>> 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)

hi everybody welcome back to re invent 2022. this is thecube's exclusive coverage we're here at the satellite set it's up on the fifth floor of the Venetian Conference Center and this is part of the global startup program the AWS startup showcase series that we've been running all through last year and and into this year with AWS and featuring some of its its Global Partners Ed wallson series the CEO of chaos search many times Cube Alum and Kevin Miller there's also a cube Alum vice president GM of S3 at AWS guys good to see you again yeah great to see you Dave hi Kevin this is we call this our Super Bowl so this must be like your I don't know uh World Cup it's a pretty big event yeah it's the World Cup for sure yeah so a lot of S3 talk you know I mean that's what got us all started in 2006 so absolutely what's new in S3 yeah it's been a great show we've had a number of really interesting launches over the last few weeks and a few at the show as well so you know we've been really focused on helping customers that are running Mass scale data Lakes including you know whether it's structured or unstructured data we actually announced just a few just an hour ago I think it was a new capability to give customers cross-account access points for sharing data securely with other parts of the organization and that's something that we'd heard from customers is as they are growing and have more data sets and they're looking to to get more out of their data they are increasingly looking to enable multiple teams across their businesses to access those data sets securely and that's what we provide with cross-count access points we also launched yesterday our multi-region access point failover capabilities and so again this is where customers have data sets and they're using multiple regions for certain critical workloads they're now able to to use that to fail to control the failover between different regions in AWS and then one other launch I would just highlight is some improvements we made to storage lens which is our really a very novel and you need capability to help customers really understand what storage they have where who's accessing it when it's being accessed and we added a bunch of new metrics storage lens has been pretty exciting for a lot of customers in fact we looked at the data and saw that customers who have adopted storage lens typically within six months they saved more than six times what they had invested in turning storage lens on and certainly in this environment right now we have a lot of customers who are it's pretty top of mind they're looking for ways to optimize their their costs in the cloud and take some of those savings and be able to reinvest them in new innovation so pretty exciting with the storage lens launch I think what's interesting about S3 is that you know pre-cloud Object Store was this kind of a niche right and then of course you guys announced you know S3 in 2006 as I said and okay great you know cheap and deep storage simple get put now the conversations about how to enable value from from data absolutely analytics and it's just a whole new world and Ed you've talked many times I love the term yeah we built chaos search on the on the shoulders of giants right and so the under underlying that is S3 but the value that you can build on top of that has been key and I don't think we've talked about his shoulders and Giants but we've talked about how we literally you know we have a big Vision right so hard to kind of solve the challenge to analytics at scale we really focus on the you know the you know Big Data coming environment get analytics so we talk about the on the shoulders Giants obviously Isaac Newton's you know metaphor of I learned from everything before and we layer on top so really when you talk about all the things come from S3 like I just smile because like we picked it up naturally we went all in an S3 and this is where I think you're going Dave but everyone is so let's just cut the chase like so any of the data platforms you're using S3 is what you're building but we did it a little bit differently so at first people using a cold storage like you said and then they ETL it up into a different platforms for analytics of different sorts now people are using it closer they're doing caching layers and cashing out and they're that's where but that's where the attributes of a scale or reliability are what we did is we actually make S3 a database so literally we have no persistence outside that three and that kind of comes in so it's working really well with clients because most of the thing is we pick up all these attributes of scale reliability and it shows up in the clients environments and so when you launch all these new scalable things we just see it like our clients constantly comment like one of our biggest customers fintech in uh Europe they go to Black Friday again black Friday's not one days and they lose scale from what is it 58 terabytes a day and they're going up to 187 terabytes a day and we don't Flinch they say how do you do that well we built our platform on S3 as long as you can stream it to S3 so they're saying I can't overrun S3 and it's a natural play so it's it's really nice that but we take out those attributes but same thing that's why we're able to you know help clients get you know really you know Equifax is a good example maybe they're able to consolidate 12 their divisions on one platform we couldn't have done that without the scale and the performance of what you can get S3 but also they saved 90 I'm able to do that but that's really because the only persistence is S3 and what you guys are delivering but and then we really for focus on shoulders Giants we're doing on top of that innovating on top of your platforms and bringing that out so things like you know we have a unique data representation that makes it easy to ingest this data because it's kind of coming at you four v's of big data we allow you to do that make it performant on s3h so now you're doing hot analytics on S3 as if it's just a native database in memory but there's no memory SSC caching and then multi-model once you get it there don't move it leverage it in place so you know elasticsearch access you know Cabana grafana access or SQL access with your tools so we're seeing that constantly but we always talk about on the shoulders of giants but even this week I get comments from our customers like how did you do that and most of it is because we built on top of what you guys provided so it's really working out pretty well and you know we talk a lot about digital transformation of course we had the pleasure sitting down with Adam solipski prior John Furrier flew to Seattle sits down his annual one-on-one with the AWS CEO which is kind of cool yeah it was it's good it's like study for the test you know and uh and so but but one of the interesting things he said was you know we're one of our challenges going forward is is how do we go Beyond digital transformation into business transformation like okay well that's that's interesting I was talking to a customer today AWS customer and obviously others because they're 100 year old company and they're basically their business was they call them like the Uber for for servicing appliances when your Appliance breaks you got to get a person to serve it a service if it's out of warranty you know these guys do that so they got to basically have a you know a network of technicians yeah and they gotta deal with the customers no phone right so they had a completely you know that was a business transformation right they're becoming you know everybody says they're coming a software company but they're building it of course yeah right on the cloud so wonder if you guys could each talk about what's what you're seeing in terms of changing not only in the sort of I.T and the digital transformation but also the business transformation yeah I know I I 100 agree that I think business transformation is probably that one of the top themes I'm hearing from customers of all sizes right now even in this environment I think customers are looking for what can I do to drive top line or you know improve bottom line or just improve my customer experience and really you know sort of have that effect where I'm helping customers get more done and you know it is it is very tricky because to do that successfully the customers that are doing that successfully I think are really getting into the lines of businesses and figuring out you know it's probably a different skill set possibly a different culture different norms and practices and process and so it's it's a lot more than just a like you said a lot more than just the technology involved but when it you know we sort of liquidate it down into the data that's where absolutely we see that as a critical function for lines of businesses to become more comfortable first off knowing what data sets they have what data they they could access but possibly aren't today and then starting to tap into those data sources and then as as that progresses figuring out how to share and collaborate with data sets across a company to you know to correlate across those data sets and and drive more insights and then as all that's being done of course it's important to measure the results and be able to really see is this what what effect is this having and proving that effect and certainly I've seen plenty of customers be able to show you know this is a percentage increase in top or bottom line and uh so that pattern is playing out a lot and actually a lot of how we think about where we're going with S3 is related to how do we make it easier for customers to to do everything that I just described to have to understand what data they have to make it accessible and you know it's great to have such a great ecosystem of partners that are then building on top of that and innovating to help customers connect really directly with the businesses that they're running and driving those insights well and customers are hours today one of the things I loved that Adam said he said where Amazon is strategically very very patient but tactically we're really impatient and the customers out there like how are you going to help me increase Revenue how are you going to help me cut costs you know we were talking about how off off camera how you know software can actually help do that yeah it's deflationary I love the quote right so software's deflationary as costs come up how do you go drive it also free up the team and you nail it it's like okay everyone wants to save money but they're not putting off these projects in fact the digital transformation or the business it's actually moving forward but they're getting a little bit bigger but everyone's looking for creative ways to look at their architecture and it becomes larger larger we talked about a couple of those examples but like even like uh things like observability they want to give this tool set this data to all the developers all their sres same data to all the security team and then to do that they need to find a way an architect should do that scale and save money simultaneously so we see constantly people who are pairing us up with some of these larger firms like uh or like keep your data dog keep your Splunk use us to reduce the cost that one and one is actually cheaper than what you have but then they use it either to save money we're saving 50 to 80 hard dollars but more importantly to free up your team from the toil and then they they turn around and make that budget neutral and then allowed to get the same tools to more people across the org because they're sometimes constrained of getting the access to everyone explain that a little bit more let's say I got a Splunk or data dog I'm sifting through you know logs how exactly do you help so it's pretty simple I'll use dad dog example so let's say using data dog preservability so it's just your developers your sres managing environments all these platforms are really good at being a monitoring alerting type of tool what they're not necessarily great at is keeping the data for longer periods like the log data the bigger data that's where we're strong what you see is like a data dog let's say you're using it for a minister for to keep 30 days of logs which is not enough like let's say you're running environment you're finding that performance issue you kind of want to look to last quarter in last month in or maybe last Black Friday so 30 days is not enough but will charge you two eighty two dollars and eighty cents a gigabyte don't focus on just 280 and then if you just turn the knob and keep seven days but keep two years of data on us which is on S3 it goes down to 22 cents plus our list price of 80 cents goes to a dollar two compared to 280. so here's the thing what they're able to do is just turn a knob get more data we do an integration so you can go right from data dog or grafana directly into our platform so the user doesn't see it but they save money A lot of times they don't just save the money now they use that to go fund and get data dog to a lot more people make sense so it's a creativity they're looking at it and they're looking at tools we see the same thing with a grafana if you look at the whole grafana play which is hey you can't put it in one place but put Prometheus for metrics or traces we fit well with logs but they're using that to bring down their costs because a lot of this data just really bogs down these applications the alerting monitoring are good at small data they're not good at the big data which is what we're really good at and then the one and one is actually less than you paid for the one so it and it works pretty well so things are really unpredictable right now in the economy you know during the pandemic we've sort of lockdown and then the stock market went crazy we're like okay it's going to end it's going to end and then it looked like it was going to end and then it you know but last year it reinvented just just in that sweet spot before Omicron so we we tucked it in which which was awesome right it was a great great event we really really missed one physical reinvent you know which was very rare so that's cool but I've called it the slingshot economy it feels like you know you're driving down the highway and you got to hit the brakes and then all of a sudden you're going okay we're through it Oh no you're gonna hit the brakes again yeah so it's very very hard to predict and I was listening to jassy this morning he was talking about yeah consumers they're still spending but what they're doing is they're they're shopping for more features they might be you know buying a TV that's less expensive you know more value for the money so okay so hopefully the consumer spending will get us out of this but you don't really know you know and I don't yeah you know we don't seem to have the algorithms we've never been through something like this before so what are you guys seeing in terms of customer Behavior given that uncertainty well one thing I would highlight that I think particularly going back to what we were just talking about as far as business and digital transformation I think some customers are still appreciating the fact that where you know yesterday you may have had to to buy some Capital put out some capital and commit to something for a large upfront expenditure is that you know today the value of being able to experiment and scale up and then most importantly scale down and dynamically based on is the experiment working out am I seeing real value from it and doing that on a time scale of a day or a week or a few months that is so important right now because again it gets to I am looking for a ways to innovate and to drive Top Line growth but I I can't commit to a multi-year sort of uh set of costs to to do that so and I think plenty of customers are finding that even a few months of experimentation gives them some really valuable insight as far as is this going to be successful or not and so I think that again just of course with S3 and storage from day one we've been elastic pay for what you use if you're not using the storage you don't get charged for it and I think that particularly right now having the applications and the rest of the ecosystem around the storage and the data be able to scale up and scale down is is just ever more important and when people see that like typically they're looking to do more with it so if they find you usually find these little Department projects but they see a way to actually move faster and save money I think it is a mix of those two they're looking to expand it which can be a nightmare for sales Cycles because they take longer but people are looking well why don't you leverage this and go across division so we do see people trying to leverage it because they're still I don't think digital transformation is slowing down but a lot more to be honest a lot more approvals at this point for everything it is you know Adam and another great quote in his in his keynote he said if you want to save money the Cloud's a place to do it absolutely and I read an article recently and I was looking through and I said this is the first time you know AWS has ever seen a downturn because the cloud was too early back then I'm like you weren't paying attention in 2008 because that was the first major inflection point for cloud adoption where CFO said okay stop the capex we're going to Opex and you saw the cloud take off and then 2010 started this you know amazing cycle that we really haven't seen anything like it where they were doubling down in Investments and they were real hardcore investment it wasn't like 1998 99 was all just going out the door for no clear reason yeah so that Foundation is now in place and I think it makes a lot of sense and it could be here for for a while where people are saying Hey I want to optimize and I'm going to do that on the cloud yeah no I mean I've obviously I certainly agree with Adam's quote I think really that's been in aws's DNA from from day one right is that ability to scale costs with with the actual consumption and paying for what you use and I think that you know certainly moments like now are ones that can really motivate change in an organization in a way that might not have been as palatable when it just it didn't feel like it was as necessary yeah all right we got to go give you a last word uh I think it's been a great event I love all your announcements I think this is wonderful uh it's been a great show I love uh in fact how many people are here at reinvent north of 50 000. yeah I mean I feel like it was it's as big if not bigger than 2019. people have said ah 2019 was a record when you count out all the professors I don't know it feels it feels as big if not bigger so there's great energy yeah it's quite amazing and uh and we're thrilled to be part of it guys thanks for coming on thecube again really appreciate it face to face all right thank you for watching this is Dave vellante for the cube your leader in Enterprise and emerging Tech coverage we'll be right back foreign

>>The queue presents Coon and cloud native con Europe, 2022, brought to you by red hat, the cloud native computing foundation and its ecosystem partners. >>Welcome to Licia Spain, a Coon cloud native con Europe, 2022. I'm your host, Keith Townson, along with Paul Gillon senior editor, enterprise architecture for Silicon angle. Paul, we're gonna talk to some amazing people this week. Coon, what the energy here, what, what, what, what would you say about >>It? I'd say it's reminiscent of, of early year, uh, early stage conferences I've seen with other technologies. There is a lot of startup activity. Here's a lot of money in the market, despite the sell off in the stock market lately. Uh, a lot of anticipation that there are, there could be big exits. There could be big things ahead for these companies. You don't see that when you go to the big established conferences, uh, you see just, uh, anticipation here that I don't think you see, uh, you you'll see maybe in a couple years, so it's fun to be here right now. I'm sure it'll be a very different experience in two or three years. >>So welcome to our guest cube alum. Batam Tobar the founder and CEO of Upbound. Welcome back. >>Thank you. Yeah, pleasure to be on, on the show again. >>So Paul, tell us the we're in this phase of migrations and, and moving to cloud native stacks. Are we another replatforming generation? I mean, we've done, the enterprise has done this, you know, time and time again, whether it's from Java to.net or do net to Java or from bare metal to VMs, but are we in another age of replatforming? >>You know, it's interesting. Every company has now become a tech company and every tech company needs to build a very model, you know, modern digital platform for them to actually run their business. And if they don't do that, then they'll probably be out of business. And, um, it is interesting to think about how companies are platforming and replatforming. Like, you know, as you said, just a, a few years back, you know, we were on people using cloud Foundry or using Heroku, you hear Heroku a lot, or, you know, now it's cloud native and Kubernetes and, and it, it begs the question, you know, is this the end? That to your point, is this, you know, do we have a, you know, what, what makes us sure that this is the, you know, the last platform or the future proof platform that, that people are building, >>There's never a last platform, right? There's always something around the core. The question is, is Kubernetes Linux, or is it windows? >>That, that's a good question. Um, it's more like more like Linux. I think, um, you know, the, you know, you've heard this before, but people talk about Kubernetes as a platform off platforms. Um, you can use it to build other platforms and if you know what you're doing, you can probably put, assemble a set of pieces around it and arrive at something that looks and can work for your business, but it requires a ton of talent. It requires a lot of people that actually can act, you know, know how to put this stick together to, to work for your business. It is, there's not a lot of guidance. I, we were, I think we were chatting earlier about the CSCF landscape and, and, um, how there are all these different projects and companies around it, but, but they don't come together in meaningful ways that you have, they act the enterprise itself has to figure out how to bring them together. Right. And that's the combination of what they do there organically or not is their platform. Right. And that changes. It can change over time. >>Do you think they really do. They really want to put these things together? I mean, there's, that's not what enterprise is like to do. They want to find someone who's gonna come in and, uh, turnkey do it all for them. >>Yeah. And, and if there was, this is the, this is the things like EV every week now you hear about another platform that says, this is the new Heroku. This is the new cloud Foundry, this replaces every, you know, some vendor has, and you can see them all around here. You know, companies that are basically selling platform solutions, um, that do put 'em together. And the problem with it is that you typically outgrow these, like you are, um, it might solve 80% of the use cases you care about, but the other 20% are not represented. And so you end up outgrowing the platform itself, right? And the, the choice has been mostly around, you know, do you buy something off the shelf that solves 80% of your use cases, or do you build something on your own? And then you have to spend all your resources actually going through and building all of it. And that's been the dilemma, you know, people who talk about this as a platform dilemma, but it's been, it's been the way for a long time. Like you, every, we go through this cycle every few years and, you know, people end up essentially oscillating between buying something off the, you know, that's off the shelf or building it, building it themselves. >>So what's the payoff. If I'm a CIO and I'm looking at the landscape, I don't need to understand, you know, I don't know to know what a pod is to know that looking at 200 plus projects in co and at, in cloud native, uh, foundation and the bevy of, of co-located projects and, and conferences before they, even the start of this, what's the payoff >>Increasing the pace of innovation. I mean, that literally is when we talk to customers, they all say roughly the same thing. They want something that works for their business. They want something that helps them take their, you know, line of business applications to production in a much quicker way, lets them innovate, lets them create higher engineers that can, don't have to understand everything about every system, but can actually specialize and focus on the, the parts that they sh they care about. Um, but it's all in the context of, you know, people want to be able to innovate at a very high pace, otherwise they get disrupted. >>So I was at the, you know, my favorite part of, of Coon in general is the hallway track and talking to people on the ground, doing cool things. I was talking to a engineer who was able to take their Java, stack