>> From Cambridge, Massachusetts, it's theCUBE. Covering MIT Chief Data Officer and Information Quality Symposium 2019. Brought to you by SiliconANGLE Media. (futuristic music) >> Welcome back to Cambridge, Massachusetts everybody. You're watching theCUBE, the leader in live tech coverage. We go out to the events and we extract the signal from the noise. This is day two, we're sort of wrapping up the Chief Data Officer event. It's MIT CDOIQ, it started as an information quality event and with the ascendancy of big data the CDO emerged and really took center stage here. And it's interesting to know that it's kind of come full circle back to information quality. People are realizing all this data we have, you know the old saying, garbage in, garbage out. So the information quality worlds and this chief data officer world have really come colliding together. Robert Abate is here, he's the Vice President and CDO of Global IDS and also the co-chair of next year's, the 14th annual MIT CDOIQ. Robert, thanks for coming on. >> Oh, well thank you. >> Now you're a CDO by background, give us a little history of your career. >> Sure, sure. Well I started out with an Electrical Engineering degree and went into applications development. By 2000, I was leading the Ralph Lauren's IT, and I realized when Ralph Lauren hired me, he was getting ready to go public. And his problem was he had hired eight different accounting firms to do eight different divisions. And each of those eight divisions were reporting a number, but the big number didn't add up, so he couldn't go public. So he searched the industry to find somebody who could figure out the problem. Now I was, at the time, working in applications and had built this system called Service Oriented Architectures, a way of integrating applications. And I said, "Well I don't know if I could solve the problem, "but I'll give it a shot." And what I did was, just by taking each silo as it's own problem, which was what EID Accounting Firm had done, I was able to figure out that one of Ralph Lauren's policies was if you buy a garment, you can return it anytime, anywhere, forever, however long you own it. And he didn't think about that, but what that meant is somebody could go to a Bloomingdale's, buy a garment and then go to his outlet store and return it. Well, the cross channels were different systems. So the outlet stores were his own business, retail was a different business, there was a completely different, each one had their own AS/400, their own data. So what I quickly learned was, the problem wasn't the systems, the problem was the data. And it took me about two months to figure it out and he offered me a job, he said well, I was a consultant at the time, he says, "I'm offering you a job, you're going to run my IT." >> Great user experience but hard to count. >> (laughs) Hard to count. So that's when I, probably 1999 was when that happened. I went into data and started researching-- >> Sorry, so how long did it take you to figure that out? You said a couple of months? >> A couple of months, I think it was about two months. >> 'Cause jeez, it took Oracle what, 10 years to build Fusion with SOA? That's pretty good. (laughs) >> This was a little bit of luck. When we started integrating the applications we learned that the messages that we were sending back and forth didn't match, and we said, "Well that's impossible, it can't not match." But what didn't match was it was coming from one channel and being returned in another channel, and the returns showed here didn't balance with the returns on this side. So it was a data problem. >> So a forensics showdown. So what did you do after? >> After that I went into ICICI Bank which was a large bank in India who was trying to integrate their systems, and again, this was a data problem. But they heard me giving a talk at a conference on how SOA had solved the data challenge, and they said, "We're a bank with a wholesale, a retail, "and other divisions, "and we can't integrate the systems, can you?" I said, "Well yeah, I'd build a website "and make them web services and now what'll happen is "each of those'll kind of communicate." And I was at ICICI Bank for about six months in Mumbai, and finished that which was a success, came back and started consulting because now a lot of companies were really interested in this concept of Service Oriented Architectures. Back then when we first published on it, myself, Peter Aiken, and a gentleman named Joseph Burke published on it in 1996. The publisher didn't accept the book, it was a really interesting thing. We wrote the book called, "Services Based Architectures: A Way to Integrate Systems." And the way Wiley & Sons, or most publishers work is, they'll have three industry experts read your book and if they don't think what you're saying has any value, they, forget about it. So one guy said this is brilliant, one guy says, "These guys don't know what they're talking about," and the third guy says, "I don't even think what they're talking about is feasible." So they decided not to publish. Four years later it came back and said, "We want to publish the book," and Peter said, "You know what, they lost their chance." We were ahead of them by four years, they didn't understand the technology. So that was kind of cool. So from there I went into consulting, eventually took a position as the Head of Enterprise and Director of Enterprise Information Architecture with Walmart. And Walmart, as you know, is a huge entity, almost the size of the federal government. So to build an architecture that integrates Walmart would've been a challenge, a behemoth challenge, and I took it on with a phenomenal team. >> And when was this, like what timeframe? >> This was 2010, and by the end of 2010 we had presented an architecture to the CIO and the rest of the organization, and they came back to me about a week later and said, "Look, everybody agrees what you did was brilliant, "but nobody knows how to implement it. "So we're taking you away, "you're no longer Director of Information Architecture, "you're now Director of Enterprise Information Management. "Build it. "Prove that what you say you could do, you could do." So we built something called the Data CAFE, and CAFE was an acronym, it stood for: Collaborative Analytics Facility for the Enterprise. What we did was we took data from one of the divisions, because you didn't want to take on the whole beast, boil the ocean. We picked Sam's Club and we worked with their CFO, and because we had information about customers we were able to build a room with seven 80 inch monitors that surrounded anyone in the room. And in the center was the Cisco telecommunications so you could be a part of a meeting. >> The TelePresence. >> TelePresence. And we built one room in one facility, and one room in another facility, and we labeled the monitors, one red, one blue, one green, and we said, "There's got to be a way where we can build "data science so it's interactive, so somebody, "an executive could walk into the room, "touch the screen, and drill into features. "And in another room "the features would be changing simultaneously." And that's what we built. The room was brought up on Black Friday of 2013, and we were able to see the trends of sales on the East Coast that we quickly, the executives in the room, and these are the CEO of Walmart and the heads of Sam's Club and the like, they were able to change the distribution in the Mountain Time Zone and west time zones because of the sales on the East Coast gave them the idea, well these things are going to sell, and these things aren't. And they saw a tremendous increase in productivity. We received the 2014, my team received the 2014 Walmart Innovation Project of the Year. >> And that's no slouch. Walmart has always been heavily data-oriented. I don't know if it's urban legend or not, but the famous story in the '80s of the beer and the diapers, right? Walmart would position beer next to diapers, why would they do that? Well the father goes in to buy the diapers for the baby, picks up a six pack while he's on the way, so they just move those proximate to each other. (laughs) >> In terms of data, Walmart really learned that there's an advantage to understanding how to place items in places that, a path that you might take in a store, and knowing that path, they actually have a term for it, I believe it's called, I'm sorry, I forgot the name but it's-- >> Selling more stuff. (laughs) >> Yeah, it's selling more stuff. It's the way you position items on a shelf. And Walmart had the brilliance, or at least I thought it was brilliant, that they would make their vendors the data champion. So the vendor, let's say Procter & Gamble's a vendor, and they sell this one product the most. They would then be the champion for that aisle. Oh, it's called planogramming. So the planogramming, the way the shelves were organized, would be set up by Procter & Gamble for that entire area, working with all their other vendors. And so Walmart would give the data to them and say, "You do it." And what I was purporting was, well, we shouldn't just be giving the data away, we should be using that data. And that was the advent of that. From there I moved to Kimberly-Clark, I became Global Director of Enterprise Data Management and Analytics. Their challenge was they had different teams, there were four different instances of SAP around the globe. One for Latin America, one for North America called the Enterprise Edition, one for EMEA, Europe, Middle East, and Africa, and one for Asia-Pacific. Well when you have four different instances of SAP, that means your master data doesn't exist because the same thing that happens in this facility is different here. And every company faces this challenge. If they implement more than one of a system the specialty fields get used by different companies in different ways. >> The gold standard, the gold version. >> The golden version. So I built a team by bringing together all the different international teams, and created one team that was able to integrate best practices and standards around data governance, data quality. Built BI teams for each of the regions, and then a data science and advanced analytics team. >> Wow, so okay, so that makes you uniquely qualified to coach here at the conference. >> Oh, I don't know about that. (laughs) There are some real, there are some geniuses here. >> No but, I say that because these are your peeps. >> Yes, they are, they are. >> And so, you're a practitioner, this conference is all about practitioners talking to practitioners, it's content-heavy, There's not a lot of fluff. Lunches aren't sponsored, there's no lanyard sponsor and it's not like, you know, there's very subtle sponsor desks, you have to have sponsors 'cause otherwise the conference's not enabled, and you've got costs associated with it. But it's a very intimate event and I think you guys want to keep it that way. >> And I really believe you're dead-on. When you go to most industry conferences, the industry conferences, the sponsors, you know, change the format or are heavily into the format. Here you have industry thought leaders from all over the globe. CDOs of major Fortune 500 companies who are working with their peers and exchanging ideas. I've had conversations with a number of CDOs and the thought leadership at this conference, I've never seen this type of thought leadership in any conference. >> Yeah, I mean the percentage of presentations by practitioners, even when there's a vendor name, they have a practitioner, you know, internal practitioner presenting so it's 99.9% which is why people attend. We're moving venues next year, I understand. Just did a little tour of the new venue, so, going to be able to accommodate more attendees, so that's great. >> Yeah it is. >> So what are your objectives in thinking ahead a year from now? >> Well, you know, I'm taking over from my current peer, Dr. Arka Mukherjee, who just did a phenomenal job of finding speakers. People who are in the industry, who are presenting challenges, and allowing others to interact. So I hope could do a similar thing which is, find with my peers people who have real world challenges, bring them to the forum so they can be debated. On top of that, there are some amazing, you know, technology change is just so fast. One of the areas like big data I remember only five years ago the chart of big data vendors maybe had 50 people on it, now you would need the table to put all the vendors. >> Who's not a data vendor, you know? >> Who's not a data vendor? (laughs) So I would think the best thing we could do is, is find, just get all the CDOs and CDO-types into a room, and let us debate and talk about these points and issues. I've seen just some tremendous interactions, great questions, people giving advice to others. I've learned a lot here. >> And how about long term, where do you see this going? How many CDOs are there in the world, do you know? Is that a number that's known? >> That's a really interesting point because, you know, only five years ago there weren't that many CDOs to be called. And then Gartner four years ago or so put out an article saying, "Every company really should have a CDO." Not just for the purpose of advancing your data, and to Doug Laney's point that data is being monetized, there's a need to have someone responsible for information 'cause we're in the Information Age. And a CIO really is focused on infrastructure, making sure I've got my PCs, making sure I've got a LAN, I've got websites. The focus on data has really, because of the Information Age, has turned data into an asset. So organizations realize, if you utilize that asset, let me reverse this, if you don't use data as an asset, you will be out of business. I heard a quote, I don't know if it's true, "Only 10 years ago, 250 of the Fortune 10 no longer exists." >> Yeah, something like that, the turnover's amazing. >> Many of those companies were companies that decided not to make the change to be data-enabled, to make data decision processing. Companies still use data warehouses, they're always going to use them, and a warehouse is a rear-view mirror, it tells you what happened last week, last month, last year. But today's businesses work forward-looking. And just like driving a car, it'd be really hard to drive your car through a rear-view mirror. So what companies are doing today are saying, "Okay, let's start looking at this as forward-looking, "a prescriptive and predictive analytics, "rather than just what happened in the past." I'll give you an example. In a major company that is a supplier of consumer products, they were leading in the industry and their sales started to drop, and they didn't know why. Well, with a data science team, we were able to determine by pulling in data from the CDC, now these are sources that only 20 years ago nobody ever used to bring in data in the enterprise, now 60% of your data is external. So we brought in data from the CDC, we brought in data on maternal births from the national government, we brought in data from the Census Bureau, we brought in data from sources of advertising and targeted marketing towards mothers. Pulled all that data together and said, "Why are diaper sales down?" Well they were targeting the large regions of the country and putting ads in TV stations in New York and California, big population centers. Birth rates in population centers have declined. Birth rates in certain other regions, like the south, and the Bible Belt, if I can call it that, have increased. So by changing the marketing, their product sales went up. >> Advertising to Texas. >> Well, you know, and that brings to one of the points, I heard a lecture today about ethics. We made it a point at Walmart that if you ran a query that reduced a result to less than five people, we wouldn't allow you to see the result. Because, think about it, I could say, "What is my neighbor buying? "What are you buying?" So there's an ethical component to this as well. But that, you know, data is not political. Data is not chauvinistic. It doesn't discriminate, it just gives you facts. It's the interpretation of that that is hard CDOs, because we have to say to someone, "Look, this is the fact, and your 25 years "of experience in the business, "granted, is tremendous and it's needed, "but the facts are saying this, "and that would mean that the business "would have to change its direction." And it's hard for people to do, so it requires that. >> So whether it's called the chief data officer, whatever the data czar rubric is, the head of analytics, there's obviously the data quality component there whatever that is, this is the conference for, as I called them, your peeps, for that role in the organization. People often ask, "Will that role be around?" I think it's clear, it's solidifying. Yes, you see the chief digital officer emerging and there's a lot of tailwinds there, but the information quality component, the data architecture component, it's here to stay. And this is the premiere conference, the premiere event, that I know of anyway. There are a couple of others, perhaps, but it's great to see all the success. When I first came here in 2013 there were probably about 130 folks here. Today, I think there were 500 people registered almost. Next year, I think 600 is kind of the target, and I think it's very reasonable with the new space. So congratulations on all the success, and thank you for stepping up to the co-chair role, I really appreciate it. >> Well, let me tell you I thank you guys. You provide a voice at these IT conferences that we really need, and that is the ability to get the message out. That people do think and care, the industry is not thoughtless and heartless. With all the data breaches and everything going on there's a lot of fear, fear, loathing, and anticipation. But having your voice, kind of like ESPN and a sports show, gives the technology community, which is getting larger and larger by the day, a voice and we need that so, thank you. >> Well thank you, Robert. We appreciate that, it was great to have you on. Appreciate the time. >> Great to be here, thank you. >> All right, and thank you for watching. We'll be right back with out next guest as we wrap up day two of MIT CDOIQ. You're watching theCUBE. (futuristic music)

>> Live from San Diego, California, it's theCUBE, covering Cisco Live US 2019. Brought to you by Cisco and its ecosystem partners. >> Welcome back to theCUBE. Our coverage of Cisco Live day three is in full effect. I am Lisa Martin with Dave Vellante and we have a couple of guests joining us. We've got Mike Bundy, head of Cisco Strategic Alliances from, guess where? The jacket should give it away, Pure Storage. And Tom Koppelman, the VP of Architecture Sales America for Cisco, hi guys! >> Hi. >> Hi. >> How ya doing? >> Thanks for bringing more brightness to our set. >> Yeah I forgot my sunglasses. >> I know, we're in the buzzy, bright DevNet Zone. We've been here all week. Great event, massive event, my goodness. 28,000 folks or so, Mike let's start with you. Give us a status of the Pure-Cisco relationship, the evolution of that, where you guys are now. What is exciting? >> Sure, so the relationship, it's unbelievable in terms of the amount of synergies and energy we have together. In fact, Tom at Cisco was really involved in the early genesis of this relationship, prior to me joining the company. And, in the last couple years, we've probably doubled in terms of our go-to-market and sell to customers together. So, tremendous growth. Partnership brings a value to us because of the strong heritage that we have from a DevNet tie-in, in terms of all the automation that we have on the platform, so. It's just a tremendous, tremendous, great partnership. >> And Tom, Cisco has a massive partner ecosystem, a lot of choice. What is it about Pure Storage that is providing advantages to Cisco? Where it's helping customers really kind of bridge this gap between hyper-converged, multi-cloud hybrid, all that jazz? >> Right, so Pure was a first mover in terms of flash storage, right. We saw demand from our customers wanting that technology to improve their data center environments. And when we partnered up early, we were able to kind of capture that momentum, right. And, when I think about our go-to-market with Pure, which is really where I kind of focus, there's very little friction in that relationship, right. There's not competitive overlap. There's not things like that. It's technology that the customers want, that they ask for, and a good field go-to-market in leadership on both sides that are willing to invest and get engaged and move the relationship forward. >> So what else are you guys doing besides just the go-to-market partnership because I got a hold of this timeline of Cisco Validated Designs that Pure and Cisco have put out over the last five years, four years. >> Right. >> And there's like 13 milestones on there. So that's roughly three a year. Of course, it started with Pure's IPO. So that's when Cisco said, all right, these guys are real. Start working with them. And in the early days, of course, you started with FlashStack. That was the flagship product. And then VDI, everybody does VDI, analysts are like, yeah, yeah, everybody does VDI. But then it started really accelerating the cadence. So it's more than just go-to-market. What's beneath that go-to-market? >> Yeah, good question. >> You want to? >> You hit the highlights of the CVD's and whatnot. >> I would say that Pure, this is our number one partnership that we have from an alliance perspective. The investment is far exceeding other partnerships we have. So, the amount of product integration that we're doing is tremendous, as you see there. We've focused on ACI and multi data centers the last couple years. We've focusing on AI and machine learning, most recently. And beyond that, we just signed an agreement and have released resell of Cisco SAN switches in the marketplace. It's the resell agreement we've ever done as a company and it just further shows the commitment in resources that we're willing to put into making sure the partnership is successful and continues to grow and evolve. >> And on top of that the investment in Cisco Intersight, in integrating with Cisco Intersight, the management platform, which is very important to us, it just shows commitment of the partnership. >> Let's talk more about that. So, how does that work? What problems is that solving for customers? >> Well, Cisco Intersight is our cloud based management offering for compute and Pure has integrated their storage platform as part of that solution. So allowing customers, whether it's a converged solution, just raw compute, a hyper-converged solution, but allowing them to manage those pools and deliver that via a cloud solution. >> So Pure plugs into the Cisco API. Now you're part of that stack, essentially. So it's transparent to the customer. And, Cisco's management plane takes care of all that. >> That's exactly right, correct, yes. >> Its' a big deal for us because it was the first integration with Intersight from any storage partner that Cisco has, right. So first to market. We want to embrace hyper-convergence, which is a big important priority for Cisco, and also bridging that gap. So as we compete against single vendor stacks, we have the right solution that customers are looking for. And ultimately, that's why it's so important for us. >> Yeah, Pure is big on firsts. First to flash, you just mentioned another first, you were the first with NVMe, before that you were with the evergreen. I mean, you like being first. >> First orange sport coat. >> That's definitely first there. (laughing) >> Let's talk about customer value though. Obviously, that's what it's all about. As we look at, not just the tremendous amount of choice that customer have when it comes to technology partners, but also the amount of data that's being generated, that's growing astronomically. Yet, organizations are getting so little value out of that because they can't extract the insights. What are you guys doing together leveraging the superpowers of AI and machine learning to help customers in any industry search a really, not just monetize that data, but really accelerate their businesses. Tom you're smiling so let's start with you. >> Yeah, so we came out with an AI server, right, our ML 480, and we've integrated that. Pure has invested, we've both invested and done an integration between FlashBlade, and I'll let Mike talk a little about FlashBlade and the value proposition of FlashBlade, but integrated that with our AI server. And our AI server is an Nvidia powered server, so it essentially gives you scale of processing and capabilities to allow you capitalize on all that data so the customers can get the information they need out of that. If you want to take a second on FlashBlade. >> And you know, AI is the buzz. It's the hot two letter acronym in the industry these days. $13 billion infrastructure opportunity, et cetera, et cetera. So, what Pure is really focused on is, data is the new oil of commodities for customers and clients. What we've built is a platform called FlashBlade, an architecture called the Data Hub, that allows you to not have to copy data and move it around and create silos in data warehouses. So, you can much easier execute a data strategy with the Data Hub architecture, using FlashBlade. When you look at machine learning in terms of how you build a data pipeline so that you can then get to quicker results from a business application standpoint with AI. That's what we've built together with Cisco. We're uber, uber, super excited a number of customers already in the last couple months. >> So I'm going to push a little on that, AI server, AI storage, people don't associate storage and server guys with AI. But if I hear you correctly, there's a $13 billion opportunity for workloads. To manage workloads running on your servers and your storage. >> Correct. >> And so you're optimizing them for AI workloads. >> Absolutely, exactly right. >> So you're not necessarily inventing AI. You're providing infrastructure so that people can leverage AI, is that right? >> Yes. >> Yeah, and the same way that we've built APIs together to work with Intersight, we do that in a way that allows our customers to leverage Cafe, other applications that can help build that data pipeline. We build the platform from the infrastructure level, it makes the management easy and we partner with all of the applications at the top end, but also the middleware and that software prepackage layer that connects via APIs to us. So, it's easy, it's agile, it's manageable, it's a cloud-like experience for the customers, right. >> Easy, agile, all awesome but security. Absolutely critical today. What are you guys doing, Tom I'll start with you, how are you guys working together infuse and integrate security into the technology so that from a customer's perspective, those risks dial down. >> So, Cisco is integrating security across all of our product portfolio, right. And, that includes our data center portfolio, all the way through our campus, our WAN, all those portfolios. We continue to look for opportunities to integrate, whether it's dual-factor authentication or things like secure data center where the highly scalable, multi-instance firewall in front of a data center, things like that. So we're definitely looking for areas and angles and opportunities for us to, not only integrate it from a product standpoint, but also ensure that we are talking that story with our customers so that they know they can leverage Cisco for the full architecture from a security standpoint. >> And the same thing on the storage of the data from an encryption perspective, and as the data gets moved or is mobile, that level of security and policy follows it wherever the data is moved. >> So, what should we expect, what's next in the time? What's 14 going to look like? You don't have top give us specifics but are we going to see blockchain CVDs? What should observers think about the partnership going forward? What could we look forward to? >> Yeah, I mean, the adoption of Container capability is tremendous in our customers environment. Cisco has a cloud Container platform available today. We're integrating that into FlashStack in the very near future. Embracing the cloud. Disaster recovery and data protection it's very hot for customers. Improving that experience so that you have faster restoration times, you're able to look at multi-tier strategy that's very easy to manage from a storage perspective, leveraging S3 with Amazon, Azure, et cetera. So, that's a couple things that are on the short term building block together. >> Yeah, I was going to comment on certainly multi cloud and Containers, those would be two of the big ones that I'd hit on, right. And, in the event of multi cloud leveraging, converged and hyper-converged together to better solve a customer's problems. >> So I was going to ask you. So hyper-converged now becomes a bridge to the cloud if, in fact, that's where customers want to go. >> Yes, it can be. >> Absolutely. >> Yeah, it can be, yes. >> Absolutely. >> Well guys thank you so much for joining Dave and me on the program, sharing with us the momentum that the Pure-Cisco relationship has and what excites you for the future. We appreciate your time. >> Thank you. >> Thank guys. >> For Dave Vellante, I'm Lisa Martin, you're watching theCUBE live from Cisco Live San Diego. Thanks for watching. (electronic music)

