufacturing and how we improve also retail experience and warehousing, and we are working with some of the largest companies in the world on pilots, on deploying IoT solutions to make their businesses, their processes, and their businesses, you know, more competitive, and some of them you can see in the demo environment. Lenovo itself already is managing 55 million devices in an IoT fashion connecting to our own cloud, and constantly improving the experience by learning from the behavior of these devices in an IoT way, and we are collecting significant amount of data to really improve the performance of these systems and our future generations of products on a ongoing base. We have a very strong partnership with a company called ADLINK from Taiwan that is one of the leading manufacturers of manufacturing PC and hardened devices to create solutions on the IoT platform. The next area that we are very actively investing in is commercial augmented reality. I believe augmented reality has by far more opportunity in commercial than virtual reality, because it has the potential to ultimately improve every single business process of commercial customers. Imagine in the future how complex surgeries can be simplified by basically having real-time augmented reality information about the surgery, by having people connecting into a virtual surgery, and supporting the surgery around the world. Visit a furniture store in the future and see how this furniture looks in your home instantly. Doing some maintenance on some devices yourself by just calling the company and getting an online manual into an augmented reality device. Lenovo is exploring all kinds of possibilities, and you will see a solution very soon from Lenovo. Early when we talked about smart office, I talked about the importance of creating a software platform that really run all these use cases for a smart office. We are creating a similar platform for augmented reality where companies can develop and run all their argumented reality use cases. So you will see that early in 2019 we will announce an augmented reality device, as well as an augmented reality platform. So, I know you're very interested on what exactly we are rolling out, so we will have a first prototype view available there. It's still a codename project on the horizon, and we will announce it ultimately in 2019, but I think it's good for you to take a look what we are doing here. So, I just wanted to give you a peek on what we are working beyond smart office and the device productivity in terms of really how we make businesses smarter. It's really about increasing productivity, providing you the most secure solutions, increase workplace collaboration, increase IT efficiency, using new computing devices and software and services to make business smarter in the future. There's no other company that will enable to offer what we do in commercial. No company has the breadth of commercial devices, software solutions, and the same data center capabilities, and no other company can do more for your intelligent transformation than Lenovo. Thank you very much. (audience applauding) >> Thanks mate, give me that. I need that. Alright, ladies and gentlemen, we are done. So firstly, I've got a couple of little housekeeping pieces at the end of this and then we can go straight into going and experiencing some of the technology we've got on the left-hand side of the room here. So, I want to thank Christian obviously. Christian, awesome as always, some great announcements there. I love the P1. I actually like the Aston Martin a little bit better, but I'll take either if you want to give me one for free. I'll take it. We heard from YY obviously about the industry and how the the fourth Industrial Revolution is impacting us all from a digital transformation perspective, and obviously Kirk on DCG, the great NetApp announcement, which is going to be really exciting, actually that Twitter and some of the social media panels are absolutely going crazy, so it's good to see that the industry is really taking some impact. Some of the publications are really great, so thank you for the media who are obviously in the room publishing right no. But now, I really want to say it's all of your turn. So, all of you up the back there who are having coffee, it's your turn now. I want everyone who's sitting down here after this event move into there, and really take advantage of the 15 breakouts that we've got set there. There are four breakout sessions from a time perspective. I want to try and get you all out there at least to use up three of them and use your fourth one to get out and actually experience some of the technology. So, you've got four breakout sessions. A lot of the breakout sessions are actually done twice. If you have not downloaded the app, please download the app so you can actually see what time things are going on and make sure you're registering correctly. There's a lot of great experience of stuff out there for you to go do. I've got one quick video to show you on some of the technology we've got and then we're about to close. Alright, here we are acting crazy. Now, you can see obviously, artificial intelligence machine learning in the browser. God, I hate that dance, I'm not a Millenial at all. It's effectively going to be implemented by healthcare. I want you to come around and test that out. Look at these two guys. This looks like a Lenovo management meeting to be honest with you. These two guys are actually concentrating, using their brain power to race each others in cars. You got to come past and give that a try. Give that a try obviously. Fantastic event here, lots of technology for you to experience, and great partners that have been involved as well. And so, from a Lenovo perspective, we've had some great alliance partners contribute, including obviously our number one partner, Intel, who's been a really big loyal contributor to us, and been a real part of our success here at Transform. Excellent, so please, you've just seen a little bit of tech out there that you can go and play with. I really want you, I mean go put on those black things, like Scott Hawkins our chief marketing officer from Lenovo's DCG business was doing and racing around this little car with his concentration not using his hands. He said it's really good actually, but as soon as someone comes up to speak to him, his car stops, so you got to try and do better. You got to try and prove if you can multitask or not. Get up there and concentrate and talk at the same time. 62 different breakouts up there. I'm not going to go into too much detai, but you can see we've got a very, very unusual numbering system, 18 to 18.8. I think over here we've got a 4849. There's a 4114. And then up here we've got a 46.1 and a 46.2. So, you need the decoder ring to be able to understand it. Get over there have a lot of fun. Remember the boat leaves today at 4:00 o'clock, right behind us at the pier right behind us here. There's 400 of us registered. Go onto the app and let us know if there's more people coming. It's going to be a great event out there on the Hudson River. Ladies and gentlemen that is the end of your keynote. I want to thank you all for being patient and thank all of our speakers today. Have a great have a great day, thank you very much. (audience applauding) (upbeat music) ♪ Ba da bop bop bop ♪ ♪ Ba da bop bop bop ♪ ♪ Ba da bop bop bop ♪ ♪ Ba da bop bop bop ♪ ♪ Ba da bop bop bop ♪ ♪ Ba da bop bop bop ♪ ♪ Ba da bop bop bop ba do ♪

Published Date : Sep 13 2018

SUMMARY :

and those around you, Ladies and gentlemen, we ask that you please take an available seat. Ladies and gentlemen, once again we ask and software that transform the way you collaborate, Good morning everyone! Ooh, that was pretty good actually, and have a look at all of the breakout sessions. and the industries demand to be more intelligent, and the strategies that we have going forward I'm going to give you the stage and allow you to say is that the first products are orderable and being one of the largest device companies in the world. and exactly what's going on with that. I think I'll need that. Okay, Christian, so obviously just before we get down, You're in Munich? and it's a great place to live and raise kids, And I miss it a lot, but I still believe the best sushi in the world and I have had sushi here, it's been fantastic. (Christian laughing) the real Oktoberfest in Munich, in relation to Oktoberfest, at the Lower East Side in Avenue C at Zum Schneider, and consequently ended up with you. and is reconfiguring it based on the work he's doing and a carbon fiber roll cage to protect what's inside, and that is the workstation business . and then finding an appropriate model of desktop, in the wind tunnel, which isn't alway easy, I hate to use the word game changer, is certainly going to ensure that future. And the core of this is that we need to be, and distribute the best tire in the world, okay? that would fit to that. and thank you for the introduction. and the technology you are deploying and more productive during the meeting. how IT gets hardware in the hands of end-users, You imagine looking at all the devices we use. and we really appreciate the partnership. and it's going to be starting up next year for sure. and how many operators in the world Ladies and gentlemen that is the end of your keynote.

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Keynote Analysis | OpenStack Summit 2018


 

>> Announcer: Live, fro-- >> Announcer: Live from Vancouver, Canada it's theCUBE! Covering OpenStack Summit North America 2018. Brought to you by Red Hat, the OpenStack Foundation, and it's ecosystem partners. >> Hi and welcome to SiliconANGLE Media's production of theCUBE here at OpenStack Summit 2018 in Vancouver. I'm Stu Miniman with my cohost, John Troyer. We're here for three days of live wall-to-wall coverage at the OpenStack Foundation's show they have it twice a year John, pleasure to be with you again, you and I were together at the OpenStack show in Boston, a year ago, little bit further trip for me. But views like this, I'm not complaining. >> It's a great time to be in Vancouver, little bit overcast but the convention center's beautiful and the people seem pretty excited as well. >> Yeah so if you see behind us, the keynote let out. So John, we got to get into the first question of course for some reason the last month people are always Hey Stu where are you, what're you doing and when I walk through the various shows I'm doing when it comes to this one they're like, why are you going to the OpenStack show? You know, what's going on there, hasn't that been replaced by everything else? >> I got the same thing, there seems to be kind of a almost an antireligious thing here in the industry maybe more emotional perhaps at other projects. Although frankly look, we're going to take the temperature of the community, we're going to take the temperature of the projects, the customers, we got a lot of customers here, that's really the key here is that our people actually using this, being productive, functional, and is there enough of a vendor and a community ecosystem to make this go forward. >> Absolutely, so three years ago, when we were actually here in Vancouver, the container sessions were overflowing, people sitting in the aisles. You know containers, containers, containers, docker, docker, docker, you know, we went through a year or two of that. Then Kubernetes, really a wave that has taken over, this piece of the infrastructure stack, the KubeCon and CloudNativeCon shows, in general, I think have surpassed this size, but as we know in IT, nothing ever dies, everything is always additive, and a theme that I heard here that definitely resonated is, we have complexity, we need to deal with interoperability, everybody has a lot of things and that's the, choose your word, hybrid, multi-cloud world that you have, and that's really the state of opensource, it's not a thing, it's there's lots of things you take all the pieces you need and you figure out how to put 'em together, either buy them from a platform, you have some integrator that helps, so somebody that puts it all together, and that's where, you know, we live here, which is, by they way, I thought they might rename the show in the open, and they didn't, but there's a lot of pieces to discuss. >> Definitely an open infrastructure movement, we'll probably talk about that, look I loved the message this morning that the cloud is not consolidating, in fact it's getting more complicated, and so that was a practical message here, it's a little bit of a church of opensource as well, so the open message was very well received and, these are the people that are working on it, of course, but yeah, the fact that, like last year I thought in Boston, there was a lot of, almost confusion around containers, and where containers and Kubernetes fit in the whole ecosystem, I think, now in this year in 2018 it's a lot more clear and OpenStack as a project, or as a set of projects, which traditionally was, the hit on it was very insular and inward facing, has at least, is trying to become outward facing, and again that's something we'll be looking at this week, and how well will they integrate with other opensource projects. >> I mean John, you and I are both big supporters of the opensource movements, love the community at shows like this, but not exclusively, it's, you know, Amazon participating a little bit, using a lot of opensource, they take opensource and make it as a service, you were at Red Hat Summit last week, obviously huge discussion there about everything opensource, everything, so a lot going on there, let me just set for, first of all the foundation itself in this show, the thing that I liked, coming into it, one of the things we're going to poke at is, if I go up to the highest level, OpenStack is not the only thing here, they have a few tracks they have an Edge computer track, they have a container track, and there's a co-resident OpenDev Show happening a couple floors above us and, even from what the OpenStack Foundation manages, yes it OpenStack's the main piece of it, and all those underlying projects but, they had Katacontainers, which is, you know, high level project, and the new one is Zuul, talking about CI/CD, so there are things that, will work with OpenStack but not exclusively for OpenStack, might not even come from OpenStack, so those are things that we're seeing, you know, for example, I was at the Veeam show last week, and there was a software company N2WS that Veeam had bought, and that solution only worked on Amazon to start and, you know, I was at the Nutanix show the week before, and there's lots of things that start in the Amazon environment and then make their way to the on-premises world so, we know it's a complex world, you know, I agree with you, the cloud is not getting simpler, remember when cloud was: Swipe the credit card and it's super easy, the line I've used a lot of times is, it is actually more complicated to buy, quote, a server equivalent, in the public could, than it is if I go to the website and have something that's shipped to my data center. >> It's, yeah, it's kind of ironic that that's where we've ended up. You know, we'll see, with Zuul, it'll be very interesting, one of the hits again on OpenStack has been reinvention of the wheel, like, can you inter-operate with other projects rather than doing it your self, it sounds like there's some actually, some very interesting aspects to it, as a CI/CD system, and certainly it uses stuff like Ansible so it's, it's built using opensource components, but, other opensource components, but you know, what does this give us advantage for infrastructure people, and allowing infrastructure to go live in a CI/CD way, software on hardware, rather than, the ones that've been built from the dev side, the app side. I'm assuming there's good reasons, or they wouldn't've done it, but you know, we'll see, there's still a lot of projects inside the opensource umbrella. >> Yeah, and, you know, last year we talked about it, once again, we'll talk about it here, the ecosystem has shifted. There are some of the big traditional infrastructure companies, but what they're talking about has changed a lot, you know. Remember a few years ago, it was you know, HP, thousand people, billion dollar investment, you know, IBM has been part of OpenStack since the very beginning days, but it changes, even a company like Rackspace, who helped put together this environment, the press release that went was: oh, we took all the learnings that we did from OpenStack, and this is our new Kubernetes service that we have, something that I saw, actually Randy Bias, who I'll have on the show this week, was on, the first time we did this show five years ago, can't believe it's the sixth year we're doing the show, Randy is always an interesting conversation to poke some of the sacred cows, and, I'll use that analogy, of course, because he is the one that Pets vs Cattle analogy, and he said, you know, we're spending a lot of time talking about it's not, as you hear, some game, between OpenStack and Kubernetes, containers are great, isn't that wonderful. If we're talking about that so much, maybe we should just like, go do that stuff, and not worry about this, so it'll be fun to talk to him, the Open Dev Show is being, mainly, sponsored by Mirantis who, last time I was here in Vancouver was the OpenStack company, and now, like, I saw them a year ago, and they were, the Kubernetes company, and making those changes, so we'll have Boris on, and get to find out these companies, there's not a lot of ECs here, the press and analysts that are here, most of us have been here for a lot of time so, this ecosystem has changed a lot, but, while attendance is down a little bit, from what I've heard, from previous years, there's still some good energy, people are learning a lot. >> So Stu, I did want to point out, that something I noticed on the stage, that I didn't see, was a lot of infrastructure, right? OpenStack, clearly an infrastructure stack, I think we've teased that out over the past couple years, but I didn't see a lot of talk about storage subsystems, networking, management, like all the kind of, hard, infrastructure plumbing, that actually, everybody here does, as well as a few names, so that was interesting, but at the end of the day, I mean, you got to appeal to the whole crowd here. >> Yeah, well one of the things, we spent a number of years making that stuff work, back when it was, you know, we're talkin' about gettin' Cinder, and then all the storage companies lined up with their various, do we support it, is it fully integrated, and then even further, does it actually work really well? So, same stuff that went through, for about a decade, in virtualization, we went through this in OpenStack, we actually said a couple years ago, some of the basic infrastructure stuff has gotten boring, so we don't need to talk about it anymore. Ironic, it's actually the non-virtualized environments, that's the project that they have here, we have a lot of people who are talking bare metal, who are talking containers, so that has shifted, an interesting one in the keynote is that you had the top level sponsors getting up there, Intel bringing around a lot of their ecosystem partners, talking about Edge, talking about the telecommunications, Red Hat, giving a recap of what they did last week at their summit, they've got a nice cadence, the last couple of years, they've done Red Hat Summit, and OpenStack Summit, back-to-back so that they can get that flow of information through, and then Mark Shuttleworth, who we'll have on a little bit later today, he came out puchin', you know, he started with some motherhood in Apple Pi about how Ubuntu is everywhere but then it was like, and we're going to be so much cheaper, and we're so much easier than the VMwares and Red Hats of the world, and there was a little push back from the community, that maybe that wasn't the right platform to do it. >> Yeah, I think the room got kind of cold, I mean, that's kind of a church in there, right, and everyone is an opensource believer and, this kind of invisible hand of capitalism (laughs) reached in and wrote on the wall and, you know, having written and left. But at the end of the day, right, somebody's got to pay for babies new shoes. I think that it was also very interesting seeing, at Red Hat Summit, which I covered on theCUBE, Red Hat's argument was fairly philosophical, and from first principles. Containers are Linux, therefore Red Hat, and that was logically laid out. Mark's, actually I loved Mark's, most of his speech, which was very practical, this, you know, Ubuntu's going to make both OpenStack and containers simpler, faster, quicker, and cheaper, so it was clearly benefits, and then, for the folks that don't know, then he put up a couple a crazy Eddy slides like, limited time offer, if you're here at the show, here's a deal that we've put together for ya, so that was a little bit unusual for a keynote. >> Yeah, and there are a lot of users here, and some of them'll hear that and they'll say: yeah, you know, I've used Red Hat there but, you can save me money that's awesome, let me find out some more about it. Alright, so, we've got three days of coverage here John, and we get to cover this really kind of broad ecosystem that we have here. You talked about what we don't discuss anymore, like the major lease was Queens, and it used to be, that was where I would study up and be like oh okay, we've got Hudson, and then we got, it was the letters of the alphabet, what's the next one going to be and what are the major features it's reached a certain maturity level that we're not talking the release anymore, it's more like the discussions we have in cloud, which is sometimes, here's some of the major things, and oh yeah, it just kind of wraps itself in. Deployments still, probably aren't nearly as easy as we'd like, Shuttleworth said two guys in under two weeks, that's awesome, but there's solutions we can put, stand up much faster than that now, two weeks is way better than some of the historical things we've done, but it changes quite a bit. So, telecommunications still a hot topic, Edge is something, you know what I think back, it was like, oh, all those NFE conversations we've had here, it's not just the SDN changes that are happening, but this is the Edge discussion for the Telcos, and something people were getting their arms around, so. >> It's pretty interesting to think of the cloud out on telephone poles, and in branch offices, in data centers, in closets basically or under desks almost. >> No self-driving cars on the keynote stage though? >> No, nothing that flashy this year. >> No, definitely not too flashy so, the foundation itself, it's interesting, we've heard rumors that maybe the show will change name, the foundation will not change names. So I want to give you last things, what're you looking for this week, what were you hearing from the community leading up to the show that you want to validate or poke at? >> Well, I'm going to look at real deployments, I'd like to see how standard we are, if we are, if an OpenStack deployment is standardized enough that the pool of talent is growing, and that if I hire people from outside my company who work with OpenStack, I know that they can work with my OpenStack, I think that's key for the continuation of this ecosystem. I want to look at the general energy and how people are deploying it, whether it does become really invisible and boring, but still important. Or do you end up running OpenShift on bare metal, which I, as an infrastructure person, I just can't see that the app platform should have to worry about all this infrastructure stuff, 'cause it's complicated, and so, I'll just be looking for the healthy productions and production deployments and see how that goes. >> Yeah, and I love, one of the things that they started many years ago was they have a super-user category, where they give an award, and I'm excited, we have actually have the Ontario Institute for Cancer Research is one of our guests on today, they won the 2018 super-user group, it's always awesome when you see, not only it's like, okay, CERN's here, and they're doing some really cool things looking for the Higgs boson, and all those kind of things but, you know, companies that are using technology to help them attack the battle against cancer, so, you know, you can't beat things like that. We've got the person from the keynote, Melvin, who was up on stage talking about the open lab, you know, community, ecosystem, definitely something that resonates, I know, one of the reasons I pulled you into this show in the last year is you're got a strong background there. >> Super impressed by all the community activity, this still feels like a real community, lots of pictures of people, lots of real, exhortations from stage to like, we who have been here for years know each other, please come meet us, so that's a real sign of also, a healthy community dynamic. >> Alright, so John first of all, I want to say, Happy Victoria Day, 'cause we are here in Vancouver, and we've got a lot going on here, it's a beautiful venue, hope you all join us for all of the coverage here, and I have to give a big shout out to the companies that allowed this to happen, we are independent media, but we can't survive without the funding of our sponsors so, first of all the OpenStack Foundation, helps get us here, and gives us this lovely location overlooking outside, but if it wasn't for the likes of our headline sponsor Red Hat as well as Canonical, Kontron, and Nuage Networks, we would not be able to bring you this content so, be sure to checkout thecube.net for all the coverage, for John Troyer, I'm Stu Miniman, thanks so much for watching theCUBE. (bubbly music)