their, their, uh, net stack and start to create APIs between and break 'em into microservices. Now teams are working across from one another realizing that, that, that promise of innovation, but that was the end point. They they're there. Yeah. As companies are thinking about replatforming where like, where do we start? I mean, looking at the, the CNCF, the, the map and it's 200 plus projects, where do I start? >>Do you typically today start with Kubernetes and, and um, a lot of companies have now deployed Kubernetes to production as a container orchestrator, whether they're going through a vendor or not, but now you are seeing all the things around it, whether it's C I C D or GI ops that they're looking at, you know, or the starting to build consoles around, you know, their, their platforms or looking at managing more than just containers. And that's a theme that, you know, we're seeing a lot now, people want, people want to actually bring this modern stack to manage, not just container workloads, but start looking at databases and cloud workloads and everything else that they're doing around it. Honestly, everybody's trying to do the same thing. They're trying to arrive at a single point of control, a single, you know, a platform that can do it all that they can centralize policy centralized controls to compliance governance, cost controls, and then expose a self-service experience to developers. Like they're all trying to build what we probably call an internal cloud platform. They don't know, they talk about it in different ways, but almost everyone is trying to build some internal platform that sits on top of, on premises. And on top of cloud, depending on their scenarios, >>You make an interesting point, which is that everyone here is to some extent trying to do the same thing. And there's fine points of granularity between now they're approaching it as you walk around this floor. Do you understand what all of these companies are doing? >>I'm not sure I understand all of them, but I, I do. I do recognize a lot of them. Yes. >>And in terms of your approach, you, you use the term control plane, uh, what is distinctive about your approach? >>Very good question. So, you know, we, we end up out take a, um, we we're trying to solve, uh, this problem as well. We're trying to help people build their own platforms. Um, but let me, let me, you know, there's a lot to it. So let me actually step back and talk about the architecture of this. But if you were to look at any cloud platform, let's take the largest one. AWS, if you peek behind the scenes at AWS, you know, um, it's basically a set of independent services, EC two S3 databases, et cetera, um, that are, you know, essentially working on different parts of, you know, like offer completely different pricing, different services, et cetera. They come together because they all integrate into a control plan. >>It's the thing that serves an API. It's the thing that gives it all a common field. It's where you do access control. It's where you do, um, billing, metering, cost control policy, et cetera. Right? And so our realization was if the enterprises are platforming and replatforming, why shouldn't they build their platform in the same way that the cloud vendors build theirs? And so we started this project almost four years ago, now three and a half years, um, called cross plain, which is a, essentially an open source control plane that can become the integration point for all services. And essentially gives you a universal control plane for cloud. >>So you mentioned the idea of the orchestrating or managing stuff other than containers, as I think about companies that built amazing platforms, enterprise companies, building amazing applications on AWS 10 years ago, and they're adopting the AWS control plane. And now I'm looking at Kubernetes is Kubernetes the way to multi-cloud to be able to control those discrete applic, uh, services in a AWS or Google cloud Azure or Oracle cloud is cetera. >>We kind of have the tease it, the parts. So there are really two parts to Kubernetes and everybody thinks of Kubernetes as a container orchestration platform. Right? And, um, you know, there is a sense that people say, if I was to run Kubernetes on everywhere and can build everything on top of containers, that I get some kind of portability across clouds, right. That I can put things in containers. And then they magically run, you know, in different environments. Um, in reality, what we've seen is not everything fits in containers. It's not gonna be the world is not gonna look like containers on the bottom. Everything else is on top. Instead, what we're gonna see is essentially a set of services that people are using across the different vendors. So if you look at like, you could be at AWS shop primarily, but I bet you're using confluent or elastic or data breaks or snowflake or Mongo or other services. >>I bet you're using things that are on premises, right? And so when you look at that and you say to build my platform as an enterprise, I have to consume services from multiple vendors. Even it's just one major cloud vendor, but I'm consuming services from others. How do I bring them together in meaningful ways so that I can, you know, build my platform on top of the collection of them and offer something that my developers can consume. And self-service on. That's not a, that's not just containers. What's interesting though, is if you look at Kubernetes and, you know, look inside it, Kubernetes built a control plane. That's actually quite useful and applicable outside of container scenarios. So this whole notion of CRDs and controllers, if you've heard that term, um, the ability, you know, like there are two parts to Kubernetes, there is the control plane, and then there's the container container, uh, workloads and the control plane is generic. >>It could be used literally across, you know, you can use it to manage things that are completely outside of container workloads. And that's what we did with cross plain. We took the control plane of Kubernetes and then built bindings providers that connected to AWS, to Google, to Azure, to digital ocean, to all these different environments. So you can bring the way of managing, you know, the style of managing that Kubernetes invented to more than just containers. You can now manage cloud services, using the same approach that you are now using with Kubernetes and using the entire ecosystem of tooling around it. >>Enterprise have been under pressure replatform for a long time. It was first go to Unix then to Linux and virtualize then to move to the cloud. Now, Kubernetes, do you think that this is the stack that enterprises can finally commit to? >>I think if you take the orientation of your deploying a control plane within your enterprise, that is extensible, that enables you to actually connect it to all the things that are under your domain, um, that that actually can be a Futureproof way of doing a platform. And, you know, if you look at the largest cloud platforms, AWS has been around for at least 15 years now, uh, and they really haven't changed the architecture of AWS significantly. It's still a control plane, a set of control planes that are managing services. >>It's a legacy >>They've added a lot of services. They've have a ton of diversity. They've added so many different things, but the architecture is still a hub and spoke that they've built, right? And if the enterprise can take the same orientation, put a control plane, let it manage all the things that are, you know, about today, arrive at a single point of control, have a single point where you can enforce policy compliance, cost controls, et cetera, mm-hmm <affirmative>, and then expose a self-service experience to your developers that actually can become future proof. >>So we've heard this promise before the cloud of clouds, basically. Yes, the, the, to be able to manage everything, what we find is the devils in the details. The being able to say, you know, a load balancer issuing a, a command to, to deploy a load balancer in AWS is different than it is in Azure, which is different than it is in GCP. How do, how do enterprises know that we can talk to a single control plane to do that? I mean, that just seems extremely difficult to manage. Oh >>Yeah. That, um, the approach is not, you're not trying to create a lowest common denominator between clouds. That's a really, really hard problem. And in fact, you get relegated to just using this, you know, really shallow features of each, if you're, if you're gonna do that, like your, your example of load balancers, load balances look completely different between between cloud vendors. Um, the approach that we kind of advocate for is that you shouldn't think of them as you shouldn't try to unify them in a way that makes them, you know, there's a, uh, there's a global abstraction that says, oh, there's a load balancer. And it somehow magically works across the different cloud vendors. I think that's a really, really hard thing to say, to do as you point out. However, if you bring them all under a same control plane, As different as they are, you're able to now apply policies. You're able to set cost controls. You're able to expose a self-service experience on top of them, even, even if they are very different. And that's, that's something that I think is, you know, been hard to do in the past. >>So BAAM, we'll love to dig deeper into this in future segments. And I'm gonna take a look at the, the, the product and project <laugh> and see where you folks land in this conversation from Valencia Spain, I'm Keith towns. And along with Paul Gillon, and you're watching the leader in high tech.

>>The cube presents, Coon and cloud native con Europe 22 brought to you by the cloud native computing foundation. >>Welcome to Licia Spain in Coon cloud native con Europe, 2022. I'm your host, Keith Townson, along with Paul Gillon senior editor, enterprise architecture for Silicon angle. Paul, we're gonna talk to some amazing people this week. Coon, what the energy here, what, what, what would you say about >>It? I'd say it's reminiscent of, of early year, early stage conferences I've seen with other technologies. There is a lot of startup activity. Here's a lot of money in the market, despite the selloff in the stock market lately, a lot of anticipation that there are, there could be big exits. There could be big things ahead for these companies. You don't see that when you go to the big established conferences, you see just anticipation here that I don't think you see you you'll see maybe in a couple of years. So it's fun to be here right now. I'm sure it'll be a very different experience in two or three years. >>So welcome to our guest Q alum. BAAM Tobar the founder and CEO of Upbound. Welcome back. >>Thank you. Yeah, pleasure to be on, on the show again. >>So Paul, tell us the we're in this phase of migrations and, and moving to cloud native stacks. Are we another re-platforming generation? I mean, we've done, the enterprise has done this, you know, time and time again, and whether it's from Java to.net or net to Java or from bare metal to VMs, but are we in another age of replatforming? >>You know, it's interesting. Every company has now become a tech company and every tech company needs to build a very model, you know, modern digital platform for them to actually run their business. And if they don't do that, then they'll probably be out of business. And it is interesting to think about how companies are platforming and replatforming. Like, you know, as you said, just a, a few years back, you know, we were on people using cloud Foundry or using Heroku, you hear Heroku a lot, or, you know, now it's cloud native and Kubernetes and, and it, it begs the question, you know, is this the end that the tr point is this, you know, do we have a, you know, what, what makes us sure that this is the, you know, the last platform or the future proof platform that, that people are building, >>There's never a last platform, right? There's always something around the core. The question is, is Kubernetes Linux, or is it windows? >>That, that's a good question. It's more like more like Linux. I think, you know, the, you know, you've heard this before, but people talk about Kubernetes as a platform off platforms, you can use it to build other platforms. And if you know what you're doing, you can probably put, assemble a set of pieces around it and arrive at something that looks and can work for your business. But it requires a ton of talent. It requires a lot of people that actually can act, you know, know how to put the stick together to, to work for your business. It is, there's not a lot of guidance. I, we were, I think we were chatting earlier about the CSCF landscape and, and how there all these different projects and companies around it. But, but they don't come together in meaningful ways that you have, they act the enterprise itself has to figure out how to bring them together. Right. And that's the combination of what they do there organically or not is their platform. Right. And that changes. It can change over time. >>Do you think they really do. They really want to put these things together? I mean, there's, that's not what enterprise is like to do. They want to find someone who's gonna come in and turnkey do it all for >>Them. Yeah. And, and if there were, this is the, this is the things like EV every week now you hear about another platform that says, this is the new Heroku. This is the new cloud Foundry. This replaces every, you know, some vendor has, and you can see them all around here. You know, companies that are basically selling platform solutions that do put 'em together. And the problem with it is that you typically outgrow these, like you are, it might solve 80% of the use cases you care about, but the other 20% are not represented. And so you end up outgrowing the platform itself, right? And the, the choice has been mostly around, you know, do you buy something off the shelf that solves 80% of your use cases? Or do you build something on your own? And then you have to spend all your resources actually going through and building all of it. And that's been the dilemma, you know, people who talk about this as a platform dilemma, but it's been, it's been the way for a long time. Like you, every, we go through this cycle every few years and, you know, people end up essentially oscillating between buying something off the, you know, that's off the shelf or building it, building it themselves. >>So what's the payoff. If I'm a CIO and I'm looking at the landscape, I don't need to understand, you know, I don't know what a pod is to know that looking at 200 plus projects in co and at, in cloud native foundation and the bevy of, of co-located projects and, and conferences before the, even the start of this, what's the payoff >>Increasing the pace of innovation. I mean, that literally is when we talk to customers, they all say roughly the same thing. They want something that works for their business. They want something that helps them take their, you know, line of business applications to production in a much quicker way, lets them innovate, lets them create higher engineers that can, don't have to understand everything about every system, but can actually specialize and focus on the, the parts that they sh they care about. But it's all in the context of, you know, people want to be able to innovate at a very high pace. Otherwise they get disrupted. >>So I was at the, you know, my favorite part of coan in general is the hallway track and talking to people on the ground, doing cool things. I was talking to a engineer who was able to take their Java, stack their, their.net stack and start to create APIs between and break 'em into microservices. Now teams are working across from one another realizing that, that, that promise of innovation, but that was the end point. They they're there. Yeah. As companies are thinking about replatforming where like, where do we start? I mean, I'm looking at the, the C CNCF, the, the map and it's 200 plus projects. Where, where do I start? >>You typically today start with Kubernetes. And, and a lot of companies have now deployed Kubernetes to production as a container orchestrator, whether they're going through a vendor or not. But now you're seeing all the things around it, whether it's C I C D or GI ops that they're looking at, you know, or they're starting to build consoles around, you know, their, their platforms or looking at managing more than just containers. And that's a theme that, you know, we're seeing a lot now, people want, people want to actually bring this modern stack to manage, not just container workloads, but start looking at databases and cloud workloads and everything else that they're doing around it. Honestly, everybody's trying to do the same thing. They're trying to arrive at a single point of control, a single, you know, a platform that can do it all that they can centralize policies, centralized controls to compliance governance, cost controls, and then expose a self-service experience to the developers. Like they're all trying to build what we probably call an internal cloud platform. They don't know, they talk about it in different ways, but almost everyone is trying to build some internal platform that sits on top of, on premises. And on top of cloud, depending on their scenarios, >>You make an interesting point, which is that everyone here is to some extent trying to do the same thing. And there's fine points of granularity between now they're approaching it as you walk around this floor. Do you understand what all of these companies are doing? >>I'm not sure I understand all of them, but I, I do. I do recognize a lot of them. Yes. >>And in terms of your approach, you, you use the term control plane. What is distinctive about your approach? >>Very good question. So, you know, we, we end, Upbound take a, we we're trying to solve this problem as well. We're trying to help people build their own platforms, but let me, let me, you know, there's a lot to it. So let me actually step back and, and talk about the architecture of this. But if you were to look at any cloud platform, let's take the largest one. AWS, if you peek behind the scenes at AWS, you know, it's basically a set of independent services, EC two S three databases, et cetera, that are, you know, essentially working on different parts of, you know, like offer completely different pricing, different services, et cetera. They come together because they all integrate into a control plan. >>It's the thing that serves an API. It's the thing that gives it all a common feel. It's where you do access control. It's where you do billing metering, cost control policy, et cetera. Right? And so our realization was if the enterprises are platforming and replatforming, why shouldn't they build their platform in the same way that the cloud vendors build theirs? And so we started this project almost four years ago, now three and a half years called cross plain, which is a, essentially an open source control plane that can become the integration point for all services. And essentially gives you a universal control plane for cloud. >>So you mentioned the idea of if orchestrating or managing stuff other than containers, as I think about companies that built amazing platforms, enterprise companies, building amazing applications on AWS 10 years ago, and they're adopting the AWS control plane. And now I'm looking at Kubernetes is Kubernetes the way to multi-cloud to be able to control those discrete services in a AWS or Google cloud Azure or Oracle cloud, is that true? >>We kind have the tease it, the parts. So there are really two parts to Kubernetes and everybody thinks of Kubernetes as a container orchestration platform. Right? And you know, there is a sense that people say, if I was to run Kubernetes on everywhere and can build everything on top of containers, that I get some kind of portability across clouds, right. That I can put things in containers. And then they magically run, you know, in different environments. In reality, what we've seen is not everything fits in containers. It's not gonna be the world is not gonna look like containers on the bottom. Everything else is on top. Instead, what we're gonna see is essentially a set of services that people are using across the different vendors. So if you look at like, you could be at AWS shop primarily, but I bet you're using confluent or elastic or data breaks or snowflake or Mongo or other services. >>I bet you're using things that are on premises, right? And so when you look at that and you say to build my platform as an enterprise, I have to consume services from multiple vendors. Even if it's just one major cloud vendor, but I'm consuming services from others. How do I bring them together in meaningful ways so that I can, you know, build my platform on top of the collection of them and offer something that my developers can consume. And self-service on. That's not a, that's not just containers. What's interesting though, is if you look at Kubernetes and, you know, look inside it, Kubernetes built a control plane. That's actually quite useful and applicable outside of container scenarios. So this whole notion of CRDs and controllers, if you've heard that term, the ability, you know, like there are two parts to Kubernetes, there is a control plane, and then there's the container container workloads. >>And the control plane is generic. It could be used literally across, you know, you can use it to manage things that are completely outside of container workloads. And that's what we did with cross mind. We took the control plane of Kubernetes and then built bindings providers that connected to AWS, to Google, to Azure, to digital ocean, to all these different environments. So you can bring the way of managing, you know, the style of managing that Kubernetes invented to more than just containers. You can now manage cloud services, using the same approach that you are now using with Kubernetes and using the entire ecosystem of tooling around it. >>Enterprise has been under pressure to replatform for a long time. It was first go to Unix then to Linux and virtualize then to move to the cloud. Now, Kubernetes, do you think that this is the stack that enterprises can finally commit to? >>I think if you take the orientation of your deploying a control plane within your enterprise, that is extensible, that enables you to actually connect it to all the things that are under your domain, that that actually can be a Futureproof way of doing a platform. And, you know, if you look at the largest cloud platforms, AWS has been around for at least 15 years now, and they really haven't changed the architecture of AWS significantly. It's still a control plane, a set of control planes that are managing services. >>It's a legacy >>They've added a lot of services. They've have a ton of diversity. They've added so many different things, but the architecture is still a hub and spoke that they've built, right? And if the enterprise can take the same orientation, put a control plane, let it manage all the things that are, you know, about today, arrive at a single point of control, have a single point where you can enforce policy compliance, cost controls, et cetera, and then expose a self-service experience to your developers that actually can become future proof. >>So we've heard this promise before the cloud of clouds, basically, yes, the, the, to be able to manage everything, what we find is the devils in the details. The being able to say, you know, a load balancer issuing a, a command to, to deploy a load balancer in AWS is different than it is in Azure, which is different than it is in GCP. How do, how do enterprises know that we can talk to a single control plane to do that? I mean, that just seems extremely difficult to manage. >>Oh yeah. That the approach is not, you're not trying to create a lowest common denominator between clouds. That's a really, really hard problem. And in fact, you get relegated to just using this, you know, really shallow features of each, if you're, if you're gonna do that, like your, your example of load balancers, load balances look completely different between between cloud vendors, the approach that we kind of advocate for is that you shouldn't think of them as you shouldn't try to unify them in a way that makes them, you know, there's a, there's a global abstraction that says, oh, there's a load balancer. And it somehow magically works across the different cloud vendors. I think that's a really, really hard thing to say, to do as you pointed out. However, if you bring them all under a same control plane, as different as they are, you're able to now apply policies. You're able to set cost controls. You're able to expose a self-service experience on top of them, even, even if they are very different. And that's, that's something that I think is, you know, been hard to do in the past. >>So BAAM, we'll love to dig deeper into this in future segments. And I'm gonna take a look at the, the, the product and project and see where you folks land in this conversation from Valencia Spain, I'm Keith towns, along with Paul Gillon and you're watching the leader in high tech coverage.