(upbeat trumpet music) >> From our studios in the heart of Silicon Valley, Palo Alto, California. This is a CUBE Conversation. >> Welcome, everybody, I'm Dave Vellante and we're here in our Palo Alto studios and we're going to talk about big data. For the last ten years, we've seen organizations come to the realization that data can be used to drive competitive advantage and so they dramatically lowered the cost of collecting data. We certainly saw this with Hadoop, but you know what data is plentiful, insights aren't. Infrastructure around big data is very challenging. I'm here with Kumar Sreekanti, co-founder and CEO of BlueData, and a long time friend of mine. Kumar, it's great to see you again. Thanks so much for coming to theCUBE. >> Thank you, Dave, thank you. Good to see you as well. >> We've had a number of conversations over the years, the Hadoop days, on theCUBE, you and I go way back, but I said up front, big data sounded so alluring, but it's very, very complex to get started and we're going to get into that. I want to talk about BlueData. Recently sold to company to HPE, congratulations. >> Thank you, thank you. >> It's fantastic. Go back, why did you start BlueData? >> When I started BlueData, prior to that I was at VMware and I had a great opportunity to be in the driving seat, working with many talented individuals, as well as with many customers and CIOs. I saw while VMware solved the problem of single instance of virtual machines and transform the data center, I see the new wave of distributed systems, vis-a-vis first example of that is Hadoop, were quite rigid. They were running on bare metal and they were not flexible. They were having, customers, a lot of issues, the ones that you just talked about. There's a new stack coming up everyday. They're running on bare metal. I can't run the production and the DevOps on the same systems. Whereas the cloud was making progress so we felt that there is an opportunity to build a Vmware-like platform that focuses on big data applications. This was back in 2013, right. That was the early genesis. We saw that data is here and data is the new oil as many people have said and the organizations have to figure out a way to harness the power of that and they need an invisible infrastructure. They need very innovative platforms. >> You know, it's funny. We see data as even more valuable than oil because you can only once. (Kumar laughs) You can use data many, many times. >> That's a very good one. >> Companies are beginning to realize that and so talk about the journey of big data. You're a product guy. You've built a lot of products, highly technical. You know a lot of people in the valley. You've built great teams. What was the journey like with BlueData? >> You know, a lot of people would like it to be a straight line from the starting to that point. (Dave laughs) It is not, it's fascinating. At the same time, a stressful, up and downs journey, but very fulfilling. A, this is probably one of the best products that I've built in my career. B, it actually solves a real problem to the customers and in the process you actually find a lot of satisfaction not only building a great product. It actually building the value for the customers. Journey has been very good. We were very blessed with extremely good advisors from the right beginning. We were really fortunate to have good investors and I was very, as you said, my knowledge and my familiarity in the valley, I was able to build a good team. Overall, an extremely good journey. It's putting a bow on the top, as you pointed out, the exit, but it's a good journey. There's a lot of nuance I learned in the process. I'm happy to share as we go through. >> Let's double-click on the problem. We talked a little bit about it. You referenced it. Everyday there's a new open source project coming out. There's The Scoop and The Hive and a new open open source database coming out. Practitioners are challenged. They don't have the skillsets. The Ubers and the Facebooks, they could probably figure it out and have the engineers to do it, but the average enterprise may not. Clearly complexity is the problem, but double-click on that and talk a little bit about, from your perspective, what that challenge is. >> That's a very good point. I think when we started the company, we exactly noticed that. There are companies that have the muscle to hire the set of engineers and solve the problem, vertically specific to their application or their use case, but the average, which is Fortune 500 companies, do not have that kind of engineering man power. Then I also call this day two operations. When you actually go back to Vmware or Windows, as soon as you buy the piece of software, next day it's operational and you know how to use it, but with these new stacks, by the time stack is installed, you already have a newer version. It's actually solutions-led meaning that you want to have a solution understanding, but you want to make the infrastructure invisible meaning, I want to create a cluster or I want to funnel the data. I don't want to think about those things. I just wanted to directly worry about what is my solution and I want BlueData to worry about creating me a cluster, automating it. It's automation, automation, automation, orchestration, orchestration, orchestration. >> Okay, so that's the general way in which you solve this problem. Automate, you got to take the humans out of the equation. Talk specifically about the BlueData architecture. What's the secret sauce behind it? >> We were very fortunate to see containers as the new lightweight virtual machines. We have taken an approach. There are certain applications, particularly stateful, need a different handling than cloud-native non-stateful applications so what we said was, in fact our architecture predates Kubernetes, so we built a bottoms-up, pure white-paper architecture that is geared towards big data, AIML applications. Now, actually, even HPC is starting to move into that direction. >> Well, tell me actually, talk a little bit about that in terms of the evolution of the types of workloads that we've seen. You know, it started all out, Hadoop was batch, and then very quickly that changed. Talk about that spectrum. >> It's actually when we started, the highest ask from the customers were Hadoop and batch processing, but everybody knew that was the beginning and with the streaming and the new streaming technologies, it's near realtime analytics and moving to now AIML applications like H2O and Cafe and now I'm seeing the customer's asking and say, I would like to have a single platform that actually runs all these applications to me. The way we built it, going back to your previous question, the architecture is, our goal is for you to be able to create these clusters and not worry about the copying the data, single copy of the data. We built a technology called DataTap which we talked about in the past and that allows you to have a single copy of the data and multiple applications to be able to access that. >> Now, HPC, you mentioned HPC. It used to be, maybe still is, this sort of crazy crowd. (laughter) You know, they do things differently and everybody bandwidth, bandwidth, bandwidth and very high-end performance. How do you see that fitting in? Do you see that going mainstream? >> I'm glad you pointed out because I'm not saying everything is moving over, but I am starting to see, in fact, I was in a conversation this morning with an HPC team and an HPC customer. They are seeing the value of the scale of distributed systems. HPC tend to be scale up and single high bandwidth. They are seeing the value of how can I actually bring these two pieces together? I would say it's in infancy. Don't take me to say, look how long Hadoop take, 10 years so it's probably going to take a longer time, but I can see enterprises thinking of a single unified platform that's probably driven by Kubernetes and have these applications instantiated, orchestrated, and automated on that type. >> Now, how about the cloud? Where does that fit? We often say in theCUBE that it's not Moore's Law anymore. The innovation cocktail is data, all this data that we've collected, applying machine intelligence, and then scaling with the cloud. Obviously cloud is hugely important. It gobbled up the whole Hadoop business, but where do you see it fitting? >> Cloud is a big elephant in the room. We all have to acknowledge. I think it provides significant advantages. I always used to say this, and I may have said this in my previous CUBE interviews, cloud is all about the innovation. The reason cloud got so much traction, is because if you compare the amount of innovation to on-prem, they were at least five years ahead of that. Even the BlueData technology that we brought to the barer, EMR on Amazon was in front of the data, but it was only available Amazon. It's what we call an opinionated stack. That means you are forced to use what they give you as opposed to, I want to bring my own piece of software. We see cloud, as well as on-prem pretty much homogenous. In fact, BlueData software runs both on-prem, on the cloud, in a hybrid fashion. Same software and you can bring your stack on the top of the BlueData. >> Okay, so hybrid was the next piece of it. >> What we see is cloud has, at least from the angle from my exposure, cloud is very useful for certain applications, especially what I'm seeing is, if you are collecting the large amounts of data on the cloud, I would rather run a batch processing and curate the data and bring the very important amount of data back into the on-prem and run some realtime. It's just one example. I see a balance between the two. I also see a lot of organizations still collecting terabits of data on-prem and they're not going to take terabits of data overnight to the cloud. We are seeing all the customers asking, we would like to see a hybrid solution. >> The reason I like the acquisition by HPE because not only is it a company started by a friend and someone that I respect and knows how to build solid technology that can last, but it's software. HPE, as a company, my view needs more software content. (Kumar laughs) Software's eating the world as Marc Andressen says. It would be great to see that software live as an independent entity. I'm sure decisions are still being made, but how do you see that playing out? What are the initial discussions like? What can you share with us? >> That's a very, very, well put there. Currently, the goal from my boss and the teams there is, we want to keep the BlueData software independent. It runs on all x86 hardware platforms and we want to drive the roadmap driven by the customer needs on the software like we want to run more HPC applications. Our roadmap will be driven by the customer needs and the change in the stack on the top, not by necessarily the hardware. >> Well, that fits with HPE's culture of always trying to give optionality and we've had this conversation many, many times with senior-level people like Antonio. It's very important that there's no lock-in, open mindset, and certainly HPE lives up to that. Thanks so much for coming-- >> You're welcome. Back into theCUBE. >> I appreciate you having me here as well. >> Your career has been amazing as we go back a long time. Wow. From hardware, software, all these-- >> Great technologies. (laughter) >> Yeah, solving hard problems and we look forward to tracking your career going forward. >> Thank you, thank you. Thanks so much. >> And thank you for watching, everybody. This is Dave Vellante from our Palo Alto Studios. We'll see ya next time. (upbeat trumpet music)

>> From Sand Hill Road in the heart of Silicon Valley, it's theCUBE. Presenting, the People First Network, insights from entrepreneurs and tech leaders. >> Hello everyone I'm John Furrier with theCUBE, we are here in Sand Hill Road up at Mayfield Venture Capital Firm for their 50th anniversary, their People First Network series, produced with theCUBE and Mayfield, I'm John Furrier, with Bill Schlough, the Chief Information Officer of the San Francisco Giants, CUBE alumni, great to see you thanks for joining me today for this People First Series we're doing with Mayfield's 50th anniversary, thanks for coming in. >> Good to be here, John. >> So, been a while since we chatted, it's been a year, A lot's happening in tech, you can't go a year, that's like seven dog years in tech, lot happening, you're managing, as the CIO for the Giants, a lot of things going on in baseball, what's the priorities for you these days, obviously, you guys, great social, great fan experience, what's new for you, what's the priority? >> Man, there's always something new. It's what I love about it, this'll be my 20th season with the Giants comin' up. And, it never gets old, there's always new challenges. On the field, in the seats, off the field, you name it. As we look toward next year, really excited about bringin' in a new video board, which we haven't publicly announced, maybe I just did publicly announce, we're breaking news on theCUBE today. So we're puttin' in a new video board, it'll be over three times the size of the one we have today. That's big news, we're doing a lot of exciting things in the ticketing world. The ticketing world is really transforming right before our eyes in terms of the way fans buy tickets. It's changed a lot. Once up on a time you could call a game a sellout, and we sold out 530 straight games at AT&T Park, but really there's no such thing as a sellout anymore I mean, at any point you can get a great ticket, so we have to adapt to that and change the product that we're delivering to fans, so making some changes on the ticketing front, the fan experience, the ballpark with the video board, and another thing that's changing a lot is the way fans consume our game when they're not at the ballpark. It's rare that you're going to see somebody sit on a couch for three plus hours and watch a game continuously anymore. Fans are consuming through mobile devices, streaming, catching clips here and there, all different methods, and it's fun to be a part of that, because, fans still love the game, but they're just consuming it in different ways. >> Yeah, I love having chats with you on theCUBE because one of the things that have always been the same from nine years doing theCUBE is, the buzzword of consumerization of IT has been out there, overused, but you're living it, you have a consumer product, the ultimate consumer product, in Major League Baseball, and the Giants, great franchise, in a great city, in a great stadium, with a rabid fanbase, and they know tech, so you have all the elements of tech, but the expectation of consumers, and the experiences are changing all the time, you got to deliver on the expectations and introduce new experiences that become expectations, and this is the flywheel of innovation, and it's really hard, but I really respect what you guys are doing over there, and that's why I'm always curious, but, always, the question comes back to, is, can I get faster wifi in the stadium? (laughs) It's always the number one question >> It's funny that you ask that because it is AT&T Park, you know, so, honestly, we got to check that box, and we've had to for years, all the way back to when we first rolled it out, way back in 2004 when we first rolled out wifi in the park, people weren't asking for it then, people were coming to the ballpark with a laptop and plugging a card into it, and there were about a hundred of them that were accessing it, but today, what's interesting is, who knows what next, but we're not talkin' about wifi as much, wifi is just kind of, expected, you got to have it, like water. You're talkin' about 5G networks, and new ways to connect. Honestly, this past season, our wifi usage in terms of the number of fans that use wifi, what we call the take rate, the percentage of fans, was actually down 30% from the previous year. Not because we had less fans in the stadium, because this is the take rate, a percentage of fans in the stadium, went down, because AT&T made some massive investments in their cellular infrastructure at the ballpark, and if you're just connecting, and you got great bandwidth, you don't feel the need to switch over to wifi, so who knows what the future will hold? That's a great point, and you see the LTE networks have so much more power, it used to be you needed wifi to upload your photos, so you'd go in, log in, and if they auto login that's cool, but people don't need to. >> Not with photos, what they need it now for is when we see it really maxing out is events, like our Eagles concert, or Journey concert, or a really big game, like opening day, or honestly, Warriors playoffs game, 49ers football games, that's when folks are streamin' to video. For streamin' to video, they're still goin' to that wifi. Yeah, that's the proven method, plus they don't want to jack up their charges on the AT&T site, but I won't go there, Let's talk about innovat-- Most say unlimited, I will go there, most say unlimited these days. >> Really, I got to find that plan, my daughter's killin' me with her watchin' Netflix on LTE, I tell her. Innovation is changing, I want to get your thoughts on this, 'cause I know you're on the front end of a lot of innovations, you do a lot of advising here at Mayfield. The VC's always trying to read the tea leaves, you're living it, what's the innovation formula look like now for you 'cause as you're sittin' in your staff meetings, as you look at the team of people around you, you guys want to foster, you do foster, innovation culture. What's the formula, what do you guys do when you have those meetings, when everyone's sitting around the table sayin', what do we do next? "How do we create a better experience? "How can we get better fans, and better product "in their hands as fast as possible?" What's your strategy? >> You know, it's funny, people talk about the secret sauce for innovation, what's the formula? I would say, for us, it's really a symbiotic relationship with a lot of things, first of all, where we are, geographically, we've got folks like Mayfield, down the street, and many others, that we can talk to, that are, when innovation is happening, when the startups are incubating, they're being funded by these guys, a lot of times they are here, and our phones are ringing off the hook with a lot of folks so my formula for innovation is answer the phone and take the meetings, but, to be honest, that creates its own problems, because there's so many great ideas out there, if you try to do all of them, you're going to fail at all of them. You got to pick a very small few to try to experiment with, give it a shot, we just don't have the bandwidth, we only have 250 full-time staff on the business side. For us, geographically, you have to really be laser-focused and say okay, there are so many great ideas out here, which are the three or four that we're going to focus on this year, and really give it a try, that's really going to drive, propel our business forward, enhance our product on the field, whatever it might be, but I'll tell you where it really truly starts. It's from the top with our CEO. And, I've had a few different bosses over the years, but with the Giants, our CEO is singularly focused on all of us doing things folks have never done before regardless of what business unit you're in. Whether you're in ticketing, finance, marketing, sales, what drives him, and drives all of us, is innovation. And his eyes glaze over when I talk to him about cost-cutting, and his eyes can glaze over really fast. But when I talk to him about doing something no one's ever done before, that's when he sits forward in his chair, he gets engaged, and I just have a great boss, Larry Baer, he's been with us for 25 years wit the Giants, and he is the driver for it, he creates the culture from the top, where all of us, we want to impress him, and to impress him, you got to do sometin' nobody's ever done before, and what's even more interesting is there are some challenges and some changes talking place across our industry, as I said before, ticketing and other areas, and I've sat in meetings with him where somebody might raise their hand and say, "But this is happening across the industry, "so it's just a macro trend," and he'll get upset, be like, "I don't care about macro trends. "We are here in the Bay Area, "we're the San Francisco Giants, "we're going to do it our way." >> And so when you do it your way, he promotes risk-taking, so that's a great culture. What are some of the things you have tried that were risky, and/or risque, or maybe an experiment, that went well, and maybe ones that didn't go well, can you share some color commentary around that? >> Sure, over 20 years we've had some of all of those. I would say, I've had some real scary moments, our culture is collaborative, but I wouldn't call it combative, but we all have strong opinions, a lot of us have been there a long time, and we have strong opinions and so we'll battle, internally, a lot, but then once the battle is over, we'll all align behind the victory. Thinking back, one of the most stressful times for me at the ballpark was related to wifi, when we decided to take our antennas and put 'em under people's seats. No one had ever done that before, and there were two major concerns with that. One is, honestly are people going to get cancer from these antennas under their seats, it's never been done before, what's going to happen, and whether it's going to happen or not, what's the perception of our fans going to be, because, these are, the bread and butter is, the golden goose here, all the fans, so, yeah it's great that they're going to be, have faster connection here at AT&T Park, but if they think they're going to get cancer, they're going to cancel their season ticket plans, we got to problem. Number two is, we're taking away a little storage space also, under the seats, so it was very controversial internally, we did all of our research, we proved that having a wifi antennae under your seat is the equivalent to having a cell phone in your pocket, most people do that, so we're pretty safe there, and from the storage space perspective, honestly, it actually elevates your stuff, if somebody spills a Coke behind ya, it'll fall all around your purse, which is sitting on top of that wifi antenna so we came up with a good solution, but that was an example of something that was really controversial >> So beer goes on the antennae not your bag. (laughs) >> Exactly, your bag stays dry, we found a way to spin that but, there have been so many, I can go way back in time, back to the days when it was the PalmPilot that ruled the day instead of the apple >> Well you guys also did a good job on social media, I got to give you guys props, because, you're one of the first early adopters on making the fan experience very interactive. That was, at that time, not viewed as standard. Yeah, built the @Cafe at our ballpark, which is still there really to try to bring social media to the fans. >> I think you're the first ballpark to have a kale garden, too, I think. >> That's a little off topic, but yes, driven by one of our players, who's a big kale fan, yeah, the garden out in center field. >> So sustainibility's certainly important, okay, I got to ask the question around your role in the industry, because one of the things