Published Date : May 21 2018

SUMMARY :

the OpenStack Foundation, and it's ecosystem partners. at the OpenStack Foundation's show they have it twice a year and the people seem pretty excited as well. for some reason the last month people are always I got the same thing, there seems to be kind of a and that's really the state of opensource, it's not a thing, so the open message was very well received and, one of the things we're going to poke at is, one of the hits again on OpenStack has been and he said, you know, that something I noticed on the stage, that I didn't see, an interesting one in the keynote is that you had But at the end of the day, right, it's more like the discussions we have in cloud, It's pretty interesting to think of the cloud the foundation will not change names. I just can't see that the app platform I know, one of the reasons I pulled you into this show Super impressed by all the community activity, the companies that allowed this to happen,

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Matt Watts, NetApp & Kenneth Cukier, The Economist | NetApp Insight Berlin 2017


 

>> Narrator: Live from Berlin, Germany, it's theCUBE. Covering NetApp Insight 2017. Brought to you by NetApp. (techno music) Welcome back to theCUBE's live coverage of NetApp Insight here in Berlin, Germany. I'm your host, Rebecca Knight, along with my cohost Peter Burris. We have two guests for this segment. We have Matt Watts, he is the director and data strategist and director of technology at NetApp, and Kenneth Cukier, a senior editor at The Economist, and author of the best-selling book Big Data, and author of a soon to be best-selling book on AI. Welcome. Thank you. Thank you much for coming on the show. Pleasure to be here. So, this is the, we keep hearing NetApp saying this is the day of the data visionary. I'd love to hear both of you talk about what a data visionary is, and why companies, why this is a necessary role in today's companies. Okay, so I think if you look at the generations that we've been through in the late nineties, early 2000's, it was all about infrastructure with a little bit of application and some data associated to it. And then as we kind of rolled forward to the next decade the infrastructure discussion became less. It became more about the applications and increasingly more about the data. And if we look at the current decade that we're in right now, the infrastructure discussions have become less, and less, and less. We're still talking about applications, but the focus is on data. And what we haven't seen so much of during that time is the roles changing. We still have a lot of infrastructure people doing infrastructure roles, a lot of application people doing application roles. But the real value in this explosion of data that we're seeing is in the data. And it's time now that companies really look to put data visionaries, people like that in place to understand how do we exploit it, how do we use it, what should we gather, what could we do with the information that we do gather. And so I think the timing is just right now for people to be really considering that. Yeah, I would build on what Matt just said. That, functionally in the business and the enterprise we have the user of data, and we have the professional who collected the data. And sometimes we had a statistician who would analyze it. But pass it along to the user who is an executive, who is an MBA, who is the person who thinks with data and is going to present it to the board or to make a decision based on it. But that person isn't a specialist on data. That person probably doesn't, maybe doesn't even know math. And the person is thinking about the broader issues related to the company. The strategic imperatives. Maybe he speaks some languages, maybe he's a very good salesperson. There's no one in the middle, at least up until now, who can actually play that role of taking the data from the level of the bits and the bytes and in the weeds and the level of the infrastructure, and teasing out the value, and then translating it into the business strategy that can actually move the company along. Now, sometimes those people are going to actually move up the hierarchy themselves and become the executive. But they need not. Right now, there's so much data that's untapped you can still have this function of a person who bridges the world of being in the weeds with the infrastructure and with the data itself, and the larger broader executives suite that need to actually use that data. We've never had that function before, but we need to have it now. So, let me test you guys. Test something in you guys. So what I like to say is, we're at the middle of a significant break in the history of computing. The first 50 years or so it was known process, unknown technology. And so we threw all our time and attention at understanding the technology. >> Matt: Yeah. We knew accounting, we knew HR, we even knew supply-chain, because case law allowed us to decide where a title was when. [Matt] Yep. But today, we're unknown process, known technology. It's going to look like the cloud. Now, the details are always got to be worked out, but increasingly we are, we don't know the process. And so we're on a road map of discovery that is provided by data. Do you guys agree with that? So I would agree, but I'd make a nuance which is I think that's a very nice way of conceptualizing, and I don't disagree. But I would actually say that at the frontier the technology is still unknown as well. The algorithms are changing, the use cases, which you're pointing out, the processes are still, are now unknown, and I think that's a really important way to think about it, because suddenly a lot of possibility opens up when you admit that the processes are unknown because it's not going to look like the way it looked in the past. But I think for most people the technology's unknown because the frontier is changing so quickly. What we're doing with image recognition and voice recognition today is so different than it was just three years ago. Deep learning and reinforcement learning. Well it's going to require armies of people to understand that. Well, tell me about it. This is the full-- Is it? For the most, yes it's a full employment act for data scientists today, and I don't see that changing for a generation. So, everyone says oh what are we going to teach our kids? Well teach them math, teach them stats, teach them some coding. There's going to be a huge need. All you have to do is look at the society. Look at the world and think about what share of it is actually done well, optimized for outcomes that we all agree with. I would say it's probably between, it's in single percents. Probably between 1% and 5% of the world is optimized. One small example: medical science. We collect a lot of data in medicine. Do we use it? No. It's the biggest scandal going on in the world. If patients and citizens really understood the degree to which medical science is still trial and error based on the gumption of the human mind of a doctor and a nurse rather than the data that they actually already collect but don't reuse. There would be Congressional hearings everyday. People, there would be revolutions in the street because, here it is the duty of care of medical practitioners is simply not being upheld. Yeah, I'd take exception to that. Just, not to spend too much time on this, but at the end of the day, the fundamental role of the doctor is to reduce the uncertainty and the fear and the consequences of the patient. >> Kenneth: By any means necessary and they are not doing that. Hold on. You're absolutely right that the process of diagnosing and the process of treatment from a technical standpoint would be better. But there's still the human aspect of actually taking care of somebody. Yeah, I think that's true, and think there is something of the hand of the healer, but I think we're practicing a form of medicine that looks closer to black magic than it does today to science. Bring me the data scientist. >> Peter: Alright. And I think an interesting kind of parallel to that is when you jump on a plane, how often do you think the pilot actually lands that plane? He doesn't. No. Thank you. So, you still need somebody there. Yeah. But still need somebody as the oversight, as that kind of to make a judgment on. So I'm going to unify your story, my father was a cardiologist who was also a flight surgeon in the Air Force in the U.S., and was one of the few people that was empowered by the airline pilots association to determine whether or not someone was fit to fly. >> Matt: Right. And so my dad used to say that he is more worried about the health of a bus driver than he is of an airline pilot. That's great. So, in other words we've been gah-zumped by someone who's father was both a doctor and a pilot. You can't do better than that. So it turns out that we do want Sully on the Hudson, when things go awry. But in most cases I think we need this blend of the data on one side and the human on the other. The idea that the data just because we're going to go in the world of artificial intelligence machine learning is going to mean jobs will be eradicated left and right. I think that's a simplification. I think that the nuance that's much more real is that we're going to live in a hybrid world in which we're going to have human beings using data in much more impressive ways than they've ever done it before. So, talk about that. I mean I think you have made this compelling case that we have this huge need for data and this explosion of data plus the human judgment that is needed to either diagnose an illness or whether or not someone is fit to fly a plane. So then where are we going in terms of this data visionary and in terms of say more of a need for AI? Yeah. Well if you take a look at medicine, what we would have is, the diagnosis would probably be done say for a pathology exam by the algorithm. But then, the health care coach, the doctor will intervene and will have to both interpret this for, first of what it means, translate it to the patient, and then discuss with the patient the trade-offs in terms of their lifestyle choices. For some people, surgery is the right answer. For others, you might not want to do that. And, it's always different with all of the patients in terms of their age, in terms of whether they have children or not, whether they want the potential of complications. It's never so obvious. Just as we do that, or we will do that in medicine, we're going to do that in business as well. Because we're going to take data that we never had about decisions should we go into this market or that market. Should we take a risk and gamble with this product a little bit further, even though we're not having a lot of sales because the profit margins are so good on it. There's no algorithm that can tell you that. And in fact you really want the intellectual ambition and the thirst for risk taking of the human being that defies the data with an instinct that I think it's the right thing to do. And even if we're going to have failures with that, and we will, we'll have out-performance. And that's what we want as well. Because society advances by individual passions, not by whatever the spreadsheet says. Okay. Well there is this issue of agency right? So at the end of the day a human being can get fired, a machine cannot. A machine, in the U.S. anyway, software is covered under the legal strictures of copywriting. Which means it's a speech act. So, what do you do in circumstances where you need to point a finger at something for making a stupid mistake. You keep coming back to the human being. So there is going to be an interesting interplay over the next few years of how this is going to play out. So how is this working, or what's the impact on NetApp as you work with your customers on this stuff? So I think you've got the AI, ML, that's kind of one kind of discussion. And that can lead you into all sorts of rat holes or other discussions around well how do we make decisions, how do we trust it to make decisions, there's a whole aspect that you have to discuss around that. I think if you just bring it back to businesses in general, all the businesses that we look at are looking at new ways of creating new opportunities, new business models, and they're all collecting data. I mean we know the story about General Electric. Used to sell jet engines and now it's much more about what can we do with the data that we collect from the jet engines. So that's finding a new business model. And then you vote with a human role in that as well, is well is there a business model there? We can gather all of this information. We can collect it, we can refine it, we can sort it, but is there actually a new business model there? And I think it's those kind of things that are inspiring us as a company to say well we could uncover something incredible here. If we could unlock that data, we could make sure it's where it needs to be when it needs to be there. You have the resources to bring to bed to be able to extract value from it, you might find a new business model. And I think that's the aspect that I think is of real interest to us going forward, and kind of inspires a lot of what we're doing. Great. Kenneth, Matt, thank you so much for coming on the show. It was a really fun conversation. Thank you. Thank you for having us. We will have more from NetApp Insight just after this. (techno music)

Published Date : Nov 14 2017

SUMMARY :

and the enterprise we and the consequences of the patient. of the hand of the healer, in the Air Force in the U.S., You have the resources to bring to bed

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Dr. Mark Ramsey & Bruno Aziza | BigData NYC 2017


 