>>The cube presents, Coon and cloud native con Europe 22, brought to you by the cloud native computing foundation. >>Welcome to Valencia Spain and Coon cloud native con Europe, 2022. I'm Keith towns, along with Paul Gillon, senior editor, enterprise architecture and Silicon angle. We are going to talk to some amazing folks, especially in today's segment. Paul, uh, there's a lot of companies here, like what what's been the, the consistent theme you've heard so far in the show. >>Well, you know, one thing that's different from this show, it seems to me than others I've attended is it's all around open source. We're not seeing a lot of companies bringing new proprietary technology to market. We are seeing them try to piece together, open source components with some kind of, perhaps there's a proprietary element to it, but to create some kind of a, a common management interface or control plane, and that's quite different from what I think we've seen in the past open source business models have been difficult to make work historically. Uh, and these companies are all taking their, their own approaches to it. But I think the, the degree to which this, the people here of coalesced around the importance of open source is building blocks to the future of, of applications is something I've not seen quite this way before. >>Well, with our current segment, guess we're gonna go deep into kind of these challenges and how enterprises are addressing, and their partners are addressing with those challenges we have with us, a flower head of cloud native HCL technologies. We'll get into how a system integrator is helping with this transition to Ramon neon, senior product manager, redhead. Welcome to the show. You're now cube alum. Welcome. Thanks for having us. So we're gonna get right off, uh, off the bat. We're gonna talk about this. What are some of the trends you're seeing when it comes to application migration? You've done, I'm assuming at this point, thousands of them, what are some of the common trends? >>Well, it's a very good question. And clearly ACL we've helped thousands of clients move tens of thousands of applications to what we would call a cloud native, um, you know, environment. I think the overwhelming trend that we're seeing of course is clients realize it's a particularly complex, sophisticated journey. It requires a certain set of skills and capability clients increasingly us for anything that we can do to simplify and accelerate the journey, cuz what's really important to clients. If you're on a transformation journey to cloud is you wanna see some value very quickly. So I don't wanna wait three to five years to transform my applications portfolio. If you can do something in three to five days, that would be perfect. Thank you. >>Well, three to five days, that sounds more akin to when we were doing, uh, P to V or V to V migrations. I'm sure. Uh, HCL is at this point done in the millions of those types of migrations. What are some of the challenges or the nuance in doing a traditional migration from a traditional MI monolithic application to a cloud native? >>Well, it's another good question. Of course you notice that there's a general trend in the industry. Clients don't really want to lift and shift anymore. Lift and shift doesn't really bring any transformational value to my, to my company. So clients are looking for increasingly what we could recall, cloud native modernization. I want my applications to really take advantage of the cloud native environment. They need to be elastic and kind of more robust than maybe before now in particular, I think a lot of clients have realized that this state of Nirvana, which was we're gonna modernize everything to be a cloud native microservices based application. That is a tremendous journey, but no client really has the time patient or resources to fully refactor or rearchitect all of their applications. They're looking for more immediate kind of impact. So a key trend that we've seen of course is clients still want to refactor and modernize applications, but they're focusing those resources on those applications that will bring greater impact to their business. >>What they now see as a better replacement for lift and shift is probably what we would call replatforming, where they want all of the advantages of a cloud native environment, but they haven't necessarily got the time to modernize the code base. They wanna refactor to Kubernetes in re replatform to Kubernetes in particular, and they want us to take them there quickly. And that's why, for example, this week at cuon eight sellers announced a new set of tools called KMP based on conveyor, an open source project supported by red hat. And the key attraction of KMP is it lets me replatform my applications to Kubernetes immediately, right? Within two or three minutes, I can bring an application from a legacy platform directly onto Kubernetes and I can take it straight into production. That's the kind of acceleration that clients are looking for today. Isn't >>That just a form of lift and shift though? >>Well, no lift and shift typically of course, was moving virtual machines from one place to another. You know, the focus of Kubernetes of course is containerization of solutions. And it's not just about containerizing the solution and moving it. It's the DevOps tool chain around the solution as well. And of course, when I take that application into production in a Kubernetes based environment, I'm expecting to operate it in a different way as well. So that's where we see tremendous focus on what we would call cloud native operations clients expecting to use practices like site reliability engineering, to run these replatformed applications in a different way to, so >>It sounds like you're saying, I, I mean, replatforming has been a, a spectrum of options. I think Gartner has seven different types of re-platforming. Uh, are you seeing clients take more mature attitude now toward replatforming? Are they looking more carefully at the characteristics of their legacy applications and, and trying to try to make maybe more nuanced choices about what to replatform, what to just leave >>Alone? I think clients and I I'm sure Ramon's got some comments on this too, but clients have a lot more insight now in terms of what works for them. They they've realized that this, this promise of maybe a microservices based applications estate is a good one, but I can't do that for every application. If I am a large enterprise with several thousand applications in my portfolio, I can't refactor everything to become microservices based. So clients see replatforming possibly is a middle ground. I, I get a lot of the advantages from a cloud native environment. My applications are inherently more efficient, hopefully a lot more performance. >>Yeah. It's, it's a matter of software delivery performance. Yeah. So, uh, legacy workloads will definitely benefit from, uh, being brought into Kubernetes in the software delivery per performance department. So, uh, it's a matter of, uh, somehow Rebump your, your legacy applications and getting the benefits in, in life's application, life cycle management, a, uh, full tolerance and all that stuff. It's about leveraging the, what Kubernetes offers. >>When you say bringing legacy applications into Kubernetes. It's not that simple, right? I mean, what's involved in doing that. >>It, it, isn't, it's just a matter of taking a holistic view at your application portfolio and understanding the nuances of each application type within your organization and trying to come up with a suitable migration strategy for each one of these application types. And for that, what we're trying to do is provide a series of standardized, um, tools and methodologies, uh, from a community perspective, uh, we created this conveyor community. Uh, it, it was kick started by red hat and IBM, but we are trying to bring as many vendors and GSI, uh, as possible to try to set up these standards to make these, uh, road towards Kubernetes as easy as >>Possible. So we've done a little bit of, uh, app modernization in the CTO advisor hybrid infrastructure. And one of the things that we've found, there's plenty of Avan advantages. If I take a monolithic application that has, uh, that I've traditionally had to scale off to, uh, game performance, I can take selective parts of that, and now I can add auto-scaling to it. Exactly. However, as I look at a landscape Allen of thousands of applications, uh, I need to dedicate developer resources to get that done and my traditional environment, but my traditional environment is busy building new. My traditional or my developers are building new applications and new capabilities. I just don't have the resources to do that. How does HCL and red hat team together to kind of fast track that capability? >>Well, um, I'll comment on two things in particular, actually the, the first thing when it comes to skilling, I think the thing that's really surprised us at HCL is so many of our clients around the world have said, we are desperately short of skills. We cannot hire ourselves out of this problem. We need to get our existing developer community re-skilled around platforms like OpenShift, conveyor, and other projects too. So the first thing that's happened to us at eight still is we've been incredibly busy undertaken, probably what we would call developer workforce modernization, right, where we have to help the client reskill their entire technical and developer community to give them the skills, right. So we will help the clients develop a community, build the cloud native understanding, help them understand how to modernize tools for example, uh, or applications. But the second thing I mention is, and this comes back to a comment that Ramon made around around conveyor. >>It's been really encouraging to see the open source community start to invest in building the supporting frameworks around my kind of modernization journey, because if I'm a developer that's re-skilling and I'm attempting to maybe modernize an application, being able to dip into an open source project, I mean, a good example would be tackled part of the conveyor project. Exactly. You now have open source based tools that will help you analyze your applications. They will go into the source code and they will give the developer guidance in terms of what would be effective treatments to undertake. So perhaps a development team that are new to this modernization journey, they would benefit from a project like conveyor, for example, because I need to know where can I safely modernize my application now for experience organizations like HCL that comes naturally to us, but for people who are just starting this journey, if I can take an open source tool like tackle or the rest of the conveyor, for example, and use that to accelerate my journey, it takes a lot of pressure off, off my organization, but it also accelerates the journey too. >>And it's not just a matter of, of tooling. We we're also opensourcing, uh, the, the modernization methodology that we've been using in red hat consulting for years. So this whole conveyor communities, it's all about knowledge sharing on one hand and building a set of tools together, based on that knowledge that we are sharing to make it as easy as possible. >>And what role does red hat play in all that, I mean, is your you've carved out this position for yourself as the, as the true open source company. Is that, does that position you for a leadership role in helping companies make this >>Transition? I wouldn't say we should be leading the whole thing. Uh, we, we kick started it, but we want to get other vendors on board for this thing. One cool thing about the Camira community is that IBM is, uh, opensourcing a lot of their IP. So IBM research is on board. In this thing, we have some really crazy stuff related to a AI being applied to application analysis. We have some machine learning in place. We have very cool stuff that has been sitting on a, on a corner in IBM research for quite some years that now it's being open sourced and integrated in a, uh, unified user experience to streamline the, uh, modernization process as much as >>Possible. So let's talk about the elephant of the room. Uh, HCL was leading the conversation around cloud Foundry circa five plus years ago. And as customers are thinking about their journey to cloud native, how should they think about that cloud Foundry to cloud native or Kubernetes, uh, replatforming? >>Well within the cloud Foundry community, we've, we've been quite staunched supporters of Kubernetes for quite some time, right? It's, it's quite a, a stated intent of the cloud Foundry foundation to, to move across to Kubernetes platform right now that is a significant engineering journey for cloud Foundry to take. Now we're in this position where a lot of large users of cloud Foundry have a certain urgency to their journey. They, they want to consolidate on a single Kubernetes based, okay. Um, infrastructure. We, we see a lot of traction around OpenShift, for example, from red hat in terms of its market leadership. So a lot of clients are saying we would like to consolidate all of our platforms around a single kind of Kubernetes vendor, whether that's red hat or anyone else, you know, quite frankly. So what ATL is doing right now with the tools and the solutions we've announced this week is we're simply accelerating that journey for clients. If I've got a large installed base of applications running in my cloud Foundry environment, and I've also started to invest in standardize on Kubernetes based platforms like OpenShift, most clients would see it as quite a sensible choice to now try and consolidate those two environments into one. And that's simply what we're doing at HCL. We're making it very, very easy. In fact, we fully automated the journey so I can move all of my applications from cloud Foundry into for example, OpenShift pretty much immediately. And it just simplifies the entire journey. >>So the, as we start to wrap up the segment, I like to know customer stories. What, what, how customers either surprised or challenged when they get into, even with the help of an ACL in redhead, why are they seeing the most difficult parts of their migrations? >>Well, my, my simple comment would be maybe complexity, right? And the, the associated requirement for skilled people to undertake this modernization work, right? We spoke about this, of course, in terms of clients now are a lot more realistic. They understand that their ambition now needs to be somewhat tempered by their ability to sort of drive modernization quickly. So we see a lot of clients when they look at their very large global portfolios of applications, they're trying to invest their resources in the higher priority applications, the revenue generative applications in particular, but they have to bring everything else with them as well. Now, a common kind of separation point was we see a lot of clients who might say I'm gonna properly modernize and refactor, maybe five to 10% of my portfolio, but the other 90% also needs to come on the journey as well. And that's really where replatforming in particular kicks in. So, so the key trend again, is, is clients send to us, I've gotta take the entire journey. All right, I've got the resources and the skills to really focus on this much of my application base. Can someone simplify the overall journey so I can afford to bring everything on a cloud native journey? >>So the key to success here is having a holistic view at the application portfolio, segmenting the application portfolio in different application types and ordering the, the priorities of these application types and come up with suitable migration strategies for each one of them is >>Really necess necessary to move everything though. >>Not necessarily no, or, uh, not necessarily. Yeah, absolutely not everything. But, uh, it would make sense. Uh, as we were saying before, it will definitely move, make sense to move legacy applications towards Kubernetes, to leverage all the, uh, software delivery >>That's >>That's project, right? >>It is. If >>You're gonna restructure the application around APIs and microservices, >>That it should be taken the, the way I've seen, uh, organizations succeeding the most in this, uh, road towards cloud native and Kubernetes in general is trying to address the whole portfolio. Maybe not move everything, but try, try to have this holistic view and not leave anything behind, because if you try to do this isolated, uh, initiatives of bringing this or that applications in a, in isolation, you're Def you, you will miss part of the picture and you might be, uh, doomed to fail >>There. Yeah. It's been my experience that if you don't have a plan to migrate your applications to a cloud native operating model, then you're doomed to follow lift and shift examples to the public cloud. Yeah. Whether you're, uh, going to any other clouds, if you don't make that, that operational transition. Last question on operational transition, we've talked a lot about the replatforming process itself. What about day two, uh, at the I've landed to the cloud? What are some of the top considerations for, for compliance, uh, op op observability, just making sure my apps stay up and transitioning my workforce to that model. >>I, I, I think the over, you know, the overarching trend or theme that, that I see is clients now are, are asking for what I would call cloud native operations. Now in particular, there's a very solid theme around what we would call reliability engineering. So think about site reliability, engineering, SRE platform, reliability engineering, PR E. These are the dominant topics that clients and I want to engage, uh, HCL on in particular, because the point you make is a valid one. I've modernized my application. Now I need to modernize the way that I operate the application in production. Otherwise I won't see those benefits. So that general theme of SRE is keeping us really busy. We're busy, re-skilling all of those operations teams around the world as well, because they need to know how to run these environments appropriately too. >>And also being able to measure your progress while your transitioning is important. And that's one of the concerns that we are addressing as well in the premier community with a tool called polars to, to measure, to effectively measure the software delivery performance of, of the organization after the transition has been done. >>And this is a really good point by the way, cuz most, most people think it's a bit of a black art. How do I understand how I modernize my application? How do I understand how I've improved my kind of value chain around software creation and many people thought you needed to bring in very expensive consultants to advise you on these, on these black lives? No, >>Definitely >>Not. But in open source projects like conveyor from, from red hat, the availability of these tools available on an open source model means exactly any engineer, any developer can get these tools off the shelf and get that immediate benefit. >>Well, a flower head of creative labs at HCL at Ramon neon, senior product manager, redhead. Thank you for joining the QPI. Now Cuba alum, uh, you'll have a nice profile like the profile picture on here. Awesome. >>Absolutely. Thank you. >>From Valencia Spain. I'm Keith towns, along with Paul Gillon and you're watching the cue, the leader in high tech coverage.