that's happening more and more in Major League Baseball and certainly as it crosses over to tech her at Mayfield Venture Capital, there's a lot of collaboration going on, and it's a very people-centric culture where, it used to be people would meet at conferences, or you'd do conference calls, now people are in touch in real time, so these networks are forming. It takes a village to create innovative products, whether you're inside the Giants, or outside in the ecosystem, how have you personally navigated that, and can you share some experiences to the folks watching, how you became successful working in an environment where it's collaborative inside the walls of the San Francisco Giants, but also outside? >> %100, the topic is near and dear to my heart, and from when I started with the Giants, that's what I love about our industry We compete on the field, and only on the field. When you look at who the Giants competitors are, from a business perspective, honestly the Dodgers are not a competitor from a business perspective. The A's are barely a competitor from a business perspective. We got a lot of competitors and very few of them are in our actual industry, so we collaborate all day, and it's been amazing, I can count on one hand, across all of sports, folks who have not been collaborative. There's a very small group of teams, your favorite team, the Boston Red Sox, are not on that list, they are very collaborative, but their arch rival, well there's a few others out there that may be less collaborative, but most of them are highly collaborative, from top down, and so, what I did from when I first started the first trip I made, was to Cleveland. And this was many years ago, Cleveland Indians had a reputation of being very progressive so I called up my counterpart there, I said, "I'm new to the industry, can I come out, "can I learn from you?" And that's where it started, and ever since, every year, we travel to two cities, I take at least four of my staff, to two cities each year and we meet with all the sports teams in those cities. This year, we went to Milwaukee and we met with the Brewers, and we did the Packers as well. Every year, over the 20 years we've visited pretty much every professional sports city, and we just go through it again, and always, red carpet, open door, and you build those face-to-face relationships, that you can pick up the phone and make the call, in a few weeks we're all going to get together in Denver at our MLB IT Summit, my job at the IT Summit every year is I host the golf classic, so I bring all the golfers, the hackers, the duffers out, and we have a great time on the golf course and build those relationships and again, the only thing that we don't really talk about that much is the technology we use to enhance the product on the field. Everything else is fair game. >> So share the business side, but the competitive advantage, where the battle's really having Dodger and Giants obviously on the field, highly competitive-- >> But what's cool about that is then I can meet with the other sports teams to talk about that, so I'll leave the teams nameless, but we've had some awesome collaborative discussions with NBA teams especially to talk about what they're doing to assess talent, and there's no competition there. >> So there's kind of rules of the road, kind of like baseball, unwritten rules. >> Right. >> So talk about the coolest thing that you guys have done this year, share something that you personally feel proud of, or fans love, what were some of the cool things this year that pops out for you? >> Sure, the technology that we invested in this year that I thought was a game-changer, we saw, we experimented with last season, but this year, we've been experimenting with VR and AR a little bit. But, a technology that we thought was really cool is called 4DReplay, it's a company out of Korea. And we saw them, we did an experiment with them, and then we implemented them for the full season this year and we've seen them at some other venues as well, the Warriors tried them at the Playoffs, but we had 'em full year and what we did was they put in about 120 cameras, spaced approximately five feet apart, between the bases. 120 of 'em, and they focus on the pitcher and the batter, so when you have a play, you can 3D, or 4D, 4D rotate around that play and watch the ball as it's moving off the bat, and get it from that full perspective, it's awesome for the fan experience, it gives them a perspective they never have, I love watching the picture, because you can see that hand, in full 4D glory pronating as it comes through on every pitch, if you can watch that hand carefully you can predict what kind of pitch it is, it's something that a fan has never had access to before, we did that for the first time this year. >> I had a new experience, obviously you see Statcast on TV now, a lot of this overlayed stuff happening, kind of creates like an esports vibe to the table. Esports is just coming. >> And it's just the beginning >> Your thoughts on esports, competitor, natural evolution, baseball's going to be involved in it, obviously, thing in the emerging technology's looking interesting, and the younger generation wants the hot, young... Sure, we feel like our game has been around a long time, and it still is, the rules haven't changed that much, but fans still enjoy it, but they just consume it differently and our game can be incredibly exciting in moments, but, there's also some gaps in there when you can build relationships. Some of the younger generation may fill those gaps with watching somethin' else, or two other things on their devices, but that's okay, we embrace that at the ballpark, but in terms of the emergence of esports, and the changing demographic of our fanbase, what we're trying to do is just package our game differently. One thing I'm really excited about, and startin' to see, we're in the early days, I consider with virtual reality, we experiment with it, maybe two or three years ago we've been doing some stuff with it, but I'd say it feels like we're in the second or third inning with virtual reality, where we're really going, and I've seen Intel doin' some of this stuff, I was out working with Intel in Pyeongchang, at the Olympics this past year, working with their PR team, and where it's going I can already visualize what this is going to be like, this concept of volumetric video. Where, it's not about having that courtside seat, in basketball, or that seat right behind home plate, it's about being wherever you want to be, anywhere in the action. And to me it's not about doin' it live, because in baseball, you don't know where the ball's going to go, it's about doin' it, replay, right after, okay, that ball was shot to Brandon Crawford, he made the most amazing diving play, picked it up, gunned it to first, where do you want to watch that from? Everybody's different, some people might want to watch it from right behind first base, some people might want to watch it right Brandon Crawford, behind the batter, with volumetric video and the future of VR, you'll be able to do that, and this esports generation, this fan's instant gratification want, unique experiences, that's what's going to deliver it. >> This is such an immersive environment, we're looking at this kind of volumetric things from Intel, and you got VR and AR, immersion, is a new definition, and it's not, I won't say putting pressure, it's evolving the business model, who would've thought that DraftKings and these companies would be around and be successful, that's gambling, okay, you now you got that, your VR so the business model's changing, I've been hearing even token and cryptocurrency, maybe baseball cards will be tokenized. So these are kind of new, crazy ideas that might be new fan experience and a business model for you guys. Your thoughts on those kind of wacky trends. >> That's why I love working with companies like Mayfield 'cause they're seeing the future before we see it, and I love being where we are, so we can talk to them, and learn about these companies. Another example, along those lines is, how are fans going to get to the ballpark five years from now, and how do we adapt to that because we're doing a major development right adjacent to the ballpark, we've got 4,000 parking spaces. Are we going to need those five years from now? Well we're going to build out that whole parking lot, we're going to put a structure in there. But five, ten years from now, we're building that structure so it can be adaptable, because, is anyone going to need to park? Is parking going to be like typing, you know on a typewriter, 10, 15 years now because everybody is in either self-driving cars, or ride shares, and the cars just, poof, go away, and they come back when you need 'em. >> Like I said, everything that's been invented's been on Star Trek except for the transporter room, but maybe they could transport to the game. >> We could use that in San Francisco. >> Bill, got to ask you about your role with Mayfield, because one of the things I've always been impressed with you is that you always have a taste for innovation, you're not afraid to put the toe in the water or jump in the deep end where the technology is, these guys are lookin' for some trends, too. How do you advise some of these guys, how do you work with Mayfield, what's the relationship, how are they to work with, what's the intersection between Mayfield and you? >> Well the one thing that Mayfield does is they put together a conference, each Summer, that I love comin' down to, and I get to meet a lot of my counterparts and we talked about meeting with my counterparts in sports, but I love meetin' with my counterparts across all industries, and Mayfield makes that possible, they bring us all together with some really interesting speakers on a variety of topics not all directly tech related, so it's a great opportunity for me to just get outside of the daily routine, get outside the box, open my mind, and I just have to drop down the road to do it. So that's an example, another thing is, Mayfield, and other firms will come to me, and just say, "Hey, here's a technology we're evaluating, "they think it would be a great fit in sports, "what do you think?" And so, I can give them some valuable feedback, on company's they're evaluating, companies will come to us, and I might throw them their way, so it's really a two way street >> Great relationship, so you're a sounding board for some ideas, you get to peek into the future, I mean, we've interviewed entrepreneurs, successful entrepreneurs here, it's a seven, eight year build out, so it's almost like an eight year peek into the future. >> Yeah, and it's super valuable, especially given where we are geographically and our inclination toward being on the leading edge. >> I want to just end the segment by sayin', thanks for comin' in, and I want you to show the ring there, 'cause I always, can't stop starin' at the hardware, you got the ring there, the world champion. >> It's a few years old at the moment, we're going to have to get a new one sometime soon. >> We got to work on that, so is there any cutting edge technology to help you evaluate the best player, who you lookin' at next year, what's goin' on? What's the trades goin' on, share us-- >> Are we off the record now, 'cause I have a feeling you're asking this for personal reasons, for your squad, so. >> I'm a Red Sox fan of the AL, obviously, moved here 20 years ago, big fan of the Giants, I love comin' to the games, you guys do a great job, fan experience is great, you guys do great job and I'm looking forward to seeing a great season. >> Thanks, yeah, hope springs eternal this time of year, we always block off October and expect to be busy, but when we have it back, it just gives us an opportunity to get a head start on everybody. >> Well Bill, thanks for coming in, Bill Schlough, CIO for the San Francisco Giants, here on Sand Hill Road talkin' about the 50th anniversary of Mayfield, and this is the People First Network, getting ideas from entrepreneurs, industry executives, and leaders. I'm John Furrier with theCUBE, thanks for watching. (electronic music)

>> Live from Orlando, Florida. It's theCUBE, covering Cisco Live 2018. Brought to you by Cisco, NetApp, and theCUBE's ecosystem partners. >> Okay welcome back everyone, we're here live in the Cisco DevNet Zone, at Cisco Live 2018. It's theCUBE's exclusive coverage. This is Go Live, I'm John Furrier with Stu Miniman there, here with Suzie Wee who is the CTO and Vice President of Cisco. This is her baby DevNet, the fastest growing developer program in Cisco history, only four years old. Welcome to theCUBE, good to see you again. >> Hey John good to see you, hey Stu. >> I made that stat, it was only four years old. So DevNet, obviously just for color commentary, really successful developer program, only in it's fourth year or so for Cisco. But it's really changing the face of Cisco. It's showing that a new collaboration, a new co-development, a new developer framework is being built on top of networks and it's on a collision course with Cloud Native. Kay, this is a great path for network engineers. It really changed the show vibe so congratulations. >> Thank you, thank you. Yeah, and why do you say collision course? There's like a whole new paradigm, right? And it's pretty amazing, it's pretty amazing. >> Well some of the things that we've been seeing here, obviously CCIE's or 25 years of excellence and stats was out here >> Yes, Yes. >> The key note from the CEO, Chuck Robbins, talks about an old way and new way. Developers are clearly in the driver's seat here and network engineers, Cisco partners, customers technical folks and engineers. They're at the keys to the kingdom and you introduced a concept called Network Dev Ops. >> Yes. >> Okay, a few years ago when we first had you on theCUBE. Where is that now? Where is Network Dev Ops now? What's the vibe internally? Is there a full acceptance to it? Is there embracing it? >> It's amazing and ya know it's like, when we were pushing it we were just saying, "Hey, the network is changing, the network "is gonna be programmable, the network "is going to have API's", and you go back four years and then you're just like, "What was the buzz?" The buzz was SDN, y'know the buzz was SDN. SDN was open flow, it was separation of control plain from data plain. But, it was still kind of research. And what we knew is like, it wouldn't become real until the people who are building and operating the World's networks were ready to adopt it. And so, at first of course, it was like, there were the people who were like, "Okay this network thing, this programmability "is gonna come to the network, but what can we do there?" And since then, people have jumped in, they've like really gotten in. And like here at this Cisco Live, what we're seeing is that people are ready to code. And so the concept of, I'm a networker, now there's software built into my entire network programming portfolio. How do I build the skills? I'm a developer, and the networkers are getting comfortable with understanding that they need to code, they need to understand these skills. But one thing that we did, was we actually separated out, like, the definition of developer. >> Yep. >> Y'know. >> You guys done a good job of really defining a path for the network engineer, who can extend their skill set and solve network problems, be creative, and also do great business outcome oriented things. So, I want you to take a minute to explain the DevNet story because you guys just didn't throw a PowerPoint at this. You dug in, you built it up, and you threw a lot of resources for Cisco, I mean small for Cisco's scale, but you guys dug in, you did the homework and you're doing new things. So take us to the DevNet story and what's happening this year in the momentum. Take us through that little journey. >> Yeah, so the story was back in actually 2013. Cisco was saying, "Hey, we're gonna get into software "we're doing software, we have a software strategy." And all of that is fantastic, either... But the thing that was missing, was like, Hey, we need an ecosystem, like the reason you do software is to have an ecosystem. And in order to have an ecosystem you want people to build upon your stuff. You need to expose your API's. It doesn't happen by itself, you need to have a developer program so that you can actually really let people use all of that and partake in the ecosystem. So we, kind of, I evangelized, evangelized, evangelized, gave a couple hundred pitches, got the okay to start DevNet, and that was in 2014. And then in 2014, then we said okay. So now we got the okay to start a developer program for Cisco. But, y'know, it's still not a sure shot that it would work. >> Yeah. >> And then we said our dream is to have a developer conference at Cisco Live. And so we wanted to have that developer conference at Cisco Live and then three months later, we had it. And we're like okay, 24 hour hack-a-thon, deep dive API sessions, but would the people come? Would they be ready? And then, they came. Like, they came, it was packed. It was just like wall to wall of people, who are excited to learn about software. So now you go and then you fast forward, y'know, four years, and now we just hit 500,000 developers. 500,000 people have registered for DevNet. And you can be like, "Well what does that mean?" We have half a million developers. Is it a real number? Well, my team kept scrubbing the database. Like so, we had hit 400,000 and then our numbers got lower and I was like "Come on guys, stop it!" And they were like, "No, no, no, we have to scrub it, "we gotta out the duplicates." And then finally we got it up and we've grown it. It basically is at 500,000 registered developers. And what that means is like, now we have a community. We have a community of people who are getting up on network API's, we have a community of people who can develop, and once you do that you hit this completely different inflection point. Where at first our mission was just to help networkers be developers, to help the app developers understand that the network has API's and to do stuff there. That's still our goal, to enable developers. But now we have a community, what we can do is really catalyze that community into business and impact. >> Suzie, first of all congratulations. It's been so much fun to be here in the DevNet Zone. It'd been a few years since I'd been to Cisco Live. And y'know, people in these sessions every time. And you go, people are coding, they're white-boarding, they're, y'know building. Playing with Legos, they're doing all sorts of stuff. Over the last five years, y'know, we all knew that, y'know, developers of the new Kingmakers. It's been talked about a lot. But we've seen many infrastructure companies try. They create little developer conferences, they bring in speakers, they'll get some momentum, and then after a year or two, it kind of fizzles out. >> Yes. >> Give us a little bit behind the scenes, as to, y'know is it because networking people are worried about their jobs and they're getting on-board? Is it, y'know, I know part of it is your team and the ecosystem you've built here. But, give is some of the reasons why this has succeeded when so many other have, kind of, come and gone. >> Yeah well, I mean we're very fortunate that we've kind of executed in a way that it has continued to be here and we know that's really hard to do. It takes executive support, it takes the troops, it takes fighting anti-bodies, and kind of all of that kind of stuff. But I think, like, the key has been that we've been working with the community. When we had that first DevNet Zone, that first developer conference at Cisco Live four years ago, people came. And that told Cisco something, right? And then as we've continued to build it out, we've actually been not doing it as a silo within Cisco. We've been doing it with our sales organization, with our partner organization, we've been doing it with our ecosystem and our partners and out there. We've just continuously been doing it based on what their needs are. >> And Suzie, I love that, because there are some of the events I saw, they were like, "Well, the developer "is this special unicorn", and we're gonna have this special area, it's velvet rope, we're gonna treat 'em really well. But, this is the first thing you see when you come in, you're very approachable. The line I've heard from your team is, "We are going to meet them where they are." There are no, y'know, "Gosh I haven't "touched programming in 20 years." No, no, no, you're fine, you're good come on in. I'm not sure if I'm really (mumbles). Well you're not programming, you're coding. So, I think that's part of the success, is these people. Y'know, this is their careers, and you're giving them that path forward. >> It is, and when we look at like, developer programs, you'd think it would be easy to start a developer program. But, there's no formula for it, y'know? And when we did it for Cisco, like as we've grown this, it depends on the products that we have, it depends on the community that we have, the types of solutions, what our customers want. And basically what happens is, we did have a core set of networkers who are scared. And we, instead of making DevNet the elite place for the elite developers, we said it is the place to bring in the community. We're gonna be welcoming, we're bringing them in on the journey, because they're the ones who need to be there. And so we've really tried this more open approach. And if you look at Cisco's community of networkers, they're amazing, like, they are developing and installing and operating networks around the World in every country. They've been dedicated, but they are scared of that transition to software and programmability. And they've been dedicated to us, we're dedicated to them, getting to that next level. >> You just did a good job of bringing that tribe kind of mentality and co-development, co-creation, people who are learning. So you have first time learners kicking the tires on coding and growing and experts. So Cisco Champions coming in; Powerhouse developers. >> Yeah >> Not Cisco employees, it's Cisco Champions, and so a nice balance. So that's a good sign of success. >> And you're right, that's key because it's not just, like just beginners. I mean, first of all, there is a very large stage of new people who are just coming in and then wanting to get started and that's awesome. And in addition, very advanced folks, who are like, y'know, just the most advanced developer you'd find, who also has networking expertise. And then of course, the app developers. We're talking to app developers and cloud developers and DevOps pros, and they're coming in as well. >> Yea, and Suzie you bring up a great point. Cause one of the challenges when you have the cool new innovation stuff, is the business, like well how does that connect back? So help connect the dots, we heard Chuck Robbins on stage. Not only was it just DevNet and 500,000 but the new products that are coming out just tie right into it. >> It's crazy, like yea, it's awesome. Because what happens is, programmability, Cisco, is building programmability into our entire portfolio. It's not that we have one product that has API's, I mean that's where we were a few years ago. But now we look... Our enterprise networking products, y'know, for the data center, for service provider, for wireless. All of those products are programmable. Our security products are programmable. IoT, collaboration, our entire portfolio is now programmable, so it gives you this kind of whole portfolio of programmability to play with, and that cross-domain. Who covers that many domains? And that's really powerful. When we take a look at the programmability, it was like for the network devices themselves. Like those have Asics that are programmable. So if there's like a new protocol that comes up to handle IoT things, we can actually re-program the Asics to get that going at line rates. You can do like, on-board application hosting on those network devices. We have controller levels, so you can hit the network, and then now you have like analytics and insights that you can do to pull out information from the network, and then be able to, y'know, operate at that level as well. >> So a strategic advantage architecturally for Cisco, certainly in the network side and scaling up at the stack with Kubernetes and (mumbles). We saw Google on-stage, kinda giving an indicator of where it's going. I want to ask you about the culture question for DevNet. Obviously people are fascinated with the success of DevNet, we've been great to follow the success through your journey and being part of it. But for the folks that are now seeing the success, and want to join: What can they expect, if I join the DevNet mission? What's the expectation? What's gonna be the vibe? What would you share to someone watching, that's gonna jump in and join the journey, what can they expect? >> Well, I think that first of all, it's going to be very welcoming. Like, they're gonna feel welcome. And I'm just proud of my team, because people come in and they actually say, "Wow, sometimes you go to developer conferences "and it's a little bit intimidating." And yea, you might be intimidated, but here you're going to feel welcome. Because, y'know, we really want things to happen. And then there's gonna be this kind of like, intrigue in terms of what you can build. Because what we're building is different. It's not a well known area, like everyone knows how to build apps for a mobile device. People don't know how to build applications for programmable infrastructure. Like, the fact that hey, your wireless access points now give you location and proximity information. I can write an indoor location app. Sounds simple, but it's awesome. >> Connect a camera to it. >> It's amazing, right? >> Hello! >> And then what happens is, as you're doing that, you have like, connect a camera, you're like put a Playstation into a hospital... The Children's Hospital of L.A came and spoke, and they were talking about the business problem. They had a patient, who was very sick, a young boy. And his wish was to have Playstation so he could play it. And then they had to go to their networkers cause you don't put Playstations in hospitals. They had to make that happen and intent-based networking lets you make that wish, and then activate that in the