>> Live from Mid Town Manhattan. It's the Cube, covering BIGDATA New York City 2017. Brought to you by, SiliconANGLE Media and it's ecosystems sponsors. >> Hey welcome back everyone live here in New York City for the Cube special presentation of BIGDATA NYC. Here all week with the Cube in conjunction with Strata Data even happening around the corner. I'm John Furrier the host. James Kobielus, our next two guests Doctor Mark Ramsey, chief data officer and senior vice president of R&D at GSK, Glasgow Pharma company. And Bruno as he's the CMO at Fscale, both Cube alumni. Welcome back. >> Thank for having us. >> So Bruno I want to start with you because I think that Doctor Mark has some great use cases I want to dig into and go deep on with Jim. But Fscale, give us the update of the company. You guys doing well, what's happening? How's the, you have the vision of this data layer we talked a couple years ago. It's working so tell us, give us the update. >> A lot of things have happened since we talked last. I think you might have seen some of the news in terms of growth. Ten X growth since we started and mainly driven around the customer use cases. That's why I'm excited to hear from Mark and share his stories with the rest of the audience here. We have a presentation at Strata tomorrow with Vivens. It's a great IOT use case as well. So what we're seeing is the industry is changing in terms of how it's spying the idea platforms. In the past, people would buy idea platforms vertically. They'd buy the visualization, they'd buy the sementic and buy the best of great integration. We're now live in a world where there's a multitude of BI tools. And the data platforms are not standardized either. And so what we're kind of riding as a trend is this idea of the need for the universal semantic layer. This idea that you can have a universal set of semantics. In a dictionary or ontology. that can be shared across all types of business users and business use cases. Or across any data. That's really the trend that's driving our growth. And you'll see it today at this show with the used cases and the customers. And of course some of the announcements that we're doing. We're announcing a new offer with cloud there and tableau. And so we're really excited about again how they in space and the partner ecosystems embracing our solutions. >> And you guys really have a Switzerland kind of strategy. You're going to play neutral, play nicely with everybody. Because you're in a different, your abstraction layer is really more on the data. >> That's right. The whole value proposition is that you don't want to move your data. And you don't want to move your users away from the tools that they already know but you do want them to be able to take advantage of the data that you store. And this concept of virtualized layer and you're universal semantic layer that enables the use case to happen faster. Is a big value proposition to all of them. >> Doctor Mark Ramsey, I want to get your quick thoughts on this. I'm obviously your customer so. I mean you're not bias, you ponder pressure everyday. Competitive noise out there is high in this area and you're a chief data officer. You run R&D so you got that 20 miles stare into the future. You've got experience running data at a wide scale. I mean there's a lot of other potential solutions out there. What made it attractive for you? >> Well it feels a need that we have around really that virtualization. So we can leave the data in the format that it is on the platform. And then allow the users to use like Bruno was mentioning. Use a number of standardized tools to access that information. And it also gives us an ability to learn how folks are consuming the data. So they will use a variety of tools, they'll interact with the data. At scale gives us a great capability to really look under the cover, see how they're using the data. And if we need to physicalize some of that to make easier access in the long term. It gives us that... >> It's really an agility model kind to data. You're kind of agile. >> Yeah its kind of a way to make, you know so if you're using a dash boarding tool it allows you to interact with the data. And then as you see how folks are actually consuming the information. Then you can physicalize it and make that readily available. So it is, it gives you that agile cycles to go through. >> In your use of the solution, what have you seen in terms of usage patterns. What are your users using at scale for? Have you been surprised by how they're using it? And where do you plan to go in terms of the use cases you're addressing going forward with this technology? >> This technology allows us to give the users the ability to query the data. So for example we use standardized ontologies in several of the areas. And standardized ontologies are great because the data is in one format. However that's not necessarily how the business would like to look at the data and so it gives us an ability to make the data appear like the way the users would like to consume the information. And then we understand which parts of the model they're actually flexing and then we can make the decision to physicalize that. Cause again it's a great technology but virtualization there is a cost. Because the machines have to create the illusion of the data being a certain way. If you know it's something that's going to be used day in and day out then you can move it to a physicalized version. >> Is there a specific threshold when you were looking at the metrics of usage. When you know that particular data, particular views need to be physicalized. What is that threshold or what are those criteria? >> I think it's, normally is a combination of the number of connections that you have. So the joins of the data across the number of repositories of data. And that balanced with the volume of data so if you're dealing with thousands of rows verses billions of rows then that can lead you to make that decision faster. There isn't a defined metric that says, well we have this number of rows and this many columns and this size that it really will lead you down that path. But the nice thing is you can experiment and so it does give you that ability to sort of prototype and see, are folks consuming the data before you evoke the energy to make it physical. >> You know federated, I use the word federated but semantic virtualization layers clearly have been around for quite sometime. A lot of solution providers offer them. A lot of customers have used them for disparate use cases. One of the wraps traditionally again estimating virtualization is that it's simply sort of a stop gap between chaos on the one end. You know where you have dozens upon dozens of databases with no unified roll up. That's a stop gap on the way to full centralization or migration to a big data hub. Did you see semantic virtualization as being sort of your target architecture for your operational BI and so forth? Or do you on some level is it simply like I said a stop gap or transitional approach on the way to some more centralized environment? >> I think you're talking about kind of two different scenarios here. One is in federated I would agree, when folks attempted to use that to bring disparate data sources together to make it look like it was consolidated. And they happen to be on different platforms, that was definitely a atop gap on a journey to really addressing the problem. Thing that's a little different here is we're talking about this running on a standardized platform. So it's not platformed disparate it's on the platform the data is being accessed on the platform. It really gives us that flexibility to allow the consumer of the data to have a variety of views of the data without actually physicalizing each of them. So I don' know that it's on a journey cause we're never going to get to where we're going to make the data look as so many different ways. But it's very different than you know ten, 15 years ago. When folks were trying to solve disparate data sources using federation. >> Would it be fair to characterize what you do as agile visualization of the data on a data lake platform? Is that what it's essentially about? >> Yeah that, it certainly enables that. In our particular case we use the data lake as the foundation and then we actually curate the data into standardized ontologies and then really, the consumer access layer is where we're applying virtualization. In the creation of the environment that we have we've integrated about a dozen different technologies. So one of the things we're focused on is trying to create an ecosystem. And at scale is one of the components of that. It gives us flexibility so that we don't have to physicalize. >> Well you'd have to stand up any costs. So you have the flexibility with at scale. I get this right? You get the data and people can play with it without actually provisioning. It's like okay save some cash, but then also you double down on winners that come in. >> Things that are a winner you check the box, you physicalize it. You provide that access. >> You get crowd sourcing benefits like going on in your. >> You know exactly. >> The curation you mentioned. So the curation goes on inside of at scale. Are you using a different tool or something you hand wrote in house to do that? Essentially it's a data governance and data cleansing. >> That is, we use technology called Tamer. That is a machine learning based data curation tool, that's one of our fundamental tools for curation. So one of the things in the life sciences industry is you tend to have several data sources that are slightly aligned. But they're actually different and so machine learning is an excellent application. >> Lets get into the portfolio. Obviously as a CTO you've got to build a holistic view. You have a tool chest of tools and a platform. How do you look at the big picture? On that scale if it's been beautifully makes a lot of sense. So good for those guys. But you know big picture is, you got to have a variety of things in your arsenal. How do you architect that tool shed or your platform? Is everything a hammer, everything's a nail. You've got all of them though. All the things to build. >> You bring up a great point cause unfortunately a lot of times. We'll use your analogy, it's like a tool shed. So you don't want 12 lawnmowers right? In your tool shed right? So one of the challenges is that a lot of the folks in this ecosystem. They start with one area of focus and then they try to grow into area of focuses. Which means that suddenly everybody's starts to be a lawnmower, cause they think that's... >> They start as a hammer and turn into a lawn mower. >> Right. >> How did that happen, that's called pivoting. >> You can mow your lawn with a hammer but. So it's really that portfolio of tools that all together get the job done. So certainly there's a data acquisition component, there's the curation component. There's visualization machines learning, there's the foundational layer of the environment. So all of those things, our approach has been to select. The kind of best in class tools around that and then work together and... Bruno and the team at scale have been part of this. We've actually had partner summits of how do we bring that ecosystem together. >> Is your stuff mostly on prime, obviously a lot of pharma IP there. So you guys have the game that poll patent thing which is well documented. You don't want to open up the kimono and start the cloth until it's releasing so. You obviously got to keep things confidential. Mix of cloud, on prime, is it 100 percent on prime? Is there some versing for the cloud? Is it a private cloud, how do you guys look at the cloud piece? >> Yeah majority of what we're doing is on prime. The profile for us is that we persist the data. So it's not. In some cases when we're doing some of the more advanced analytics we burst to the cloud for additional processors. But the model of persisting the data means that it's much more economical to have on prime instance of what we're doing. But it is a combination, but the majority of what we're doing is on prime. >> So will you hold on Jim, one more question. I mean obviously everyone's knocking on your door. You know how to get in that account. They spend a lot of money. But you're pretty disciplined it sounds like you've got to a good view of you don't want people to come in and turn into someone that you don't want them to be. But you also run R&D so you got to have to understand the head room. How do you look at the head room of what you need down the road in terms of how you interface with the suppliers that knock on your door. Whether it's at scale currently working with you now. And then people just trying to get in there and sell you a hammer or a lawn mower. Whatever they have they're going to try, you know you're dealing with the vendor pressure. >> Right well a lot of that is around what problem we're trying to solve. And we drive all of that based on the use cases and the value to the business. I mean and so if we identify gaps that we need to address. Some of those are more specific to life sciences types of challenges where they're very specific types of tools that the population of partners is quite small. And other things. We're building an actual production, operational environment. We're not building a proof of concept, so security is extremely important. We're coberosa enabled end to end to out rest inflight. Which means it breaks some of the tools and so there's criteria of things that need to be in place in order to... >> So you got anything about scale big time? So not just putting a beach head together. But foundationally building out platform. Having the tools that fit general purpose and also specialty but scales a big thing right? >> And it's also we're addressing what we see is three different cohorts of consumers of the data. One is more in the guided analytics, the more traditional dashboards, reports. One is in more of computational notebooks, more of the scientific using R, Python, other languages. The third is more kind of almost at the bare middle level machine learning, tenser flow a number of tools that people directly interact. People don't necessarily fit nicely into those three cohorts so we're also seeing that, there's a blend. And that's something that we're also... >> There's a fourth cohort. >> Yeah well you know someone's using a computational notebook but they want to draw upon a dashboard graphic. And then they want to run a predefined tenser flow and pull all that together so. >> And what you just said, tied up the question I was going to ask. So it's perfect so. One of my core focuses is as a Wikibon analyst is on deep learning. On AI so in semantic data virtualization in a life sciences pharma context. You have undoubtedly a lot of image data, visual data. So in terms of curating that and enabling you know virtualized access to what extent are you using deep learning, tenser flow, convolutional neural networks to be able to surface up the visual patterns that can conceivably be searched using a variety of techniques. Is that a part of your overall implementation of at scale for your particular use cases currently? Or do you plan to go there in terms of like tenser flow? >> No I mean we're active, very active. In deep learning, artificial intelligence, machine learning. Again it depends on which problem you're trying to solve and so we again, there's a number of components that come together when you're looking at the image analytics. Verses using data to drive out certain decisions. But we're acting in all of those areas. Our ultimate goal is to transform the way that R&D is done within a pharmaceutical company. To accelerate the, right now it takes somewhere between five and 15 years to develop a new medicine. The goal is to really to do a lot more analytics to shorten that time significantly. Helps the patients, gets the medicines to market faster. >> That's your end game you've got to create an architecture that enables the data to add value. >> Right. >> The business. Doctor Mark Ramsey thanks so much for sharing the insight from your environment. Bruno you got something there to show us. What do you got there? He always brings a prop on. >> A few years ago I think I had a tattoo on my neck or something like this. But I'm happy that I brought this because you could see how big Mark's vision is. the reason why he's getting recognized by club they're on the data awards and so forth. Is because he's got a huge vision and it's a great opportunity for a lot of CTOs out there. I think the average CEO spent a 100 million dollars to deploy big data solutions over the last five years. But they're not able to consumer all the data they produce. I think in your case you consume about a 100 percent of the instructor data. And the average in this space is we're able to consume about one percent of the data. And this is essentially the analogy today that you're dealing with if you're on the enterprise. We'd spent a lot of time putting data in large systems and so forth. But the tool set that we give, that you did officers in their team is a cocktail straw lik this in order to drink out of it. >> That's a data lake actually. >> It's an actual lake. It's a Slurpee cup. Multiple Slurpees with the same straw. >> Who has the Hudson river water here? >> I can't answer that question I think I'd have to break a few things if I did. But the idea here is that it's not very satisfying. Enough the frustration business users and business units. When at scale's done is we built this, this is the straw you want. So I would kind of help CTOs contemplate this idea of the Slurpee and the cocktail straw. How much money are you spending here and how much money are you spending there. Because the speed at which you can get the insights to the business user. >> You got to get that straw you got to break it down so it's available everywhere. So I think that's a great innovation and it makes me thirsty. >> You know what, you can have it. >> Bruno thanks for coming from at scale. Doctor Mark Ramsey good to see you again great to have you come back. Again anytime love to have chief data officers on. Really a pioneering position, is the critical position in all organizations. It will be in the future and will continue being. Thanks for sharing your insights. It's the Cube, more live coverage after this short break. (tech music)

Published Date : Sep 27 2017

SUMMARY :

Brought to you by, And Bruno as he's the CMO at Fscale, So Bruno I want to start with you And of course some of the announcements that we're doing. And you guys really have a Switzerland And you don't want to move your users You run R&D so you got that in the format that it is on the platform. It's really an agility model kind to data. So it is, it gives you that agile cycles to go through. And where do you plan to go and day out then you can move it to a physicalized version. When you know that particular data, particular views But the nice thing is you can experiment You know where you have dozens upon dozens of databases So it's not platformed disparate it's on the platform So one of the things we're focused on So you have the flexibility with at scale. Things that are a winner you check the box, You get crowd sourcing benefits So the curation goes on So one of the things in the life sciences industry you got to have a variety of things in your arsenal. So one of the challenges is that a lot of the folks Bruno and the team at scale have been part of this. So you guys have the game that poll patent thing but the majority of what we're doing is on prime. of what you need down the road and the value to the business. So you got anything about scale big time? more of the scientific using R, Python, other languages. Yeah well you know someone's using to what extent are you using deep learning, Helps the patients, gets the medicines to market faster. that enables the data to add value. Bruno you got something there to show us. that you did officers in their team is a cocktail straw It's a Slurpee cup. Because the speed at which you can get the insights you got to break it down so it's available everywhere. Doctor Mark Ramsey good to see you again

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Chip Coyle, Infor | Inforum 2017


 

>> Announcer: Live from the Javits Center in New York City, it's theCUBE. Covering Inforum 2017, brought to you by Infor. >> Welcome back to theCUBE's coverage of Inforum 2017, I am your host, Rebecca Knight, along with my co-host, Dave Vellante. We are joined by Chip Coyle. He is Infor's CMO. Thanks so much for sitting down with theCUBE today. >> Thank you for having me. >> So we just kicked off the show, the general session, Charles Philips, a lot of other Infor executives up there on the main stage talking. Lay it out for us. How many people are here. What are sort of the big themes that you're trying to get across here. >> Yeah, well, first of all it's great for Infor to be having our conference here at the Javits Center. It's about 10 blocks from our home-- >> Rebecca: Your own back yard. >> In New York City, and so this year, we've got nearly 7,000 attendees over the course of the week. Many component programs as we do every year with our partner summit, with our various conferences for the different individual customer constituencies, and executive forum, and of course, a big customer appreciation event happening tomorrow night. >> You've also made some big announcements. I'm talking mostly about Coleman AI, and Burst. I want you, if you can unpack those for our viewers a little bit. >> Yeah, I would say the theme of the conference this year is the age of networked intelligence. And what does that mean? Well, we've had, for the last several years, a layered strategy in our business, starting at the foundation with very deep industry functional applications. Purpose built for the different industries. We've taken all of that technology and moved it to the cloud, so that you get the benefits of the efficiencies and the network capability of taking your applications to the cloud. We recently, a year ago, acquired GT Nexus, which expands our capability, in a broader sense, to a commerce network, and we're able to incorporate that into our traditional applications in different industries. And then, just a couple of months ago, we acquired a business intelligence software company, Burst, which brings some really great technology for business intelligence that we can layer on top of all of our applications in this network environment. And then finally, today, the big announcement was Coleman, as you said, and that was to take our new artificial intelligence platform and really create just profound new ways that the workers in the different industries and their different companies across the networked enterprise, can interact in a business setting, much like people do in a commercial setting today. >> Can you, Chip, talk about the evolution of the brand promise. So when we first met Infor, at AWS Reinvent, it was like who was Infor? Trying to educate people on who Infor is. And so I felt like last year was your sort of stamp of this is how Infor and why Infor is relevant, and now, there seems to be sort of an undertone of innovation. So can you talk about the evolution of the brand and what you see as the brand promise. >> Well, we are very consistent in our branding and positioning of Infor as really the first industry cloud company. We're the ones who have been, at an accelerated pace, bringing the most deep, industry-rich, functional applications to the cloud. And that has created a great layer now, for all of these future innovations that we have talked about today with the benefits of business intelligence enabled applications built right in, so that you can truly have all the information you need at the right time, for the right purpose to make immediate business decisions. And then the potential and capability of artificial intelligence on top of that. >> As the chief marketing officer, can you talk a little bit about how these innovations change how you do your job, and how they make your life easier, in terms of making the right decision at the right time, making the decision better, having the right data? >> Yeah, well some of the other announcements that we're making this week, actually are in my particular line of business, which is marketing, and one of those, for example, is we're broadening our Infor CRM suite, with a link to LinkedIn's Sales Navigator. So that brings a whole set of important data to, about customers, to enable better customer interactions, for our customers. So that's something that we look to be using in our business, along with Marketo, which is a new business partner, as the engine, or the marketing automation platform to fuel our marketing business. So that's how it's impacting me directly in what I do. >> So I wonder if you could help us sort of debunk some of the myths. So Oracle would say enterprise apps aren't moving to the cloud, and we are the company to move them to the cloud, and we're the only company that can move them to the cloud. You know, SAP, it's got it sort of some cloud going on, but most of the stuff remains on prem. We heard today 55% of your revenue comes from cloud. And we know you made a decision years ago to run on AWS. Help us understand, I mean these are core, hard core enterprise apps that are running in the cloud. So help us debunk some of those myths and add some color to that. >> The traditional processes of rolling out major applications and enterprise applications in an enterprise is completely changing. And it's also changing because of the capabilities of the cloud. And the approach that Infor takes, which is very easy to assemble and configure with our Ion technology and collaboration technology, such as Mingle, to put these applications in place in a much faster way for our customers than some of the traditional players in the ERP market have been accustomed to do. And they just don't have the current technology approach or foundation to be able to move quickly to the cloud, as we do at Infor. >> In talking about Infor, you talked a little bit about the brand evolution, how are you getting the word out? Infor is really a sleeping giant in the technology industry. How are you getting your name out there? >> Well one thing that we want to do with our brand is show, well first of all, introduce Infor to the world at large, that hasn't heard of us. And the way that we want to do that is by showing what kind of benefits we can give to customers in different industries. So we just recently launched our first-ever TV commercials. They have run on shows like Meet the Press, and some of the CNBC and MSNBC shows. That has, incidental, all of that was developed entirely, 100% in house, with Hook and Loop, our creative in-house creative agency. So we're very proud of that. We're looking to do more of that with TV. We also have a relationship with the Brooklyn Nets here in New York, where on the business side, we're enabling them with performance and team analytics with a whole slew of applications of that with biometric readings and imagery, when they're moving around on the court. That can then be used to help fine tune and make decisions on which personnel to use, which, what are the best players to be able to, say, shoot a free throw after one day of rest versus two days of rest. That level of analytics. So we are, in that partnership with the Nets, are also in a branding way, going to be on the Nets jersey starting this September with an Infor patch on the jersey. And we're announcing that also, this week. >> Awesome. This is definitely a New York theme here. We're here at the Javits Center, Brooklyn Nets, Hudson Yards, another huge project that you guys are intimately involved in. Not a lot of vendors are explicitly mentioned in that. Maybe talk about that a little bit. >> Well, Hudson Yards as a development is unique in that it is really a completely self-contained city in all respects. Where the concept is to be able to network the data and information of anybody within that city, with respect to where they live in the high-rises, where they shop in the retail stores or grocery stores, where they eat in the restaurants, and where they work with all of the businesses that are locating there, too. So that gives you so much potential to rethink how information can enable, just the way that you move about, even in the city. From keyless entry into facilities, to voice-activated tasks, like, can you please restock in my groceries in my refrigerator in my condo. So there's so many ways that that can be a broad showcase for the true smart city of the future. >> These are high-end clientele. This is very New York. I want to shift gears and talk about the eco system a little bit. There's a few names that I, maybe they were here before, but I hadn't seen them, at least prominently, certainly IBM, you mentioned Marketo, a great interesting partner, hot company, and some of the SIs are sort of coming out of the woodwork. >> Chip: Yes. >> Now when you think about your strategy for sort of micro verticals, the SIs, I always say, they love to eat at the trough. And if there's not a lot of customizations, they're not interested. However, you've attracted them, because you've now got a substantial enough estate. So talk about that evolution of the eco system. >> We're proud to have as our diamond sponsors this year, AVAAP, as well as Marketo. And AVAAP has been a longstanding partner for, implementation partner for us, in expanding areas. Their heritage is with Lawson in health care and they're doing a lot of implementations across our business in all geographies, in all industries. But what's new this year is we also have attracted some new, some of the big SIs, such as Deloitte and Accenture, Capgemini, Grant Thornton. So they have all come in as sponsors and we're really on the cusp of some big and bigger and better things with them in the different businesses. >> The other thing I wanted to ask you about is Infor has a unique way of attracting interesting speakers. I've done probably five or six thousand interviews in the last five or six years, and some of the most interesting have been at Inforum. Deborah Norville came on in New Orleans, last year Lara Logan, Naomi Tutu, Karina Hollekim, amazing three women interviews. >> Rebecca: This year Susan Rice. >> This year Susan Rice was here, so what's that all about? They're not techies, they're just interesting people. What are you trying to do there? >> Well, we have a program, the Women's Infor Network, WIN, that was created by Pam Murphy, our chief operating officer, and starting a few Inforums ago, we wanted to use Inforum as a platform to showcase innovative women in the world. And it's a little bit of a departure from our product and technology messages. And this year, we've got, as you mentioned, some great inspiring women, like Jill Biden, the former first, vice president-- >> Rebecca: Second lady. >> And also, Susan Rice, as you mentioned. So, it's going to be, it's always a very popular session. >> Yes, and we're looking forward to having those women on theCUBE, too, tomorrow. >> Chip: Absolutely. >> Chip, thanks so much for joining us, it's been a pleasure. >> Thank you for having me. >> I'm Rebecca Knight, for Dave Vellante. We'll have more from Inforum 2017 after this. (techno music)