>>Hey, welcome to the Cubes coverage of Red Hat Summit 2021, the virtual experience, I'm lisa martin, I have two guests joining me. One is a cube alum kamal Shah is back, he's now the VP of cloud platforms at Brent had come on, it's great to have you back on the program. You're in a new role, we're going to talk about that. Thank you. And Kirsten newcomer is here as well. She's the Director of cloud and Death stickups strategy at Red Hat, Kirsten, Welcome and thank you for bringing the red hat vibe to the segment. >>Absolutely, very happy to be here. >>So looking forward to this conversation that we're going to be having in the next 20 minutes or so. We're gonna be talking about the last time come on, you were on, you were the ceo of stack rocks In January of 2021. The announcement that red hat plans to acquire stack rocks, it wouldn't be talking all about that. But I'd like to start with Kirsten, give us your perspective from red hats perspective, why is red hat a good fit for stack rocks? >>You know, there are so many reasons first of all as as you know, right? Red hat has been working with product Izing kubernetes since kubernetes one dato. Right, so so open shift three dato shipped with kubernetes one dot Oh, so we've been working with kubernetes for a long time, stack rocks embraces kind of is kubernetes native security embraces the declarative nature of kubernetes and brings that to security. Red hats, Custer's red hat enterprise customers, we have a great set across different verticals that are very security conscious and and during my five years at red hat, that's where I spend the majority of my time is talking with our customers about container and kubernetes security. And while there's a great deal of security built in to open shift as it goes to market out of the box, customers need the additional capabilities that stack rock springs. Historically, we've met those needs with our security partners. We have a great ecosystem of security partners. And with the stack rocks acquisition, we're now in a position to offer additional choice. Right. If a customer wants those capabilities from Red hat tightly integrated with open shift, we'll have those available and we continue to support and work with our broad ecosystem of security partners. >>Excellent customers always want choice. Come on. Give me your perspective. You were at the helm the ceo of stack rocks as you were last time you were on the cube. Talk to me about the redhead acquisition from your seat. >>Yeah. So as as Kirsten mentioned, we were partners of red hat. You're part of the red hat partner ecosystem. And uh, what we found is that was both a great strategic fit and a great cultural fit between our two companies. Right? And so the discussions that we had were how do we go and quickly enable our customers to accelerate their digital transformation initiatives to move workloads to the cloud, to containerized them, to manage them through kubernetes and make sure that we seamlessly addressed their security concerns. Right? Because it continues to be the number one concern for large enterprises and medium sized enterprises and frankly any enterprise that uh, you know, uh, working out today. So, so that was kind of the impetus behind it. And I must say that so far the the acquisition has been going on very smoothly. So we had two months in roughly and everybody and has been very welcoming, very collaborative, very supportive. And we are already working hand in hand to to integrate our companies and to make sure that we are working closely together to make our customers successful. >>Excellent. We're gonna talk about that integration in a second. But I can imagine challenging going through an acquisition during a global pandemic. Um but that is one of the things that I think lends itself to the cultural alignment. Kamal that you talked about, Kirsten. I want to get your perspective. We know we talk about corporate culture and corporate culture has changed a lot in the last year with everybody or so many of us being remote. Talk to me about kind of the core values that red hat and stack rocks share >>actually, you know, that's been one of the great joys doing during the acquisition process in particular, Kamal and and ali shared kind of their key values and how they um how they talked to talk with their team And some of the overlap was just so resonated so much for all of us. In particular the sense of transparency, uh, that the, that the team the stack rocks executive team brings and approaches. That's a that's a clear value for red hat um strongly maintained. Uh, that was one of the key things the interest in um uh, containers and kubernetes. Right. So the technology alignment was very clear. We probably wouldn't have proceeded without that. But again, um and I think the investment in people and the independence and the and the strong drive of the individuals and supporting the individuals as they contribute to the offering so that it really creates that sense of community um and collaboration that is key. Uh and and it's just really strong overlap in in cultural values and we so appreciated that >>community and collaboration couldn't be more important these days. And ultimately the winner is the customers. So let's dig in. Let's talk about what stack rocks brings to open shift Kirsten take it away >>man. So as I said earlier, um so I think we we really believe in continuous security at red hat and in defense and depth. And so when we look at an enterprise kubernetes distribution that involves security at the real core os layer security and kubernetes adding the things into the distribution, making sure they're there by default, that any distribution needs to be secured to be hardened, auditing, logging, identity, access management, just a wealth of things. And Red hat has historically focused on infrastructure and platform security, building those capabilities into what we bring to market stack rocks enhances what we already have and really adds workload protection, which is really when it comes down to it. Especially if you're looking at hybrid cloud, multi cloud, how you secure, not just the platform, but how you secure your workloads changes. And we're moving from a world where, you know, you're deploying anti virus or malware scanners on your VMS and your host operating system to a world where those work clothes may be very short lived. And if they aren't secured from the get go, you miss your opportunity to secure them right? You can't rely on, you know, you do need controls in the infrastructure but they need to be kubernetes native controls and you need to shift that security left. Right? You never patch a running container. You always have to rebuild and redeploy if you patch the running container the next time that container images deployed, you've missed, you've lost that patch. And so the whole ethos the whole shift left. The Deb sec ops capabilities that stack rock springs really adds such value. Right? You can't just do DEF SEc or set cops. You need to do a full infinity loop to really have def SEc ops and stack rocks. I'm gonna let Kamal tell you about it, but they have so many capabilities that that really drive that shift left and enable that closed loop. We're just so excited that they're part of our offerings. >>So can you take us through that? How does stack rocks facilitate the shift left? >>Yeah, absolutely. So stack rocks, which we we announced at summit is now being rebranded as red hat. Advanced cluster security was really purpose built to help our customers address the use cases across the entire application lifecycle. Right? So from bill to deploy to run time. So this is the infinite loop that Kirsten mentioned earlier and one of our foundations was to be kubernetes native to ensure that security is really built into the application is supposed to bolt it on. So specifically, we help our customers shift left by securing the supply chain and we're making sure that we identifying vulnerabilities early during the build process before they make it to a production environment. We helped them secure the infrastructure by preventing miS configurations again early in the process because as we all know, MIS configurations often lead to breaches at at runtime. Right? We help them address uh compliance requirements by ensuring that we can check for CS benchmarks are regulatory requirements around the C I P C I, hip hop and this and and that's uh you know, just focusing on shift left, doesn't really mean that you ignore the right side or ignore the controls you need uh when your applications are running in production. So we help them secure that at runtime by identifying preventing breaches the threat detection, prevention and incident response. >>That built in security is you both mentioned that built in versus bolt on Kirsten? Talk to me about that, that as really kind of a door opener. We talked a lot about security issues, especially in the last year. I don't know how many times we've talked about miS configurations leading to breaches that we've seen so many security challenges present in the last year. We talked to me a little bit Kirsten about >>what >>customers appetites are for going. All right now, I've got cloud native security, I'm going to be able to, I'm going to feel more comfortable with rolling out production deployments. >>It's, it's a great place to go. So there are a number of elements to think about. And if I could, I could, I could start with by building on the example that Kamal said, Right, So when we think about um I need to build security into my pipeline so that when I deliver my containerized workloads, they're secure. What if I miss a step or what if a new vulnerability is discovered after the fact? Right. So one of the things that stack rocks or redhead a CS offers is it has built in policy checks to see whether a container or running image has something like a package manager in it. Well, a package manager can be used to load software that is not delivered with the container. And so the idea of ensuring that you are including workload, built in workload, protect locks with policies that are written for you. So you can focus on building your applications. You don't necessarily have to learn everything there is to know about the new attack vectors that are really just it it's new packaging, it's new technology. It's not so much there are some new attack vectors, but mostly it's a new way of delivering and running your applications. That requires some changes to how you implement your security policies. And so ensuring that you have the tools and the technology that you're running on have those capabilities built in. So that when we have conversations with our security conscious customers, we can talk with them about the attack vectors they care about. We can illustrate how we are addressing those particular concerns. Right? One of them being malware in a container, we can look for stack. Rocks can look for a package manager that could be used to pull in, you know, code that could be exploited and you can stop a running container. Um, we can do deeper data collection with stack rocks. Again, one of the challenges when you're looking at moving your security capabilities from a traditional application environment is containers come and go all the time. In a kubernetes cluster nodes, your servers can come and go in a cloud native kubernetes cluster, right? If you're running on on cloud public cloud infrastructure, um, those things are the nodes are ephemeral to, they're designed to be shut down and brought back up. So you've got a lot more data that you need to collect and that you need to analyze and you need to correlate the information between these. Right? I no longer have one application stack running on one or more VMS, it's just things are things are moving fast so you want the right type of data collection and the right correlation to have good visibility into your environment. >>And if I can just build on that a little bit. The whole idea here is that these policies really serve as god rails right for the developers. So the it allows developers to move quickly to accelerate the speed of development without having to worry about hundreds of potential security issues because there are guardrails that will notify that with concrete recommendations early in the process. And the analogy I often use is that you know the reason we have breaks in our cars, it's not to slow us down but to allow us to go faster because we know we can slow down when we need to write. So similarly these policies are really it's really designed to accelerate the speed of development and accelerate digital transformation initiatives that our customers are embarking on >>and come on. I want to stick with you on the digital transformation front. We've talked so much about how accelerated that has been in the last year with everything going on in such a dynamic market. Talk to me Kamal about some of the feedback that you've gotten from stack rocks customers about the acquisition and how it is that maybe that facilitator of the many pivots that businesses have had to do in the last year to go from survival mode to thriving business. >>Yeah. Yes, absolutely. The feedback from all of our customers bar none has been very very positive. So it's been it's allowed us to invest more in the business and you know, we publicly stated that we are going to invest more in adding more capabilities. We are more than doubling the size of our teams as an example. And really working hand in hand with our uh the broader team at Red had to uh further accelerate the speed of development and digital transformation initiatives. So it's been extremely positive because we're adding more resources, We're investing more. We're accelerating the product roadmap uh based on uh compared to what we could do as a, as a start up as you can imagine. And and the feedback has been nothing but positive. So that's kind of where we are today. And what we're doing with the summit is rolling out a new bundle called open shift uh, Open shift platform plus, which includes not just Red hat A CS which used to be Stock rocks, but also red hat open shift hybrid cloud platform as well as Red hat advanced uh container cluster management, ACM capabilities as well as create the container registry. So we're making it easier for our customers to get all the capabilities that they need to for the drive digital transformation initiatives to get. It goes back to this whole customer centric city team that red hat has, that was also core value of stack rocks and and the winner and all of this, we believe ultimately is our, our our customers because that's where we exist to serve them, >>right. And I really like that if I could chime in kind of on top of that a little bit. Um so, so I think that one of the things we've seen with the pandemic is more of the red Hat customers are accelerating their move to public cloud and away from on premises data centers. Uh and and you know, that's just part partly because of so many people working remotely. Um it just has really pushed things. And so with Hybrid cloud becoming even more key to our joint customer base and by hybrid cloud, I mean that they have some environments that are on premises as they're making this transition. Some of those environments may stay that footprint may stay on premises, but it might be smaller, they may not have settled on a single public cloud. They could, in fact, they often are picking a public cloud based on where their development focuses. Google is very popular for ai and ml workloads. Amazon of course is just used, you know, by pretty much everybody. Um and then Azzurri is popular with um a subset of customers as well. And so we see our customers investing in all of these environments and stack rocks red hat A CS like open shift runs in all these environments. So with open shift platform plus you get a complete solution that helps with multi cluster management with a C. M with security across all of these environments, right? You can take one approach to how you secure your cluster, how you secure your workloads, how you manage configurations, You get one approach no matter where you're running your containers and kubernetes platform when you're doing this with open shift platform plus. So you also get portability. If today you want to be running an amazon maybe tomorrow you need to spin up a cluster in google, you can do that if you're working with the K s or G K E, you can or a Ks, you can do that with red hat a CS as well. So we really give you everything you need to be successful in this move and we give you back to that choice word, right? We give you the opportunity to choose and to migrate at the speed that works for you. >>So that's simplicity. That streamlining. I gotta ask you the last question here in our last couple of minutes. Come on, what's the integration process been like? as we said the acquisition just a couple of months in. But talk to me about that integration process. What that's been like? >>Yeah, absolutely. So as I mentioned earlier, the process has been very smooth so far, so two months in and it's largely driven by the common set of culture and core values that exists between our two companies. And so uh you know, from a product standpoint, we've been working hand in hand because I mentioned earlier, we were partners are working hand in hand on accelerating the road map the joint roadmap that we have here uh from a go to market perspective teams are well integrated. We are going to be rolling out the rolling out the bundle and we're gonna be rolling out additional uh options for our customers. We've also publicly announced that will be open sourcing uh red hat A. C. S. Uh formerly known as Stock Rock. So stay tuned for further news and that announcement. And, and so you know, uh, again two months and everybody's been super collaborative. Super helpful, super welcoming. And the team is the well settled and we're looking forward to now focusing on our primary objective is just to make sure that our customers are successful. >>Absolutely. That customer focus is absolutely critical. But also so is the employee experience. And it sounds like we both talked about the ethos and the and the core value alignment. They're probably being pretty critical to doing an integration during a very challenging time globally. I appreciate both of you joining me on the program today, sharing what's going on stack rocks now asks the opportunities for customers to have that built in cuBA and the security. Thanks so much for your time. >>Thank you. Thank >>you for Camel shaw and Kirsten newcomer. I'm lisa martin. You're watching the cubes coverage of Red Hat Summit, The virtual experience. Mhm

>> Announcer: From theCUBE studios in Palo Alto, in Boston, connecting with thought leaders all around the world. This is theCUBE conversation. >> Hi everyone, this is Dave Vellante and welcome to this CUBE conversation where we're going to dig in to this, the area of cloud databases. And Gartner just published a series of research in this space. And it's really a growing market, rapidly growing, a lot of new players, obviously the big three cloud players. And with me are three experts in the field, two long time industry analysts. Marc Staimer is the founder, president, and key principal at Dragon Slayer Consulting. And he's joined by David Floyer, the CTO of Wikibon. Gentlemen great to see you. Thanks for coming on theCUBE. >> Good to be here. >> Great to see you too Dave. >> Marc, coming from the great Northwest, I think first time on theCUBE, and so it's really great to have you. So let me set this up, as I said, you know, Gartner published these, you know, three giant tomes. These are, you know, publicly available documents on the web. I know you guys have been through them, you know, several hours of reading. And so, night... (Dave chuckles) Good night time reading. The three documents where they identify critical capabilities for cloud database management systems. And the first one we're going to talk about is, operational use cases. So we're talking about, you know, transaction oriented workloads, ERP financials. The second one was analytical use cases, sort of an emerging space to really try to, you know, the data warehouse space and the like. And, of course, the third is the famous Gartner Magic Quadrant, which we're going to talk about. So, Marc, let me start with you, you've dug into this research just at a high level, you know, what did you take away from it? >> Generally, if you look at all the players in the space they all have some basic good capabilities. What I mean by that is ultimately when you have, a transactional or an analytical database in the cloud, the goal is not to have to manage the database. Now they have different levels of where that goes to as how much you have to manage or what you have to manage. But ultimately, they all manage the basic administrative, or the pedantic tasks that DBAs have to do, the patching, the tuning, the upgrading, all of that is done by the service provider. So that's the number one thing they all aim at, from that point on every database has different capabilities and some will automate a whole bunch more than others, and will have different primary focuses. So it comes down to what you're looking for or what you need. And ultimately what I've learned from end users is what they think they need upfront, is not what they end up needing as they implement. >> David, anything you'd add to that, based on your reading of the Gartner work. >> Yes. It's a thorough piece of work. It's taking on a huge number of different types of uses and size of companies. And I think those are two parameters which really change how companies would look at it. If you're a Fortune 500 or Fortune 2000 type company, you're going to need a broader range of features, and you will need to deal with size and complexity in a much greater sense, and a lot of probably higher levels of availability, and reliability, and recoverability. Again, on the workload side, there are different types of workload and there're... There is as well as having the two transactional and analytic workloads, I think there's an emerging type of workload which is going to be very important for future applications where you want to combine transactional with analytic in real time, in order to automate business processes at a higher level, to make the business processes synchronous as opposed to asynchronous. And that degree of granularity, I think is missed, in a broader view of these companies and what they offer. It's in my view trying in some ways to not compare like with like from a customer point of view. So the very nuance, what you talked about, let's get into it, maybe that'll become clear to the audience. So like I said, these are very detailed research notes. There were several, I'll say analysts cooks in the kitchen, including Henry Cook, whom I don't know, but four other contributing analysts, two of whom are CUBE alum, Don Feinberg, and Merv Adrian, both really, you know, awesome researchers. And Rick Greenwald, along with Adam Ronthal. And these are public documents, you can go on the web and search for these. So I wonder if we could just look at some of the data and bring up... Guys, bring up the slide one here. And so we'll first look at the operational side and they broke it into four use cases. The traditional transaction use cases, the augmented transaction processing, stream/event processing and operational intelligence. And so we're going to show you there's a lot of data here. So what Gartner did is they essentially evaluated critical capabilities, or think of features and functions, and gave them a weighting, or a weighting, and then a rating. It was a weighting and rating methodology. On a s... The rating was on a scale of one to five, and then they weighted the importance of the features based on their assessment, and talking to the many customers they talk to. So you can see here on the first chart, we're showing both the traditional transactions and the augmented transactions and, you know, the thing... The first thing that jumps out at you guys is that, you know, Oracle with Autonomous is off the charts, far ahead of anybody else on this. And actually guys, if you just bring up slide number two, we'll take a look at the stream/event processing and operational intelligence use cases. And you can see, again, you know, Oracle has a big lead. And I don't want to necessarily go through every vendor here, but guys, if you don't mind going back to the first slide 'cause I think this is really, you know, the core of transaction processing. So let's look at this, you've got Oracle, you've got SAP HANA. You know, right there interestingly Amazon Web Services with the Aurora, you know, IBM Db2, which, you know, it goes back to the good old days, you know, down the list. But so, let me again start with Marc. So why is that? I mean, I guess this is no surprise, Oracle still owns the Mission-Critical for the database space. They earned that years ago. One that, you know, over the likes of Db2 and, you know, Informix and Sybase, and, you know, they emerged as number one there. But what do you make of this data Marc? >> If you look at this data in a vacuum, you're looking at specific functionality, I think you need to look at all the slides in total. And the reason I bring that up is because I agree with what David said earlier, in that the use case that's becoming more prevalent is the integration of transaction and analytics. And more importantly, it's not just your traditional data warehouse, but it's AI analytics. It's big data analytics. It's users are finding that they need more than just simple reporting. They need more in-depth analytics so that they can get more actionable insights into their data where they can react in real time. And so if you look at it just as a transaction, that's great. If you're going to just as a data warehouse, that's great, or analytics, that's fine. If you have a very narrow use case, yes. But I think today what we're looking at is... It's not so narrow. It's sort of like, if you bought a streaming device and it only streams Netflix and then you need to get another streaming device 'cause you want to watch Amazon Prime. You're not going to do that, you want one, that does all of it, and that's kind of what's missing from this data. So I agree that the data is good, but I don't think it's looking at it in a total encompassing manner. >> Well, so before we get off the horses on the track 'cause I love to do that. (Dave chuckles) I just kind of let's talk about that. So Marc, you're putting forth the... You guys seem to agree on that premise that the database that can do more than just one thing is of appeal to customers. I suppose that makes, certainly makes sense from a cost standpoint. But, you know, guys feel free to flip back and forth between slides one and two. But you can see SAP HANA, and I'm not sure what cloud that's running on, it's probably running on a combination of clouds, but, you know, scoring very strongly. I thought, you know, Aurora, you know, given AWS says it's one of the fastest growing services in history and they've got it ahead of Db2 just on