network, that's now a programmable infrastructure. So the types of problems that you can solve are different, it's amazing. >> The new apps are coming out and you're creating a new, first generation green field of networked apps. >> Yes. (chuckles heartily) >> Like what iPhone did for mobile apps, you guys are doing for networks. >> That's right, that's right. >> So that's awesome, it's super cool. Programmable infrastructure, all DevOps kinda geeky stuff. For the next steps, as you guys are now at the beginning of the next inflection point. >> Yes. >> What're you guys focused on? What's happening with the team? What's happening with some of the initiatives you're doing? Also demos get better and better. The training classes are still going on. What's your focus? >> So with some of the things that are happening now, which is... So we've hit this milestone of half a million developers. But what does that mean? What that means is that, we have half a million people who can use network API's. What that means also, is that they're contributing code. So it's no longer just, "Here I'm gonna help "you use your API", but now it's also like, they're contributors back. And what we're doing, is we're actually embracing that and making that part of the innovation model for networking. So, you're not just taking Cisco's platforms and the innovation there, which is of course growing tremendously, but now you can also add in innovation by the community. And I know it's a straight forward concept for software. It's not a straightforward concept for networking and infrastructure. >> To bring an open-source ethos, to code sharing, co-contributing. >> Exactly, and something that we've released is code exchange, definite code exchange. And what it is, is just a list of curated software. Software that's out of GitHub, that works for our platforms, y'know. But the thing that developers are always like, "Okay there's a lot of software out there, "which one should I use?" and then basically giving them like, the curated list of here's the stuff that you can use. >> So Suzie, it's been fun to watch the transformation of Cisco overall. As we look at... Before, we used to measure in boxes and ports. What's the measurement internally? When you talk about saying, "Okay how are we doing "on our journey to become a software company?" Give us a little insight as to internally how Cisco measures that. >> The way that we measure that now is, we're talking to our customers and our partners and their adoption of API's, of programmability, their ability to execute on that and to be successful in this business. And so, it's really an external looking view. So it's all just like okay, how much do they get it? How much can they use it? How much are they building the skills? So it's really looking at the success of the community and being able to build the skills and use these products and build solutions with them. >> Suzie, congratulations on continuing growing, hitting a major milestone, 500,000 developers, half a million developers, that's a real community. It's just the beginning now, it's the start line. >> (chuckling) The start line, it is. >> One finish line is another start line. >> It is a start line, it's absolutely the start line. >> And you guys had a great event last night at the Mango party, the Mango Cafe. Talk about that, you had a celebration. Turns out a lot of people showed up. It was supposed to be a little private party. >> It was a little private party, yea. So we, y'know, just wanted to thank the team and thank our community. Because, quite honestly, to get to this half a million it wasn't just the people who work for me who got it there. It's the fact that, there's of course our team who's very dedicated to that, but then it's our partners. It's even you guys, right? It's our partners who have like... I understand this mission, I'm gonna jump in, I'm gonna help it happen. It's our systems engineers, it's our partners, it's our innovation folks, it's people from the community who understand the mission and have joined in to push it forward. So we had this party last night at Mango Cafe, you guys were there. The people were callin it kinda the best one. It's really just appreciation for our community and what they've done to get it there. Because it's not us, it's our community who've done it. >> This is the open ethos. Cisco becoming open. What's it like to be on the inside and seeing Cisco open up like this? >> It's, I mean, it's amazing. And what's amazing is like, when I started DevNet you'd think like okay, "I'm gonna run a developer program." The thing that surprises me is just, how hurtful it is to so many people. Like, people, they find a path. They see a new opportunity, they figure out a new way they wanna advance their businesses and their careers. And it's like, all heart. And that's how it grew. Like with the resources, it's just because people who had felt this heart and this connection into this mission and drive, they're taking it to the next level so it's amazing >> Like open-source software, people love to be part of a great project. >> It is, it is. >> And DevNet certainly is. And DevNet Create. Don't forget DevNet Create is your other event that bring the cloud native world with the networking world together. >> It is. >> Great project. >> You were with us at DevNet Create and that's where it's this mixing of communities of like, the app developers with the networkers who are getting out there. And what's funny is, we didn't know how those communities would interact. And they're mixing, they're getting it. They're just like "Okay, I have this location software, "I need to work together with the guys "who are gonna install the network and then "we can make this amazing experience." And they're mixing and when they do it the right things happening. >> Very complimentary, there's love going wild. >> App guys love the network guys to take care of the network and the network guys love the app guys that take care of the apps. >> Exactly! Exactly. >> It's a win-win. Great stuff, congratulations. Again, a new way to program. Just like we saw the iPhone creating the app store. Networking now is programmable. We expect to see a lot of great creativity, new problems, new things being created. And that's an opportunity for all. We're here at theCUBE bringing you all the action from the DevNet Zone at Cisco Live. More live coverage. Day three, stay with us, I'm John Furrier with Stu Miniman, we'll be right back. (upbeat music)

(ambient Music) >> Announcer: Live from Vancouver, Canada, it's theCUBE. Covering OpenStack Summit North America 2018. Brought to you by Red Hat, The OpenStack Foundation and its ecosystem partners. >> Welcome back, this is theCUBE's coverage of OpenStack Summit 2018 in Vancouver. I'm Stu Miniman, my cohost for the week is John Troyer, happy to welcome back to the program two CUBE alumni, we have Margaret Dawson and John Alessio. Margaret is the vice-president of Portfolio Product Marketing and John is the vice-president of Global Services. Thanks so much for joining us. >> Thank you. >> Thanks for having us. >> Good to be here. >> Alright so, John has gotten the week and a half now of the red hat greatness of being at summit last week, I unfortunately missed Summit, first time in five years I hadn't been at the show, did watch some of the interviews, caught up on it, and of course we talked to a lot of your team but, Margaret, let's start with you >> Margaret: Okay. >> One of the things we were looking at was, really, it's not just a maturation of OpenStack, but it's beyond where we were, how it fits into the greater picture, something we've been observing is when you think about open sourced projects, it's not one massive stack that you just deploy, it's you take what you need, it kind of gets embedded all over the place, and help us frame for us where we are today. >> Wow, that's a big question. So I think there's a couple things, I mean, in talking to customers, I think there's a couple trends that are happening. One is one you've probably talked about a lot and we probably covered at the Red Hat Summit which is just this overall digital transformation, digital leadership, whatever you want to call it, digital disruption tends to be a thing, and open sources definitely playing, really, the critical role of that, right, you will not be able to innovate and disrupt or even manage a disruption if you're not able to get to those technologies and innovations quickly and be able to adapt to it and have it work with other things. So the need for openness, for open APIs, for open technologies, inner-operability allows us to move faster and have that innovation and agility that every enterprise and organization needs world wide. And tied to that is kind of this overall hybrid cloud, so it's not just, OpenStack is a part of a much bigger kind of solution or goal that enterprises have in order to win and transform and be a digital leader. >> Margaret, I love that. Digital transformation, absolutely something we hear time and again from customers. >> Margaret: Yup. >> John, I've got a confession to make. I'm an infrastructure person and sometimes we're always like, why, come on, we spend all our time talking about how all the widgets and doo-dads and things-- >> Margaret: Blinky lights. >> Blinky lights, up on stage we have the-- >> He missed the blinking lights >> He did miss the blinking light. >> They had a similar stack up on stage yesterday. >> Oh, that's right. >> Same fans you could hear in the back of the room. But the whole goal of infrastructure always, of course, is to run the application, the whole reason for applications is to run and transform and do-- >> John: Serve the business >> Yeah, so that's where I'm going with this is we're talking more about not only that foundational layer of OpenStack but everything that goes with it and on it so maybe you could talk about the services-- >> Sure. So I think, Stu, that's exactly what we're seeing. So if you think about the last year and what we're seeing with services and projects here on OpenStack, I think the first thing to talk about is the fact that it's been growing quite a bit, in fact, from a 2017 versus 2018 perspective, our number of OpenStack projects have increased 36% year on year globally. So we're seeing a lot of demand, but we're seeing the projects be a lot more comprehensive. So these are OpenStack projects, but they're OpenStack with Open Shift, with Cloud Form, with Suff, as an example, and this combination is, really, a very very powerful combination. In fact, it's been so powerful that we started to see some common patterns of customers building a hybrid cloud solution, using OpenStack as their kind of private cloud infrastructure, but then using Open Shift as their way to kind of deploy applications in containers in that hybrid way, that we created a whole solution, which we announced two weeks ago, when John was at our Red Hat Summit, called Containers on Cloud. And that's taking all of our best practices around combining these products together in a very comprehensive, programmatic approach to deploying those solutions together. >> And I think it's really important, I mean, as you know, I think you and I met when we were both in networking, so coming from that infrastructure background but we really all need to talk about the workload down, starting with the application, starting with the business goal, and then how the infrastructure is almost becoming a services-based abstraction layer where you just need it to be always there. >> John: Yup. >> And whether it's public cloud or private cloud or traditional infrastructure, what developers in the business want is that agility and flexibility and containers provide that. There's other kind of architectural fabrics that allow that consistency and that's when it gets really exciting. >> One thing that's really interesting to me this week at OpenStack, as we've drilled into different customers, and talking to different people, even at lunch, is one, it's real. Everyone I've talked to, stuff in deployment, it went quickly, it's rock solid, it's powering, as we know, actually a lot of that is technical infrastructure that's powering a lot of the world's infrastructure at this point. >> That's right. >> The other thing that was interesting to me is some folks I talked to were saying, "Well, actually we have enough knowledge "that we're actually doing a lot of it ourselves, "we're going upstream." However, so that's great, and that's right for some people, but what I'm kind of been interested in both just coming from Red Hat Summit is both the portfolio, the breadth of the stack, and then all the different offerings that Red Hat, you know, it's not Rel anymore, it's not just Linux anymore, there's everything that's been built up and around and on top for orchestration and management, and then also the training, the services, the support, and that sort of thing, and I was wondering, that's kind of a two-part question, but maybe you all could tackle that. What does Red Hat bring to the table then? >> So, let me just start with, again, just to kind of position what we do as global services, our number one priority is customer success with Red Hat technology, that's the first and foremost thing we do and second is really around building expertise in the ecosystem so our customers have choice and where to go to get that expertise. So, if you start to look at kind of what's been going on as it relates to OpenStack, and, again, many customers are using Upstream bits, but many customers are using Red Hat bits, we see that and we look at the number of people who are getting trained around our technology. So over the last three years, we've trained, through our fee-based programs, 55,000 people on our OpenStack portfolio and in fact from 2017 to 2018 that was up 50% year on year and so the momentum is super super strong. So, that's the first point. The second is it's not just our customers. So part of my remit is, yes, to run consulting and, yes, to drive customer enablement and training, but it's also to build an ecosystem through our business partners. Our business partners use a program we call OPEN, Online Partner Enablement Network, which actually will just be celebrating five years just like OpenStack will, we'll be celebrating five years for OPEN. And our business partner accreditations on OpenStack specifically are up 49% year on year. So we're seeing the momentum in our regional systems integrators, our global systems integrators, our partners at large, building their solutions and capabilities around OpenStack, which I think is fantastic. >> No and it helps a lot with the verticalization of that, right, 'cause every industry has slightly different things they need. The thing I that would add to that, in terms of do-it-yourself community versus a dis-ter that's supported from someone like Red Hat, is it really comes down to core competency. And so even though OpenStack has become vastly simplified from a day one, day two, ongoing management, it is still a complex project. I mean that's the power of it, it can be highly customizable, right, it is an incredibly powerful infrastructure capability and so for most people their core competency is not that, and they need that support at least initially to get it going. What we have done is a couple things. I've actually talked to customers a lot about doing that training earlier and it's for a couple reasons, one is so that they actually have the people in house that have that competency but, two, you're giving infrastructure folks a chance to be part of that future cool stuff, right? I mean, OpenStack's written in Python and there's other languages that are newer and sexier, I guess, but it's still kind of moving them towards that future and for a lot of guys that have been in the data center and the ops world for a long time, they're looking out there at developers and going, I'm not the cool kid anymore, right? So OpenStack actually is a little bit of a window, not just to help companies go through that digital transformation, but actually help your ops personnel get a taste of that future and be part of that transformation instead of being stuck in just mainframe land or whatever, so training them early in the process is a really powerful way to do a lot of things. You know, skillset, retention, as well as then you can manage more of that yourself. >> And then all the way up to Stack, right? I mean, we're talking about containers, and then there's containers but then there's container data storage, container data networking. I mean, you've got the rest of the pieces in that, in Open Shift, in the rest. >> Absolutely. >> That is correct. >> And I think, John, you were at Red Hat Summit, we had a number of different innovation award winners. So I think one good example of kind of this kind of transformation from a digital transformation perspective, but also kind of leveraging a lot of what our Stack has to offer is Cafe Pacific. And so we talked about Cafe, they were one of our innovation award winners and what their challenge really was is how do they create a new modern infrastructure that gave them more flexibility so they could be more responsive to their customers. >> Yeah. >> In the airline industry. And so what they were really looking for was really, truly a hybrid cloud solution. They wanted to be able to have some things run in their infrastructure, have some things run in the public cloud, and we worked with them over the last, little over a year now, Red Hat consulting, Red Hat training, the Red Hat engineering team, in really building a solution that leverage OpenStack, yes, but also a number of other capabilities in the Red Hat portfolio, Open Shift, so they can deploy these applications, containerized applications now both to the public cloud as well as to the private cloud, but also automation through Ansible, which we're hearing a lot about Ansible and products like Ansible here at the conference-- >> Well the Open Stock and Ansible communities are starting to really work well together, just like Kubernetes, you've got a lot of this collaboration happening at the project level not to mention when we actually productize it and take it to customers. >> Yeah, so it's been super super powerful and I think it's a good one where it really hit on what Margaret was saying, which was giving the guys in infrastructure an opportunity to be a part of this huge transformation that Cafe went through, 'cause they were a very very key part of it. >> Yeah. Well, I think we're seeing that also with the open innovation labs. So this is something, which is really an innovation incubation process, it's agile, scrum, whatever, and in those we're not just talking to the developers, we're actually combining developers, functional lines of business leaders, infrastructure, architects, who all come together in a very typical six week kind of agile methodology and what comes out of that, I don't know, I've seen it a couple times, it's magical is all I can say, but having those different perspectives and having those different people work together to innovate is so powerful and they all feel like they're moving that forward and you come out with pilots, and we've seen things where they come out with two apps at the end of six weeks or eight weeks, it's just incredible when they're all focused on that and you start to understand those different perspectives and to me that's open source culture, right? It's awesome. >> And, Margaret, I'd love to hear your perspective also on that hybrid cloud discussion because so many people look at OpenStack and be like, oh, that's private cloud. >> Margaret: Right. >> And, of course, every customer we talk to, they have a cloud strategy. And they're doing lots of SaaS, they've got public cloud, multiple, Red Hat, I know you play across all of them, big announcement with Microsoft last week, last year was Amazon big partnerships with, so is Kubernetes the story, or is Kubernetes a piece of the story, how do all these play together for customers? >> I think Kubernetes is one and so, especially when you look at the broader architectural level, OpenStack becomes obviously the private cloud and enables them to start to do things that are more cloud-native even in their own data center, or if it's hosted or management or more traditional infrastructure, but it really has to be fluid. And a lot of customers initially were saying that their strategy was cloud first, and they would say, "Oh, we're going to put "everything in the public cloud." And then you actually start going through the workloads, you start going through the cost, you start going through the data privacy, or whatever the criteria capabilities are, and that's just not practical, frankly. And so this hybrid reality with private cloud, traditional, and public is going to be the reality for a very very long time, if not forever. There's always going to be things that you want to have better control of. And so Kubernetes at the orchestration layer becomes really critical to be able to have that agility across all those environments, but you have other fabrics like that in your architecture too, we talked about Ansible, it allows you to have common automation and do those play books that you can use across all those different infrastructure, KVM, what's your virtualization fabric, and can KVM take you from traditional virtualization all through public cloud? The answer is yes. So we're going to see increasingly these kind of layers of the overall architecture that allows you to have that flexibility, that management that's still the consistency, which is what you need to keep your policies the same, your access controls, you security, your compliance, and your sanity, whereas before it was kind of Ad Hoc. People would be like, oh, we're just going to put this here, go to public cloud. We're going to do this here, and now people are finding standardizing on things like even Red Hat Enterprise Linux, that's my OS layer, and that allows me to easily do Linux containers in a secure way, et cetera, et cetera. So, doing hybrid cloud means both the agility but you got to have some consistency in order to have the security and control that you need. So it's a little bit different than what we were talking about a few years ago, even. >> And I think one of the things that we've learned in the services world is that we started this idea about 18 months ago, we called these journey adoption programs, which were really the fact that some of these transformations are big, they're not about a single project that's going to last four to six weeks, it's a journey that the customer's going to go on and so when we talk about hybrid cloud, we've actually created this adoption program which can really start with the customer in this whole discovery phase, really, what are you trying to accomplish from a business perspective then take them into a design phase, take them into a deployment phase, take them into an enablement phase, and then take them into a sustainment phase. And there's a number of different services that we'll do across consulting, training, even within Marco Bill Peters Organization, which is our customer experience and engagement organization, around what role a technical account manager can play and really help our customer in the operational phases. And so we've learned this from some of the very large deployments, like Verizon, where we've seen some very-- >> And it's cyclical, right? You can do that many times. >> We do. In fact, you absolutely do. And so we've created now a program, specifically, around hybrid clouded option to try and de-mistify it. >> Yeah. >> Because it is complex. >> Well, and the reality is, there's somewhere around 30% of organizations still do not actually have a clear cloud strategy. And we see that in our own research, our own experiences, but industry analysts come up with the exact same number. >> And Margaret, by the way, the other 70%, the ink still pretty-- >> Yeah. >> Still wet! (laughing) >> Yes, it is. I'll tell you, I love saying cloud first to people because they kind of giggle. It's like, yeah, that's our strategy but we know we don't really know what that means. >> Which cloud? >> Exactly. >> Exactly. >> All the clouds. >> Exactly. >> Alright, well Margaret and John, want to give you a final word, key takeaways you want to have or anything new to the show that you want to point out? >> I would just say we are still in early days. I think sometimes we forget that we, both in the open source communities, in the industry for a long time, tend to be 10 years ahead of where most people are and so when you hear jokes about, oh, is OpenStack still viable or is everything doing this, it's like right now we only have a very small percentage of actual enterprise workloads in the cloud and so we need to just now get to the point where we're all getting mature in this and really start to help our customers and our partners and our communities take this to the next level and work on inter-operability, and ease of use, and management. We're so mature now in technology, now let's put the polish on it, so that the consumption and the utilization can really go to the next level. >> Yeah, and I'll play off what Margaret said. I think it's very very key. When I look at where we've had the biggest success, as defined by, in that discovery phase, the customer lays out for us, here's what our business objectives were, did we achieve those business objectives, it's all about figuring out how we can create the solution and integrate into their environment today. So Margaret said I think very very well which is we have to integrate into these other solutions and every one of these big customer deployments has some Red Hat software, but it also has some other software that we're integrating into because customers have investments. So it's not about rip and replace, it's about integrate, it's about leverage, it's about time to market, and that's what most of the customers I've talked to, they're very worried about time to value, and so that's what we're trying to focus in, I think as a whole company, around Red Hat. >> Margaret: Agree. >> Absolutely. Summed it up very well. John Alessio, Margaret Dawson, thanks so much for joining us again. >> Thanks again. >> For John Troyer, I'm Stu Miniman, watch more coverage here from OpenStack Summit 2018 in Vancouver. Thanks for watching theCUBE.