Published Date : Jul 11 2017

SUMMARY :

Covering Inforum 2017, brought to you by Infor. Welcome back to theCUBE's coverage What are sort of the big themes that you're trying to be having our conference here at the Javits Center. for the different individual customer constituencies, for our viewers a little bit. to the cloud, so that you get the benefits of the brand promise. for the right purpose to make immediate business decisions. to be using in our business, along with Marketo, hard core enterprise apps that are running in the cloud. in the ERP market have been accustomed to do. about the brand evolution, how are you getting the word out? And the way that we want to do that you guys are intimately involved in. Where the concept is to be able to network the data and some of the SIs are sort of coming out of the woodwork. So talk about that evolution of the eco system. in the different businesses. of the most interesting have been at Inforum. What are you trying to do there? And this year, we've got, as you mentioned, And also, Susan Rice, as you mentioned. Yes, and we're looking forward to having it's been a pleasure. I'm Rebecca Knight, for Dave Vellante.

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Teresa Carlson, AWS - AWS Public Sector Summit 2017


 

>> Announcer: Live from Washington, D.C., it's theCUBE covering AWS Public Sector Summit 2017. Brought to you by Amazon Web Services and it's partner ecosystem. >> Welcome back, live here on theCUBE along with John Furrier, I'm John Walls. Welcome to AWS Public Sector Summit 2017. Again, live from Washington, D.C., your nation's capital, our nation's capital. With us now is our host for the week, puts on one heck of a show, I'm want to tell you, 10,000 strong here, jammed into the Washington Convention Center, Theresa Carlson from World Wide Public Sector. Nice to have you here, Theresa. >> Hi, good afternoon. >> Thanks for joining us. >> Love theCUBE and thank you for being here with us today. >> Absolutely. >> All week in fact. >> It's been great, it really has. Let's just talk about the show first off. Way back, six years ago, we could probably get everybody there jammed into our little area here, just about I think. >> Pretty much. >> Hard to do today. >> That's right. >> How do you feel about when you've seen this kind of growth not only of the show, but in your sector in general? >> I think at AWS we're humbled and excited and, on a personal level because I was sort of given the charge of go create this Public Sector business world-wide, I'm blown away, I pinch myself every time because you did hear my story. The first event, we had about 50 people in the basement of some hotel. And then, we're like, okay. And today, 10,000 people. Last year we had it at the Marriott Wardman Park and we shut down Connecticut Avenue so we knew we needed to make a change. (laughing) But it's great, this is really about our customers and partners. This is really for them. It's for them to make connections, share, and the whole theme of this is superheroes and they are our superheroes. >> One of the heroes you had on the stage today, John Edwards from the CIA, one of your poster-children if you will for great success and that kind of collaboration, said something to the effect of quote, "The best decision we ever made at the CIA "was engaging with AWS in that partnership." When you hear something like that from such a treasured partner, you got to feel pretty good. >> You just have to drop the microphone, boom, and you're sort of done. They are doing amazing work and their innovation levels are really leading, I would say, in the US Public Sector for sure and also, not just in US Public Sector but around the world. Their efforts of what they're doing and the scale and reach at which they're doing it so that's pretty cool. >> John, you've talked about the CIA moment, I'd like to hear the story, share with Theresa. >> Oh, you're going to steal my thunder here? >> No, I'm setting you up. That's what a good partner does. It's all yours. >> Well, John, we've talked multiple times already so I'll say it for the third time. The shot heard around the cloud was my definition of seminal moment, in big mega-trends there's always a moment. It was when Obama tweeted, Twitter grew, plane landing on the Hudson, there's always a seminal moment in major trends that make or break companies. For you guys, it was the CIA. Since then, it's just been a massive growth for you guys. That deal was interesting because it validated Shadow IT, validated the cloud, and it also unseated IBM, the behemoth sales organization that owned the account. In a way, a lot of things lined up. Take us through what's happened then, and since then to now. >> Well, you saw between yesterday at Werner Vogels' keynote and my keynote this morning, just the breadth and depth of the type of customers we have. Everything from the UK government, GCHQ, the Department of Justice with the IT in the UK, to the centers for Medicare for HHS, to amazing educational companies, Cal. Polytech., Australian Tax Office. That's just the breadth and depth of the type of customers we have and all of their stories were impactful, every story is impactful in their own way and across whatever sector they have. That really just tells you that the type of workloads that people are running has evolved because I remember in the early days, when you and I first talked, we talked about what are the kind of workloads and we were talking a little bit about website hosting. That's, of course, really evolved into things like machine learning, artificial intelligence, a massive scale of applications. >> Five or six years ago when we first chatted at re:Invent, it's interesting 'cause now this is the size of re:Invent what it was then so you're on a same trajectory from a show size. Again, validation to the growth in Public Sector. But I was complimenting you on our opening today, saying that you're tenacious because we've talked early days, it was a slog in the early days to get going in the cloud, you were knocking on a lot of doors, convincing people, hey, the future's going to look his way and I don't want to say they slammed the proverbial door in your face but it was more of, woah, they don't believe the cloud is ever going to happen for the government. Share some of those stories because now, looking back, obviously the world has changed. >> It has and, in fact, it's changed in many aspects of it, from policy makers, which I think would be great for you all to have on here sometime to get their perspective on cloud, but policy makers who are now thinking about, we just had a new modernization of IT mandate come out in the US Federal Government where they're going to give millions and millions of dollars toward the modernization of IT for US Government agencies which is going to be huge. That's the first time that's ever happened. To an executive order around cyber-security which is pretty much mandated to look at cloud and how you use it. You're seeing thing like that to even how grants are given where it used to be an old-school model of hardware only to now use cloud. Those ideas and aspects of how individuals are using IT but also just the procurements that are coming out. The buying vehicles that you're seeing come out of government, almost all of them have cloud now. >> John and I were talking about D.C. and the political climate. Obviously, we always talk about it on my show, comment on that. But, interesting, theCUBE, we could do damage here in D.C.. So much target-rich environment for content but more than ever, to me, is the tech scene here is really intrinsically different. For example, this is not a shiny new toy kind of trend, it is a fundamental transformation of the business model. What's interesting to me is, again, since the CIA shot heard around the cloud moment, you've seen a real shift in operating model. So the question I have for you, Theresa, if you can comment on this is: how has that changed? How has the procuring of technology changed? How has he human side of it changed? Because people want to do a good job, they're just on minicomputers and mainframes from the old days with small incremental improvement over the years in IT but now to a fundamental, agile, there's going to be more apps, more action. >> You said something really important just a moment ago, this is a different kind of group than you'll get in Silicon Valley and it is but it's very enterprise. Everybody you see here, every project they work on, we're talking DoD, the enterprise of enterprises. They have really challenging and tough problems to solve every day. How that's changed, in the old days here in government, they know how to write acquisitions for a missile or a tank or something really big in IT. What's changing is their ability to write acquisitions for agile IT, things like cloud utility based models, moving fast, flywheel approach to IT acquisitions. That's what's changing, that kind of acquisition model. Also, you're seeing the system integrator community here change. Where they were, what I call, body shops to do a lot of these projects, they're having to evolve their IT skills, they're getting much more certified in areas of AWS, at the system admin to certified solution architects at the highest level, to really roll these projects out. So training, education, the type of acquisition, and how they're doing it. >> What happened in terms of paradigm shift, mindset? Something had to happen 'cause you brought a vision to the table but somebody had to buy it. Usually, when we talk about legacy systems, it was a legacy mindset too, resistant, reluctant, cautious, all those things. >> Theresa: Well, everything gets thrown out. >> What happened? Where did it tip the other way? Where did it go? >> I think, over time, it's different parts of the government but culture is the hardest thing to, always, change. Other elements of any changes, you get there, but culture is fundamentally the hardest thing. You're seeing that. You've always heard us say, you can't fight gravity, and cloud is the new normal. That's for the whole culture. People are like, I cannot do my project anymore without the use of cloud computing. >> We also have a saying, you can't fight fashion either, and sometimes being in fashion is what the trends are going on. So I got to ask you, what is the fashion statement in cloud these days with your customers? Is it, you mentioned there, moving much down in the workload, is it multi-cloud? Is it analytics? Where's the fashionable, cool action right now? >> I think, here, right now, the cool thing that people really are talking about are artificial intelligence and machine learning, how they take advantage of that. You heard a lot about recognition yesterday, Poly and Lex, these new tools how they are so differentiating anything that they can possibly develop quickly. It's those kind of tools that really we're hearing and of course, IOT for state and local is a big deal. >> I got to ask you the hard question, I always ask Andy a hard question too, if he's watching, you're going to get this one probably at re:Invent. Amazon is a devops culture, you ship code fast and you make all these updates and it's moving very, very fast. One of the things that you guys have done well, but I still think you need some work to do in terms of critical analysis, is getting the releases out that are on public cloud into the GovCloud. You guys have shortened that down to less than a year on most things. You got the east region now rolled out so full disaster recovery but government has always been lagging behind most commercial. How are you guys shrinking that window? When do you see the day when push button commercial, GovCloud are all lockstep and pushing code to both clouds? >> We could do that today but there's a couple of big differentiators that are important for the GovCloud. That is it requires US citizenship, which as you know, we've talked about the challenges of technology and skills. That's just out there, right? At Amazon Web Services, we're a very diverse company, a group of individuals that do our coding and development, and not all of them are US citizens. So for these two clouds, you have to be a US citizen so that is an inhibitor. >> In terms of developers? In terms of building the product? >> Not building but the management aspect. Because of their design, we have multiple individuals managing multiple clouds, right? Now, with us, it's about getting that scale going, that flywheel for us. >> So now it's going to be managed in the USA versus made in the USA with everything as a service. >> Yeah, it is. For us, it's about making sure, number one, we can roll them out, but secondly, we do not want to roll services into those clouds unless they are critical. We are moving a lot faster, we rolled in a lot more services, and the other cool thing is we're starting to do some unique things for our GovCloud regions which, maybe the next time, we can talk a little bit more about those things. >> Final question for me, and let John jump in, the CIA has got this devops factory thing, I want you to talk about it because I think it points to the trend that's encouraging to me at least 'cause I'm skeptical on government, as you know. But this is a full transformation shift on how they do development. Talk about these 4000 developers that got rid of their development workstations, are now doing cloud, and the question is, who else is doing it? Is this a trend that you see happening across other agencies? >> The reason that's really important, I know you know, in the old-school model, you waited forever to provision anything, even just to do development, and you heard John talk about that. That's what he meant on this sort of workstation, this long period of time it took for them to do any kind of development. Now, what they do is they just use any move they have and they go and they provision the cloud like that. Then, they can also not just do that, they can create armies of cores or Amazon machine images so they have super-repeatable tools. Think about that. When you have these super-repeatable tools sitting in the cloud, that you can just pull down these machine images and begin to create both code and development and build off those building blocks, you move so much faster than you did in the past. So that's sort of a big trend, I would say they're definitely leading it. But other key groups are NASA, HHS, Department of Justice. Those are some of the key, big groups that we're seeing really do a lot changes in their dev. >> I got to ask you about the-- >> Oh, I have to say DHS, also DHS on customs and border patrols, they're doing the same, really innovators. >> One of the things that's happening which I'm intrigued by is the whole digital transformation in our culture, right, society. Certainly, the Federal Government wants to take care of the civil liberties of the citizens. So it's not a privacy question, it's more about where smart cities is going. We're starting to see, I call, the digital parks, if you will, where you're starting to see a digital park go into Yosemite and camping out and using pristine resources and enjoying them. There's a demand for citizens to democratize resources available to them, supercomputing or datasets, what's your philosophy on that? What is Amazon doing to facilitate and accelerate the citizen's value of technology so it can be in the hands of anyone? >> I love that question because I'll tell you, at the heart of our business is what we call citizen service, paving the way for disruptive innovation, making the world a better place. That's through citizen's services and they're access. For us, we have multiple things. Everything from our dataset program, where we fund multiple datasets that we put up on the cloud and let everybody take advantage of them, from the individual student to the researcher, for no fee. >> John F.: You pick up the cost on that? >> We do, we fund, we put those datasets in completely, we allow them to go and explore and use. The only time they would ever pay is if they go off and start creating their own systems. The most highly curated datasets up there right now are pretty much on AWS. You heard me talk about the earth, through AWS Earth that we have that shows the earth. We have weather datasets, cancer datasets, we're working with so many groups, genomic, phenotypes, genomes of rice, the rice genome that we've done. >> So this is something that you see that you're behind, >> Oh, completely. >> you're passionate about and will continue to do? >> Because you never know when that individual student or small community school is out there and they can access tools that they never could've accessed before. The training and education, that creativity of the mind, we need to open that up to everybody and we fundamentally believe that cloud is a huge opportunity for that. You heard me tell the 1000 genomes story in the past of where took that cancer dataset or that genome dataset from NIH, put it into AWS for the first time, the first week we put it up we had 3200 new researchers crowdsource on that dataset. That was the first time, that I know of, that anyone had put up a major dataset for researchers. >> And the scale, certainly, is a great resource. And smart cities is an interesting area. I want to get your thoughts on your relationship with Intel. They have 5G coming out, they have a full network transformation, you're going to have autonomous vehicles out there, you're going to have all kinds of digital. How are you guys planning on powering the cloud and what's the role that Intel will play with you guys in the relationship? >> Of course, serverless computing comes into play significantly in areas like that because you want to create efficiencies, even in the cloud, we're all about that. People have always said, oh, AWS won't do that 'cause that's disrupting themselves. We're okay with disrupting ourselves if it's the right thing. We also don't want to hog resourcing of these tools that aren't necessary. So when it comes to devices like that and IOT, you need very efficient computing and you need tools that allow that efficient computing to both scale but not over-resource things. You'll see us continue to have models like that around IOT, or lambda, or serverless computing and how we access and make sure that those resources are used appropriately. >> We're almost out of time so I'd like to shift over if we can. Really impressed with the NGO work, the non-profit work as well and your work in the education space. Just talk about the nuance, differences between working with those particular constituents in the customer base, what you've learned and the kind of work you're providing in those silos right now. >> They are amazing, they are so frugal with their resources and it makes you hungry to really want to go out and help their mission because what you will find when you go meet with a lot of these not-for-profits, they are doing some of the most amazing work that even many people have really not heard of and they're being so frugal with how they resource and drive IT. There's a program called Feed the World and I met the developer of this and it's like two people. They've fed millions of people around the world with like three developers and creating an app and doing great work. To everything from like the American Heart Association that has a mission, literally, of stopping heart disease which is our number one killer around the world. When you meet them and you see the things they're doing and how they are using cloud computing to change and forward their mission. You heard us talk about human trafficking, it's a horrible, misunderstood environment out there that more of us need to be informed on and help with but computing can be a complete differentiator for them, cloud computing. We give millions of dollars of grants away, not just give away, we help them. We help them with the technical resourcing, how they're efficient, and we work really hard to try to help forward their mission and get the word out. It's humbling and it's really nice to feel that you're not only doing things for big governments but you also can help that individual not-for-profit that has a mission that's really important to not only them but groups in the world. >> It's a different level of citizen service, right? I mean, ocean conservancy this morning, talking about that and tidal change. >> What's the biggest thing that, in your mind, personal question, obviously you've been through from the beginning to now, a lot more growth ahead of you. I'm speculating that AWS Public Sector, although you won't disclose the numbers, I'll find a number out there. It's big, you guys could run the table and take a big share, similar to what you've done with startup and now enterprise market. Do you have a pinch-me moment where you go, where are we? Where are you on that spectrum of self-awareness of what's actually happening to you and this world and your team? In Public Sector, we operate just like all of AWS and all of Amazon. We really have treated this business like a startup and I create new teams just like everybody else does. I make them frugal and small and I say go do this. I will tell you, I don't even think about it because we are just scratching the surface, we are just getting going, and today we have customers in 155 countries and I have employees in about 25 countries now. Seven years ago, that was not the case. When you're moving that fast, you know that you're just getting going and that you have so much more that you can do to help your customers and create a partner ecosystem. It's a mission for us, it really is a mission and my team and myself are really excited, out there every day working to support our customers, to really grow and get them moving faster. We sort of keep pushing them to go faster. We have a long way to go and maybe ask me five years from now, we'll see. >> How about next year? We'll come back, we'll ask you again next year. >> Yeah, maybe I'll know more next year. >> John W.: Theresa, thank you for the time, very generous with your time. I know you have a big schedule over the course of this week so thank you for being here with us once again on theCUBE. >> Thank you. >> Many time CUBE alum, Theresa Carlson from AWS. Back with more here from the AWS Public Sector Summit 2017, Washington, D.C. right after this. (electronic music)