functionality, which is pretty impressive. I love Google Spanner, you know, love the... What they're trying to accomplish there. You know, you go down to Microsoft is, they're kind of the... They're always good enough a database and that's how they succeed and et cetera, et cetera. But David, it sounds like you agree with Marc. I would say, I would think though, Amazon kind of doesn't agree 'cause they're like a horses for courses. >> I agree. >> Yeah, yeah. >> So I wonder if you could comment on that. >> Well, I want to comment on two vectors. The first vector is that the size of customer and, you know, a mid-sized customer versus a global $2,000 or global 500 customer. For the smaller customer that's the heart of AWS, and they are taking their applications and putting pretty well everything into their cloud, the one cloud, and Aurora is a good choice. But when you start to get to a requirements, as you do in larger companies have very high levels of availability, the functionality is not there. You're not comparing apples and... Apples with apples, it's two very different things. So from a tier one functionality point of view, IBM Db2 and Oracle have far greater capability for recovery and all the features that they've built in over there. >> Because of their... You mean 'cause of the maturity, right? maturity and... >> Because of their... Because of their focus on transaction and recovery, et cetera. >> So SAP though HANA, I mean, that's, you know... (David talks indistinctly) And then... >> Yeah, yeah. >> And then I wanted your comments on that, either of you or both of you. I mean, SAP, I think has a stated goal of basically getting its customers off Oracle that's, you know, there's always this urinary limping >> Yes, yes. >> between the two companies by 2024. Larry has said that ain't going to happen. You know, Amazon, we know still runs on Oracle. It's very hard to migrate Mission-Critical, David, you and I know this well, Marc you as well. So, you know, people often say, well, everybody wants to get off Oracle, it's too expensive, blah, blah, blah. But we talked to a lot of Oracle customers there, they're very happy with the reliability, availability, recoverability feature set. I mean, the core of Oracle seems pretty stable. >> Yes. >> But I wonder if you guys could comment on that, maybe Marc you go first. >> Sure. I've recently done some in-depth comparisons of Oracle and Aurora, and all their other RDS services and Snowflake and Google and a variety of them. And ultimately what surprised me is you made a statement it costs too much. It actually comes in half of Aurora for in most cases. And it comes in less than half of Snowflake in most cases, which surprised me. But no matter how you configure it, ultimately based on a couple of things, each vendor is focused on different aspects of what they do. Let's say Snowflake, for example, they're on the analytical side, they don't do any transaction processing. But... >> Yeah, so if I can... Sorry to interrupt. Guys if you could bring up the next slide that would be great. So that would be slide three, because now we get into the analytical piece Marc that you're talking about that's what Snowflake specialty is. So please carry on. >> Yeah, and what they're focused on is sharing data among customers. So if, for example, you're an automobile manufacturer and you've got a huge supply chain, you can supply... You can share the data without copying the data with any of your suppliers that are on Snowflake. Now, can you do that with the other data warehouses? Yes, you can. But the focal point is for Snowflake, that's where they're aiming it. And whereas let's say the focal point for Oracle is going to be performance. So their performance affects cost 'cause the higher the performance, the less you're paying for the performing part of the payment scale. Because you're paying per second for the CPUs that you're using. Same thing on Snowflake, but the performance is higher, therefore you use less. I mean, there's a whole bunch of things to come into this but at the end of the day what I've found is Oracle tends to be a lot less expensive than the prevailing wisdom. So let's talk value for a second because you said something, that yeah the other databases can do that, what Snowflake is doing there. But my understanding of what Snowflake is doing is they built this global data mesh across multiple clouds. So not only are they compatible with Google or AWS or Azure, but essentially you sign up for Snowflake and then you can share data with anybody else in the Snowflake cloud, that I think is unique. And I know, >> Marc: Yes. >> Redshift, for instance just announced, you know, Redshift data sharing, and I believe it's just within, you know, clusters within a customer, as opposed to across an ecosystem. And I think that's where the network effect is pretty compelling for Snowflake. So independent of costs, you and I can debate about costs and, you know, the tra... The lack of transparency of, because AWS you don't know what the bill is going to be at the end of the month. And that's the same thing with Snowflake, but I find that... And by the way guys, you can flip through slides three and four, because we've got... Let me just take a quick break and you have data warehouse, logical data warehouse. And then the next slide four you got data science, deep learning and operational intelligent use cases. And you can see, you know, Teradata, you know, law... Teradata came up in the mid 1980s and dominated in that space. Oracle does very well there. You can see Snowflake pop-up, SAP with the Data Warehouse, Amazon with Redshift. You know, Google with BigQuery gets a lot of high marks from people. You know, Cloud Data is in there, you know, so you see some of those names. But so Marc and David, to me, that's a different strategy. They're not trying to be just a better data warehouse, easier data warehouse. They're trying to create, Snowflake that is, an incremental opportunity as opposed to necessarily going after, for example, Oracle. David, your thoughts. >> Yeah, I absolutely agree. I mean, ease of use is a primary benefit for Snowflake. It enables you to do stuff very easily. It enables you to take data without ETL, without any of the complexity. It enables you to share a number of resources across many different users and know... And be able to bring in what that particular user wants or part of the company wants. So in terms of where they're focusing, they've got a tremendous ease of use, tremendous focus on what the customer wants. And you pointed out yourself the restrictions there are of doing that both within Oracle and AWS. So yes, they have really focused very, very hard on that. Again, for the future, they are bringing in a lot of additional functions. They're bringing in Python into it, not Python, JSON into the database. They can extend the database itself, whether they go the whole hog and put in transaction as well, that's probably something they may be thinking about but not at the moment. >> Well, but they, you know, they obviously have to have TAM expansion designs because Marc, I mean, you know, if they just get a 100% of the data warehouse market, they're probably at a third of their stock market valuation. So they had better have, you know, a roadmap and plans to extend there. But I want to come back Marc to this notion of, you know, the right tool for the right job, or, you know, best of breed for a specific, the right specific, you know horse for course, versus this kind of notion of all in one, I mean, they're two different ends of the spectrum. You're seeing, you know, Oracle obviously very successful based on these ratings and based on, you know their track record. And Amazon, I think I lost count of the number of data stores (Dave chuckles) with Redshift and Aurora and Dynamo, and, you know, on and on and on. (Marc talks indistinctly) So they clearly want to have that, you know, primitive, you know, different APIs for each access, completely different philosophies it's like Democrats or Republicans. Marc your thoughts as to who ultimately wins in the marketplace. >> Well, it's hard to say who is ultimately going to win, but if I look at Amazon, Amazon is an all-cart type of system. If you need time series, you go with their time series database. If you need a data warehouse, you go with Redshift. If you need transaction, you go with one of the RDS databases. If you need JSON, you go with a different database. Everything is a different, unique database. Moving data between these databases is far from simple. If you need to do a analytics on one database from another, you're going to use other services that cost money. So yeah, each one will do what they say it's going to do but it's going to end up costing you a lot of money when you do any kind of integration. And you're going to add complexity and you're going to have errors. There's all sorts of issues there. So if you need more than one, probably not your best route to go, but if you need just one, it's fine. And if, and on Snowflake, you raise the issue that they're going to have to add transactions, they're going to have to rewrite their database. They have no indexes whatsoever in Snowflake. I mean, part of the simplicity that David talked about is because they had to cut corners, which makes sense. If you're focused on the data warehouse you cut out the indexes, great. You don't need them. But if you're going to do transactions, you kind of need them. So you're going to have to do some more work there. So... >> Well... So, you know, I don't know. I have a different take on that guys. I think that, I'm not sure if Snowflake will add transactions. I think maybe, you know, their hope is that the market that they're creating is big enough. I mean, I have a different view of this in that, I think the data architecture is going to change over the next 10 years. As opposed to having a monolithic system where everything goes through that big data platform, the data warehouse and the data lake. I actually see what Snowflake is trying to do and, you know, I'm sure others will join them, is to put data in the hands of product builders, data product builders or data service builders. I think they're betting that that market is incremental and maybe they don't try to take on... I think it would maybe be a mistake to try to take on Oracle. Oracle is just too strong. I wonder David, if you could comment. So it's interesting to see how strong Gartner rated Oracle in cloud database, 'cause you don't... I mean, okay, Oracle has got OCI, but you know, you think a cloud, you think Google, or Amazon, Microsoft and Google. But if I have a transaction database running on Oracle, very risky to move that, right? And so we've seen that, it's interesting. Amazon's a big customer of Oracle, Salesforce is a big customer of Oracle. You know, Larry is very outspoken about those companies. SAP customers are many, most are using Oracle. I don't, you know, it's not likely that they're going anywhere. My question to you, David, is first of all, why do they want to go to the cloud? And if they do go to the cloud, is it logical that the least risky approach is to stay with Oracle, if you're an Oracle customer, or Db2, if you're an IBM customer, and then move those other workloads that can move whether it's more data warehouse oriented or incremental transaction work that could be done in a Aurora? >> I think the first point, why should Oracle go to the cloud? Why has it gone to the cloud? And if there is a... >> Moreso... Moreso why would customers of Oracle... >> Why would customers want to... >> That's really the question. >> Well, Oracle have got Oracle Cloud@Customer and that is a very powerful way of doing it. Where exactly the same Oracle system is running on premise or in the cloud. You can have it where you want, you can have them joined together. That's unique. That's unique in the marketplace. So that gives them a very special place in large customers that have data in many different places. The second point is that moving data is very expensive. Marc was making that point earlier on. Moving data from one place to another place between two different databases is a very expensive architecture. Having the data in one place where you don't have to move it where you can go directly to it, gives you enormous capabilities for a single database, single database type. And I'm sure that from a transact... From an analytic point of view, that's where Snowflake is going, to a large single database. But where Oracle is going to is where, you combine both the transactional and the other one. And as you say, the cost of migration of databases is incredibly high, especially transaction databases, especially large complex transaction databases. >> So... >> And it takes a long time. So at least a two year... And it took five years for Amazon to actually succeed in getting a lot of their stuff over. And five years they could have been doing an awful lot more with the people that they used to bring it over. So it was a marketing decision as opposed to a rational business decision. >> It's the holy grail of the vendors, they all want your data in their database. That's why Amazon puts so much effort into it. Oracle is, you know, in obviously a very strong position. It's got growth and it's new stuff, it's old stuff. It's, you know... The problem with Oracle it has like many of the legacy vendors, it's the size of the install base is so large and it's shrinking. And the new stuff is.... The legacy stuff is shrinking. The new stuff is growing very, very fast but it's not large enough yet to offset that, you see that in all the learnings. So very positive news on, you know, the cloud database, and they just got to work through that transition. Let's bring up slide number five, because Marc, this is to me the most interesting. So we've just shown all these detailed analysis from Gartner. And then you look at the Magic Quadrant for cloud databases. And, you know, despite Amazon being behind, you know, Oracle, or Teradata, or whomever in every one of these ratings, they're up to the right. Now, of course, Gartner will caveat this and say, it doesn't necessarily mean you're the best, but of course, everybody wants to be in the upper, right. We all know that, but it doesn't necessarily mean that you should go by that database, I agree with what Gartner is saying. But look at Amazon, Microsoft and Google are like one, two and three. And then of course, you've got Oracle up there and then, you know, the others. So that I found that very curious, it is like there was a dissonance between the hardcore ratings and then the positions in the Magic Quadrant. Why do you think that is Marc? >> It, you know, it didn't surprise me in the least because of the way that Gartner does its Magic Quadrants. The higher up you go in the vertical is very much tied to the amount of revenue you get in that specific category which they're doing the Magic Quadrant. It doesn't have to do with any of the revenue from anywhere else. Just that specific quadrant is with that specific type of market. So when I look at it, Oracle's revenue still a big chunk of the revenue comes from on-prem, not in the cloud. So you're looking just at the cloud revenue. Now on the right side, moving to the right of the quadrant that's based on functionality, capabilities, the resilience, other things other than revenue. So visionary says, hey how far are you on the visionary side? Now, how they weight that again comes down to Gartner's experts and how they want to weight it and what makes more sense to them. But from my point of view, the right side is as important as the vertical side, 'cause the vertical side doesn't measure the growth rate either. And if we look at these, some of these are growing much faster than the others. For example, Snowflake is growing incredibly fast, and that doesn't reflect in these numbers from my perspective. >> Dave: I agree. >> Oracle is growing incredibly fast in the cloud. As David pointed out earlier, it's not just in their cloud where they're growing, but it's Cloud@Customer, which is basically an extension of their cloud. I don't know if that's included these numbers or not in the revenue side. So there's... There're a number of factors... >> Should it be in your opinion, Marc, would you include that in your definition of cloud? >> Yeah. >> The things that are hybrid and on-prem would that cloud... >> Yes. >> Well especially... Well, again, it depends on the hybrid. For example, if you have your own license, in your own hardware, but it connects to the cloud, no, I wouldn't include that. If you have a subscription license and subscription hardware that you don't own, but it's owned by the cloud provider, but it connects with the cloud as well, that I would. >> Interesting. Well, you know, to your point about growth, you're right. I mean, it's probably looking at, you know, revenues looking, you know, backwards from guys like Snowflake, it will be double, you know, the next one of these. It's also interesting to me on the horizontal axis to see Cloud Data and Databricks further to the right, than Snowflake, because that's kind of the data lake cloud. >> It is. >> And then of course, you've got, you know, the other... I mean, database used to be boring, so... (David laughs) It's such a hot market space here. (Marc talks indistinctly) David, your final thoughts on all this stuff. What does the customer take away here? What should I... What should my cloud database management strategy be? >> Well, I was positive about Oracle, let's take some of the negatives of Oracle. First of all, they don't make it very easy to rum on other platforms. So they have put in terms and conditions which make it very difficult to run on AWS, for example, you get double counts on the licenses, et cetera. So they haven't played well... >> Those are negotiable by the way. Those... You bring it up on the customer. You can negotiate that one. >> Can be, yes, They can be. Yes. If you're big enough they are negotiable. But Aurora certainly hasn't made it easy to work with other plat... Other clouds. What they did very... >> How about Microsoft? >> Well, no, that is exactly what I was going to say. Oracle with adjacent workloads have been working very well with Microsoft and you can then use Microsoft Azure and use a database adjacent in the same data center, working with integrated very nicely indeed. And I think Oracle has got to do that with AWS, it's got to do that with Google as well. It's got to provide a service for people to run where they want to run things not just on the Oracle cloud. If they did that, that would in my term, and my my opinion be a very strong move and would make make the capabilities available in many more places. >> Right. Awesome. Hey Marc, thanks so much for coming to theCUBE. Thank you, David, as well, and thanks to Gartner for doing all this great research and making it public on the web. You can... If you just search critical capabilities for cloud database management systems for operational use cases, that's a mouthful, and then do the same for analytical use cases, and the Magic Quadrant. There's the third doc for cloud database management systems. You'll get about two hours of reading and I learned a lot and I learned a lot here too. I appreciate the context guys. Thanks so much. >> My pleasure. All right, thank you for watching everybody. This is Dave Vellante for theCUBE. We'll see you next time. (upbeat music)

>> From the SiliconAngle media office in Boston, Massachusetts, it's theCUBE! Now, here's your host, Stu Miniman. >> Hi, I'm Stu Miniman, and welcome to theCUBE's Boston-area studio. Welcome back to the program, CUBE alum, Ashok Ramu, Vice President and General Manager of Cloud at Actifio, great to see you. >> Happy New Year, Stu, happy to be here. >> 2020, hard to believe it said, it feels like we're in the future here. And talking about future, we've watched Actifio for many years, we remember when copy data management, the category, was created, and really, Actifio, we were talking a lot before Cloud was the topic that we spent so much talking about, but Actifio has been on this journey with its customers in Cloud for many years, and of course, that is your role is working, building the product, the team working all over it, so give us a little bit of a history, if you would, and give us the path that led to 10C announcement. >> Sure thing. We started the Cloud journey early on, in 2014 or 2013-ish, when Amazon was the only Cloud that really worked. We built our architecture, in fact, we took our enterprise architecture and put it on the Cloud and realized, "Oh my god," you know, it's a world of difference. The economics don't work, the security model is different, the scale is different. So, I think with the 8.0 version that came out in 2017, we really kind of figured out the architecture that worked for large enterprises, particularly enterprises that have diverse data sets and have requirements around, you know, marrying different applications to data sets anywhere they want, so we came up with efficient use of object, we came up with the capability of migrating workloads, taking VMware VMs, bringing up on Azure, bringing up on DCP, et cetera. So that was the first foray into Actifio's Cloud, and since then, we've been just building strength after strength, you know. It's been a building block, understanding our customers, and thank you to the customers and the hyperscalers that actually led us to the 10C release. So this, I believe, we've taken it up a notch wherein, we understand the Cloud, we understand the infrastructure, the software auto-tunes itself to know where it's running on, taking the guessing game out of the equation. So 10C really represents what we see as a launchpad for the rest of the Cloud journey that Actifio's going to embark upon. We have enabled a number of new use cases like AI and ML, data transformation is key, we tackled really complicated workloads like HANA and Sybase and MySQL, et cetera, and in addition to that, we also adopt different native Cloud technologies, like Cloud snapshots, like recovery orchestration of the Cloud, et cetera. >> Yeah, I think it's worth reminding our audience there that Actifio's always been software. And when you talk about, you know, I think back to 2013, 2014, it was the public Cloud versus the data center, and we have seen the public Cloud in many ways looks more and more like what the enterprise has been used to. >> Absolutely. >> And the data centers have been trying to Cloud-ify for a number of years, and things like containerization and Kubernetes is blurring the line, and of course, every hyperscaler out there now has something that reaches their public Cloud into the data center and of course, technologies like VMware are also extending into the public Cloud, or, SAP now, of course is all of the Cloud environment. So with hybrid Cloud and multi-Cloud as kind of the waves of driving, help us understand that Actifio lives in all of these environments, and they're all a little bit different, so how does Actifio make sure that it can provide the functionality and experience that users want, regardless of where it is? >> Absolutely, you said it right. Actifio has always been a software company. And it is our customers that showed us, by Cloudifying their data centers, that we had to operate in the Cloud. So we had on premises VMware Clouds, not before we had Amazon and Azure and Google. So that evolution started much early on. And so, from what, you know, Actifio's a very customer-driven company, be it, you know, all segments of the company are driven by the customers, and in 2019, and even before, when you see a strong trend to migrate workloads, to move workloads, we realized, there is a significant opportunity, because the hardest thing to migrate is the volume of data because it's ever-changing, and it is ever-growing. So, the key element of neutrality was the application itself. Microsoft SQL's a SQL no matter how you run it. It could be on a big Windows machine in your data center or a NGCP, it makes no difference. So Actifio's approach to start application down basically gave us the freedom to say, we're going to create SQL to SQL. I don't know if you're running in Azure, Google, DOP data center, or AliCloud, it makes no difference to me. I understand SQL, I understand SQL's availability groups, I understand logs, I can capture it and give it back to you, so when we took that approach, it kind of automatically gave us infrastructure neutrality, really didn't care. So when we have a conversation with a customer, it basically goes around lines of, "Okay, Mr. Customer, how much data do you have? And what are your key applications? Can you categorize them in terms of priority?" It usually comes out to be databases are the crown jewels, so they're the number one priority in terms of data management, migration, test Av, et cetera. And then, we basically drill down into the ecosystem the databases live into. So, because we walk application down, the conversation is the same whether the customer is in the data center, or in the Cloud. So that is how we've evolved, and that's how we're thinking from a product standpoint, from a support standpoint, and then the overall company is built that way. So it makes it easy for us to adapt a new platform that comes in. So, when you talked about, you know, how does, each Cloud is different, you're absolutely right, the security concepts are different, right? Microsoft is built on active directory, Google is built on something very different. So how do you utilize and how do you make this work? We do have an infrastructure layer that basically provides Cloud-specific capabilities for various Cloud platforms. And that has gotten to a point where it understands and tunes itself from a security standpoint and a performance standpoint. Once that's taken care of, the rest of the application stack, which is over 90% of our software, stays the same, there's no change. And so that is how we kind of tackle this. Because the ecosystem we live in, we have to keep up with two people. We have to keep up with the infrastructure people who are making it bigger, faster, and we also have to keep up with the application people who are making it fancier and more complicated. So that's unfortunately the ecosystem we live in, and taking this approach has given us a mechanism to insulate us from a lot of the complexities of these two environments. >> Yeah, that's great, 'cause when you talk to customers and you say, "What's going on in your environment," change is difficult. So, how many different pieces of what I'm doing do I need to move to be able to take advantage of the modern economics. On the one hand, you know, if I have an application and I like it, well, maybe I could just lift and shift it, but if I'm just lifting, shifting, I'm not necessarily taking advantage of the full Cloud native environments, but I need to make sure that my data is protected, backup, you mentioned security, are of course the top concerns, so. It sounds like, in many ways, you're talking, helping customers work through some of those initiatives, being able to take advantage of new