(music playing) >> Narrator: Live, from downtown San Francisco It's the Cube. Covering IBM Chief Data Officer Startegy Summit 2018. Brought to you by: IBM >> Welcome back to San Francisco everybody we're at the Parc 55 in Union Square. My name is Dave Vellante, and you're watching the Cube. The leader in live tech coverage and this is our exclusive coverage of IBM's Chief Data Officer Strategy Summit. They hold these both in San Francisco and in Boston. It's an intimate event, about 150 Chief Data Officers really absorbing what IBM has done internally and IBM transferring knowledge to its clients. Steven Eluk is here. He is one of those internal practitioners at IBM. He's the Vice President of Deep Learning and the Global Chief Data Office at IBM. We just heard from him and some of his strategies and used cases. He's joined by Sumit Gupta, a Cube alum. Who is the Vice President of Machine Learning and deep learning within IBM's cognitive systems group. Sumit. >> Thank you. >> Good to see you, welcome back Steven, lets get into it. So, I was um paying close attention when Bob Picciano took over the cognitive systems group. I said, "Hmm, that's interesting". Recently a software guy, of course I know he's got some hardware expertise. But bringing in someone who's deep into software and machine learning, and deep learning, and AI, and cognitive systems into a systems organization. So you guys specifically set out to develop solutions to solve problems like Steven's trying to solve. Right, explain that. >> Yeah, so I think ugh there's a revolution going on in the market the computing market where we have all these new machine learning, and deep learning technologies that are having meaningful impact or promise of having meaningful impact. But these new technologies, are actually significantly I would say complex and they require very complex and high performance computing systems. You know I think Bob and I think in particular IBM saw the opportunity and realized that we really need to architect a new class of infrastructure. Both software and hardware to address what data scientist like Steve are trying to do in the space, right? The open source software that's out there: Denzoflo, Cafe, Torch - These things are truly game changing. But they also require GPU accelerators. They also require multiple systems like... In fact interestingly enough you know some of the super computers that we've been building for the scientific computing world, those same technologies are now coming into the AI world and the enterprise. >> So, the infrastructure for AI, if I can use that term? It's got to be flexible, Steven we were sort of talking about that elastic versus I'm even extending it to plastic. As Sumit you just said, it's got to have that tooling, got to have that modern tooling, you've got to accommodate alternative processor capabilities um, and so, that forms what you've used Steven to sort of create new capabilities new business capabilities within IBM. I wanted to, we didn't touch upon this before, but we touched upon your data strategy before but tie it back to the line of business. You essentially are a presume a liaison between the line of business and the chief data office >> Steven: Yeah. >> Officer office. How did that all work out, and shake out? Did you defining the business outcomes, the requirements, how did you go about that? >> Well, actually, surprisingly, we have very little new use cases that we're generating internally from my organization. Because there's so many to pick from already throughout the organization, right? There's all these business units coming to us and saying, "Hey, now the data is in the data lake and now we know there's more data, now we want to do this. How do we do it?" You know, so that's where we come in, that's where we start touching and massaging and enabling them. And that's the main efforts that we have. We do have some derivative works that have come out, that have been like new offerings that you'll see here. But mostly we already have so many use cases that from those businesses units that we're really trying to heighten and bring extra value to those domains first. >> So, a lot of organizations sounds like IBM was similar you created the data lake you know, things like "a doop" made a lower cost to just put stuff in the data lake. But then, it's like "okay, now what?" >> Steven: Yeah. >> So is that right? So you've got the data and this bog of data and you're trying to make more sense out of it but get more value out of it? >> Steven: Absolutely. >> That's what they were pushing you to do? >> Yeah, absolutely. And with that, with more data you need more computational power. And actually Sumit and I go pretty far back and I can tell you from my previous roles I heightened to him many years ago some of the deficiencies in the current architecture in X86 etc and I said, "If you hit these points, I will buy these products." And what they went back and they did is they, they addressed all of the issues that I had. Like there's certain issues... >> That's when you were, sorry to interrupt, that's when you were a customer, right? >> Steven: That's when I was... >> An external customer >> Outside. I'm still an internal customer, so I've always been a customer I guess in that role right? >> Yep, yep. >> But, I need to get data to the computational device as quickly as possible. And with certain older gen technologies, like PTI Gen3 and certain issues around um x86. I couldn't get that data there for like high fidelity imaging for autonomous vehicles for ya know, high fidelity image analysis. But, with certain technologies in power we have like envy link and directly to the CPU. And we also have PTI Gen4, right? So, so these are big enablers for me so that I can really keep the utilization of those very expensive compute devices higher. Because they're not starved for data. >> And you've also put a lot of emphasis on IO, right? I mean that's... >> Yeah, you know if I may break it down right there's actually I would say three different pieces to the puzzle here right? The highest level from Steve's perspective, from Steven's teams perspective or any data scientist perspective is they need to just do their data science and not worry about the infrastructure, right? They actually don't want to know that there's an infrastructure. They want to say, "launch job" - right? That's the level of grand clarity we want, right? In the background, they want our schedulers, our software, our hardware to just seamlessly use either one system or scale to 100 systems, right? To use one GPU or to use 1,000 GPUs, right? So that's where our offerings come in, right. We went and built this offering called Powder and Powder essentially is open source software like TensorFlow, like Efi, like Torch. But performace and capabilities add it to make it much easier to use. So for example, we have an extremely terrific scheduling software that manages jobs called Spectrum Conductor for Spark. So as the name suggests, it uses Apache Spark. But again the data scientist doesn't know that. They say, "launch job". And the software actually goes and scales that job across tens of servers or hundreds of servers. The IT team can determine how many servers their going to allocate for data scientist. They can have all kinds of user management, data management, model management software. We take the open source software, we package it. You know surprisingly ugh most people don't realize this, the open source software like TensorFlow has primarily been built on a (mumbles). And most of our enterprise clients, including Steven, are on Redhat. So we, we engineered Redhat to be able to manage TensorFlow. And you know I chose those words carefully, there was a little bit of engineering both on Redhat and on TensorFlow to make that whole thing work together. Sounds trivial, took several months and huge value proposition to the enterprise clients. And then the last piece I think that Steven was referencing too, is we also trying to go and make the eye more accessible for non data scientist or I would say even data engineers. So we for example, have a software called Powder Vision. This takes images and videos, and automatically creates a trained deep learning model for them, right. So we analyze the images, you of course have to tell us in these images, for these hundred images here are the most important things. For example, you've identified: here are people, here are cars, here are traffic signs. But if you give us some of that labeled data, we automatically do the work that a data scientist would have done, and create this pre trained AI model for you. This really enables many rapid prototyping for a lot of clients who either kind of fought to have data scientists or don't want to have data scientists. >> So just to summarize that, the three pieces: It's making it simpler for the data scientists, just run the job - Um, the backend piece which is the schedulers, the hardware, the software doing its thing - and then its making that data science capability more accessible. >> Right, right, right. >> Those are the three layers. >> So you know, I'll resay it in my words maybe >> Yeah please. >> Ease of use right, hardware software optimized for performance and capability, and point and click AI, right. AI for non data scientists, right. It's like the three levels that I think of when I'm engaging with data scientists and clients. >> And essentially it's embedded AI right? I've been making the point today that a lot of the AI is going to be purchased from companies like IBM, and I'm just going to apply it. I'm not going to try to go build my own, own AI right? I mean, is that... >> No absolutely. >> Is that the right way to think about it as a practitioner >> I think, I think we talked about it a little bit about it on the panel earlier but if we can, if we can leverage these pre built models and just apply a little bit of training data it makes it so much easier for the organizations and so much cheaper. They don't have to invest in a crazy amount of infrastructure, all the labeling of data, they don't have to do that. So, I think it's definitely steering that way. It's going to take a little bit of time, we have some of them there. But as we as we iterate, we are going to get more and more of these types of you know, commodity type models that people could utilize. >> I'll give you an example, so we have a software called Intelligent Analytics at IBM. It's very good at taking any surveillance data and for example recognizing anomalies or you know if people aren't suppose to be in a zone. Ugh and we had a client who wanted to do worker safety compliance. So they want to make sure workers are wearing their safety jackets and their helmets when they're in a construction site. So we use surveillance data created a new AI model using Powder AI vision. We were then able to plug into this IVA - Intelligence Analytic Software. So they have the nice gooey base software for the dashboards and the alerts, yet we were able to do incremental training on their specific use case, which by the way, with their specific you know equipment and jackets and stuff like that. And create a new AI model, very quickly. For them to be able to apply and make sure their workers are actually complaint to all of the safety requirements they have on the construction site. >> Hmm interesting. So when I, Sometimes it's like a new form of capture says identify "all the pictures with bridges", right that's the kind of thing you're capable to do with these video analytics. >> That's exactly right. You, every, clients will have all kinds of uses I was at a, talking to a client, who's a major car manufacturer in the world and he was saying it would be great if I could identify the make and model of what cars people are driving into my dealership. Because I bet I can draw a ugh corelation between what they drive into and what they going to drive out of, right. Marketing insights, right. And, ugh, so there's a lot of things that people want to do with which would really be spoke in their use cases. And build on top of existing AI models that we have already. >> And you mentioned, X86 before. And not to start a food fight but um >> Steven: And we use both internally too, right. >> So lets talk about that a little bit, I mean where do you use X86 where do you use IBM Cognitive and Power Systems? >> I have a mix of both, >> Why, how do you decide? >> There's certain of work loads. I will delegate that over to Power, just because ya know they're data starved and we are noticing a complication is being impacted by it. Um, but because we deal with so many different organizations certain organizations optimize for X86 and some of them optimize for power and I can't pick, I have to have everything. Just like I mentioned earlier, I also have to support cloud on prim, I can't pick just to be on prim right, it so. >> I imagine the big cloud providers are in the same boat which I know some are your customers. You're betting on data, you're betting on digital and it's a good bet. >> Steven: Yeah, 100 percent. >> We're betting on data and AI, right. So I think data, you got to do something with the data, right? And analytics and AI is what people are doing with that data we have an advantage both at the hardware level and at the software level in these two I would say workloads or segments - which is data and AI, right. And we fundamentally have invested in the processor architecture to improve the performance and capabilities, right. You could offer a much larger AI models on a power system that you use than you can on an X86 system that you use. Right, that's one advantage. You can train and AI model four times faster on a power system than you can on an Intel Based System. So the clients who have a lot of data, who care about how fast their training runs, are the ones who are committing to power systems today. >> Mmm.Hmm. >> Latency requirements, things like that, really really big deal. >> So what that means for you as a practitioner is you can do more with less or is it I mean >> I can definitely do more with less, but the real value is that I'm able to get an outcome quicker. Everyone says, "Okay, you can just roll our more GPU's more GPU's, but run more experiments run more experiments". No no that's not actually it. I want to reduce the time for a an experiment Get it done as quickly as possible so I get that insight. 'Cause then what I can do I can get possibly cancel out a bunch of those jobs that are already running cause I already have the insight, knowing that that model is not doing anything. Alright, so it's very important to get the time down. Jeff Dean said it a few years ago, he uses the same slide often. But, you know, when things are taking months you know that's what happened basically from the 80's up until you know 2010. >> Right >> We didn't have the computation we didn't have the data. Once we were able to get that experimentation time down, we're able to iterate very very quickly on this. >> And throwing GPU's at the problem doesn't solve it because it's too much complexity or? >> It it helps the problem, there's no question. But when my GPU utilization goes from 95% down to 60% ya know I'm getting only a two-thirds return on investment there. It's a really really big deal, yeah. >> Sumit: I mean the key here I think Steven, and I'll draw it out again is this time to insight. Because time to insight actually is time to dollars, right. People are using AI either to make more money, right by providing better customer products, better products to the customers, giving better recommendations. Or they're saving on their operational costs right, they're improving their efficiencies. Maybe their routing their trucks in the right way, their routing their inventory in the right place, they're reducing the amount of inventory that they need. So in all cases you can actually coordinate AI to a revenue outcome or a dollar outcome. So the faster you can do that, you know, I tell most people that I engage with the hardware and software they get from us pays for itself very quickly. Because they make that much more money or they save that much more money, using power systems. >> We, we even see this internally I've heard stories and all that, Sumit kind of commented on this but - There's actually sales people that take this software & hardware out and they're able to get an outcome sometimes in certain situations where they just take the clients data and they're sales people they're not data scientists they train it it's so simple to use then they present the client with the outcomes the next day and the client is just like blown away. This isn't just a one time occurrence, like sales people are actually using this right. So it's getting to the area that it's so simple to use you're able to get those outcomes that we're even seeing it you know deals close quicker. >> Yeah, that's powerful. And Sumit to your point, the business case is actually really easy to make. You can say, "Okay, this initiative that you're driving what's your forecast for how much revenue?" Now lets make an assumption for how much faster we're going to be able to deliver it. And if I can show them a one day turn around, on a corpus of data, okay lets say two months times whatever, my time to break. I can run the business case very easily and communicate to the CFO or whomever the line of business head so. >> That's right. I mean just, I was at a retailer, at a grocery store a local grocery store in the bay area recently and he was telling me how In California we've passed legislation that does not allow plastic bags anymore. You have to pay for it. So people are bringing their own bags. But that's actually increased theft for them. Because people bring their own bag, put stuff in it and walk out. And he didn't want to have an analytic system that can detect if someone puts something in a bag and then did not buy it at purchase. So it's, in many ways they want to use the existing camera systems they have but automatically be able to detect fraudulent behavior or you know anomalies. And it's actually quite easy to do with a lot of the software we have around Power AI Vision, around video analytics from IBM right. And that's what we were talking about right? Take existing trained AI models on vision and enhance them for your specific use case and the scenarios you're looking for. >> Excellent. Guys we got to go. Thanks Steven, thanks Sumit for coming back on and appreciate the insights. >> Thank you >> Glad to be here >> You're welcome. Alright, keep it right there buddy we'll be back with our next guest. You're watching "The Cube" at IBM's CDO Strategy Summit from San Francisco. We'll be right back. (music playing)