Published Date : Jun 14 2017

SUMMARY :

Brought to you by Amazon Web Services Nice to have you here, Theresa. Let's just talk about the show first off. and the whole theme of this is superheroes One of the heroes you had on the stage today, and the scale and reach at which they're doing it I'd like to hear the story, share with Theresa. No, I'm setting you up. that owned the account. of the type of customers we have. the cloud is ever going to happen for the government. and how you use it. and the political climate. at the system admin to but somebody had to buy it. and cloud is the new normal. in the workload, is it multi-cloud? the cool thing that people really are talking about One of the things that you guys have done well, that are important for the GovCloud. Not building but the management aspect. So now it's going to be managed in the USA but secondly, we do not want to roll services are now doing cloud, and the question is, and you heard John talk about that. Oh, I have to say DHS, also DHS the digital parks, if you will, from the individual student to the researcher, for no fee. You heard me talk about the earth, that creativity of the mind, with you guys in the relationship? and you need tools that allow that efficient computing and the kind of work you're providing and I met the developer of this and it's like two people. It's a different level of citizen service, right? and that you have so much more that you can do We'll come back, we'll ask you again next year. I know you have a big schedule over the course of this week Back with more here from the AWS Public Sector Summit 2017,

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Jay Jamison, HPE - Red Hat Summit 2017


 

>> Narrator: Live from Boston, Massachusetts, it's theCUBE. Covering Red Hat Summit 2017. Brought to you by Red Hat. >> Welcome back to theCUBE's coverage of the Red Hat Summit here in Boston, Massachusetts. I'm your host, Rebecca Knight, along with my cohost, Stu Miniman. We are joined by Jay Jamison, he is the Vice President for Strategy Software Defined and Cloud Division at HPE. Thanks so much for joining us Jay. >> Oh thanks for having me. >> So I was just in your keynote session and you talked about making hybrid IT simpler. You talked about the imperative that you heard from customers to bring solutions not silos. Can you tell our viewers a little bit about the specific feedback you were hearing from customers that really made you want to tighten your focus? >> Yeah, I think that, so, first off thanks for having me, and I would say that, absolutely, customers have been very clear at the excitement and the opportunity that they see ahead of them in terms of digital transformation and moving to cloud and taking advantage of all these new capabilities and technologies that seem to be showing up all the time. Whether it's containers, whether it's Kubernetes, whether it's internet of things, all that stuff's super exciting, but at the same time customers are saying, "You know, look, I've got thousands of applications "in a traditional estate, or a virtualized estate "that aren't going to be moving to anything "like a cloud any time soon. "And what I need is a way to start thinking about "how do I manage that whole estate "so that I can get my existing footprint optimized, "I can keep that running smoothly, "make sure it's secure, make sure it's reliable, "make sure it's low cost? "While I want to continue to reduce budget "where possible there and I want to start spinning up "more of my new efforts and more of my new investment "onto these new things so that I can be more responsive "to the business that I'm trying to run. "I can get new products and services out to my customers, "I can engage partners and my existing customer base "in ways that they want to be engaged. "I can enter new businesses." And so that challenge of how do I manage that hybrid estate, whether it's a mix of on-prem or off-prem, whether it's a mix of traditional and virtualized applications and workloads with new cloud native or containerized, or even server-less now, those kinds of things, that is really what I see as the problem of hybrid IT today. And our customers tell us that, "Geez, it's complicated "and getting more so each and every day." And that presents a tremendous opportunity for HPE, and partners like Red Hat, to be able to come forward and say, "Look, we can start helping you with solutions "that start bringing together a comprehensive approach "to trying to solve for making that entire estate simpler, "make it more solution oriented, "and less a set of different silos and people "that are all kind of sort of stuck "in whatever technology stack they might be running with." >> Jay, very interesting point. One of the messages we heard from Red Hat is that application spectrum you talked about. I've got most enterprises hundreds if not thousands of applications. They have the new ones that they're modernizing and building but even the old ones we need to at least re-platform them. The term, we used to call it lift and shift, re-platform seems to be the cool new way to kind of talk about it. But, you know, really modernizing the platform that I'm on, being more software driven, being ready to take that, if I'm breaking it down, and componentizing, containerizing it, starting to build micro services, but how are you working with Red Hat? How does HPE cloud offerings and infrastructure pieces playing in that re-platforming and then moving up the spectrum? >> Yeah, so I think really across the board, I think there are a couple of pieces. I think first of all you're absolutely right that customers will say, "Look, I have "an existing estate of applications and workloads "that I absolutely have to use." So for example I often think about if you think about a mobile application that you might use a lot from a mainstream customer. Like think of your, like getting your flight reservation on your mobile phone. Of course there are parts of that mobile application that are going to be very modern. Like I can order an Uber from the mobile app that I use on my airline often, and that's, of course, very modern, I'm using APIs, I'm using all this nice stuff to plug in what Uber offers that airline vendor to be able to say, "You can have that transaction "flow through a partner flow." But things like what time's the flight taking off, whether it's delayed, those are existing systems that aren't newfangled if you will. And so what customers are telling me is, "Look, I've got a corpus of data, "a corpus of application logic "that I absolutely need to be able to access "and use and deliver in new ways." And so in many senses I think that resonates very strongly, this notion of, re-platforming it's going on and it's a reality of, again, how do I make this mix of data application tools that may exist, and the desires I have to do new stuff. How do I bring it together in a way that lands effectively for a customer so that they have a delightful experience? Now what we're doing with Red Hat I think is really exciting in terms of providing opportunities for, in manners, where together we're sort of taking the best of both worlds. So a great example that I talked about in my keynote is saying, "Look, we're trying to take", we're working very closely with Red Hat, and specifically their Ansible team, to say, "Look, what customers, what users of Ansible love "is building playbooks that enable them "to automate infrastructure using Ansible playbooks", that's what's it all about. And what Ansible has been great with those playbooks is setting up and running and automating virtual machines, well what we're doing, because HPE tends to have infrastructure and great infrastructure management tools that say, "Look, down at the hardware level "we want to make it easier and more fungible "for IT shops to be able "to manage that physical infrastructure." And so what we've done is we've partnered up with Ansible to say, "Look, we want your users of Ansible "to just have their playbooks and we will "connect our OneView APIs", which is our infrastructure management software that sits on top of hardware. Say, "It connects, and so when your users "build an Ansible playbook that wants "to change how the infrastructure works "we'll take care of it all in OneView. "It's not something that your users have "to change or learn anything new "it's just all of a sudden Ansible gets more powerful "because it's connecting to HPE hardware and providing "a richer more flexible infrastructure experience." And so that's some of the stuff that we're doing now to make our hardware more flexible and more modern in the context of an Ansible developer, or Ansible user, but over time that's going to get even better. So the stuff, the things that we're doing with Synergy, which is our new brand that is focused on building hardware infrastructure that has composability, which basically says, "Look". It looks like hardware device but from an operators point of view it's very fungible, you can refactor and make your blend of compute, or storage, or networking, kind of shift on the fly. So a very cloud-like experience with on-premises infrastructure. And what we're doing is we want to work with great technologies that are very cloud-centric such as OpenShift from Red Hat to say, "Look, we want to be able to enable customers "to using APIs spin up bear metal instances of OpenShift." Very powerful in terms of time to value message for a cloud native customer that says, "Look, I need to run cloud native applications, "I want to have containers but I want to do it on-premise" This solution will be something that we think is a really powerful message for, particularly our Red Hat OpenShift style customer looking to build applications. >> Jay, and I'm familiar with the Synergy platform and composable infrastructure, like the ideas, you can break that down into smaller components. What we hear all the time is, "I need to build distributed architectures", and, as they talked in the keynote, predicting and forecasting where that's going to go is tough. So big challenge customers always have is like, "I buy these boxes and three years "into it I'm only using 40% of it." The utilization inside of data centers is horrible. Even with server virtualization it helped a little bit but not as much as what you see server founders in clouds and the like. So where are we with the rollout with Synergy? Do you have any proof points of customers that are saying, "Oh, I'm getting better utilization, "my OP-X is much better"? >> Yeah, what I would say is, so first of all I would strongly agree with you in the sense that if you talk to most mainstream enterprise customers today about their data center utilization rates it's often very poor. And I think one of the big draws that customers have when they look at public cloud opportunities is they'll say, "Well a nice thing about a public cloud is "I feel as though I'm getting much higher utilization rates "because of the way the payment structures work and so on." Now that may not always be true, you'll have, at times people will say, "Well these things are sitting dormant." But that's the instinct, right? >> We had server sprawl, we have VM sprawl, and now we have cloud sprawl. >> Now you have cloud sprawl, exactly right. >> And server less will fix it all too right? >> Exactly right, but you absolutely have the challenge of under-utilized data centers. And so it's imperative for HPE, and I think really the industry, to say, "Look, the solutions that we're putting forward, "whenever we talk about hybrid cloud solutions, "or hybrid IT solutions, or private cloud solutions or whatever to me it comes down to look, am I able to show you in concrete terms how am I increasing the utilization of your data center and how am I helping you lower your costs? And Synergy will, over time, become a great solution and platform in that manner because, for a couple of reasons. One, you've described, the fungibility and the composability of resources makes that something that is very much simpler from a technology standpoint. But then at the same time when you couple it with pay as you go style business models, that HPE makes available to its customers through our financial services, you start to then say, "Look, you're not "just sort of writing us a big check in CAPEX "and waiting three years and then being disappointed." "What you're doing is you're going to start getting the notion "that says, "Look this is going to show up, "you'll have a small amount of POD, "you're paying as you use." And we're able to then work together to forecast when will capacity requirements get to a place where you absolutely need to add more capability and refresh that hardware, or extend that hardware, excuse me. On the customer adoption, it's a new platform, and it's just coming out and we're getting great early adoption, and I think particularly from users that were in the beta. We had very satisfied beta users and we're starting to see, I think, really strong early adoption of the product. We actually had someone at our most recent Discover talk where I was talking with them later and they were, I think it was Hudson Alpha, which is a biotech researching style institute that often tries many of our things. And what they were saying that I thought was really interesting point which I'd not heard of in the context of, "Hey, what does composability do and how does "this drive up utilization rates and many of these things?" One of the things that he was saying that I thought was really interesting is he was starting to use Synergy to deliver what he called spot instant style on-premise infrastructure where someone could run a workload for a period of time and then if someone else needed the infrastructure more badly and he had a way to sort of basically just blast away the old thing and put in the new thing there. And he said, "This is great because during the day "there's a certain set of workloads we have to do. "At night there are a different set of workloads "I want to do and Synergy gives me the capability "to do all of that very simply." And so I think that those kinds of capabilities, while still early, will be very powerful value propositions for customers that are looking to solve the problem you're describing of, "How do I get out of a data center "that's under 20% utilized? "I need to get more efficiency here in order "to lower the cost and be responsive "to what my customers need." >> Jay before you were at HPE you worked as a venture capitalist at Blue Run Ventures, in particular looking at opportunities in mobile and consumer internet enterprise software. If you could put on your investor hat here and talk a little bit about the cloud market and the cloud industry, what excites you and what gives you pause in terms of where you see the market heading and where companies are putting their money? >> Oh that's a really good question. I think that, well I would say that putting an investor hat on, I think that particularly in the enterprise space, I think it's a really exciting time, particularly for, and not to be super self serving for what HPE is doing, but I think there is a set of problems that are out there that are big and broad where there will be large companies that get created. One area that we're very interested in at HPE that I think is an area of investor interest, whether it's HPE making the investment or whether it's venture capitalists or what have you. It's really in the notion of what I describe as hybrid management. And what that basically means is, "Look, I'm a user that's going to have some VMware. "I'm going to have some cloud stuff running on AWS, "I'm going to have a desire to use Kubernetes, "and containers and so on." "Help me get one pane of glass that gives "me a way to think about seeing "those different applications, understanding how they're running. "I want one way to do things like firmware updates "for the stuff that needs firmware updates. "I want one way to do application firewalls, "I want one way to do this." And I think that's going to be a very interesting and sticky market to go off an win. So if I were in the investment space that would be an area that I would be looking at very deeply. Another area that I think is going to be really interesting and important, we talk a lot about AI and machine learning in the context of everything in the world of enterprise, seems to have this label of, "Hey, we're using AI and machine learning." But I think what you really have to get back to is what about artificial intelligence and machine learning is actually going to help you solve a problem? How is it going to make your business actually better? And I think that often we're, I think right now at a place where we're a little bit too over our skis in terms of saying, "Look, these are really interesting technologies, "AI's going to do everything and drive out cars "and basically make us little house pets in the corners "'cause they're doing so much in our lives." But I think that there tends to be, one customer was saying to me, "You know what's really interesting is "dozens of startups will come and tell me "about how AI's going to solve "a hundred problems I didn't know I have. "What I'd really like someone to come "and talk to me about is about, "I want them to talk to me about "one of the problems I know I have. "'Cause I've got a hundred problems "I know I've got that I want solutions to." And so I think a big opportunity is really to try and figure out how do these new technologies particularly in that space and around big data and so on, how do those become things that are really truly impactful to making a mainstream business that may have a hybrid estate, how does it make them more effective? And that can have an impact in terms of how to make their IT ops more efficient, how to predict outages, how to be more secure, all that sort of stuff, all the way to "How do I do a better job delighting my customers "and predicting where the next new markets are going to be?" So those are some of the areas that I'd be most interested in as an investor and really as an operator and a strategist at HPE. >> And yet you remain a little skeptical of what you're hearing about the AI and machine learning in terms of where it really truly is at right now and the opportunities that? >> Yeah, what I would says is, I think it's if it's, the technology's really exciting and developing very very quickly. That I have no question about. What I often have questions about and I hear customers questioning is is this a technology in search of a solution or is it just kind of, we're saying, "Hey this is a really cool new thing "that it can go solve everything "but I haven't thought specifically about how "to actually solve this specific problem "that exists at hand." And that's the challenge. It's ultimately, I think of it, to dig in a little deeper, it's really a product management question or problem of "Hey, do I really understand what problem "my customer's trying to solve "and am I using this tactic to do a great job?" As a quick example machine learning, those kinds of things are great for what computers do well. One thing a computer does really well is the same repetitive task thousands and thousands of times. So things like email marketing automation, or thinking about how you use a business development manager to reach, do outbound selling. That you can have a computer do a lot that imitates a human being to say, "Hey I'm going to send you an email "and try and sell you something "and get you interested in a call." I don't need to have a human being do a lot of that stuff. That to me is really straight down the fairway, really clear business problem that AI and machine learning can do a great job, bots, all that sort of stuff, can do a great job starting to have an impact on. But to think it's just going to do everything out of the box is something that you have to think about. Okay where does this tool and technology really provide the value that customers are going to see. >> Jay. We've had HPE on theCUBE lots of time. You were at Discover in London, so I think we're pretty close to where you cloud strategy is but I look at next week's Open Stack Summit, some in the industry was like, "Oh, HPE pulled out completely of Open Stack." You've got HPE Discover coming up in Vegas, soon after that, and we'll have theCUBE there also. I know John and Dave are really looking forward to it. Give our audience a little bit of an update as to where HPE is and isn't when it comes to Open Stack, specifically and just kind of cloud positioning in general. >> Yeah, right, so what I would say is I think that it's a really good question because I think there's been a lot of transition and I think that customers are still, and the market, are still trying to figure out, "Okay, what and where does, is HPE playing?" And I think that what I was talking about today in the keynote and what I think represents where we're going and what we are doing is we're really focused in on this notion of saying, "Look we want to build a set of solutions that make a customer's hybrid estate simpler" and that hybrid estate, as I describe it, cuts across proprietary virtualization technologies like VMware of like Hyper view with Microsoft, it's going to cut across openstack, it's going to cut across doctor, it's going to cut across public clouds, et cetera. And I would say that where HPE is most focused, short of, when we look at how do we help customers get better leverage and value across that whole mix of estate, what we would talk about is, we think we're moving a little bit more up stack into this sort of notion of saying we want to invest and be really great at managing across that estate, so when I was talking about areas that I'd be interested in as an adventure investor, you know, it wouldn't surprise you that HPE were really, we talked a little bit about this concept of new stack and it really is this notion of saying, we want to be great at managing a hybrid estate across public and private, across proprietary and open source. So what that generally means, what that means then, as it pertains to, okay, what are we doing with openstack what are we doing with respect to cloud founder in this case or redhat, open ship, it means we're a lot more partner centric, because our assertion is that we believe the customers love a mix of, it's not going to be an all openstack world within a data center, we think it's going to be a mix openstack's going to be part of the estate, we also think doctor is going to be part of the estate, we think VMware is going to be part of the estate, we think that's where things are going, and so if you've seen us do in terms of the work we're doing with, whether it's red hat, at some levels, whether it's SUSA, whether it's even VMware, whether it's Microsoft, whether it's doctor, we've done, worked in partnership with all of them, and I think you'll see that partner centric approach continue. We certainly are interested in helping support customers that are existing and we'll move forward with respect to openstack with cloud founder in terms of what we're doing there, but I think that, increasingly over time, there's going to be a deep alliance on partners as we look at those infrastructures, service paz layers, because we look at that and say, there's a tremendous amount of world class talent, that's off building off those distributions in the openstack communities and other big opensource communities and those areas where we can most likely partner and have those take advantage of things like our infrastructure management layer of one view, can be very well leveraged within our new stack product and project that we're working on and so on, so that's really where we're heading and how we're approaching it. >> Jay Jameson, thank you so much for joining us, it's been great. >> It's been a pleasure thank you so much. >> I'm Rebecca Knight for Stu Miniman and we will return with more of theCUBE after this.