environments, but not need to completely change everything. Maybe, I'd love to hear a little bit, when you talk about the developers and DevOps initiatives that are happening inside customers, where does that impact, where does that connect with what Actifio's doing? >> Well, that's a great question. So, let me start with a real customer example. We have this customer, SEI Investments, who basically, their business model is to grow by acquisition, so they're adding on tens, hundreds of developers every quarter. So it's impossible to keep up with infrastructure needs when you grow at that pace. They decided to adopt a Cloud platform. And with each Cloud platform comes some platform-specific piece that all these developers now have to re-tool themselves. So, I'm a developer, I used to come in the morning, open up my machine and start working away on the application, now I have to do something different, and if there is 300 of me, and the cost of moving to the Cloud was a lot less than training the developers. It was much harder to train the developers because it has been ongoing process. So we were presented the challenge of how do you avoid it? So, when we are able to separate the application layer from the data layer, because of the way we operate, what we present as a solution was to say, just move your, what is the heaviest layer you have? That's the database, okay. And what are the copies you're creating? I'm creating hundreds of copies of my Oracle database, okay. Let's just move that to the Cloud. All of the front-end application doesn't see a change, thanks to the great infrastructure work the Cloud providers do, you add 10 Gigabyte to everywhere. So network is not a problem, computer's not a problem, it's just available on an API call, so you provision that. All they did was a data movement, moved it from Point A to Point B, gives you the flexibility to spend up any number of copies you want in the Cloud, now, your developer tool sets haven't changed, so there's no training required for developers, but from an operations standpoint, you've completely eased the burden of creating a hundred more copies every month, because Cloud is built for that. So you take the elasticity of the Cloud, advantage of that, and provide the data in the last mile to the Cloud, thereby, developers, they will access the application with the same level of ease. So, that is the paradigm we're seeing, we're seeing, you know, in some of our customers, there is faster and better storage provision for Actifio because there are 190 developers working off Actifio, where there's only about a handful of people running production. So, it's a paradigm shift is where we see it. And the pace at which we bring up the application wherein we're able to bring up 150 terabyte article database in three hours. Before Actifio, it used to be, maybe, 30 days, if you were lucky. So it's not just an order of magnitude, it's what you can do with that data, is where we're seeing the shift going to. >> Yeah, it's interesting, when you go back and look at some of the changes that have happened in the Cloud, Cloud storage was one of the earliest discussed use cases there, and backup to the Cloud was one of the earlier pieces of the Cloud storage discussion. Yet, we've seen changes and maturation into what can actually be done, explain a little bit how Actifio enables even greater functionality when you're talking about backup to the Cloud. >> Absolutely. You know, the object storage technology, it's probably the most scalable and stable piece of storage known to mankind, because nobody can build that level of scale that Amazon, Azure, and Google have put into it. From a security standpoint, performance standpoint, and scale standpoint. So I'm able to drop my data in Boston and pick it up in Tokyo seamlessly, right? That's unheard of before. And the biggest impediment to that, was a lot of legacy application data didn't know how to consume this object storage. So what Actifio came up with on onboard technology was to light up the object storage for everybody, and basically make it a performance neutral platform, wherein you take the guessing game out of the customer. The customer doesn't need to go research S3 or Google Nearline or Google Persistent Disk and say I want ten copies there versus five copies there, Actifio figures it out for you. You give us your SLA, you give us your RTOs and RPOs, and we tell you, okay, this is the most cost effective way to store your data. You get the multi-year retention for free, you get the GDPR, appchafe and protection for free, you get the geo-redundancy for free. All this is built into the platform. In addition, you also can run DevOps off the object store. You can run DR off the object store. So we enabled a lot of the legacy use cases using this new technology, so that is kind of where we see the cusp, wherein, in the Cloud, there's always a question and a debate, does D-doop make sense? D-doop consumes a lot of compute, takes a lot of memory, you need to have that memory and compute whether you want it or not. We're seeing a lot more adoption of encryption, where the data is encrypted at source. When you encrypt data, D-doop is just a big compute-churning platform, it doesn't do much for you. So we went through this debate actively, I think four or five years ago, and we figured out, object store's the way to go. You cannot get storage, I mean, it's a buck a terabyte in Google, and dropping. How can you get storage that's reliable, scalable, at a lower cost? All we had to do was actuate the use of that storage, which is what we did. >> Yeah. I'm just laughing a little bit because, you know, gosh, I think back a dozen years ago, the industry knew that the future of storage would be object, yet it's taken a long time to really be able to leverage it and use it, and the Cloud, the hyperscalers of course, have been a huge enabler on that, but we don't want customers to have to think about that it's object underneath, and that's the bridging the gap that I think we've been looking for. There, what else. We talk about really being able to extract the value out of Cloud, you know, data protection, disaster recovery, migrations are all things that are top of mind. >> Yeah, absolutely. All those use cases, and we're seeing some of the top rating CIOs talk about AI and ML. We've had a couple of customers who want to basically take their manufacturing data from remote sites and pump it into Google bit query. Now we all know manufacturing happens in Taiwan and Singapore and all those locations, now how do you take data from all those applications, normalize it, and pump it into Google bit query and get your predictable results on a quarterly basis, it's a challenge. Because the data volumes are large. So with our Cloud technology and our onboard capability, we're able to funnel data directly into Google Nearline, and on a quarterly basis, on a scheduled basis, transform it, push it into bit query, and bring out the results for the end user. So that journey is pretty transformated, from a customer standpoint. What they used to have five people do maybe once a year, now with a push of a button happens every quarter. So it's a change in how the AI and ML analytics evolve. The other element is also you know, our partnership with IBM, we're working very closely with their Cloud bag for data. Cloud bag for data is an awesome platform built to analyze any kind of data that you might have. With Actifio's normalization platform, you basically can feed any data into Actifio and it presents a unified interface into the slow pack, so you can build your analytics workloads very quickly and easily. >> So we've talked a lot about Cloud, one of the other C's of course in 10C is containers, if we look at containerization, when it first started, it was stateless applications, most applications that are running in containers are running for very short period of time, so help us understand where Actifio fits there, what's the problem statement that you are solving? >> Oh, absolutely. So containers are coming up, up and coming and out of reality, and as we see more applications flow into containers, you see the data lives outside the container. Because containers are short-lived, they're microservices, they come up and they go down, and the state is maintained in a storage platform outside the container, so Actifio tackles containers by taking the data protection strategy we have for the storage platform already, Bell defined, but enhancing the data presentation into the container as it comes up. So a container can be brought up in seconds, maybe less. But the container is only brought to life when it can lead to data and start working again, so that's the bridge Actifio actuates. So we understand, you know, the architecture of how a container is put together, how the container system is put together, and basically, we marry the storage and the application consistent in the storage into the container so that the container's databases, or applications, come to life. >> And that could be in a customer's data center, in a public Cloud, Kubernetes enabled, all of that? >> Absolutely, it can be anywhere, and with 10C, what we have done is we've also integrated with Cloud Native Snapshot, so if you talk about net neutrality for the container platform, if it's on premises, we have all kinds of access to the storage, the infrastructure, and the platforms so our processing is very different. If you take it to the Cloud, let's say Google, Google Kubernetes platform is fairly, it's a black box. You get some storage, and you get containers. And you have an API access to the storage. So in Google, we automatically autotune and start taking the Google snapshots to take the storage perfection, so that's the other way we've kind of neutralized the platform. >> Yeah, you've got a, thinking about it just from a customer's standpoint, one of the big challenges there is they've got everything from their big monoliths, they're big databases, through these microservice Cloud native architectures there, and it sounds like you know, is that just one of the fundamental architectural designs to make sure that you can span across those environments and give customers a common look and feel between those environments? >> Absolutely. The single pane of glass is a big askt and a big focus for us, not just across infrastructure, it's across geos and across all platforms. So you could have workloads running AIX6, VMware, in the Cloud, all the way through containers, and manage it all to a single console, to know when was the last good backup, how many copies of the database am I running, and each of these databases could have their own security constructs. So we normalize all of those elements and put them in a single console. >> Okay, 10C, shipping today? >> 10C shipping today, we have early access to a few customers, the general availability releases possibly in the February timeframe. >> Okay, and if I'm an existing Actifio customer, what's the path for me to get to 10C? >> Our support will reach out and do a simple software upgrade, it's available on all Cloud platforms, it's available everywhere, so you will see that on all the marketplaces and the regular upgrade process will get you that. >> Okay, and if I'm not an Actifio customer today, how easy is it for me to try this out? >> Oh, it is very easy, with our Actifio go SAS platform, it's a one-click download, you can download and try it out, try all the capabilities of the platform, it's also available on all the Cloud marketplaces for you to go and access that. >> All right, well, Ashok, a whole lot of pieces inside of 10C, congratulations to you and the team for building that, and definitely look forward to hearing more about the customer deployments. >> Thank you, we have exciting times ahead. >> All right. Lots more coverage from theCUBE throughout 2020, be sure to check out theCUBE.net, I'm Stu Miniman, thanks for watching theCUBE. (techno music)

(upbeat music) >> Woman Voiceover: From our studios in the heart of Silicon Valley, Palo Alto, California this is a CUBE Conversation. >> Hello and welcome to this special CUBE Conversation here in Palo Alto, California, CUBE Studios, I'm John Furrier, host of theCUBE, here with Sri Ambati. He's the founder and CEO of H20.ai. CUBE Alum, hot start up right in the action of all the machine learning, artificial intelligence, with democratization the role of data in the future, it's all happening with Cloud 2.0, DevOps 2.0, Sri, great to see you. Thanks for coming by. You're a neighbor, you're right down the street from us at our studio here. >> It's exciting to be at theCUBE Com. >> That's KubeCon, that's Kubernetes Con. CUBEcon, coming soon, not to be confused with KubeCon. Great to see you. So tell us about the company, what's going on, you guys are smoking hot, congratulations. You got the right formula here with AI. Explain what's going on. >> It started about seven years ago, and .ai was just a new fad that arrived that arrived in Silicon Valley. And today we have thousands of companies in AI, and we're very excited to be partners in making more companies become AI-first. And our vision here is to democratize AI, and we've made it simple with our open source, made it easy for people to start adapting data science and machine learning in different functions inside their large organizations. And apply that for different use cases across financial services, insurance, health care. We leapfrogged in 2016 and built our first closed source product, Driverless AI, we made it on GPUs using the latest hardware and software innovations. Open source AI has funded the rise of automatic machine learning, Which further reduces the need for extraordinary talent to fill the machine learning. No one has time today, and then we're trying to really bring that automatic machine learning at a very significant crunch time for AI, so people can consume AI better. >> You know, this is one of the things that I love about the current state of the market right now, the entrepreneur market as well as startups and growing companies that are going to go public. Is that there's a new breed of entrepreneurship going on around large scale, standing up infrastructure, shortening the time it takes to do something. Like provisioning. The old AIs, you got to be a PHD. And we're seeing this in data science, you don't have to be a python coder. This democratization is not just a tag line, actually the reality is of a business opportunity. Whoever can provide the infrastructure and the systems for people to do it. It is an opportunity, you guys are doing that. This is a real dynamic. This is a new way, a new kind of dynamic and an industry. >> The three real characteristics on ability to adopt AI, one is data is a team sport. Which means you've got to bring different dimensions within your organization to be able to take advantage of data and AI. And you've got to bring in your domain scientists, work closely with your data scientists, work closely with your data engineers, produce applications that can be deployed, and then get your design on top of it that can convince users or strategists to make those decisions that data is showing up So that takes a multi-dimensional workforce to work closely together. The real problem in adoption of AI today is not just technology, it's also culture. So we're kind of bringing those aspects together in formal products. One of our products, for example, Explainable AI. It's helping the data scientists tell a story that businesses can understand. Why is the model deciding I need to take this test in this direction? Why is this model giving this particular nurse a high credit score even though she doesn't have a high school graduation? That kind of figuring out those democratization goes all the way down. Why is the model deciding what it's deciding, and explaining and breaking that down into English. And building a trust is a huge aspect in AI right now. >> Well I want to get to the talent, and the time, and the trust equation on the next talk, but I want to get the hard news out there. You guys have some news, Driverless AI is one of your core things. Explain the news, what's the big news? >> The big news has been that... AI's a money ball for business, right? And money ball as it has been played out has been the experts were left out of the field, and algorithms taking over. And there is no participation between experts, the domain scientists, and the data scientists. And what we're bringing with the new product in Driverless AI, is an ability for companies to take our AI and become AI companies themselves. The real AI race is not between the Googles and the Amazons and the Microsofts and other AI companies, AI software companies. The real AI race is in the verticals and how can a company which is a bank, or an insurance giant, or a healthcare company take AI platforms and become, take the data and monetize the data and become AI companies themselves. >> Yeah, that's a really profound statement I would agree with 100% on that. I think we saw that early on in the big data world around Hadoop, well Hadoop kind of died by the wayside, but Dave Vellante and the WikiBon team have observed, and they actually predicted, that the most value was going to come from practitioners, not the vendors. 'Cause they're the ones who have the data. And you mentioned verticals, this is another interesting point I want to get more explanation from you on, is that apps are driven by data. Data needs domain-specific information. So you can't just say "I have data, therefore magic happens" it's really at the edge of the domain speak or the domain feature of the application. This is where the data is, so this kind of supports your idea that the AI's about the companies that are using it, not the suppliers of the technology. >> Our vision has always been how we make our customers satisfied. We focus on the customer, and through that we actually make customer one of the product managers inside the company. And the doors that open from working very closely with some of our leading customers is that we need to get them to participate and take AIs, algorithms, and platforms, that can tune automatically the algorithms, and have the right hyper parameter optimizations, the right features. And augment the right data sets that they have. There's a whole data lake around there, around data architecture today. Which data sets am I not using in my current problem I'm solving, that's a reasonable problem I'm looking at. That combination of these various pieces have been automated in Driverless AI. And the new version that we're now bringing to market is able to allow them to create their own recipes, bring their own transformers, and make an automatic fit for their particular race. So if you think about this as we built all the components of a race car, you're going to take it and apply it for that particular race to win. >> John: So that's the word driverless comes in. It's driverless in the sense of you don't really need a full operator, it kind of operates on its own. >> In some sense it's driverless. They're taking the data scientists, giving them a power tool. Historically, before automatic machine learning, driverless is in the umbrella of machine learning, they would fine tune, learning the nuances of the data, and the problem at hand, what they're optimizing for, and the right tweaks in the algorithm. So they have to understand how deep the streets are going to be, how many layers of deep learning they need, what variation of deep learning they should put, and in a natural language crossing, what context they need. Long term shot, memory, all these pieces they have to learn themselves. And there were only a few grand masters or big data scientists in the world who could come up with the right answer for different problems. >> So you're spreading the love of AI around. >> Simplifying that. >> You get the big brains to work on it, and democratization means people can participate and the machines also can learn. Both humans and machines. >> Between our open source and the very maker-centric culture, we've been able to attract some of the world's top data scientists, physicists, and compiler engineers. To bring in a form factor that businesses can use. One data scientist in a company like Franklin Templeton can operate at a level of ten or hundreds of them, and then bring the best in data science in a form factor that they can plug in and play. >> I was having a concert with Kent Libby, who works with me on our platform team. We have all this data with theCUBE, and we were just talking, we need to hire a data scientist and AI specialist. And you go out and look around, you've got Google, Amazon, all these big players spending between 3-4 million per machine learning engineer. And that might be someone under the age of 30 with no experience. So the talent bore is huge. The cost to just hire, we can't hire these people. >> It's a global war. There's talent shortage in China, there's talent shortage in India, there's talent shortage in Europe, and we have offices in Europe and India. There's a talent shortage in Toronto and Ottawa. So it's a global shortage of physicists and mathematicians and data scientists. So that's where our tools can help. And we see Driverless AI as, you can drive to New York or you can fly to New York. >> I was talking to my son the other day, he's taking computer science classes in night school. And it's like, well you know, the machine learning in AI is kind of like dog training. You have dog training, you train the dog to do some tricks, it does some tricks. Well, if you're a coder you want to train the machine. This is the machine training. This is data science, is what AI possibility is there. Machines have to be taught something. There's a base input, machines just aren't self-learning on their own. So as you look at the science of AI, this becomes the question on the talent gap. Can the talent gap be closed by machines? And you got the time, you want speed, low latency, and trust. All these things are hard to do. All three, balancing all three is extremely difficult. What's your thoughts on those three variables? >> So that's why we brought AI to help with AI. Driverless AI is a concept of bringing AI to simplify. It's an expert system to do AI better. So you can actually give to the hands of the new data scientists, so you can perform at the power of an advanced data scientist. We're not disempowering the data scientist, the part's still for a data scientist. When you start with a confusion matrix, false positives, false negatives, that's something a data scientist can understand. When you talk about feature engineering, that's something a data scientist can understand. And what Driverless AI is really doing is helping him do that rapidly, and automated on the latest hardware, that's where the time is coming into. GPUs, FPGAs, TPUs, different form of clouds. Cheaper, right. So faster, cheaper, easier, that's the democratization aspect. But it's really targeted at the data scientist to prevent experimental error. In science, the data science is a search for truth, but it's a lot of experiments to get to truth. If you can make the cost of experiments really simple, cheaper, and prevent over fitting. That's a common problem in our science. Prevent bias, accidental bias that you introduce because the data is biased, right. So trying to prevent the flaws in doing data science. Leakage, usually your signal leaks, and how do you prevent those common pieces. That's where Driverless AI is coming at it. But if you put that in a box, what that really unlocks is imagination. The real hard problems in the world are still the same. >> AI for creative people, for instance. They want infrastructure, they don't want to have to be an expert. They want that value. That's the consumerization. >> AI is really the co founder for someone who's highly imaginative and has courage, right. And you don't have to look for founders to look for courage and imagination. A lot of entrepreneurs in large companies, who are trying to bring change to their organizations. >> Yeah, we always say, the intellectual property game is changing from protocols, locked in, patented, to you could have a workflow innovation. Change one little tweak of a process with data and powerful AI, that's the new magic IP equation. It's in the workflow, it's in the application, it's new opportunities. Do you agree with that? >> Absolutely. The leapfrog from here is businesses will come up with new business processes. So we looked at business process optimization, and globalization's going to help there. But AI, as you rightfully said earlier, is training computers. Not just programming them, you're schooling them. A host of computers that can now, with data, think almost at the same level as a Go player. The world's leading Go player. They can think at the same level of an expert in that space. And if that's happening, now I can transform. My business can run 24 by 7 and the rate at which I can assemble machines and feed it data. Data creation becomes, making new data becomes, the real value that AI can- >> H20.ai announcing Driverless AI, part of their flagship product around recipes and democratizing AI. Congratulations. Final point, take a minute to explain to the folks just the product, how they buy it, what's it made of, what's the commitment, how do they engage with you guys? >> It's an annual license, a software license people can download on our website. Get a three week trial, try it on their own. >> Free trial? >> A free trial, our recipes are open-source. About a hundred recipes, built by grand masters have been made open source. And they can be plugged, and tried. Customers of course don't have to make their software open source. They can take this, make it theirs. And our vision here is to make every company an AI company. And that means that they have to embrace AI, learn it, tweak it, participate, some of the leading conservation companies are giving it back in the open source. But the real vision here is to build that community of AI practitioners inside large organizations. We are here, our teams are global, and we're here to support that transformation of some large customers. >> So my problem of hiring an AI person, you could help me solve that. >> Right today. >> Okay, so anyone who's watching, please get their stuff and come get an opening here. That's the goal. But that is the dream, we want AI in our system. >> I have watched you the last ten years, you've been an entrepreneur with a fierce passion, you want AI to be a partner so you can take your message to wider audience and build monetization around the data you have created. Businesses are the largest, after the big data warlords we have, and data privacy's going to come eventually, but I think businesses are the second largest owners of data they just don't know how to monetize it, unlock value from it, and AI will help. >> Well you know we love data, we want to be data-driven, we want to go faster. Love the driverless vision, Driverless AI, H20.ai. Here in theCUBE I'm John Furrier with breaking news here in Silicon Valley from hot startup H20.ai. Thanks for watching.