(electronic music) >> Announcer: From Boston, Massachusetts, it's theCUBE. Covering Cloud Foundry Summit 2018. Brought to you by the Cloud Foundry Foundation. >> I'm Stu Miniman, and this is theCUBE's coverage of Cloud Foundry Summit 2018, here in beautiful Boston, Massachusetts. Happy to welcome to the program first-time guest, Dr. Nic Williams, CEO of Stark and Wayne. Dr. Nic, thanks for joining me >> Thank you very much. I think you must've come to the conference from a different direction than I came. >> I'm a local, and I'm trying to get more people to come to the Boston area. We've been doing theCUBE now for, coming up on our ninth year of doing it, and it's only the third time I've done something in this convention center, so please, more tech shows to this area, Boston, the Hynes Convention Center, and things like that. >> There's plenty of tech people. I was at the Nero Cafe, everyone seemed like they were a tech person. >> Oh no, the Seaport region here is exploding. I've done two interviews today with companies here in Boston or Cambridge. There's a great tech scene. For some reason, you and I were joking, it's like, do we really need another conference in Vegas? I mean really. >> Dr. Nic: Right, no, I like the regional. >> But yeah, the weather here is unseasonably cold. It was snowing and sleeting this morning, which is not the Spring weather. >> It is April, it is mid-April, and it's almost snowing outside. >> Alright, so Dr. Nic, first of all, you get props for the T-shirt. You've got Iron Man and Doctor Doom, and we're saying that there is a connection between the superheroes and Stark and Wayne. >> Right, so Stark and Wayne is founded by two fictional superheroes. The best founders are the fictional ones, they don't go to meetings, they're too busy making, you know, films. >> Yes, but everybody knows that Tony Stark is Iron Man, but nobody's supposed to know that Bruce Wayne was Batman. >> Nic: Right, right. >> But I've heard Stark and Wayne mentioned a number of times by customers here at the conference. So, for our audience that doesn't know, what does Stark and Wayne do, and how are you involved in the Cloud Foundry ecosystem? >> So Stark and Wayne, I first found Bosh, I founded Stark and Wayne. Earlier than that I discovered Bosh, six years ago, when it was first released, became like, I claimed to be the world's first evangelist for Bosh, and still probably the number one evangelist. And so Stark and Wayne came out of that. I was VMWare Pivotal's go-to person for standing things up and then customers grew, and you know. Yeah, people want to know who to go to, and when it comes to running Cloud Foundry, that's us. >> Yeah well, there's always that discussion, right? We've got all these wonderful platforms and these things that go together, but a lot of times there's services and people that help to get those up. Pivotal, just had a great discussion with a Pivotal person, talking about the reason they bought Pivotal Labs originally was like, wow, when people got stuck, that's what Pivotal Labs helps with that whole application development, so you're doing similar things with Bosh? >> Correct. No it's, we have our mental model around what it is to run operations of a platform, where you're running complex software, but you have an end user who expects everything just to work. And they never want to talk to you, and you don't want to talk to them. So it's this new world of IT where they get what they want instantly, that's the platform and it has to keep working. >> Dr Nic, is it an unreasonable thing for people to say that, yeah I want the things to work, and it shouldn't go down, and you know-- >> What is shadow IT? Shadow IT is the rebellion against corporate IT, so we want to bring back, well, we want to bring the wonders of public services to corporate environments. >> Okay, so-- >> That's the Cloud Foundry's story. >> Yeah, so talk to me a little bit about your users. We've watched this ecosystem mature since the early days, you know, things are more mature, but what's working well? What are the challenges? What are some of the prime things that have people calling up your team? >> So our scope, our users, or our customers, are people, they're the GEs and the Fords of the world running either as a service or internally large Cloud Foundry installations. And whilst Cloud Foundry is getting better and better, the security model is better, the upgrades seem to be flawless, it does keep getting more complex. You know, you can't just add container to container networking and it not get more complicated, right? So, yeah, trying to keep up-to-date with not just the core, but even the community of projects going on is part of the novelty, but also it's trying to bring it to customers and be successful. >> Yeah, I go to a number of these shows that are open source and every time you come there, it's like, "Well, here's the main things we're talking about "but here's six other projects that come up." How's that impact some of what you were just talking about? But, maybe elaborate as to how you deal with the pace of change, and those big companies, how are they help integrate those into what they're doing, or do they, you know-- >> So my Twitter is different from your Twitter. So my Twitter is 10 years worth of collecting of people who talk about interesting things, putting in a URL, just referencing an idea they're having, so they tend to be the thought leaders. They might be wrong, or like, let's put Docker into production, like, it doesn't make it wrong, but you've got to be wary of people who are too early. And you just start to peace a picture of what's being built, and you start to know which groups and which individuals are machines, and make great stuff, and you sort of track their work. Like HashiCorp, Mitchell Hashimoto, I knew him before HashiCorp, and he is a monster, and so you tend to track their work. >> So your Twitter and my Twitter might be more alike than you think. >> Nic: No maybe, right. >> I interviewed Armon at the Cube-Con show last year. My Twitter blowing up the show was a bunch of people arguing about whether Serverless was going to eradicate this whole ecosystem. >> Well, we can argue about that if you like, I guess. >> But love, one of the things coming into this show, was, you know, how does the whole Kubernetes discussion fit into Cloud Foundry? We've heard at this show, Microsoft, Google, many others, talking about, look, open source communities, they're going to work together. >> Well Windows is going to track things 'cause they think they need to sell them, right? But then Microsoft has Service Fabric, which they've owned and operated internally for 10 years, and so, I think some really interesting products may be built on top of Service Fabric, because of what it is. Whereas, you know, Kubernetes will run things, Service Fabric may build net new projects. And then Cloud Foundry's a different experience altogether, so some people, it's what problems they experienced, comes to the solution they find, and unless you've tried to run a platform for people, you might not think the solution's a platform. You might think it's Kubernetes, but-- >> Yeah, so one of the things we always look at when we talk about platforms, is what do they get stood up for? How many applications do you get to stand up there? What don't they work for? Maybe you could help give us a little bit of color as to what you see? >> I'm pretty good at jamming anything into Cloud Foundry, so I have a pretty small scope of what doesn't fit, but typically the idea of Cloud Foundry is the assumption the user is a developer who has 10 iterations a day. Alright, so they want to deploy, test, deploy, test, and then layer pipelines on top of that. You also get, you're going to get the backend of long, stable apps, but the value is, for many people, is that the deploy experience. And then, you know, but whilst, you're going to get those apps that live forever, we still get to replace the underlying core of it. So you still maintain a security model even for the things that are relatively unloved. Andthis is really valuable, like the nice, clean separation of the security, the package, CVEs, and the base OS, then the apps is part of the-- >> Yeah, absolutely, there's been an interesting kind of push and pull lately. We need to take some of those old applications, and we may need to lift and shift them. It doesn't mean that I can necessarily take advantage of all the cool stuff, and there are some things that I can't do with them when I get them on to that new platform. But absolutely, you need to worry about security, you know, data's like the center of everything. >> If you're lifting and shifting, there probably is no developer looking after it, so it's more of an operator function, and they can put it anywhere they like. They're looking after it now, whereas the Cloud Foundry experience is that developer-led experience that has an operations backend. If you're lifting and shifting, if it fits in Cloud Foundry, great, if it fits in Kubernetes, great. It's your responsibility. >> Yeah, what interaction do you have with your clients, with some of the kind of cultural and operational changes that they need to go through? So thinking specifically, you've go the developers doing things, you know, the operators, whether they're involved, whether that be devops or not, but I'm curious-- >> So the biggest change when it comes to helping people who are running platforms. And I know many people want to talk about the cloud transformation, but let's talk about the operations transformation, is to become a service-orientated group who are there to provide a service. Yes you're internal, yes they all have the same email address that you do, but you're a service-orientated organization, and that is not technology, that is a mental mode. And if you're not service-orientated, shadow IT occurs, because they can go to Amazon and get a support organization that will respond to them, and so you're competing with Amazon, and Google, and you need to be pretty good. >> Yeah, you mentioned that, you know, your typical client is kind of a large, maybe I'm putting words in your mouth, the Fortune 1000 type companies, does this sort of-- >> We haven't got Berkshire. We haven't got Berkshire, and so if we're going to go Fortune 5, you know, we'd like, I've read my Warren Buffett biography, I reckon the FA are here to meet him I reckon. >> Right, so one of the questions, is this only for the enterprise? Can it be used for smaller businesses, for newer businesses? >> What's interesting is people think about Cloud Foundry as like, "Oh you run it on your infrastructure." Like, I did a talk in 2014, 15, when Docker was starting to be frothy, was, before you think you want to build your own pass, ring me on the hotline. Like, argue with me about why you wouldn't just use Heroku, or Pivotal Web Services, or IBM Cloud, like a public pass. Please, I beg of you, before you go down any path of running on-prem anything, answer solidly the question of why you just wouldn't use a public service. And yeah, so it really starts at that point. It's like, use someone else's, and then if you have to run your own. So, who's really going to have all these rules? It's large organization that have these, "Oh, no, no, we have to run our own." >> Well doctor, one of the things we've said for a while, is there's lots of things that enterprise suck at, that they need to realize that they shouldn't be doing. So start at the most basic level, there's like five companies in the world that are good at building data centers, nobody else should build data centers, if you're using somebody else that can do that. So as you go up and up the stack, you want to get rid of the undifferentiated lifting, things like that, so-- >> I like to joke that every CIO, the moment they get that job, like that's their ticket to get to build their own data center. It's like, what else was the point of becoming a CIO? I want to build my own data center. >> No, not anymore, please-- >> Not anymore, but you know, plus they've been around a little longer than-- >> So, what is that line? What should companies be able to consume a platform, versus where do they add the value, and do you help customers kind of understand that that-- >> By the time they're talking to us, they're pretty far along having convinced themselves about what they're doing. And they have their rules. They have their isolation rules, their data-ownership rules, and they'll have their level of comfort. So they might be comfortable on Amazon, Google, Azure, or they might still not be comfortable with public cloud, and they want the vSphere, but they still have that notion of we're going to run this ourselves. And most of them it's not running one, because that idea of we need our own, propagates throughout the entire organization, and they'll start wanting their own Cloud Foundry-- >> Look, I find that when I talk to users, we, the vendors, and those that watch the industry, always try to come up with these multi-cloud hybrid cloud-type discussion. Users, have a cloud strategy, and it's usually often siloed just like everything else, and right, they're using-- >> Developers-- >> I have some data service, and it's running on Google-- >> Developers just want to have a nice life. >> Microsoft apps. >> They just want to get their work done. They want to feel like, "Alright this is a great job, "like, I'm respected, I get interesting work, "we get to ship it, it actually goes into production." I think if you haven't ever had a project you've worked on that didn't go into production, you haven't worked long enough. Many of us work on something for it not to be shipped. Get it into production as quick as possible and-- >> So, do you have your, you know, utopian ideal world though as to, this is the step-- >> Oh, absolutely-- >> And this is how it'll be simple. >> Tell developers what the business problems are. Get them as close to the business problems, and give them responsibility to solve them. Don't put them behind layers of product managers, and IT support-- >> But Dr. Nic, the developers, they don't have the budget-- >> Speak for utopian-- >> How do we sort through that, because, right, the developer says they want to do this, but they're not tied to the person that has the budget, or they're not working with the operators, I mean, how do we sort through that? >> How do we get to utopia? >> Stu: Yeah. Well, Facebook, Google, Microsoft, they all solved utopia, right? So, this is, think more like them, and perhaps the CEO of the company shouldn't come from sales, perhaps it should be an IT person. >> Well, yeah, what's the T-shirt for the show? It was like running at scale, when you reach a certain point of scale, you either need to solve some of these things, or you will break? >> Right, alright look, hire great sales organizations, but if you don't have empathy for what your company needs to look like in five years time, you're probably not going to allow your organization to become that. The power games, alright? If everyone assumes that the marketing department becomes the top of the organization, or the, you know, then the good people are going to leave to go to organizations where they might be become CEO one day. >> Alright, Dr. Nic, want to give you the final word. For the people that haven't been able to come to the sessions, check out the environment, what are they missing at this show? What is exciting you the most in this ecosystem? >> Like any conference you go to, you come, the learning is all put online. Your show is put online, or every session is put online. You don't come just to learn. You get the energy. I live in Australia, I work from a coffee shop, my staff are all in America, and so to come and just to get the energy that you're doing the right thing, that you get surrounded by a group of people, and certainly no one walks away from a CF Summit feeling like they're in the wrong career. >> Excellent. Well, Dr. Nic, appreciate you helping us understand the infinity wars of cloud environments here. Stark and Wayne, thanks so much for joining us. I'm Stu Miniman, and you're watching theCUBE. >> Dr. Nic: Thanks Stu. (electronic music)

>> Narrator: Live from Stanford University in Palo Alto, California, it's the CUBE! Covering, Women in Data Science Conference 2018, brought to you by Stanford. >> Welcome back to the CUBE, we are live at Stanford University, we've been here all day at the third annual Women in Data Science Conference, WiDS 2018. This event is remarkable in its growth in scale, in its third year, and that is, in part by the partners and the sponsors that they have been able to glean quite early on. I'm excited to be joined by Vijay Raghavendra, the senior vice president of Merchant Technology and stores as well, from Walmart Labs. Vijay, welcome to the CUBE! >> Thank you, thank you for having me. >> Walmart Labs has been paramount to the success of WiDS, we had Margot Gerritsen on earlier, and I said, "How did you get the likes of a Walmart Labs as a partner?" And, she was telling me that, the coffee-- the coffee shop conversation >> Yeah, the Coupa Cafe! >> That she had with Walmart Labs a few years ago, and said, "Really, partners and sponsors like Walmart have been instrumental in the growth and the scale, of this event." And, we've got the buzz around, so we can hear the people here, but this is the big event at Stanford. There's 177 regional events, 177! In 53 countries. It's incredible. Incredible, the reach. So, tell me a little bit about the... From Walmart Labs perspective, the partnership with WiDS, what is it that really kind of was an "Aha! We've got to do this"? >> Yeah, it's just incredible, seeing all of these women and women data scientists here. It all started with Esteban Arcaute, who used to lead data science at Walmart Labs, and Search, before he moved on to Facebook with Margot. And, Karen in the cafe in Palo Alto, in 2015, I think. And Esteban and I had been talking about how we really expand the leverage of data and data science within Walmart, but more specifically, how we get more women into data science. And, that was really the genesis of that, and, it was really-- credit goes to Esteban, Margot, and Karen for, really, thinking through it, bringing it together, and, here we are. >> Right, I mean bringing it together from that concept, that conversation here at Stanford Cafe to the first event was six months. >> Yeah, from June to November, and, it's just incredible the way they put it together. And, from a Walmart Labs perspective, we were thrilled to be a huge part of it. And, all the way up the leadership chain there was complete support, including my boss Jeremy King, who was all in, and, that really helped. >> Margot was, when we were chatting earlier, she was saying, "It's still sort of surprising," and she said she's been, I think in, in the industry for, 30-plus years, and she said that, she always thought, back in the day, that by the time she was older, this problem would be solved, this gender gap. And she says, "Actually, it's not like it's still stagnant," we're almost behind, in a sense. When I look at the ... women that are here, in Stanford, and those that are participating via those regional events, the livestream that WiDS is doing, as well as their Facebook livestream. You know, the lofty goal and opportunity to reach 100,000 people shows you that there's clearly a demand, there's a need for this. I'd love to get your perspective on data science at Walmart Labs. Tell me a little bit about the team that you're leading, you lead a team of engineers, data scientists, product managers, you guys are driving some of the core capabilities that drive global e-commerce for Walmart. Tell me about, what you see as important for that female perspective, to help influence, not only what Walmart Labs is doing, but technology and industry in general. >> Yeah. So, the team I lead is called Merchant Technology, and my teams are responsible for, almost every aspect of what drives merchandising within Walmart, both on e-commerce and stores. So, within the purview of my teams are everything from the products our customers want, the products we should be carrying either in stores or online, to, the product catalog, to search, to the way the products are actually displayed within a store, to the way we do pricing. All of these are aspects of what my teams are driving. And, data and data science really put me at every single aspect of this. And the reason why we are so excited about women in data science and why getting that perspective is so important, is, we are in the retail business, and our customers are really span the entire spectrum, from, obviously a lot of women shop at Walmart, lot of moms, lot of millennials, and, across the entire spectrum. And, our workforce needs to reflect our customers. That's when you build great products. That's when you build products that you can relate to as a customer, and, to us that is a big part of what is driving, not just the interest in data science, but, really ensuring that we have as diverse and as inclusive a community within Walmart, so we can build products that customers can really relate to. >> Speaking of being relatable, I think that is a key thing here that, a theme that we're hearing from the guests that we're talking to, as well as some of the other conversations is, wanting to inspire the next generation, and helping them understand how data science relates to, every industry. It's very horizontal, but it also, like a tech company, or any company these days is a tech company, really, can transform to a digital business, to compete, to become more profitable. It opens up new business models, right, new opportunities for that. So does data science open up so many, almost infinite opportunities and possibilities on the career front. So that's one of the things that we're hearing, is being able to relate that to the next generation to understand, they don't have to fit in the box. As a data scientist, it sounds like from your team, is quite interdisciplinary, and collaborative. >> And, to us that is really the essence of, or the magic of, how you build great products. For us data science is not a function that is sitting on the side. For us, it is the way we operate as we have engineers, product managers, folks from the business teams, with our data scientists, really working together and collaborating every single day, to build great products. And that's, really how we see this evolving, it's not as a separate function, but, as a function that is really integrated into every single aspect of what we do. >> Right. One of the things that we talked about is, that's thematic for WiDS, is being able to inspire and educate data scientists worldwide, and obviously with the focus of helping females. But it's not just the younger generation. Some of the things that we're also hearing today at WiDS 2018 is, there's also an opportunity within this community to reinvigorate the women that have been in, in STEM and academia and industry for quite a while. Tell me a little bit more about your team and, maybe some of the more veterans and, how do you kind of get that spirit of collaboration so that those that, maybe, have been in, in the industry for a while get inspired and, maybe get that fire relit underneath them. >> That's a great question, because we, on our teams, when you look across all the different teams across different locations, we have a great mix of folks that bring very different, diverse experiences to the table. And, what we've found, especially with the way we are leveraging data, and, how that is invigorating the way we are... How people come to the table, is really almost seeing the art of what is possible. We are able to have, with data, with data science, we are able to do things that, are, really step functions in terms of the speed at which we can do things. Or, the- for example, take something as simple as search, product search, which is one of the, capabilities we own, or my team is responsible for, but, you could build the machine learning ranking, and, relevance and ranking algorithms, but, when you combine