Published Date : May 3 2017

SUMMARY :

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Next-Generation Analytics Social Influencer Roundtable - #BigDataNYC 2016 #theCUBE


 

>> Narrator: Live from New York, it's the Cube, covering big data New York City 2016. Brought to you by headline sponsors, CISCO, IBM, NVIDIA, and our ecosystem sponsors, now here's your host, Dave Valante. >> Welcome back to New York City, everybody, this is the Cube, the worldwide leader in live tech coverage, and this is a cube first, we've got a nine person, actually eight person panel of experts, data scientists, all alike. I'm here with my co-host, James Cubelis, who has helped organize this panel of experts. James, welcome. >> Thank you very much, Dave, it's great to be here, and we have some really excellent brain power up there, so I'm going to let them talk. >> Okay, well thank you again-- >> And I'll interject my thoughts now and then, but I want to hear them. >> Okay, great, we know you well, Jim, we know you'll do that, so thank you for that, and appreciate you organizing this. Okay, so what I'm going to do to our panelists is ask you to introduce yourself. I'll introduce you, but tell us a little bit about yourself, and talk a little bit about what data science means to you. A number of you started in the field a long time ago, perhaps data warehouse experts before the term data science was coined. Some of you started probably after Hal Varian said it was the sexiest job in the world. (laughs) So think about how data science has changed and or what it means to you. We're going to start with Greg Piateski, who's from Boston. A Ph.D., KDnuggets, Greg, tell us about yourself and what data science means to you. >> Okay, well thank you Dave and thank you Jim for the invitation. Data science in a sense is the second oldest profession. I think people have this built-in need to find patterns and whatever we find we want to organize the data, but we do it well on a small scale, but we don't do it well on a large scale, so really, data science takes our need and helps us organize what we find, the patterns that we find that are really valid and useful and not just random, I think this is a big challenge of data science. I've actually started in this field before the term Data Science existed. I started as a researcher and organized the first few workshops on data mining and knowledge discovery, and the term data mining became less fashionable, became predictive analytics, now it's data science and it will be something else in a few years. >> Okay, thank you, Eves Mulkearns, Eves, I of course know you from Twitter. A lot of people know you as well. Tell us about your experiences and what data scientist means to you. >> Well, data science to me is if you take the two words, the data and the science, the science it holds a lot of expertise and skills there, it's statistics, it's mathematics, it's understanding the business and putting that together with the digitization of what we have. It's not only the structured data or the unstructured data what you store in the database try to get out and try to understand what is in there, but even video what is coming on and then trying to find, like George already said, the patterns in there and bringing value to the business but looking from a technical perspective, but still linking that to the business insights and you can do that on a technical level, but then you don't know yet what you need to find, or what you're looking for. >> Okay great, thank you. Craig Brown, Cube alum. How many people have been on the Cube actually before? >> I have. >> Okay, good. I always like to ask that question. So Craig, tell us a little bit about your background and, you know, data science, how has it changed, what's it all mean to you? >> Sure, so I'm Craig Brown, I've been in IT for almost 28 years, and that was obviously before the term data science, but I've evolved from, I started out as a developer. And evolved through the data ranks, as I called it, working with data structures, working with data systems, data technologies, and now we're working with data pure and simple. Data science to me is an individual or team of individuals that dissect the data, understand the data, help folks look at the data differently than just the information that, you know, we usually use in reports, and get more insights on, how to utilize it and better leverage it as an asset within an organization. >> Great, thank you Craig, okay, Jennifer Shin? Math is obviously part of being a data scientist. You're good at math I understand. Tell us about yourself. >> Yeah, so I'm a senior principle data scientist at the Nielsen Company. I'm also the founder of 8 Path Solutions, which is a data science, analytics, and technology company, and I'm also on the faculty in the Master of Information and Data Science program at UC Berkeley. So math is part of the IT statistics for data science actually this semester, and I think for me, I consider myself a scientist primarily, and data science is a nice day job to have, right? Something where there's industry need for people with my skill set in the sciences, and data gives us a great way of being able to communicate sort of what we know in science in a way that can be used out there in the real world. I think the best benefit for me is that now that I'm a data scientist, people know what my job is, whereas before, maybe five ten years ago, no one understood what I did. Now, people don't necessarily understand what I do now, but at least they understand kind of what I do, so it's still an improvement. >> Excellent. Thank you Jennifer. Joe Caserta, you're somebody who started in the data warehouse business, and saw that snake swallow a basketball and grow into what we now know as big data, so tell us about yourself. >> So I've been doing data for 30 years now, and I wrote the Data Warehouse ETL Toolkit with Ralph Timbal, which is the best selling book in the industry on preparing data for analytics, and with the big paradigm shift that's happened, you know for me the past seven years has been, instead of preparing data for people to analyze data to make decisions, now we're preparing data for machines to make the decisions, and I think that's the big shift from data analysis to data analytics and data science. >> Great, thank you. Miriam, Miriam Fridell, welcome. >> Thank you. I'm Miriam Fridell, I work for Elder Research, we are a data science consultancy, and I came to data science, sort of through a very circuitous route. I started off as a physicist, went to work as a consultant and software engineer, then became a research analyst, and finally came to data science. And I think one of the most interesting things to me about data science is that it's not simply about building an interesting model and doing some interesting mathematics, or maybe wrangling the data, all of which I love to do, but it's really the entire analytics lifecycle, and a value that you can actually extract from data at the end, and that's one of the things that I enjoy most is seeing a client's eyes light up or a wow, I didn't really know we could look at data that way, that's really interesting. I can actually do something with that, so I think that, to me, is one of the most interesting things about it. >> Great, thank you. Justin Sadeen, welcome. >> Absolutely, than you, thank you. So my name is Justin Sadeen, I work for Morph EDU, an artificial intelligence company in Atlanta, Georgia, and we develop learning platforms for non-profit and private educational institutions. So I'm a Marine Corp veteran turned data enthusiast, and so what I think about data science is the intersection of information, intelligence, and analysis, and I'm really excited about the transition from big data into smart data, and that's what I see data science as. >> Great, and last but not least, Dez Blanchfield, welcome mate. >> Good day. Yeah, I'm the one with the funny accent. So data science for me is probably the funniest job I've ever to describe to my mom. I've had quite a few different jobs, and she's never understood any of them, and this one she understands the least. I think a fun way to describe what we're trying to do in the world of data science and analytics now is it's the equivalent of high altitude mountain climbing. It's like the extreme sport version of the computer science world, because we have to be this magical unicorn of a human that can understand plain english problems from C-suite down and then translate it into code, either as soles or as teams of developers. And so there's this black art that we're expected to be able to transmogrify from something that we just in plain english say I would like to know X, and we have to go and figure it out, so there's this neat extreme sport view I have of rushing down the side of a mountain on a mountain bike and just dodging rocks and trees and things occasionally, because invariably, we do have things that go wrong, and they don't quite give us the answers we want. But I think we're at an interesting point in time now with the explosion in the types of technology that are at our fingertips, and the scale at which we can do things now, once upon a time we would sit at a terminal and write code and just look at data and watch it in columns, and then we ended up with spreadsheet technologies at our fingertips. Nowadays it's quite normal to instantiate a small high performance distributed cluster of computers, effectively a super computer in a public cloud, and throw some data at it and see what comes back. And we can do that on a credit card. So I think we're at a really interesting tipping point now where this coinage of data science needs to be slightly better defined, so that we can help organizations who have weird and strange questions that they want to ask, tell them solutions to those questions, and deliver on them in, I guess, a commodity deliverable. I want to know xyz and I want to know it in this time frame and I want to spend this much amount of money to do it, and I don't really care how you're going to do it. And there's so many tools we can choose from and there's so many platforms we can choose from, it's this little black art of computing, if you'd like, we're effectively making it up as we go in many ways, so I think it's one of the most exciting challenges that I've had, and I think I'm pretty sure I speak for most of us in that we're lucky that we get paid to do this amazing job. That we get make up on a daily basis in some cases. >> Excellent, well okay. So we'll just get right into it. I'm going to go off script-- >> Do they have unicorns down under? I think they have some strange species right? >> Well we put the pointy bit on the back. You guys have in on the front. >> So I was at an IBM event on Friday. It was a chief data officer summit, and I attended what was called the Data Divas' breakfast. It was a women in tech thing, and one of the CDOs, she said that 25% of chief data officers are women, which is much higher than you would normally see in the profile of IT. We happen to have 25% of our panelists are women. Is that common? Miriam and Jennifer, is that common for the data science field? Or is this a higher percentage than you would normally see-- >> James: Or a lower percentage? >> I think certainly for us, we have hired a number of additional women in the last year, and they are phenomenal data scientists. I don't know that I would say, I mean I think it's certainly typical that this is still a male-dominated field, but I think like many male-dominated fields, physics, mathematics, computer science, I think that that is slowly changing and evolving, and I think certainly, that's something that we've noticed in our firm over the years at our consultancy, as we're hiring new people. So I don't know if I would say 25% is the right number, but hopefully we can get it closer to 50. Jennifer, I don't know if you have... >> Yeah, so I know at Nielsen we have actually more than 25% of our team is women, at least the team I work with, so there seems to be a lot of women who are going into the field. Which isn't too surprising, because with a lot of the issues that come up in STEM, one of the reasons why a lot of women drop out is because they want real world jobs and they feel like they want to be in the workforce, and so I think this is a great opportunity with data science being so popular for these women to actually have a job where they can still maintain that engineering and science view background that they learned in school. >> Great, well Hillary Mason, I think, was the first data scientist that I ever interviewed, and I asked her what are the sort of skills required and the first question that we wanted to ask, I just threw other women in tech in there, 'cause we love women in tech, is about this notion of the unicorn data scientist, right? It's been put forth that there's the skill sets required to be a date scientist are so numerous that it's virtually impossible to have a data scientist with all those skills. >> And I love Dez's extreme sports analogy, because that plays into the whole notion of data science, we like to talk about the theme now of data science as a team sport. Must it be an extreme sport is what I'm wondering, you know. The unicorns of the world seem to be... Is that realistic now in this new era? >> I mean when automobiles first came out, they were concerned that there wouldn't be enough chauffeurs to drive all the people around. Is there an analogy with data, to be a data-driven company. Do I need a data scientist, and does that data scientist, you know, need to have these unbelievable mixture of skills? Or are we doomed to always have a skill shortage? Open it up. >> I'd like to have a crack at that, so it's interesting, when automobiles were a thing, when they first bought cars out, and before they, sort of, were modernized by the likes of Ford's Model T, when we got away from the horse and carriage, they actually had human beings walking down the street with a flag warning the public that the horseless carriage was coming, and I think data scientists are very much like that. That we're kind of expected to go ahead of the organization and try and take the challenges we're faced with today and see what's going to come around the corner. And so we're like the little flag-bearers, if you'd like, in many ways of this is where we're at today, tell me where I'm going to be tomorrow, and try and predict the day after as well. It is very much becoming a team sport though. But I think the concept of data science being a unicorn has come about because the coinage hasn't been very well defined, you know, if you were to ask 10 people what a data scientist were, you'd get 11 answers, and I think this is a really challenging issue for hiring managers and C-suites when the generants say I was data science, I want big data, I want an analyst. They don't actually really know what they're asking for. Generally, if you ask for a database administrator, it's a well-described job spec, and you can just advertise it and some 20 people will turn up and you interview to decide whether you like the look and feel and smell of 'em. When you ask for a data scientist, there's 20 different definitions of what that one data science role could be. So we don't initially know what the job is, we don't know what the deliverable is, and we're still trying to figure that out, so yeah. >> Craig what about you? >> So from my experience, when we talk about data science, we're really talking about a collection of experiences with multiple people I've yet to find, at least from my experience, a data science effort with a lone wolf. So you're talking about a combination of skills, and so you don't have, no one individual needs to have all that makes a data scientist a data scientist, but you definitely have to have the right combination of skills amongst a team in order to accomplish the goals of data science team. So from my experiences and from the clients that I've worked with, we refer to the data science effort as a data science team. And I believe that's very appropriate to the team sport analogy. >> For us, we look at a data scientist as a full stack web developer, a jack of all trades, I mean they need to have a multitude of background coming from a programmer from an analyst. You can't find one subject matter expert, it's very difficult. And if you're able to find a subject matter expert, you know, through the lifecycle of product development, you're going to require that individual to interact with a number of other members from your team who are analysts and then you just end up well training this person to be, again, a jack of all trades, so it comes full circle. >> I own a business that does nothing but data solutions, and we've been in business 15 years, and it's been, the transition over time has been going from being a conventional wisdom run company with a bunch of experts at the top to becoming more of a data-driven company using data warehousing and BI, but now the trend is absolutely analytics driven. So if you're not becoming an analytics-driven company, you are going to be behind the curve very very soon, and it's interesting that IBM is now coining the phrase of a cognitive business. I think that is absolutely the future. If you're not a cognitive business from a technology perspective, and an analytics-driven perspective, you're going to be left behind, that's for sure. So in order to stay competitive, you know, you need to really think about data science think about how you're using your data, and I also see that what's considered the data expert has evolved over time too where it used to be just someone really good at writing SQL, or someone really good at writing queries in any language, but now it's becoming more of a interdisciplinary action where you need soft skills and you also need the hard skills, and that's why I think there's more females in the industry now than ever. Because you really need to have a really broad width of experiences that really wasn't required in the past. >> Greg Piateski, you have a comment? >> So there are not too many unicorns in nature or as data scientists, so I think organizations that want to hire data scientists have to look for teams, and there are a few unicorns like Hillary Mason or maybe Osama Faiat, but they generally tend to start companies and very hard to retain them as data scientists. What I see is in other evolution, automation, and you know, steps like IBM, Watson, the first platform is eventually a great advance for data scientists in the short term, but probably what's likely to happen in the longer term kind of more and more of those skills becoming subsumed by machine unique layer within the software. How long will it take, I don't know, but I have a feeling that the paradise for data scientists may not be very long lived. >> Greg, I have a follow up question to what I just heard you say. When a data scientist, let's say a unicorn data scientist starts a company, as you've phrased it, and the company's product is built on data science, do they give up becoming a data scientist in the process? It would seem that they become a data scientist of a higher order if they've built a product based on that knowledge. What is your thoughts on that? >> Well, I know a few people like that, so I think maybe they remain data scientists at heart, but they don't really have the time to do the analysis and they really have to focus more on strategic things. For example, today actually is the birthday of Google, 18 years ago, so Larry Page and Sergey Brin wrote a very influential paper back in the '90s About page rank. Have they remained data scientist, perhaps a very very small part, but that's not really what they do, so I think those unicorn data scientists could quickly evolve to have to look for really teams to capture those skills. >> Clearly they come to a point in their career where they build a company based on teams of data scientists and data engineers and so forth, which relates to the topic of team data science. What is the right division of roles and responsibilities for team data science? >> Before we go, Jennifer, did you have a comment on that? >> Yeah, so I guess I would say for me, when data science came out and there was, you know, the Venn Diagram that came out about all the skills you were supposed to have? I took a very different approach than all of the people who I knew who were going into data science. Most people started interviewing immediately, they were like this is great, I'm going to get a job. I went and learned how to develop applications, and learned computer science, 'cause I had never taken a computer science course in college, and made sure I trued up that one part where I didn't know these things or had the skills from school, so I went headfirst and just learned it, and then now I have actually a lot of technology patents as a result of that. So to answer Jim's question, actually. I started my company about five years ago. And originally started out as a consulting firm slash data science company, then it evolved, and one of the reasons I went back in the industry and now I'm at Nielsen is because you really can't do the same sort of data science work when you're actually doing product development. It's a very very different sort of world. You know, when you're developing a product you're developing a core feature or functionality that you're going to offer clients and customers, so I think definitely you really don't get to have that wide range of sort of looking at 8 million models and testing things out. That flexibility really isn't there as your product starts getting developed. >> Before we go into the team sport, the hard skills that you have, are you all good at math? Are you all computer science types? How about math? Are you all math? >> What were your GPAs? (laughs) >> David: Anybody not math oriented? Anybody not love math? You don't love math? >> I love math, I think it's required. >> David: So math yes, check. >> You dream in equations, right? You dream. >> Computer science? Do I have to have computer science skills? At least the basic knowledge? >> I don't know that you need to have formal classes in any of these things, but I think certainly as Jennifer was saying, if you have no skills in programming whatsoever and you have no interest in learning how to write SQL queries or RR Python, you're probably going to struggle a little bit. >> James: It would be a challenge. >> So I think yes, I have a Ph.D. in physics, I did a lot of math, it's my love language, but I think you don't necessarily need to have formal training in all of these things, but I think you need to have a curiosity and a love of learning, and so if you don't have that, you still want to learn and however you gain that knowledge I think, but yeah, if you have no technical interests whatsoever, and don't want to write a line of code, maybe data science is not the field for you. Even if you don't do it everyday. >> And statistics as well? You would put that in that same general category? How about data hacking? You got to love data hacking, is that fair? Eaves, you have a comment? >> Yeah, I think so, while we've been discussing that for me, the most important part is that you have a logical mind and you have the capability to absorb new things and the curiosity you need to dive into that. While I don't have an education in IT or whatever, I have a background in chemistry and those things that I learned there, I apply to information technology as well, and from a part that you say, okay, I'm a tech-savvy guy, I'm interested in the tech part of it, you need to speak that business language and if you can do that crossover and understand what other skill sets or parts of the roles are telling you I think the communication in that aspect is very important. >> I'd like throw just something really quickly, and I think there's an interesting thing that happens in IT, particularly around technology. We tend to forget that we've actually solved a lot of these problems in the past. If we look in history, if we look around the second World War, and Bletchley Park in the UK, where you had a very similar experience as humans that we're having currently around the whole issue of data science, so there was an interesting challenge with the enigma in the shark code, right? And there was a bunch of men put in a room and told, you're mathematicians and you come from universities, and you can crack codes, but they couldn't. And so what they ended up doing was running these ads, and putting challenges, they actually put, I think it was crossword puzzles in the newspaper, and this deluge of women came out of all kinds of different roles without math degrees, without science degrees, but could solve problems, and they were thrown at the challenge of cracking codes, and invariably, they did the heavy lifting. On a daily basis for converting messages from one format to another, so that this very small team at the end could actually get in play with the sexy piece of it. And I think we're going through a similar shift now with what we're refer to as data science in the technology and business world. Where the people who are doing the heavy lifting aren't necessarily what we'd think of as the traditional data scientists, and so, there have been some unicorns and we've championed them, and they're great. But I think the shift's going to be to accountants, actuaries, and statisticians who understand the business, and come from an MBA star background that can learn the relevant pieces of math and models that we need to to apply to get the data science outcome. I think we've already been here, we've solved this problem, we've just got to learn not to try and reinvent the wheel, 'cause the media hypes this whole thing of data science is exciting and new, but we've been here a couple times before, and there's a lot to be learned from that, my view. >> I think we had Joe next. >> Yeah, so I was going to say that, data science is a funny thing. To use the word science is kind of a misnomer, because there is definitely a level of art to it, and I like to use the analogy, when Michelangelo would look at a block of marble, everyone else looked at the block of marble to see a block of marble. He looks at a block of marble and he sees a finished sculpture, and then he figures out what tools do I need to actually make my vision? And I think data science is a lot like that. We hear a problem, we see the solution, and then we just need the right tools to do it, and I think part of consulting and data science in particular. It's not so much what we know out of the gate, but it's how quickly we learn. And I think everyone here, what makes them brilliant, is how quickly they could learn any tool that they need to see their vision get accomplished. >> David: Justin? >> Yeah, I think you make a really great point, for me, I'm a Marine Corp veteran, and the reason I mentioned that is 'cause I work with two veterans who are problem solvers. And I think that's what data scientists really are, in the long run are problem solvers, and you mentioned a great point that, yeah, I think just problem solving is the key. You don't have to be a subject matter expert, just be able to take the tools and intelligently use them. >> Now when you look at the whole notion of team data science, what is the right mix of roles, like role definitions within a high-quality or a high-preforming data science teams now IBM, with, of course, our announcement of project, data works and so forth. We're splitting the role division, in terms of data scientist versus data engineers versus application developer versus business analyst, is that the right breakdown of roles? Or what would the panelists recommend in terms of understanding what kind of roles make sense within, like I said, a high performing team that's looking for trying to develop applications that depend on data, machine learning, and so forth? Anybody want to? >> I'll tackle that. So the teams that I have created over the years made up these data science teams that I brought into customer sites have a combination of developer capabilities and some of them are IT developers, but some of them were developers of things other than applications. They designed buildings, they did other things with their technical expertise besides building technology. The other piece besides the developer is the analytics, and analytics can be taught as long as they understand how algorithms work and the code behind the analytics, in other words, how are we analyzing things, and from a data science perspective, we are leveraging technology to do the analyzing through the tool sets, so ultimately as long as they understand how tool sets work, then we can train them on the tools. Having that analytic background is an important piece. >> Craig, is it easier to, I'll go to you in a moment Joe, is it easier to cross train a data scientist to be an app developer, than to cross train an app developer to be a data scientist or does it not matter? >> Yes. (laughs) And not the other way around. It depends on the-- >> It's easier to cross train a data scientist to be an app developer than-- >> Yes. >> The other way around. Why