>> Narrator: Live from Las Vegas, it's theCUBE! Covering IBM Think 2018. Brought to you by IBM. >> Welcome back to IBM Think 2018, you're watching theCUBE, the leader in live tech coverage, my name is Dave Vellante, I'm here with my co-host Peter Burris. Gene Chao is here as the Global VP of IBM Automation and Jason Kelley, Cube Alum, is the GM of Blockchain Services. Gentlemen, welcome back to theCUBE. >> Thank you much. >> Great to see you. >> You guys, I call you heat-seeking inefficiency missiles, so, Jason's... Just a shout-out, take it from there. What are you guys up to, what are you doing? How are you helping businesses? >> Well, we're driving trust into transactions. The elusive things that we've been trying to-- >> Gene: Whoops, there goes heat-seeking. (laughing) >> Exactly. Or we're seeking the heat. It's coming after us, as soon as we say trust, someone wants to attack you. And so what we're bringing into business is that thought that, if I can add trust into transactions, I don't need a third-party to validate it. I can now say, look, you are who you are. We both know each other. All that we do, we go way back. We know each other, and what we're about to exchange is known as well. So if I can keep that validation from happening, I'm going to remove cost, labor, time, out of it. And I'm also going to then maybe avail new market opportunities of those who could not enter the system before because we didn't trust their identities. Or we didn't trust that their goods were their goods, and they were trying to exchange it. So think of that heat-seeking missile, we're trying to bring that capability and that heat is the energy in the system now going bigger, better, faster because there's trust. >> And your role is to bring those Blockchain services to market, is that right? >> That's correct, bringing the services as a whole, because see, Blockchain isn't a product. Blockchain, you know, I don't have under the table a bucket of Blockchain. >> Dave: Let me see your Blockchain. >> Sorry, no Blockchains here. So, if in fact, we're bringing this capability to the market, there's all types of services from what's the business value design? First, what's your outcome? Why say Blockchain? Believe it or not, it says it on my chest, so it means I get paid to do it, but maybe you don't need this? And so, quite simply, maybe you need to do something else. So the first thing is, let's understand the outcome that your business is running toward, and then let's understand if it's a Blockchain, and then can we bring some automation with Gene and team? >> Okay, that's the set-up for you Gene, so you're the automation piece of the puzzle. Explain. >> So, I love the commentary around the better, faster, but we're also bringing more scale. So automation has scale. What does that mean? We're really focused on two things, guys, the first thing is around taking advantage of the new technologies to enable what I'll call software-based labor. So there's a new concept of the digital workforce model that enables how transactions or how work gets done. Coupled with that is how that workflow or process, business process, IT process, whatever it is, how does that workflow fundamentally change through these technologies. Why that's important is as we look at Blockchain, as an example, as a pivot point for trusted transactions, I need to build trusted automation around it. Trusted ways to leverage these technologies in that workflow so those transactions are easily scalable, works at machine time, and runs through very quickly. >> This is fascinating stuff, 'cause look. The way that we like to characterize the big change in the industry is we say, for the first 50 years of computing, there was no process, accounting, HR, et cetera, on known technology. How do we implement? What technology do you choose to implement? The implementation choices are becoming clear. Cloud, et cetera. What's less known is the process. The unknown process, unknown technology. Now it's unknown process, known technology. And what you guys are talking about is one of the challenges when you think about processes. Who does what? Can we verify that we've done it? Did they do it right? Did they meet to do what they said they were doing? Et cetera, the whole range of issues. And the contracting process is extremely complex, but if you set it up in a Blockchain form, you've got a simple contract, a simple definition of who is trusted, simple definitions of roles, and now we can dramatically accelerate new process creation and then automate it. Have I got that right? >> I think you got it, when you think about dramatically, dramatically accelerated, you say that it means something different to everyone. But let's think about my friend Frank Yiannas at Wal-Mart, for example, where they're working on food trust. They're trying to make sure that from farm to fork, we know where that food came from. One-third of all food that's processed goes to waste. Because we lack food trust. Food is guilty until proven innocent, right? To keep that from being-- >> Spoiled. >> Spoiled, I'm... The humor is killing me. (laughing) So, no pun intended, food trust, right? So, Frank and team wanted to understand how fast they could move this thought of tracking, tracing, with transparency, this food through the system. Just as you said, there's certain contrast, think of the handshakes from getting, in their case, a mango from a farm all the way to your home, Well, it used to take them seven days. Actually, six days, twenty-some hours, in order to figure out that process. Put it on the Blockchain? 12 seconds. And then once they cured the lag and the technology, 2.2 seconds. So think of that. Now you're shrinking this to seconds versus days, what does that do to the process? What do you do when you say, now my system can go that fast. My people can go that fast. What do you do? Think of the automation that you're bringing in now, and things that you will now have to automate, out of not just necessity, but things you will say, wow, we've opened up a whole new ecosystem of possibilities in order to do business in a different way. >> Well, so let me build on that for a second. 'Cause one of the things that potentially means is that because you can handle more complex, newly designed, process, better, faster, more automated, that you can start to expand the scope of participants in a transaction? The range of characteristics of the transaction, or the type of work? That's how you build up to new businesses and new business models, right? >> Sure. >> Right, right. >> If I can jump in on that one. There's a concept in this one, and this is where Jason and I are connected at the hip. You know, we think in terms of a smarter product, we think in terms of a smarter contract, or transaction, that the guiding principle that we're using is the old way of thinking, and I carry this narrative all over with me is, the old way of thinking is you have people following your creating process, supported by that technology. So the things that you talked about, unknown technology, unknown process, continuously sourced by people? Fundamentally changed. We're now working in a world where the process is run by the technology and supported by the people. It's not that the people are going away, it's a fundamental retooling of the skills and understanding of how to support it, but that scalability, the ability to get to that exponential growth, is because the process is the king. At the top of the food chain, now. And that technology lets it expand. >> But we could do levels of complexity in that process and the number of participants in that process, unheard of! It's scale and scope. >> Yes. >> But doesn't that force... Look, we've had some conversations, Dave and I have had some conversations, with a number of big user organizations about this stuff and we keep coming back to the issue of that they can't just look at the technology, they have to focus on the design. That one of the most crucial features of this process is the design of the Blockchain. We got that right? >> You heard me use the phrase at the very beginning, if you didn't, I'll say it again, I said, business value design. Because in fact, that design is not just a UI or UX, but let's make sure that the business and technology are doing the right thing to get to the outcome. As we say, design doesn't stop until the problem is solved. And guess what, the problem's never solved. So design happens... Many people say, "Oh we're going to do some "design thinking at the beginning. "We did that," check the block, and then they run off and do something else. For us, design's like an infinity loop. You continue to do it. From the beginning all the way to the end, and then, what you're able to do, and hint-hint, this is something that we do in our services, we start with our clients, we get them started so they understand, then we help them accelerate, and then innovate. Three steps: start, accelerate, innovate. And that's a design process in and of itself. So if you start at, you know, the days of Blockchain tourism were a couple years ago, everybody wanted to kick the tires, and then last year was PoC PoV, this year's the year of production. And people are quick in saying, "How do I quickly start "production and keep moving?" >> So let's talk about some other examples. You mentioned Wal-Mart, we heard Plastic Mag this morning, I introduced somebody, I think Evercorp was the name of the company, Diamond Providence. Others that you're excited about, that have made a business impact. >> Well, I'd be remiss if I didn't mention Mike White and others at our JV with Maersk. And you know, you think of that, where you have the classic thought of a supply chain, this linear steps in the process, you know, these handshakes that have to happen. Now what we have is we have this process of thinking how we can bring transparency into all of that, and it's not just a supply chain, but a value chain. So you have where 80% of whatever you all are touching or have owned right now, with the shipping line. But not only through a shipping line, but then there was also ground and air, and ultimately to a retail location. Then you consumed it. Well, think of all of those processes now having the transparency where you can see from point of consumption all the way back to origin. Think of the supply chain visibility, that elusive thing called supply chain optimization. Now you can do that, but not only the supply chain, but the value chain. Someone's paying invoices under that big thing called a value chain. Someone's doing trade promotion management in that value chain. Now, if you have that visibility, what do you enable? How many more packages can go through the system? How much more shipping? And the estimate is 5% increase in GDP if we're able to get all of this shipping into the Blockchain. You start talking GDP? It opens eyes. >> Right now you're talking growth, right? >> Yes. >> Real growth. >> So, it's 20% of the four trillion associated with shipping? Is bound up in paperwork? >> Yes. >> So we're talking about 800 billion dollar change. >> And returning capital into the system. Returning capital. You think of this thought of opening up new opportunity, And I'll throw another example, another client, so we're not just talking, but you think of what's happening with We.Trade. Nine banks in Europe who compete. You think of Santander Bank and a Deustche Bank and those are now, they're all coming together, saying "How do we now share data and information "so that we can let small to medium size enterprises "into the system?" So now you're getting not just savings of cost and time, but now you're opening up markets. Getting greater throughput. High waters raise all boats. And that's what we're seeing in a lot of these examples with, it's not just taking out those old things, you're thinking of new processes running the business a different way. >> And Jason's a great lead guy. You asked for an example, our friends at DBS Bank. They are fundamentally looking at changing the business models within the bank across all different divisions of the bank, whether it's credit transactions, mortgages, personal wealth, and the way they approached it was, we know these new technologies are going to allow us to fundamentally look at the workflow and change it. But here's the question: Who will be looking at changing these things? What's going to enable these model changes, the workflow changes may not be human capital. It may be working alongside this sort of man plus machine element or formula-- >> Peter: Patterns. >> Right, to allow the technology to tell you where your efficiencies could be gained. Allow the technologies to make the correlations in those disparate business models, to fundamentally change how you do business. So that's happening today. >> So, phase one is what is this, phase two, POC, now you're sort of in real production, but you obviously doing a lot more POCs, you're scaling out. Where do you see this going over the next three or four years? >> Well, I think last year was a year of the PoC PoV. I think this year's a year of production. And when you think of some of the examples that we've given, we've talked about consumer trade with Wal-Mart, we talk about shipping trade with Maersk, we talk about trade finance with We.Trade. Each of those individual networks, where do we see it going? We see these networks becoming a network of networks. Where each one of them have their own ecosystems and they come together. And they come together with trusted data, with trusted information, access that's unparalleled. So that's where we see it heading. And you have to say then, okay, it sounds really simple in the way you've just described it, so where's the challenge? The challenge is going to be doing this from a business and technology perspective. There's a lot of things that have to be figured out here. How are you going to make those processes work at that speed? What do you rightfully automate and what things don't you automate? That's more than just a technology. You can't plug a technology in and solve this. It takes an end to end capability. And that's what we're seeing, becoming more of a differentiating capability for our teams, where they can say, "Gene, Jason, "can your teams talk to us together?" 'Cause, of course, they work together. That's a differentiating effect of moving at scale and at speed, and that's where we see it going. Scale and speed. >> So what Jason and the Blockchain frame does for us, is it's an accelerant. Okay, we talk about knowledge worker, automation, we talk about different areas of software-based labor, but that accelerant is doing one big thing, is it's forcing us into what I'll call vertically integrated processes or workflow. Gone are the days of segmentation of, "Oh, that's back office," or "That's front office." We now have to take that workflow and pivot that to vertical integration. Why? That accelerant is moving at the speed of light for trusted transactions, I have to make the systems supporting that. The process, the people, I have to keep up with that pace of change. If I don't vertically integrate those processes inter and intracompany? This doesn't work. It falls down. So that's our marriage. >> Tough to go to market. How do you go to market? >> How do we go to market? We go to market as fast as we can, and we go joined at the hip, with clear and simple understanding. >> Where's the Blockchain for going to market? >> Yeah, right? >> And is there partner ecosystem that... >> Absolutely. So we talk about a Blockchain, Blockchain's a team sport. And it is a true demonstration of Metcalfe's Law, you know, the network drives the value. And so we do. We go to market with this thought of, who's going to play in that network? And we have networks where its obvious value may have a founder network, like Wal-Mart, where you say look, we see the ecosystem, we have the ecosystem, we're the founding partner, or you have a consortium such as We.Trade, where they come in and they say, "Look, let's pull all this together "'cause we see the value." So we go to market with that ecosystem, knowing that they have to partner, they have to work together. >> Outstanding. >> There's three distinct chapters in our go to market strategy. One is the services architecture, the second one is software ecosystem, and the third is around platforms, like a Blockchain. So when we start-- >> No design? >> Sorry, say again? >> No design? >> No, there is absolutely design. Absolutely design. So at a service architecture's perspective, there is fundamental workflow design happening. At a platform level, that's an even further advancement of design, because of the frameworks and blueprints happening inside a Blockchain, inside the different next-gen technologies happening. So I have to be two things, I have to be an automation-led environment where I'm providing the way to do these things, differences in RPA versus other technologies, but I also have to be an automation-attached. I have to be attached into the Blockchain framework to make sure we're coupled in the different elements of that framework. So that's how we jointly go to market. >> Peter: RPAs, I'm sorry? >> I'm sorry, Robotic Process Automation companies, so these are the relatively new technologies that enable software-based labor components. They're replicating human activity. >> Software robots? >> Software robots. >> You have a path to automation anyway. >> Exactly right. Exactly right. >> And it's funny when you ask, you know, no design. Design's in there. And this is the way we work at IBM, I mean, we're past that calling it out. So if someone's calling it out, it's like you're going to buy a phone and say, "Oh yeah, we included the battery." Like, it's there now, right? So that's how we run. So is it in there? You mention IBM, anything that you're going to consume from us? Includes IBM design. By practice. >> Wow, you guys, today was Blockchain day. I mean, you must have been thrilled to see all the main tech-- >> You mean every day's not Blockchain day? >> Dave: Well, at IBM, thinks every day... >> Okay, alright, I was just checking. >> You guys sucked all of the air out of the morning. And we heard-- >> And by the way, I certainly hope not. (laughing) >> You hope not what? >> That every day is Blockchain day. >> I hope so. Jason here. >> Makes me not have to buy a new wardrobe. >> If every day's Blockchain day, it ain't working. This is going to be one of those technologies, the less we know about it, the more successful it's been. >> I agree, I agree. >> Well, gentlemen, thanks very much for coming on theCUBE. Always a pleasure. >> Thank you guys. >> Thanks very much. >> Appreciate it. >> Alright, keep it right there, buddy. We'll be back with our next guest right after this short break. You're watching theCUBE live from IBM Think 2018. Be right back.