it with, for example, a merchant that really fundamentally understands their category, and you combine data science with that, you can accelerate the learning in ways that is not possible. And when folks see that, and see that in operation that really opens up a whole, slew of other ideas and possibilities that they think about. >> And, I couldn't agree more. Looking at sort of the skillset, we talk a lot about, the obvious technical skillset, that a data scientist needs to have, but there's also, the skills of, empathy, of communication, of collaboration. Tell me about your thoughts on, what is an ideal mix, of skills that that data scientist, in this interdisciplinary function, should have. >> Yeah, in fact, I was talking with a few folks over lunch about just this question! To me, some of the technical skills, the grounding in math and analytics, are table stakes. Beyond that, what we look for in data scientists really starts with curiosity. Are they really curious about the problems they're trying to solve? Do they have tenacity? Do they settle for the more obvious answers, or do they really dig into, the root cause, or the root, core of the problems? Do they have the empathy for our customers and for our business partners, because unless you're able to put yourself in those shoes, you're going to be approaching at, maybe, in somewhat of an antiseptic way? And it doesn't really work. And the last, but one of the most important parts is, we look for folks who have a good sense for product and business. Are they able to really get into it, and learn the domain? So for example, if someone's working on pricing, do they really understand pricing, or can they really understand pricing? We don't expect them to know pricing when they come in, but, the aptitude and the attitude is really, really critical, almost as much as the core technical skills, because, in some ways, you can teach the technical skills, but not some of these other skills. >> Right, and that's an interesting point that you bring up, is, what's teachable, and, I won't say what's not, but what might be, maybe not so natural for somebody. One of the things, too, that is happening at WiDS 2018 is the first annual Datathon. And, Margot was sharing this huge number of participants that they had and they set a few ground rules like wanting the teams to be 50% female, but, tell us about the Datathon from your global visionary sponsorship level; what excites you about that in terms of, the participation in the community and the potential of, "Wow, what's next"? >> Yeah... So, it's hugely exciting for us, just seeing the energy that we've seen. And, the way people are approaching different problems, using data to solve very different kinds of problems ... across the spectrum. And for us, that is a big part of what we look for. For us it is really about, not just coming up with a solution, that's in search of a problem, but really looking at real-world problems and looking at it from the perspective of, "Can I bring data, can I bring data science to bear on this problem?", to solve it in ways that, either are not possible, or can accelerate the way we would solve the problems otherwise. And that is a big part of what is exciting. >> Yeah, and the fact that the impact that data science can make to, every element of our lives is, like I said before, it's infinite, the possibilities are infinite. But that impact is something that, I think, how exciting to be able to be in an industry or a field, that is so pervasive and so horizontal, that you can make a really big social impact. One of they other things, too, that Margot said. She mentioned that the Datathon should be fun, and I loved that, and also have an element of creativity. What's that balance of, creativity in data science? Like, what's the mixture, because we can be maybe over-creative, and maybe interpret something that's in a biased way. What is your recommendation on how much creativity can creep into, and influence, positively, data science? >> Yeah, that's a great question, and there's no perfect answer for it. Ultimately, at least my biases towards using data and data science to, solve real problems. And... As opposed to, pure research, so our focus very much is on applied learning, and applied science. And, to me, within that, I do want the data science to be creative, data scientists to be creative, because, by putting too many guardrails, you limit the way in which they would explore the data, that they may come up with insights that, well, we might not see otherwise. And, which is why, I go back to the point I made, when you have data scientists who fundamentally understand a business, and the business problems we are trying to solve, or the business domains, I think they can then come up with very interesting, innovative ways of looking at the data, and the problem, that you might not otherwise. So, I would by no means want to limit their creativity, but I do have a bias towards ensuring that it is focused on problems we are trying to solve. >> Excellent. Well, Vijay, thank you so much for stopping by the CUBE, congratulations on the continued success of the partnership with WiDS and, we're looking forward to seeing what happens the rest of the year, and we'll probably see you next year at WiDS 2019! >> Absolutely, thank you! >> Excellent, we want to thank you, you're watching the CUBE, live from Stanford University, the third annual Women in Data Science Conference. I am Lisa Martin, I'll be right back after a short break with my next guest. (cool techno music)

>> Announcer: Live from Las Vegas, it's theCUBE. Covering AWS re:Invent 2017. Presented by AWS, Intel and our ecosystem of partners. >> Hey, welcome back everyone. We're live here in Las Vegas. It's theCUBE's exclusive coverage of AWS. Amazon Web Services re:Invent 2017. Amazon web Services annual conference, 45,000 people here. Five years in a row for theCUBE, and we're going to be continuing to cover years and decades after, it's on a tear. I'm John Furrier, my co-host Stu Miniman. Exciting science, one of the biggest themes here is AI, IoT, data, Deep Learning, DeepLens, all the stuff that's been really trending has been really popular at the show. And the person behind that Amazon is Swami. He's the Vice President of Machine Learning at AWS, among other things, Deep Learning and data. Welcome to theCUBE. >> Stu: Good to see you. >> Excited to be here. >> Thanks for coming on. You're the star of the show. Your team put out some great announcements, congratulations. We're seeing new obstruction layers of complexity going away. You guys have made it easy to do voice, Machine Learning, all those great stuff. >> Swami: Yeah. >> What are you most excited about, so many good things? Can you pick a child? I don't want to pick my favorite child among all my children. Our goal is to actually put Machine Learning capabilities in the hands of all developers and data scientists. That's why, I mean, we want to actually provide different kinds of capabilities right from like machine developers who want to build their own Machine Learning models. That's where SageMakers and n21 platform that lets people build, train and deploy these models in a one-click fashion. It supports all popular Deep Learning frameworks. It can be TensorFlow, MXNet or PyCharm. We also not only help train but automatically tune where we use Machine Learning for Machine Learning to build these things. It's very powerful. The other thing we're excited about is the API services that you talked about, the new obstruction layer where app developers who do not want to know anything about Machine Learning but they want to transcribe their audio to convert from speech to text, or translate it or understand the text, or analyze videos. The other thing coming from academia where I'm excited about is I want to teach developers and students Machine Learning in a fun fashion, where they should be excited about Machine Learning. It's such a transformative capability. That's why actually we built a device meant for Machine Learning in a hands-on fashion that's called DeepLens. We have developers right on re:Invent where from the time they take to un-box to actually build a computer with an application to build Hotdog or Not Hotdog, they can do it in less than 10 minutes. It's an amazing time to be a developer. >> John: Yeah. >> Stu: Oh my God, Swami. I've had so many friends that have sat through that session. First of all, the people that sit through it they get like a kit. >> Swami: That's awesome. >> Stu: They're super excited. Last year it was the Ecodot and everybody with new skills. This year, DeepLens definitely seems to be the one that all the geeks are playing with, really programing stuff. There's a bunch of other things here, but definitely some huge buzz and excitement. >> That's awesome, glad to hear. >> Talk about the culture at Amazon. Because I know in covering you guys for so many years and now being intimate with a lot of the developers in your teams. You guys just don't launch products, you actually listen to customers. You brought up Machine Learning for developers. What specifically jumped out at you from talking to customers around making it easier? It was too hard, was it, or it was confined to hardcore math driven data scientists? Was it just the thirst and desire for Machine Learning? Or you're just doing this for side benefits, it's like a philanthropy project? >> No, in Amazon we don't build technology because it's cool. We build technology because that's what our customers want. Like 90 to 95% of our roadmap is influenced by listening to customers. The other 5 to 10% is us reading between the lines. One of the things I actually ... When I started playing with Machine Learning, having built a bunch of database storage and analytics products. When I started getting into Deep Learning and various things I realized there's a transformative capability of these technologies. It was too hard for developers to use it on a day to day fashion, because these models are too hard to build and train. Our data now, the right level of obstruction. That's why we actually think of it as in a multi-layered strategy where we cater to export practitioners and data scientists. For them we have SageMaker. Then for app developers who do not want to know anything about Machine Learning they say, "I'll give you an audio file, transcribe it for me," or "I'll give you text, get me insights or translate it." For them we actually we actually provide simple to use API services, so that they can actually get going without having to know anything about what is TensorFlow or PyCharm. >> TensorFlow got a lot of attention, because that really engaged the developer community in the current Machine Learning, because we're like, "Oh wow, this is cool." >> Swami: Yeah. >> Then it got, I won't say hard to use, but it was high end. Are you guys responding to TensorFlow in particular or you're responding to other forces? What was the driver? >> In amazon we have been using Machine Learning for like 20 years. Since the year of like 1995 we have been leveraging Machine Learning for recommendation engine, fulfillment center where we use robots to pick packages and then Elixir of course and Amazon Go. One of the things we actually hear is while frameworks like TensorFlow or PyCharm, MXNet or PyCharm is cool. It is just too hard for developers to make use of it. We actually don't mind, our users use Cafe or TensorFlow. We want the, to be successful where they take from idea to product shell. And when we talk to developers, this process took anywhere from 6 to 18 months and it should not be this hard. We wanted to do what AWS did to IT industry for compute storage and databases. We want to do the same for Machine Learning by making it really easy to get started and consumer does in utility. That was our intel. >> Swami, I wonder if you can tell us. We've been talking for years about the flywheel of customers for Amazon. What are the economies of scale that you get for the data that you have there. I think of all the training of all the Machine Learning, the developers. How can you leverage the economies of scale that Amazon has in all those kind of environments? >> When you look at Machine Learning, Machine Learning tends to be mostly the icing on the cake. Even when we talk to the expert professors who are the top 10 scientists in the world, the data that goes into the Machine Learning is going to be the determining factor for how good it is in terms of how well you train it and so forth. This is where data scientists keep saying the breath of storage and database and analytics offerings that exist really matter for them to build highly accurate models. When you talk about not just the data, but actually the underlying database technology and storage technology really is important. S3 is the world's most powerful data leg that exists that is highly secure, reliable, scalable and cost effective. We really wanted to make sure customers like Glacier Cloud who store high resolution satellite imagery on S3 and glacier. We wanted them to leverage ML capabilities in a really easy one-click fashion. That's important. >> I got to ask you about the roadmap, because you say customers are having input on that. I would agree with you that that would be true, because you guys have a track record there. But I got to put the dots that I'm connecting in my mind right now forward by saying, you guys ... And telegraphing here certainly heard well, Furner say it and Andy, data is key and opening up that data and we're seeing New Relic here, Sumo Logic. They're sharing anonymous data from usage, workloads really instructive. Data is instructive for the marketplace, but you got to feed the models on the data. The question for you is you guys get so much data. It's really a systems management dream it's an application performance dream. You got more use case data. Are you going to open that up and what's the vision behind it? Because it seems like you could offer more and more services. >> Actually we already have. If you look at x-rays and service that we launched last year. That is one of the coolest capabilities, even I am a developer during the weekends when I cool out. Being able to dive into specific capabilities so one of the performance insights where is the borderline. It's so important that actually we are able to do things like x-raying into an application. We are just getting started. The Cloud transformed how we are building applications. Now with Machine Learning, what is going to happen is we can even do various things like ... Which is going to be the borderline on what kind of datasets. It's just going to be such an amazing time. >> You can literally reimagine applications that are once dominant with all the data you have, if you opened it up and then let me bring my data in. Then that will open up a bigger aperture of data. Wouldn't that make the Machine Learning and then AI more effective? >> Actually, you already can do similar things with Lex. Lex, think of it as it's an automatic speech recognition natural language understanding where we are pre-trained on our data. But then to customize it for your own chat bots or voice applications, you can actually add your own intents and several things and we customize it underlying Deep Learning model specific to your data. You're leveraging the amount of data that we have trained in addition to specifically tuning for yours. It's only going to get better and better, to your point. >> It's going to happen, it's already happening. >> It's already happening, yeah. >> Swami, great slate of announcements on the Machine Learning side. We're seeing the products get all updated. I'm wondering if you can talk to us a little bit about the human side of things. Because we've seen a lot of focus, right, it's not just these tools but it's the tools and the people putting those together. How does Amazon going to help the data scientists, help retrain, help them get ready to be able to leverage and work even better with all these tools? >> Machine Learning, we have seen some amazing usage of how developers are using Machine Learning. For example, Mariness Analytics is a non-profit organization that its goal is to fight human trafficking. They use recognition which is our image processing. They do actually identify persons of interest and victims so that they can notify law enforcement officer. Like Royal National Institute of Blind. They actually are using audio text to speech to generate audio books for visually impaired. I'm really excited about all the innovative applications that we can do to simply improve our everyday lives using Machine Learning, and it's such in early days. >> Swami, the innovation is endless in my mind. But I want to get two thoughts from you, one startup and one practitioner. Because we've heard here in theCUBE, people come here and saying, "I can do so much more now. "I've got my EMR, it's so awesome. "I can do this solving problem." Obviously making it easy to use is super cool, that's one. I want to get your thoughts on where that goes next. And two, startups. We're seeing a lot of startups retooling on Cloud economics. I call it post-2013 >> Swami: Yeah. >> They don't need a lot of money, they can hit critical mass. They can get market product, market fit earlier. They can get economic value quicker. So they're changing the dynamics. But the worry is, how do I leverage the benefit of Amazon? Because we know Amazon is going to grow and all Clouds grow and just for you guys. How do I play with Amazon? Where is the white space? How do I engage, do I just ...? Once I'm on the platform, how do I become the New Relic or slunk? How can I grow my marketplace and differentiate? Because Amazon might come out with something similar. How do I stay in that cadence of growth, even a startup? >> If you see in AWS we have tens of thousands of partners of course, right from ISV, SIs and whatnot. Software industry is an amazing industry where it's not like winner take all market. For example, in the document management space, even though we have S3 and WorkDocs, it doesn't mean Dropbox and Box are not successful either, and so forth. What we provide in AWS is the same infrastructure for any startup or for my team, even though I build probably many of the underlying infrastructure. Nowadays for my AI team, it's literally like a startup except I probably stay in an AWS building, but otherwise I don't get any internal APIs, it's the same API so easy to S3. >> John: It's a level playing field. >> It's a level playing field. >> By the way, everyone should know, he wrote DynamoDB. As an intern or was that ...? (Swami laughs) And then SQS, rockstar techy here, so it's great to have. You're what we call a tech athlete. Great to have you on. No white space, just go for it. >> Innovation is the key. The key thing, what we have seen amazing startups who have done exceptionally well is they intently listen to customers and innovate and really look for what it matters for their customers and go for it. >> The biggest buzz of the show from your group. What's your biggest buzz from the show here? DeepLens? >> DeepLens has been ... Our idea was to actually come up with a fun way to learn Machine Learning. Machine Learning, it used to be, even until recently actually as well as last week, it was actually an intimate thing for developers to learn while there is, it's all the buzz. It's not really straight forward for developers to use it. We thought, "Hey, what is a fun way for developers "to get engaged and build Machine Learning?" That's why we actually can see DeepLens so that you can actually build fun applications. I talked about Hotdog, Not Hotdog. I'm personally going to be building what I call as a Bear Cam. Because I live in the suburbs of Seattle where we actually have bears visiting our backyard digging our trash. I want to actually have DeepLens with a pre-train model that I'm going to train to detect bears. That it sends me a message through SQS and SNS so I get a text. >> Here's an idea we want to do, maybe your team can build it for us. CUBE Cam, we put the DeepLens here and then as anyone goes by, if they're a Twitter follower of theCUBE they can send me a message. (John and Swami laughing) Swami, great stuff. Deep Learning again, more goodness coming. >> Swami: That's awesome. >> What are you most excited about? >> In Amazon we have a phrase called, "It's Day One." Even though we are a 22-year-old company, I jokingly tell my team that, "It's day one for us, "except we just woke up and we haven't even "had a cup of coffee yet." We have just scratched the surface with Machine Learning, there is so much stuff to do. I'm super excited about this space. >> Your goals for this year is what? What's your goals? >> Our goals for this year was to put Machine Learning capabilities in the hands of all developers of all skill levels. I think we have done pretty well so far I think. >> Well, congratulations Swami here on theCUBE. Vice president of Machine Learning and a lot more, all those applications that were announced Wednesday along with the Deep Leaning and the AI and the DeepLens all part of his innovative team here at Amazon. Changing the game is theCUBE doing our part bringing data to you, video and more coverage. Go to Siliconangle.com for all the stories, Wikibon.com for research and of course theCUBE.net. I'm John Furrier and Stu Miniman. Thanks for watching, we'll be right back.