is that? >> Developing code can be as difficult as the tool set one uses to develop code. Today's tool sets are very user friendly. where developing code is very difficult to teach a person to think along the lines of developing code when they don't have any idea of the aspects of code, of building something. >> I think it was Joe, or you next, or Jennifer, who was it? >> I would say that one of the reasons for that is data scientists will probably know if the answer's right after you process data, whereas data engineer might be able to manipulate the data but may not know if the answer's correct. So I think that is one of the reasons why having a data scientist learn the application development skills might be a easier time than the other way around. >> I think Miriam, had a comment? Sorry. >> I think that what we're advising our clients to do is to not think, before data science and before analytics became so required by companies to stay competitive, it was more of a waterfall, you have a data engineer build a solution, you know, then you throw it over the fence and the business analyst would have at it, where now, it must be agile, and you must have a scrum team where you have the data scientist and the data engineer and the project manager and the product owner and someone from the chief data office all at the table at the same time and all accomplishing the same goal. Because all of these skills are required, collectively in order to solve this problem, and it can't be done daisy chained anymore it has to be a collaboration. And that's why I think spark is so awesome, because you know, spark is a single interface that a data engineer can use, a data analyst can use, and a data scientist can use. And now with what we've learned today, having a data catalog on top so that the chief data office can actually manage it, I think is really going to take spark to the next level. >> James: Miriam? >> I wanted to comment on your question to Craig about is it harder to teach a data scientist to build an application or vice versa, and one of the things that we have worked on a lot in our data science team is incorporating a lot of best practices from software development, agile, scrum, that sort of thing, and I think particularly with a focus on deploying models that we don't just want to build an interesting data science model, we want to deploy it, and get some value. You need to really incorporate these processes from someone who might know how to build applications and that, I think for some data scientists can be a challenge, because one of the fun things about data science is you get to get into the data, and you get your hands dirty, and you build a model, and you get to try all these cool things, but then when the time comes for you to actually deploy something, you need deployment-grade code in order to make sure it can go into production at your client side and be useful for instance, so I think that there's an interesting challenge on both ends, but one of the things I've definitely noticed with some of our data scientists is it's very hard to get them to think in that mindset, which is why you have a team of people, because everyone has different skills and you can mitigate that. >> Dev-ops for data science? >> Yeah, exactly. We call it insight ops, but yeah, I hear what you're saying. Data science is becoming increasingly an operational function as opposed to strictly exploratory or developmental. Did some one else have a, Dez? >> One of the things I was going to mention, one of the things I like to do when someone gives me a new problem is take all the laptops and phones away. And we just end up in a room with a whiteboard. And developers find that challenging sometimes, so I had this one line where I said to them don't write the first line of code until you actually understand the problem you're trying to solve right? And I think where the data science focus has changed the game for organizations who are trying to get some systematic repeatable process that they can throw data at and just keep getting answers and things, no matter what the industry might be is that developers will come with a particular mindset on how they're going to codify something without necessarily getting the full spectrum and understanding the problem first place. What I'm finding is the people that come at data science tend to have more of a hacker ethic. They want to hack the problem, they want to understand the challenge, and they want to be able to get it down to plain English simple phrases, and then apply some algorithms and then build models, and then codify it, and so most of the time we sit in a room with whiteboard markers just trying to build a model in a graphical sense and make sure it's going to work and that it's going to flow, and once we can do that, we can codify it. I think when you come at it from the other angle from the developer ethic, and you're like I'm just going to codify this from day one, I'm going to write code. I'm going to hack this thing out and it's just going to run and compile. Often, you don't truly understand what he's trying to get to at the end point, and you can just spend days writing code and I think someone made the comment that sometimes you don't actually know whether the output is actually accurate in the first place. So I think there's a lot of value being provided from the data science practice. Over understanding the problem in plain english at a team level, so what am I trying to do from the business consulting point of view? What are the requirements? How do I build this model? How do I test the model? How do I run a sample set through it? Train the thing and then make sure what I'm going to codify actually makes sense in the first place, because otherwise, what are you trying to solve in the first place? >> Wasn't that Einstein who said if I had an hour to solve a problem, I'd spend 55 minutes understanding the problem and five minutes on the solution, right? It's exactly what you're talking about. >> Well I think, I will say, getting back to the question, the thing with building these teams, I think a lot of times people don't talk about is that engineers are actually very very important for data science projects and data science problems. For instance, if you were just trying to prototype something or just come up with a model, then data science teams are great, however, if you need to actually put that into production, that code that the data scientist has written may not be optimal, so as we scale out, it may be actually very inefficient. At that point, you kind of want an engineer to step in and actually optimize that code, so I think it depends on what you're building and that kind of dictates what kind of division you want among your teammates, but I do think that a lot of times, the engineering component is really undervalued out there. >> Jennifer, it seems that the data engineering function, data discovery and preparation and so forth is becoming automated to a greater degree, but if I'm listening to you, I don't hear that data engineering as a discipline is becoming extinct in terms of a role that people can be hired into. You're saying that there's a strong ongoing need for data engineers to optimize the entire pipeline to deliver the fruits of data science in production applications, is that correct? So they play that very much operational role as the backbone for... >> So I think a lot of times businesses will go to data scientist to build a better model to build a predictive model, but that model may not be something that you really want to implement out there when there's like a million users coming to your website, 'cause it may not be efficient, it may take a very long time, so I think in that sense, it is important to have good engineers, and your whole product may fail, you may build the best model it may have the best output, but if you can't actually implement it, then really what good is it? >> What about calibrating these models? How do you go about doing that and sort of testing that in the real world? Has that changed overtime? Or is it... >> So one of the things that I think can happen, and we found with one of our clients is when you build a model, you do it with the data that you have, and you try to use a very robust cross-validation process to make sure that it's robust and it's sturdy, but one thing that can sometimes happen is after you put your model into production, there can be external factors that, societal or whatever, things that have nothing to do with the data that you have or the quality of the data or the quality of the model, which can actually erode the model's performance over time. So as an example, we think about cell phone contracts right? Those have changed a lot over the years, so maybe five years ago, the type of data plan you had might not be the same that it is today, because a totally different type of plan is offered, so if you're building a model on that to say predict who's going to leave and go to a different cell phone carrier, the validity of your model overtime is going to completely degrade based on nothing that you have, that you put into the model or the data that was available, so I think you need to have this sort of model management and monitoring process to take this factors into account and then know when it's time to do a refresh. >> Cross-validation, even at one point in time, for example, there was an article in the New York Times recently that they gave the same data set to five different data scientists, this is survey data for the presidential election that's upcoming, and five different data scientists came to five different predictions. They were all high quality data scientists, the cross-validation showed a wide variation about who was on top, whether it was Hillary or whether it was Trump so that shows you that even at any point in time, cross-validation is essential to understand how robust the predictions might be. Does somebody else have a comment? Joe? >> I just want to say that this even drives home the fact that having the scrum team for each project and having the engineer and the data scientist, data engineer and data scientist working side by side because it is important that whatever we're building we assume will eventually go into production, and we used to have in the data warehousing world, you'd get the data out of the systems, out of your applications, you do analysis on your data, and the nirvana was maybe that data would go back to the system, but typically it didn't. Nowadays, the applications are dependent on the insight coming from the data science team. With the behavior of the application and the personalization and individual experience for a customer is highly dependent, so it has to be, you said is data science part of the dev-ops team, absolutely now, it has to be. >> Whose job is it to figure out the way in which the data is presented to the business? Where's the sort of presentation, the visualization plan, is that the data scientist role? Does that depend on whether or not you have that gene? Do you need a UI person on your team? Where does that fit? >> Wow, good question. >> Well usually that's the output, I mean, once you get to the point where you're visualizing the data, you've created an algorithm or some sort of code that produces that to be visualized, so at the end of the day that the customers can see what all the fuss is about from a data science perspective. But it's usually post the data science component. >> So do you run into situations where you can see it and it's blatantly obvious, but it doesn't necessarily translate to the business? >> Well there's an interesting challenge with data, and we throw the word data around a lot, and I've got this fun line I like throwing out there. If you torture data long enough, it will talk. So the challenge then is to figure out when to stop torturing it, right? And it's the same with models, and so I think in many other parts of organizations, we'll take something, if someone's doing a financial report on performance of the organization and they're doing it in a spreadsheet, they'll get two or three peers to review it, and validate that they've come up with a working model and the answer actually makes sense. And I think we're rushing so quickly at doing analysis on data that comes to us in various formats and high velocity that I think it's very important for us to actually stop and do peer reviews, of the models and the data and the output as well, because otherwise we start making decisions very quickly about things that may or may not be true. It's very easy to get the data to paint any picture you want, and you gave the example of the five different attempts at that thing, and I had this shoot out thing as well where I'll take in a team, I'll get two different people to do exactly the same thing in completely different rooms, and come back and challenge each other, and it's quite amazing to see the looks on their faces when they're like, oh, I didn't see that, and then go back and do it again until, and then just keep iterating until we get to the point where they both get the same outcome, in fact there's a really interesting anecdote about when the UNIX operation system was being written, and a couple of the authors went away and wrote the same program without realizing that each other were doing it, and when they came back, they actually had line for line, the same piece of C code, 'cause they'd actually gotten to a truth. A perfect version of that program, and I think we need to often look at, when we're building models and playing with data, if we can't come at it from different angles, and get the same answer, then maybe the answer isn't quite true yet, so there's a lot of risk in that. And it's the same with presentation, you know, you can paint any picture you want with the dashboard, but who's actually validating when the dashboard's painting the correct picture? >> James: Go ahead, please. >> There is a science actually, behind data visualization, you know if you're doing trending, it's a line graph, if you're doing comparative analysis, it's bar graph, if you're doing percentages, it's a pie chart, like there is a certain science to it, it's not that much of a mystery as the novice thinks there is, but what makes it challenging is that you also, just like any presentation, you have to consider your audience. And your audience, whenever we're delivering a solution, either insight, or just data in a grid, we really have to consider who is the consumer of this data, and actually cater the visual to that person or to that particular audience. And that is part of the art, and that is what makes a great data scientist. >> The consumer may in fact be the source of the data itself, like in a mobile app, so you're tuning their visualization and then their behavior is changing as a result, and then the data on their changed behavior comes back, so it can be a circular process. >> So Jim, at a recent conference, you were tweeting about the citizen data scientist, and you got emasculated by-- >> I spoke there too. >> Okay. >> TWI on that same topic, I got-- >> Kirk Borne I hear came after you. >> Kirk meant-- >> Called foul, flag on the play. >> Kirk meant well. I love Claudia Emahoff too, but yeah, it's a controversial topic. >> So I wonder what our panel thinks of that notion, citizen data scientist. >> Can I respond about citizen data scientists? >> David: Yeah, please. >> I think this term was introduced by Gartner analyst in 2015, and I think it's a very dangerous and misleading term. I think definitely we want to democratize the data and have access to more people, not just data scientists, but managers, BI analysts, but when there is already a term for such people, we can call the business analysts, because it implies some training, some understanding of the data. If you use the term citizen data scientist, it implies that without any training you take some data and then you find something there, and they think as Dev's mentioned, we've seen many examples, very easy to find completely spurious random correlations in data. So we don't want citizen dentists to treat our teeth or citizen pilots to fly planes, and if data's important, having citizen data scientists is equally dangerous, so I'm hoping that, I think actually Gartner did not use the term citizen data scientist in their 2016 hype course, so hopefully they will put this term to rest. >> So Gregory, you apparently are defining citizen to mean incompetent as opposed to simply self-starting. >> Well self-starting is very different, but that's not what I think what was the intention. I think what we see in terms of data democratization, there is a big trend over automation. There are many tools, for example there are many companies like Data Robot, probably IBM, has interesting machine learning capability towards automation, so I think I recently started a page on KDnuggets for automated data science solutions, and there are already 20 different forums that provide different levels of automation. So one can deliver in full automation maybe some expertise, but it's very dangerous to have part of an automated tool and at some point then ask citizen data scientists to try to take the wheels. >> I want to chime in on that. >> David: Yeah, pile on. >> I totally agree with all of that. I think the comment I just want to quickly put out there is that the space we're in is a very young, and rapidly changing world, and so what we haven't had yet is this time to stop and take a deep breath and actually define ourselves, so if you look at computer science in general, a lot of the traditional roles have sort of had 10 or 20 years of history, and so thorough the hiring process, and the development of those spaces, we've actually had time to breath and define what those jobs are, so we know what a systems programmer is, and we know what a database administrator is, but we haven't yet had a chance as a community to stop and breath and say, well what do we think these roles are, and so to fill that void, the media creates coinages, and I think this is the risk we've got now that the concept of a data scientist was just a term that was coined to fill a void, because no one quite knew what to call somebody who didn't come from a data science background if they were tinkering around data science, and I think that's something that we need to sort of sit up and pay attention to, because if we don't own that and drive it ourselves, then somebody else is going to fill the void and they'll create these very frustrating concepts like data scientist, which drives us all crazy. >> James: Miriam's next. >> So I wanted to comment, I agree with both of the previous comments, but in terms of a citizen data scientist, and I think whether or not you're citizen data scientist or an actual data scientist whatever that means, I think one of the most important things you can have is a sense of skepticism, right? Because you can get spurious correlations and it's like wow, my predictive model is so excellent, you know? And being aware of things like leaks from the future, right? This actually isn't predictive at all, it's a result of the thing I'm trying to predict, and so I think one thing I know that we try and do is if something really looks too good, we need to go back in and make sure, did we not look at the data correctly? Is something missing? Did we have a problem with the ETL? And so I think that a healthy sense of skepticism is important to make sure that you're not taking a spurious correlation and trying to derive some significant meaning from it. >> I think there's a Dilbert cartoon that I saw that described that very well. Joe, did you have a comment? >> I think that in order for citizen data scientists to really exist, I think we do need to have more maturity in the tools that they would use. My vision is that the BI tools of today are all going to be replaced with natural language processing and searching, you know, just be able to open up a search bar and say give me sales by region, and to take that one step into the future even further, it should actually say what are my sales going to be next year? And it should trigger a simple linear regression or be able to say which features of the televisions are actually affecting sales and do a clustering algorithm, you know I think hopefully that will be the future, but I don't see anything of that today, and I think in order to have a true citizen data scientist, you would need to have that, and that is pretty sophisticated stuff. >> I think for me, the idea of citizen data scientist I can relate to that, for instance, when I was in graduate school, I started doing some research on FDA data. It was an open source data set about 4.2 million data points. Technically when I graduated, the paper was still not published, and so in some sense, you could think of me as a citizen data scientist, right? I wasn't getting funding, I wasn't doing it for school, but I was still continuing my research, so I'd like to hope that with all the new data sources out there that there might be scientists or people who are maybe kept out of a field people who wanted to be in STEM and for whatever life circumstance couldn't be in it. That they might be encouraged to actually go and look into the data and maybe build better models or validate information that's out there. >> So Justin, I'm sorry you had one comment? >> It seems data science was termed before academia adopted formalized training for data science. But yeah, you can make, like Dez said, you can make data work for whatever problem you're trying to solve, whatever answer you see, you want data to work around it, you can make it happen. And I kind of consider that like in project management, like data creep, so you're so hyper focused on a solution you're trying to find the answer that you create an answer that works for that solution, but it may not be the correct answer, and I think the crossover discussion works well for that case. >> So but the term comes up 'cause there's a frustration I guess, right? That data science skills are not plentiful, and it's potentially a bottleneck in an organization. Supposedly 80% of your time is spent on cleaning data, is that right? Is that fair? So there's a problem. How much of that can be automated and when? >> I'll have a shot at that. So I think there's a shift that's going to come about where we're going to move from centralized data sets to data at the edge of the network, and this is something that's happening very quickly now where we can't just hold everything back to a central spot. When the internet of things actually wakes up. Things like the Boeing Dreamliner 787, that things got 6,000 sensors in it, produces half a terabyte of data per flight. There are 87,400 flights per day in domestic airspace in the U.S. That's 43.5 petabytes of raw data, now that's about three years worth of disk manufacturing in total, right? We're never going to copy that across one place, we can't process, so I think the challenge we've got ahead of us is looking at how we're going to move the intelligence and the analytics to the edge of the network and pre-cook the data in different tiers, so have a look at the raw material we get, and boil it down to a slightly smaller data set, bring a meta data version of that back, and eventually get to the point where we've only got the very minimum data set and data points we need to make key decisions. Without that, we're already at the point where we have too much data, and we can't munch it fast enough, and we can't spin off enough tin even if we witch the cloud on, and that's just this never ending deluge of noise, right? And you've got that signal versus noise problem so then we're now seeing a shift where people looking at how do we move the intelligence back to the edge of network which we actually solved some time ago in the securities space. You know, spam filtering, if an emails hits Google on the west coast of the U.S. and they create a check some for that spam email, it immediately goes into a database, and nothing gets on the opposite side of the coast, because they already know it's spam. They recognize that email coming in, that's evil, stop it. So we've already fixed its insecurity with intrusion detection, we've fixed it in spam, so we now need to take that learning, and bring it into business analytics, if you like, and see where we're finding patterns and behavior, and brew that out to the edge of the network, so if I'm seeing a demand over here for tickets on a new sale of a show, I need to be able to see where else I'm going to see that demand and start responding to that before the demand comes about. I think that's a shift that we're going to see quickly, because we'll never keep up with the data munching challenge and the volume's just going to explode. >> David: We just have a couple minutes. >> That does sound like a great topic for a future Cube panel which is data science on the edge of the fog. >> I got a hundred questions around that. So we're wrapping up here. Just got a couple minutes. Final thoughts on this conversation or any other pieces that you want to punctuate. >> I think one thing that's been really interesting for me being on this panel is hearing all of my co-panelists talking about common themes and things that we are also experiencing which isn't a surprise, but it's interesting to hear about how ubiquitous some of the challenges are, and also at the announcement earlier today, some of the things that they're talking about and thinking about, we're also talking about and thinking about. So I think it's great to hear we're all in different countries and different places, but we're experiencing a lot of the same challenges, and I think that's been really interesting for me to hear about. >> David: Great, anybody else, final thoughts? >> To echo Dez's thoughts, it's about we're never going to catch up with the amount of data that's produced, so it's about transforming big data into smart data. >> I could just say that with the shift from normal data, small data, to big data, the answer is automate, automate, automate, and we've been talking about advanced algorithms and machine learning for the science for changing the business, but there also needs to be machine learning and advanced algorithms for the backroom where we're actually getting smarter about how we ingestate and how we fix data as it comes in. Because we can actually train the machines to understand data anomalies and what we want to do with them over time. And I think the further upstream we get of data correction, the less work there will be downstream. And I also think that the concept of being able to fix data at the source is gone, that's behind us. Right now the data that we're using to analyze to change the business, typically we have no control over. Like Dez said, they're coming from censors and machines and internet of things and if it's wrong, it's always going to be wrong, so we have to figure out how to do that in our laboratory. >> Eaves, final thoughts? >> I think it's a mind shift being a data scientist if you look back at the time why did you start developing or writing code? Because you like to code, whatever, just for the sake of building a nice algorithm or a piece of software, or whatever, and now I think with the spirit of a data scientist, you're looking at a problem and say this is where I want to go, so you have more the top down approach than the bottom up approach. And have the big picture and that is what you really need as a data scientist, just look across technologies, look across departments, look across everything, and then on top of that, try to apply as much skills as you have available, and that's kind of unicorn that they're trying to look for, because it's pretty hard to find people with that wide vision on everything that is happening within the company, so you need to be aware of technology, you need to be aware of how a business is run, and how it fits within a cultural environment, you have to work with people and all those things together to my belief to make it very difficult to find those good data scientists. >> Jim? Your final thought? >> My final thoughts is this is an awesome panel, and I'm so glad that you've come to New York, and I'm hoping that you all stay, of course, for the the IBM Data First launch event that will take place this evening about a block over at Hudson Mercantile, so that's pretty much it. Thank you, I really learned a lot. >> I want to second Jim's thanks, really, great panel. Awesome expertise, really appreciate you taking the time, and thanks to the folks at IBM for putting this together. >> And I'm big fans of most of you, all of you, on this session here, so it's great just to meet you in person, thank you. >> Okay, and I want to thank Jeff Frick for being a human curtain there with the sun setting here in New York City. Well thanks very much for watching, we are going to be across the street at the IBM announcement, we're going to be on the ground. We open up again tomorrow at 9:30 at Big Data NYC, Big Data Week, Strata plus the Hadoop World, thanks for watching everybody, that's a wrap from here. This is the Cube, we're out. (techno music)