>> Announcer: Live, from Las Vegas, it's the Cube. Covering InterConnect 2017. Brought to you by, IBM. >> Okay welcome back everyone, we're here live in Las Vegas for IBM InterConnect 2017, this is the Cube coverage here, in Las Vegas for IBM's cloud and data shows. It turns out, I'm John Furrier, with my cohost Dave Vellante, next guess is Jamie Thomas, general manager of systems development and strategy at IBM, Cube Alum. Great to see you, welcome back. >> Thank you, great to see you guys as usual. >> So, huge crowds here. This is I think, the biggest show I've been to for IBM. It's got lines around the corner, just a ton of traffic online, great event. But it's the cloud show, but it's a little bit different. What's the twist here today at InterConnect? >> Well, if you saw the Keynote, I think we've definitely demonstrated that while we're focused on differentiating experience on the cloud through cloud native services, we're also interesting in bridging existing clients IT investments into that environment. So, supporting hybrid cloud scenarios, understanding how we can provide connective fabric solutions, if you will, to enable clients to run mobile applications on the cloud and take advantage of the investments they've made and their existing transactional infrastructure over a period of time. And so the Keynote really featured that combination of capabilities and what we're doing to bring those solution areas to clients and allow them to be productive. >> And the hybrid cloud is front and center, obviously. IOT on the data side, you've seen a lot of traction there. AI and machine learning, kind of powering and lifting this up, it's a systems world now, I mean this is the area that you're in. Cause you have the component pieces, the composibility of that. How are you guys facilitating the hybrid cloud journey for customers? Because now, it's not just all here it is, I might have a little bit of this and a little bit of that, so you have this component-isationer composobility that app developers are consistent with, yet the enterprises want that work load flexibility. What do you guys do to facilitate that? >> Well we absolutely believe that infrastructure innovation is critical on this hybrid cloud journey. And we're really focused on three main areas when we think about that innovation. So, integration, security, and supportive cognitive workloads. When we look at things like integration, we're focused on developers as key stake holders. We have to support the open communities and frameworks that they're leveraging, we have to support API's and allow them to tap into our infrastructure and those investments once again, and we also have to ensure that data and workload can be flexibly moved around in the future because these will allow better characteristics for developers in terms of how they're designing their applications as they move forward with this journey. >> And the insider threat, though, is a big thing too. >> Yes. >> I mean security is not only table stakes, it's a highly sensitive area. >> It's a given. And as you said, it's not just about protecting from the outside threats, it's about protecting from internal threats, even from those who may have privileged access to the systems, so that's why, with our systems infrastructure, we have protected from the chip, all the way through the levels of hardware into the software layer. You heard us talk about some of that today with the shipment of secure service containers that allow us to support the system both at install time and run time, and support the applications and the data appropriately. These systems that run Blockchain, our high security Blockchain services, LinuxONE, we have the highest certification in the industry, EAL five plus, and we're supporting FIPS 120-two, level four cryptology. So it's about protecting at all layers of the system, because our perspective is, there's not a traditional barrier, data is the new perimeter of security. So you've got to protect the data, at rest, in motion, and across the life cycle of the data. >> Let's go back to integration for a second. Give us an example of some of the integrations that you're doing that are high profile. >> Well one of the key integrations is that a lot of clients are creating new mobile applications. They're tapping back into the transactions that reside in the mainframe environment, so we've invested in ZOS Connect and this API set of capabilities to allow clients to do that. It's very prevalent in many different industries, whether it's retail banking, the retail sector, we have a lot of examples of that. It's allowing them to create new services as well. So it's not just about extending the system, but being able to create entirely new solutions. And the areas of credit card services is a good example. Some of the organizations are doing that. And it allows for developer productivity. >> And then, on the security side, where does encryption fit? You mentioned you're doing some stuff at the chip level, end to end encryption. >> Yeah it really, it's at all levels, right? From the chip level, through the firmware levels. Also, we've added encryption capability to ensure that data is encrypted at rest, as well as in motion, and we've done that in a way that encrypts these data sets that are heavily used in the main frame environment as an example, without impending on developer productivity. So that's another key aspect of how we look at this. How can we provide this data protection? But once again, not slow down the velocity of the developers. Cause if we slow down the velocity of the developers, they will be an inhibitor to achieving the end goal. >> How important is the ecosystem on that point? Because you have security, again, end to end, you guys have that fully, you're protecting the data as it moves around, so it's not just in storage, it's everywhere, moving around, in flight, as they say. But now you got ecosystem parties, cause you got API economy, you're dealing with no perimeter, but now also you have relationships as technology partners. >> Yes, well the ecosystem is really important. So if we think about it from a developer perspective, obviously supporting these open frameworks is critical. So supporting Linux and Docker and Spark and all of those things. But also, to be able to innovate at the rate and pace we need, particularly for things like cognitive workloads, that's why we created the Open Power Foundation. So we have more than 300 partners that we're able to innovate with, that allow us to create the solutions that we think we'll need for these cognitive workloads. >> What is a cognitive workload? >> So a cognitive workload is what I would call an extremely data hungry workload, the example that we can all think of is we're expecting, when we experience the world around us, we're expecting services to be brought to us, right, the digital economy understands our desires and wants and reacts immediately. So all of that is driving, that expectation is driving this growth and artificial intelligence, machine learning, deep learning type algorithms. Depending on what industry you're in, they take on a different persona, but there's so many different problems that can be solved by this, whether it's I need to have more insight into the retail offers I provide to an in consumer, to I need to be able to do fraud analytics because I'm in the financial services industry, there's so many examples of these cognitive applications. The key factors are just, tremendous amount of data, and a constrained amount of time to get business insight back to someone. >> When you do these integrations and you talk about the security investments that you're making, how do you balance the resource allocation between say, IBM platforms, mainframe, power, and the OS's, the power in those, and Linux, for example, which is such a mainstay of what you guys are doing. Are you doing those integrations on the open side as well in Linux and going deep into the core, or is it mostly focused on, sort of, IBM owned technology? >> So it really depends on what problem we're trying to solve. So, for instance, if we're trying to solve a problem where we're marrying data insight with a transaction, we're going to implement a lot of that capability on ZOS, cause we want to make sure that we're reducing data latency and how we execute the processing, if you will. If we're looking at things like new work loads and evolution of new work loads, and new things are being created, that's more naturally fit for purpose from a Linux perspective. So we have to use judgment, a lot of the new programming, the new applications, are naturally going to be done on a Linux platform, cause once again that's a platform of choice for the developer community. So, we have to think about whether we're trying to leverage existing transactions with speed, or whether we're allowing developers to create new assets, and that's a key factor in what we look at. >> Jamie, your role, is somewhat unique inside of IBM, the title of GM system's development and strategy. So what's your scope, specifically? >> So, I'm responsible for the systems development involved in our processor's mainframes, power systems, and storage. And of course, as a strategy person for a unit like that, I have responsibility for thinking about these hybrid scenarios and what do we need to do to make our clients successful on this journey? How do we take advantage of their tremendous investments they made with us over years. We have strong responsibility for those investments and making sure the clients get value. And also understanding where they need to go in the future and evolving our architecture and our strategic decisions, along those lines. >> So you influence development? >> Jamie: Yes. >> In a big way, obviously. It's a lot of roadmap work. >> Jamie: Yes. >> A lot of working with clients to figure out requirements? >> Well I have client support too, so I have to make sure things run. >> What about quantum computing? This has been a big topic, what's the road map look like? What's the evolution of that look like? Talk about that initiative. >> Well if I gave you the full road map they'd take me out of here with a hook out of this chair. >> You're too good for that, damn, almost got it from you. >> But we did announce the industries first commercial universal quantum computing project. A few weeks ago. It's called IBM Q, so we had some clever branding help, because Q makes me think of the personality in the James Bond movie who was always involved in the latest R&D research activity. And it really is the culmination of decades of research between IBM researchers and researchers around the world, to create this system that hopefully can solve problems to date, that are unsolvable today with classical computers. So, problems in areas like material science and chemistry. Last year we had announced quantum experience, which is an online access to a quantum capabilities in our Yorktown research laboratory. And over the last year, we've had more than 40,000 users access this capability. And they've actually executed a tremendous number of experiments. So we've learned from that, and now we're on this next leg of the journey. And we see a world where IBM Q could work together with our classical computers to solve really really tough problems. >> And that computing is driving a lot of the IOT, whether that's health care, to industrial, and everything in between. >> Well we're in the early stages of quantum, to be fair, but there's a lot of unique problems that we believe that it will solve. We do not believe that everything, of course, will move from classical to quantum. It will be a combination, an evolution, of the capabilities working together. But it's a very different system and it will have unique properties that allow us to do things differently. >> So, what are the basics? Why quantum computing? I presume it's performance, scale, cost, but it's not traditional, binary, computing, is that right? >> Yes. It's very, very different. In fact, if. >> Oh we just got the two minute sign. >> It's a very different computing model. It's a very different physical, computing model, right? It's built on this unit called a Q bit, and the interesting thing about a Q bit is it could be both a zero and a one at the same time. So it kind of twists our minds a little bit. But because of that, and those properties, it can solve very unique problems. But we're at the early part of the journey. So this year, our goal is to work with some organizations, learn from the commercialization of some of the first systems, which will be run in a cloud hosted model. And then we'll go from there. But, it's very promising. >> In the timeframe for commercial systems, have you guys released that? >> Well, this year, we'll start the commercial journey, but within the next few years we do plan to have a quantum computer that would then, basically, out strip the power of the largest super computers that we have today in the industry. But that's, you know, over the next few years we'll be evolving to that level. Because eventually, that's the goal, right? Is to solve the problems that we can't solve with today's classical computers. >> Talk about real quickly, in the last couple minutes, Blockchain, and where that's going, because you have a lot of banks and financial institutions looking at this as part of the messaging and the announcements here. >> Well, Blockchain is one of those workloads of course that we're optimizing with a lot that security work that I talked about earlier so. The target of our high security Blockchain services is LinuxONE, is driving a lot of encryption strategy. This week, in fact, we've seen a number of examples of Blockchain. One was talked about this morning, which was around diamond provenance, from the Everledger organization. Very clever implementation of Blockchain. We've had a number of financial institutions that are using Blockchain. And I also showed an interesting example today. Plastic Bank, which is an organization that's using Blockchain to allow ecosystem improvement, or improving our planet, if you will, by allowing communities to exchange plastic, recyclable plastic for currency. So it's really about enabling plastic to be turned into currency through the use of Blockchain. So a very novel example of a foundational research organization improving the environment and allowing communities to take advantage of that. >> Jamie thanks for stopping by the Cube, really appreciate giving the update and insight into the quantum, the Q project, and all the greatness around, all the hard work going to into the hybrid cloud, the security-osity is super important, thanks for sharing. >> It's good to see you. >> Okay we're live here, in Mandalay Bay, for IBM InterConnect 2017, stay with us for more live coverage, after this short break.

but cute at servicenow knowledge 14 is sponsored by service now here are your hosts Dave vellante and Jeff trick okay we're back this is Dave vellante with Jeff Frick were here live at moscone south and this is the knowledge 14 conference 6600 people here growing was about 4,000 last year you seen this conference grow and about the same pace as a services service now stop line they're growing at sixty percent plus on pace to do over 600 million in revenue this year on pace to be a billion-dollar company and we have the CFO here Mike Scarpelli cube alum Mike great to see you again thank you so this is amazing I mean Moscone is a great venue of the aria last year's kind of intimate you know and now you're really sort of blowing it out I would expect next year you're going to be in the into the big time of conferences well I got a budget for that Tiffany I'm a budget I know it's going to cost more just like the attendance is going up fifty sixty percent the costs are going up as well too but our partners are really important and our partners offset a lot of those costs will get over eight million in sponsorship revenue to offset that so when we expect next year will see a corresponding increase in the sponsorship revenue as well well it's impressive you have a lot of strong partners particularly the system integrator consultancy types you know we saw I hope it will miss somebody definitely saw sent you there last night we start Ernie young giving a presentation k p.m. ET le is about so cloud sherpas yeah the cloud shippers and so we had them on earlier she have a lot of these facilitators which is a great sign for you and they're realizing okay there's there's money to be made around the ServiceNow ecosystem helping customers implement so that's going to make you really happy no you know one of the things that's really important for us with the system integrators is today they haven't really brought us any deals but they've been very influential in accelerating deals and we think that theme is going to continue and based upon what they're seeing they're able to do in the ServiceNow ecosystem in terms of professional service consulting engagement we think that's going to start to motivate them to now bring us into deals that we were never in before but what they have been able to do as well besides just accelerate is have the deals grow beyond IT and we see that numerous on global 2000 accounts for us and you're not trying to land grab the professional services business that's clear effect when you talk to some of your customers when I've ever last year when your customer scoop is complaining that your your price is real high on the surface of suck which it probably makes you happy because it leaves more room for you for your partners and that's really not a long-term piece of your revenue II think you've said publicly you want to be less than fifteen percent of your business right yes yes we have a little bit of a ongoing debate internally my preference is not to see the professional service organization grow in terms of headcount with the pure implementation people the area that I would like to see it grow is more on the training side unfortunately some of our customers they insist that we are part of the professional service engagement so those are more the ones that we're going to be involved and if a customer is looking for a lower-cost alternative we want to make it fair for our partner so that we're not competing with them so they can come up with a lower price to offer a good quality service is important though that it's not going for the lowest price our partners need to make investment so it can be a quality implementations this is a number of early implementations that were done by partners that were some of our smaller partners where they really didn't meet the the expectations of those customers that we've had to go in and fix some of those engagements so the number one goal for our professional service is to ensure we have happy customers because happy customers renew and buy more which are two of the key drivers for our growth so you keep growing like crazy blew it out last quarter to get a 181 million in Billings revenues up 60-plus percent you're throwing off cash hitting all your metrics of course the stock went down oh there you go not much more you could do but you got to really be pleased with the consistent performance and really predictability it seems of the company yeah no I'm since I've been the CFO company it's going to be coming on three years suit in the summer the one thing that I will say about this business model is it's extremely predictable in terms of the the forecasting and what helps with that is the fact that we have such high renewal rates that really helps because we really since I've been here we've never lost any major accounts I think our renewal rate has been averaging north of ninety-five percent and in terms of our upsells or up sells have been very consistent on average they run about a third of our business every quarter and that was Frank has made comments before too that if we don't sign on another customer we can still grow twenty-five percent per year plus just based upon the upsell business opportunity that we have within our existing installed base of customers that's penetrating accounts deeper more seats more licenses more processes and applications yeah the main grower of our upsells are the main contributor to our upsells within our customers really has been additional seat licenses because many of our customers we still have even fully penetrated IT and as we roll out more applications or make our applications more feature-rich as we talked about as Frank his keynote he talked a little bit today aitee costing we've always had that as an application but that's going to be coming out as a much more feature-rich application it's going to be a lot more usable to some of our customers when that goes live that's going to drive more licenses because many times it's different people with an IT that are the process users behind that and then it's going outside of IT as well with the adoption of people enterprise service management concept that Frank's been talking about that will drive incremental users as well too we do have some additional products such as orchestration discovery with a vast majority of our growth and customers is additional licensing so very consistent performance like I say the stock pull back a little bits interesting you guys worked a Splunk tableau smoking hot stocks of all pullback obviously it's almost like you trade as a groupie even though completely different companies completely different business models you don't compete really at all but so you kind of got to be flattering to be in that yeah obviously but it's I looked at as X this is good in a way this is a healthy you know pull back it's maybe a buying opportunity for people that wanted to get in and there are a lot of folks that I'm sure they're looking at that do you I mean how much attention do you even pay for it i know most CFOs i took a say look we can't control it all we can control is you know what we can control and that's what we focus on but you even look at things like that you order your thoughts on you know and unfortunately there is a little bit of a psychology going on here with some of our employees and they're always asking and my comment to them is the only price that matters is the day you sell and this pullback that we've seen recently this is not uncommon was I expecting it to happen right now you know I don't if I if I could predict those things a lot of different line of business but what I will say history is the best indicator of the future and even a company like salesforce com one of our large investors last week he sent me an email and said you do realize that in the first five years of sales force being a public it had forgot if it was four or five fifty percent pullbacks in the stock price so this this happens it will happen I guarantee it will happen again sometime in the future but not just with us with all the other companies I'd be more concerned if it was we were the only company that traded down and everyone stayed up but we're all trading down we all came back today it's interesting and you kind of burned the shorts last year and they've made some money now but but you know Peter Lynch they don't ever short great companies and it's very hard to too short great companies your timing has to be perfect so and your core business you know like for instance a workday is is fundamentally very profitable or you know it should be right and because you're spending like crazy on sales and marketing you're expanding into into AP you're expanding your total available market and you're still throwing off cash what if you can talk about that a little bit you had said off camera your goal is to really be you know so throw off little cash basically be cash flow breakeven yes yes so you know you can only grow at a certain pace last quarter we added 150 new people into our sales and marketing organization that was the the largest number that we've ever added in one quarter we actually added 273 net new employees in q1 that was the most we've ever added in a quarter and even with all those ads we still had very good positive cash flow so it's pretty hard to add at any faster pace than what we're doing right now and so you know I just I don't see us being cashflow negative anytime in the future right now unless something happened and write it have to be a pretty major catastrophe thing and it's not going to be specific to service now it will be kind of across the board we're all CIOs stop spending and the other thing I learned here I thought maybe I just wasn't paying attention to earlier conference calls but the AP focus a large percentage of the global 2000 is in asia-pacific so you're out nation-building right I won't if he could talk about that sure so in two thousand and from March 31st 2013 till March 31st 2014 we open up in 10 new countries most of those were in asia-pacific there's still more countries we're going to be going into an asia-pacific and why are we going into these countries we're going into these countries because that's where the global 2000 accounts are that is our strategy because we focus on quality of customers not quantity of customers what I mean by quality of quality customers one that can grow over time to be a very large customer and even in 2013 we went into Italy and people said at the time well why are you going into Italy we went to Italy because they have global 2000 have 30-something global 2000 accounts even though the Italian economy wasn't doing well global 2000 customers still spend it's not specific to that country their global we signed to global 2000 counts in Italy last quarter so we have a history of showing that if we go into those countries we will be successful in winning those global 2000 and will continue there are some global 2000 so in geographies where it's going to take some time before we actually have a physical presence such as mainland China we do not have any sales people in mainland China today Russia we did not have any people in Russia today how about Ukraine you know we have no one in Ukraine today good thing about Hitler you get to go visit there that's your country I wanted to talk about the TAM yesterday last year we had I kind of watched it but but I was asking Colombo questions about the team because it was you know very interesting I saw a lot of potential want to try to understand how big it could be you and I talked about you had said its north eight billion of course the the stock took off i think it probably 10 billion from a value standpoint I didn't my own tam of mid year I did a blog post I had it up to 30 billion so I started to understand it was a top down it wasn't a bottom up but you guys are starting to sort of communicate to him a little bit differently you got had the help desk and then beyond that the IT Service Management and then you you've essentially got the operations strike the operations management and even now sort of enterprise and business management so I wonder if you could talk about how you look at the the tam and any attempts that you've made to quantify it sure so there's really four markets we play in that really intersect with one another in the core of our market is the IT Service Management that's kind of our beachhead and how we go into accounts in that market right now when historically when we went public gartner groups of the world they looked at it as a helpdesk replacement market they were saying as a 1.4 to 1.6 billion dollar market what they were missing is there's many other things in that space IT service management such as ppm such as our cmdb such as asset management a lot of these things aren't in your traditional help desk we think based upon the rate at which we've been extracting from the market that somewhere we can afford a six billion dollar market opportunity just IT Service Management and then IT Service Management is a subset of the overall enterprise service management market that Frank has been talking about we talked about in our analyst state we think that is potentially as high as 10x the size of our IT Service Management so that can get you up to say that 40 billion dollar plus and then you as well have the IT operations management space IT Service Management you just have the legacy vendors down there nothing innovative happening down there service relationship a lot of white space a lot of stuff that's being done in email lotus notes microsoft access sharepoint those are the markets were going after there really are no true systems in and that's in that space it's those one-off custom apps IT operations management there is a lot of innovation happening down that in that space it is very crowded with some new vendors as well as the legacy vendors the area that will plan might be the whole 18 billion dollar market at IDC talks about you know it's still early innings but it's at least two billion of that market 24 billion will be going after and then Frank brought up this concept of the whole business analytics as well too we talked about we did our acquisition in mirror 42 in 2013 and the business analytics kind of sits at the top of enterprise service relationship management the market we can go after in there that's a that's a whole market into itself at least as big as the enterprise service management but we're not going after that whole market it's just the business analytics to the extent it relates to enterprise service management so that's at least a couple billion more unfortunately this is what we believe there is no published reports out there and times going to is going to tell it similar to when Salesforce went public no one believed the opportunity in front of it and now look how big that come have a 30 billion dollar plus company valuations are depends on what time of year it is what the markets doing but over the long term you know you can sort of do valuation analysis it in the CFO world is there some kind of thought in terms of the ratio between an organization's tan and it's in its valuation you know I mean these other things raid obviously the leadership etc but but for the top companies there a relationship I personally don't get wrapped up in valuation you know I can't control that I can't control public company multiples the only thing we have control over is running our own business and we're going to stay very focused on running our business and let other we'll take care of the valuation good business you picked a good one yes no I I'm very pleased with this one excellent all right Mike well listen thanks very much for coming on the cube we're up against the clock and I always appreciate you thank you Dave time up alrighty bryce bravely request with our next guest we're live from tony south this is dave vellante with jeff record right back