Published Date : Sep 28 2016

SUMMARY :

Brought to you by headline sponsors, and this is a cube first, and we have some really but I want to hear them. and appreciate you organizing this. and the term data mining Eves, I of course know you from Twitter. and you can do that on a technical level, How many people have been on the Cube I always like to ask that question. and that was obviously Great, thank you Craig, and I'm also on the faculty and saw that snake swallow a basketball and with the big paradigm Great, thank you. and I came to data science, Great, thank you. and so what I think about data science Great, and last but not least, and the scale at which I'm going to go off script-- You guys have in on the front. and one of the CDOs, she said that 25% and I think certainly, that's and so I think this is a great opportunity and the first question talk about the theme now and does that data scientist, you know, and you can just advertise and from the clients I mean they need to have and it's been, the transition over time but I have a feeling that the paradise and the company's product and they really have to focus What is the right division and one of the reasons I You dream in equations, right? and you have no interest in learning but I think you need to and the curiosity you and there's a lot to be and I like to use the analogy, and the reason I mentioned that is that the right breakdown of roles? and the code behind the analytics, And not the other way around. Why is that? idea of the aspects of code, of the reasons for that I think Miriam, had a comment? and someone from the chief data office and one of the things that an operational function as opposed to and so most of the time and five minutes on the solution, right? that code that the data but if I'm listening to you, that in the real world? the data that you have or so that shows you that and the nirvana was maybe that the customers can see and a couple of the authors went away and actually cater the of the data itself, like in a mobile app, I love Claudia Emahoff too, of that notion, citizen data scientist. and have access to more people, to mean incompetent as opposed to and at some point then ask and the development of those spaces, and so I think one thing I think there's a and I think in order to have a true so I'd like to hope that with all the new and I think So but the term comes up and the analytics to of the fog. or any other pieces that you want to and also at the so it's about transforming big data and machine learning for the science and now I think with the and I'm hoping that you and thanks to the folks at IBM so it's great just to meet you in person, This is the Cube, we're out.

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