>> Narrator: Live from Washington D.C., it's theCUBE. Covering .conf2017. Brought to you by Splunk. >> Well welcome back here on theCUBE. We continue our coverage of .conf2017, we're in Washington D.C. Along with Dave Vellante, I'm John Walls. And Dave, you know what time it is, by the way? Just about? >> I don't know, this is the penultimate interview. >> It's almost five o'clock. >> Okay. >> And that means it's almost happy hour time. So I was thinking where might we go tonight, so-- >> There's an app for that. >> There was, and so I looked. It turns out that the Penny Whiskey Cafe is just two tenths of a mile from here. And you know how I knew that? >> How's the ratings on that? >> We got four. >> Four and half with 52. >> 52 reviews? >> Yeah, I feel good about that. >> Yeah, that's pretty good. That's a substantive base. >> I feel very solid with that one. We'll make it 53 in about a half hour. Of course I found it on Yelp. We have a couple of gentlemen from Yelp with us tonight. I don't have to tell you what Yelp does, it does everything for everybody, right. Zach Musgrave, technical lead, and Chris Gordon, software engineer at Yelp. Gentlemen, thanks for being here. And U can join us, by the way, later on, at the Penny Whiskey if you'd like to. First off, what are you doing here, right, at Splunk? What's Yelp and Splunk, what's that intersection all about? Zach, if you would. >> Sure, well Yelp uses Splunk for all sorts of purposes. Operational, intelligence, business metrics, pretty much any sort of analytics from event driven data that you can really think of, Yelp has found a way, and our engineers have found a way to get that into Splunk and derive business value from it. So Chris and I are actually here, we just gave a breakout session at .conf, talking about how we find strong business value and how we quantify that value and mutate our Splunk cluster to really drive that. >> Okay. >> So, so how do you find value then, I mean, what was? >> It's hard. Chris was one of the people who really, really drove this for us. And when we looked at this, you know I once had an engineer who came up to our team, we maintain Splunk amongst other things, and the engineer said can I ingest 10 terabytes of data a day into Splunk and then keep it forever? And I said, um, please don't. And then we talked a bit more about what that engineer was actually trying to do and why they needed this massive amount of data, and we found a better way that was much more efficient. And then where we didn't need to keep all the data forever. So, by being able to have those conversations and to quantify with the data you're already ingesting into Splunk, being able to quanitfy that and actually show how many people were searching this, how's it being used, what's the depth of the search look like, how far back are they looking in time. You can really optimize your Splunk cluster to get a lot more business value than just naively setting it up and turning it on. >> So you weren't taking a brute force approach, you were smarter about that, but you weren't deduping, you were identifying the data that was not necessary to keep, did I get that right? >> Correct. Yeah, we essentially kind of identified what are highest cost per search logs, which we basically just totaled up how many times each log was searched, and then tried to quantify how much each logs was costing us. And then this ended up being a really good metric for figuring out what we'd want to remove or something that was a candidate for dislodging the data somehow. >> So, you guys gave a talk today. We were talking off camera about pricing, that's not something you guys get involved in, but I would categorize this as sort of how do you get the most out of that asset, called Splunk, right. Is that sort of the >> Exactly. >> theme of your talk, right? >> Yeah. We talk a lot about expected value amongst our team, and in the talk we just gave. And we don't ever think about this as, oh do this so that you can spend less money on Splunk or on your infrastructure that's backing Splunk. Think about is more as we have this right now and we can utilize it more effectively. We can get more value out of what we already have. >> Okay, so, I wonder if we could just talk a little bit about your environment. We know you run on AWS. How does that cloud fit in with Splunk, paint a picture for us, if you would. What does it all look like? >> Yeah, so we have two clusters actually. One is the high value, high quality of service cluster, it's the larger generic, we call it generic prod, and then we have another one, where we kind of have our more verbose, maybe slightly less valuable per log cluster. And this runs on a D2, which is just instant storage. And then the higher performance cluster runs all on a GP2. So it's basically just SSDs. And we also do, we also have four copies of each log and we have two searchable copies of each log, so it's pretty well replicated. >> Dave: Okay, so that's how you protect the data. >> Yeah. >> Is to make copies, in what, in different zones, or? >> Yeah, we have two copies of each log in each availability zone, and then one searchable copy of each log in each availability zone. >> And you guys are cloud natives, all cloud, just out of school and graduate school. So you talked about infrastructure as code. You don't do any of that on-prem stuff, you're not like installing gear. And so it's not part of your lexicon, right? >> No. >> Okay. So I want to do a little editorial thing. Kristen Nicole, our managing editor, sent the note around today saying 101s get the best traffic on the website. So I want to do a little DevOps 101, okay. Even though, it's second nature to you, and a lot of people in our audience know what it is. How do you describe DevOps? Give us the 101 on DevOps. >> Okay so, DevOps is a complicated thing, but and occasionally you see it as like a role on like a job board or something. And that always strikes me as odd, because it's not really a role. Like it's a philosophy moreso. The way that I always see it, is it used to be like pre DevOps, was the software developers make a thing, and then they throw it over the fence, and operations just picks it up. And they're like well what do we do with this, and deploy it, okay, good luck. And so with this result in a sort of an us against them mentality, where the developers aren't incentivized to really make it resilient, or really document it well, and operations and the sys admins are not incentivized to really be flexible and to be really hard charging and move quickly, because they're the ones who are going to be on call for whatever the developers made. DevOps is a we, instead of an us verses them. So for example, product teams have an on-call rotation. Operations and sys admins write code. There are still definitely specializations, but it all comes together in a much more holistic manner. >> Okay, and the ops guys will write code, as opposed to hacking code, messing up your code, throwing it back over the fence, and saying hey your code doesn't work. >> Exactly. >> And then you say well it worked when I gave it to you. And then like you said that sort of finger pointing. >> We are totally done with works on my machine, it's over. No more. >> Okay, and the benefits obviously are higher quality, faster time to market, less food fighting. >> Yup, exactly. In the old model you'd have a new deployment of like a website like maybe once a week or maybe even once a month. Yelp deploys multiple times everyday over and over again. And each one of those is going to include changes from a dozen different engineers. So we need to be agile in that manner, just like with our Splunk cluster. >> I mean you guys are relatively new, four years and two years, perspectively. But these days it's a long time. How would you describe your Splunk journey. Where did it start and where do you want to take it? >> I would say it started, you actually had Kris Wehner on here last year, and he talked a lot about it. He was the VP of engineering at SeatMe. And he kind of got Yelp onto the whole Splunk train. And at that point it was used mostly by SeatMe and everyone at Yelp was like oh this is fantastic, we want to use this. And we started basically migrating it to our VPC. And have generally, we're starting to now get everything going, get all the kinks worked out, and really now we're trying to see where we can provide the most value and make things as easy as possible for our developers to add logs and add searches and get what they need out of it. >> So what kind of use cases are you envisioning, and where are you getting value out of it? >> So we have our operations teams get a lot of value out of it when there's some outage happening. And it's really useful for them to be able to just look at the access logs and see what's going on. And Splunk makes that very easy. And we also get a lot of value out of Yelp's application logs. Splunk has been great for figuring out when something's not right. And allowing us to dig in further. >> So yeah, at the end of the day, as consumers, what does this mean to us, ultimately? Like our searches are faster, searches are more refined, searches are more accurate? What does it mean to me at the end of the day that you're enabling what activity through this technology. >> Dave: Yeah, it'll be more secure? >> Yeah, what does it mean? >> As an end user of Yelp? >> Yes. >> So, I'll give you one example that always sticks out in my mind. So I don't know if you all know this, but you can actually do things like order food via Yelp, you can make appointments via Yelp, even with like a dentist. You can beauty appointments, all sorts of personal services. >> Hair salon came up today actually, when I was looking for a bar. >> Absolutely. That's not supposed to happen. >> Dave: Well that was the Penny Whiskey Cafe. >> You never know, but what ever's next door I don't know. >> Can you get a haircut while you drink? >> Hair salons in the District are pretty impressive. >> I wasn't planning on it, no. But anyway, I'm sorry. >> Anyway, so we work with a lot of external partners to enable all these different integrations, right. So you press start order, and then eventually you see the menu, and then you add some stuff to your cart, and then you have to pay. And so if you haven't given us your credit card information yet, then you have to enter that, and that has to go to a payment processor, the order of course has to go out to the partner who's going to fulfill your order, and so on. So there's this pipeline of many different micro services plus the main Yelp application, plus this partner who's actually fulfilling your order, plus the payment processor, and so on, and so on. And it ends up with this really complicated state machine. So the way that actually works under the hood, to be very simplistic, is there's a unique order identifier that is assigned to you when you start the order. And then that passed through the whole process. So at every step in this process a bunch of events are emitted out of the various parts of the pipeline and into Splunk, where they're then matched to show that your order is progressing. And the order didn't get stuck. Because you know what's really sad is when you order food and it doesn't show up. So we really have to guard against that. >> Yeah, we hate that. >> Yeah, everybody does. So it's really important that we're able to unify this data, from all these different places, Splunk's really great for that, and to be able to then alert on that and page somebody and say hey, something's not quite right here, we have hungry folks. >> So while I have the smartest guys that we've interviewed all week here, you mentioned, >> Please. You mentioned, aw shucks, I know. You mentioned state machine. Are you playing around with functional programming, so called server lists, probably don't like that word either, but what are you doing there? Are you finding sort of new applications in use cases for so called server lists? >> I would say not so much. I don't know, is anyone at Yelp doing that? >> Yeah, there's some Lambda stuff going on. Like core back end is doing that work right now. A lot of our infrastructure is actually build up before the AWS Lambdas were a thing. So we found other ways to do that, and we have this really cool internal platform as a service, it's a docker, and some scheduling stuff on top of that. So a lot of things, like it's really easy to just launch a batch job in there. And it takes away some of the need for the true server lists. >> Well the reason I ask is because people are saying a lot of the state list IoT apps are going to use that sort of Lambda or homegrown stuff. And I'm not sure what the play is for Yelp in Internet of Things. I would imagine there's actually a play there for you guys though, and I'm curious as to the data angle, and maybe where Splunk might fit in. >> I'm certain that we're going to be using Splunk to read data from all of those different components as they're being launched. I know that there's been a couple early forays into the Lambda space that I've seen go by in code reviews and everything. But of course, with Splunk itself we can get data out of those. So as that happens, like we already have all our pipe lining set up. And it'll be pretty easy for them to analyze their self with Splunk. >> What gets you young folks excited these days? What keeps you enthralled and passionate? What do you look for? >> I don't know I think just in general anything that empowers you to get a lot done without having to fight it constantly. And general DevOps tools have been getting really good at that recently. And yeah, I would say anything that empowers you, gives you the feeling that you can do anything really. >> Yeah, all of the infrastructure is code stuff that's going on right now. So one of the pipelines that we use to get data out of Amazon S3, but it passes notifications through this S3 event notifications to Amazon SNS, to Amazon SQS, to our Splunk forwarders. And so that's a very complicated pipeline. And you have to set it all up, it works really well, but here's the cool part. That's all defined in code. And so this means that if you set up a new integration there's a code review. And we have some verification and validation that it's correct. And furthermore, if anything goes wrong with it, we can just hit a button and it recreates itself. That's what gets me happy. When tools get in my way that's not so good. >> Well and it just leaves more time for higher value activities and that's exciting. the transformation in infrastructure over the last five years has just been mind boggling. So, thanks you guys. >> It does. It does give me a lot of pleasure when something can go catastrophically wrong, and then just like, oh wait, it's self healing, all it can take is give three plays fine. And we're all dandy. >> Well to Dave's point, while I was off camera I did a search on the two smartest guys in the room. And it said one is six feet away the other one is seven feet away, so Yelp works, I mean it really does. But thanks for the time. It's been interesting. Next generation, right? So far over us. >> Yeah, I know. It's kind of depressing, but I love it. (laughing) >> Very good, thanks guys. >> Thank you so much. >> Back with more, here on theCUBE at .conf2017. We are live, Washington D.C. >> Dave: I've kind of had it with millennial. (upbeat music)

(busy music) >> Announcer: Live from Midtown Manhattan, it's the Cube, covering Big Data New York City 2017, brought to you by SiliconANGLE Media and its ecosystem sponsors. >> Hello, and welcome to the special Cube presentation here in New York City for Big Data NYC, in conjunction with all the activity going on with Strata, Hadoop, Strata Data Conference right around the corner. This is the Cube's special annual event in New York City where we highlight all the trends, technology experts, thought leaders, entrepreneurs here inside the Cube. We have our three days of wall to wall coverage, evening event on Wednesday. I'm John Furrier, the co-host of the Cube, with Jim Kobielus, and Peter Burris will be here all week as well. Kicking off day one, Jim, the monster week of Big Data NYC, which now has turned into, essentially, the big data industry is a huge industry. But now, subsumed within a larger industry of AI, IoT, security. A lot of things have just sucked up the big data world that used to be the Hadoop world, and it just kept on disrupting, and creative disruption of the old guard data warehouse market, which now, looks pale in comparison to the disruption going on right now. >> The data warehouse market is very much vibrant and alive, as is the big data market continuing to innovate. But the innovations, John, have moved up the stack to artificial intelligence and deep learning, as you've indicated, driving more of the Edge applications in the new generation of mobile and smart appliances and things that are coming along like smart, self-driving vehicles and so forth. What we see is data professionals and developers are moving towards new frameworks, like TensorFlow and so forth, for development of the truly disruptive applications. But big data is the foundation. >> I mean, the developers are the key, obviously, open source is growing at an enormous rate. We just had the Linux Foundation, we now have the Open Source Summit, they have kind of rebranded that. They're going to see explosion from code from 64 million lines of code to billions of lines of code, exponential growth. But the bigger picture is that it's not just developers, it's the enterprises now who want hybrid cloud, they want cloud technology. I want to get your reaction to a couple of different threads. One is the notion of community based software, which is open source, extending into the enterprise. We're seeing things like blockchain is hot right now, security, two emerging areas that are overlapping in with big data. You obviously have classic data market, and then you've got AI. All these things kind of come in together, kind of just really putting at the center of all that, this core industry around community and software AI, particular. It's not just about machine learning anymore and data, it's a bigger picture. >> Yeah, in terms of a community, development with open source, much of what we see in the AI arena, for example, with the up and coming, they're all open source tools. There's TensorFlow, there's Cafe, there's Theano and so forth. What we're seeing is not just the frameworks for developing AI that are important, but the entire ecosystem of community based development of capabilities to automate the acquisition of training data, which is so critically important for tuning AI, for its designated purpose, be it doing predictions and abstractions. DevOps, what are coming into being are DevOps frameworks to span the entire life cycle of the creation and the training and deployment and iteration of AI. What we're going to see is, like at the last Spark Summit, there was a very interesting discussion from a Stanford researcher, new open source tools that they're developing out in, actually, in Berkeley, I understand, for, related to development of training data in a more automated fashion for these new challenges. The communities are evolving up the stack to address these requirements with fairly bleeding edge capabilities that will come in the next few years into the mainstream. >> I had a chat with a big time CTO last night, he worked at some of the big web scale company, I won't say the name, give it away. But basically, he asked me a question about IoT, how real is it, and obviously, it's hyped up big time, though. But the issue in all this new markets like IoT and AI is the role of security, because a lot of enterprises are looking at the IoT, certainly in the industrial side has the most relevant low hanging fruit, but at the end of the day, the data modeling, as you're pointing out, becomes a critical thing. Connecting IoT devices to, say, an IP network sounds trivial in concept, but at the end of the day, the surface area for security is oak expose, that's causing people to stop what they're doing, not deploying it as fast. You're seeing kind of like people retrenching and replatforming at the core data centers, and then leveraging a lot of cloud, which is why Azure is hot, Microsoft Ignite Event is pretty hot this week. Role of cloud, role of data in IoT. Is IoT kind of stalled in your mind? Or is it bloating? >> I wouldn't say it's stalled or that it's bloating, but IoT is definitely coming along as the new development focus. For the more disruptive applications that can derive more intelligence directly to the end points that can take varying degrees of automated action to achieve results, but also to very much drive decision support in real time to people on their mobiles or in whatever. What I'm getting at is that IoT is definitely a reality in the real world in terms of our lives. It's definitely a reality in terms of the index generation of data applications. But there's a lot of the back end in terms of readying algorithms and in training data for deployment of really high quality IoT applications, Edge applications, that hasn't come together yet in any coherent practice. >> It's emerging, it's emerging. >> It's emerging. >> It's a lot more work to do. OK, we're going to kick off day one, we've got some great guests, we see Rob Bearden in the house, Rob Thomas from IBM. >> Rob Bearden from Hortonworks. >> Rob Bearden from Hortonworks, and Rob Thomas from IBM. I want to bring up, Rob wrote a book just recently. He wrote Big Data Revolution, but he also wrote a new book called, Every Company is a Tech Company. But he mentions, he kind of teases out this concept of a renaissance, so I want to get your thoughts on this. If you look at Strata, Hadoop, Strata Data, the O'Reilly Conference, which has turned into like a marketing machine, right. A lot of hype there. But as the community model grows up, you're starting to see a renaissance of real creative developers, you're starting to see, not just open source, pure, full stack developers doing all the heavy lifting, but real creative competition, in a renaissance, that's really the key. You're seeing a lot more developer action, tons outside of the, what was classically called the data space. The role of data and how it relates to the developer phenomenon that's going on right now. >> Yeah, it's the maker culture. Rob, in fact, about a year or more ago, IBM, at one of their events, they held a very maker oriented event, I think they called it Datapalooza at one point. What it's looking at, what's going on is it's more than just classic software developers are coming to the fore. When you're looking at IoT or Edge applications, it's hardware developers, it's UX developers, it's developers and designers who are trying to change and drive data driven applications into changing the very fabric of how things are done in the real world. What Peter Burris, we had a wiki about him called Programming in the Real World. What that all involves is there's a new set of skill sets that are coming together to develop these applications. It's well beyond just simply software development, it's well beyond simply data scientists. Maker culture. >> Programming in the real world is a great concept, because you need real time, which comes back down to this. I'm looking for this week from the guests we talked to, what their view is of the data market right now. Because if you want to get real time, you've got to move from that batch world to the real time world. I'm not saying batch is over, you've still got to store data, and that's growing at an exponential rate as well. But real time data, how do you use data in real time, how do the modelings work, how do you scale that. How do you take a DevOps culture to the data world is what I'm looking for. What are you looking for this week? >> What I'm looking for this week, I'm looking for DevOps solutions or platforms or environments for teams of data scientists who are building and training and deploying and evaluating, iterating deep learning and machine learning and natural language processing applications in a continuous release pipeline, and productionizing them. At Wikibon, we are going deeper in that whole notion of DevOps for data science. I mean, IBM's called it inside ops, others call it data ops. What we're seeing across the board is that more and more of our customers are focusing on how do we bring it all together, so the maker culture. >> Operationalizing it. >> Operationalizing it, so that the maker cultures that they have inside their value chain can come together and there's a standard pattern workflow of putting this stuff out and productionizing it, AI productionized in the real world. >> Moving in from the proof of concept notion to actually just getting things done, putting it out in the network, and then bringing it to the masses with operational support. >> Right, like the good folks at IBM with Watson data platform, on some levels, is a DevOPs for data science platform, but it's a collaborative environment. That's what I'm looking to see, and there's a lot of other solution providers who are going down that road. >> I mean, to me, if people have the community traction, that is the new benchmark, in my opinion. You heard it here on the Cube. Community continues to scale, you can start seeing it moving out of open source, you're seeing things like blockchain, you're seeing a decentralized Internet now happening everywhere, not just distributed but decentralized. When you have decentralization, community and software really shine. It's the Cube here in New York City all week. Stay with us for wall to wall coverage through Thursday here in New York City for Big Data NYC, in conjunction with Strata Data, this is the Cube, we'll be back with more coverage after this short break. (busy music) (serious electronic music) (peaceful music) >> Hi, I'm John Furrier, the Co-founder of SiliconANGLE Media, and Co-host of the Cube. I've been in the tech business since I was 19, first programming on mini computers in a large enterprise, and then worked at IBM and Hewlett Packard, a total of nine years in the enterprise, various jobs from programming, training, consulting, and ultimately, as an executive sales person, and then started my first company in 1997, and moved to Silicon Valley in 1999. I've been here ever since. I've always loved technology, and I love covering emerging technology. I was trained as a software developer and love business. I love the impact of software and technology to business. To me, creating technology that starts a company and creates value and jobs is probably one of the most rewarding things I've ever been involved in. I bring that energy to the Cube, because the Cube is where all the ideas are, and where the experts are, where the people are. I think what's most exciting about the Cube is that we get to talk to people who are making things happen, entrepreneurs, CEO of companies, venture capitalists, people who are really, on a day in and day out basis, building great companies. In the technology business, there's just not a lot real time live TV coverage, and the Cube is a non-linear TV operation. We do everything that the TV guys on cable don't do. We do longer interviews, we ask tougher questions. We ask, sometimes, some light questions. We talk about the person and what they feel about. It's not prompted and scripted, it's a conversation, it's authentic. For shows that have the Cube coverage, it makes the show buzz, it creates excitement. More importantly, it creates great content, great digital assets that can be shared instantaneously to the world. Over 31 million people have viewed the Cube, and that is the result of great content, great conversations. I'm so proud to be part of the Cube with a great team. Hi, I'm John Furrier, thanks for watching the Cube. >> Announcer: Coming up on the Cube, Tekan Sundar, CTO of Wine Disco. Live Cube coverage from Big Data NYC 2017 continues in a moment. >> Announcer: Coming up on the Cube, Donna Prlich, Chief Product Officer at Pentaho. Live Cube coverage from Big Data New York City 2017 continues in a moment. >> Announcer: Coming up on the Cube, Amit Walia, Executive Vice President and Chief Product Officer at Informatica. Live Cube coverage from Big Data New York City continues in a moment. >> Announcer: Coming up on the Cube, Prakash Nodili, Co-founder and CEO of Pexif. Live Cube coverage from Big Data New York City continues in a moment. (serious electronic music)

(upbeat electronic music) >> Hello, everyone. Welcome to the Cube Studios in Palo Alto, California. I'm John Furrier, the co-host of the Cube and co-founder of SiliconANGLE Media Inc. I'm here with George Gilbert for a Wikibon conversation on the state of the big data. George Gilbert is the analyst at Wikibon covering big data. George, great to see you. Looking good. (laughing) >> Good to see you, John. >> So George, you're obviously covering big data. Everyone knows you. You always ask the tough questions, you're always drilling down, going under the hood, and really inspecting all the trends, and also looking at the technology. What are you working on these days as the big data analyst? What's the hot thing that you're covering? >> OK, so, what's really interesting is we've got this emerging class of applications. The name that we've used so far is modern operational analytic applications. Operational in the sense that they help drive business operations, but analytical in the sense that the analytics either inform or drive transactions, or anticipate and inform interactions with people. That's the core of this class of apps. And then there are some sort of big challenges that customers are having in trying to build, and deploy, and operate these things. That's what I want to go through. >> George, you know, this is a great piece. I can't wait to (mumbling) some of these questions and ask you some pointed questions. But I would agree with you that to me, the number one thing I see customers either fumbling with or accelerating value with is how to operationalize some of the data in a way that they've never done it before. So you start to see disciplines come together. You're starting to see people with a notion of digital business being something that's not a department, it's not a marketing department. Data is everywhere, it's horizontally scalable, and the smart executives are really looking at new operational tactics to do that. With that, let me kick off the first question to you. People are trying to balance the cloud, On Premise, and The Edge, OK. And that's classic, you're seeing that now. I've got a data center, I have to go to the cloud, a hybrid cloud. And now the edge of the network. We were just taking about Block Chain today, there's this huge problem. They've got the balance that, but they've got to balance it versus leveraging specialized services. How do you respond to that? What is your reaction? What is your presentation? >> OK, so let's turn it into something really concrete that everyone can relate to, and then I'll generalize it. The concrete version is for a number of years, everyone associated Hadoop with big data. And Hadoop, you tried to stand up on a cluster on your own premises, for the most part. It was on had EMR, but sort of the big company activity outside, even including the big tech companies was stand up a Hadoop cluster as a pilot and start building a data lake. Then see what you could do with sort of huge amounts of data that you couldn't normally sort of collect and analyze. The operational challenges of standing up that sort of cluster was rather overwhelming, and I'll explain that later, so sort of park that thought. Because of that complexity, more and more customers, all but the most sophisticated, are saying we need a cloud strategy for that. But once you start taking Hadoop into the cloud, the components of this big data analytic system, you have tons more alternatives. So whereas in Cloudera's version of Hadoop you had Impala as your MPP sequel database. On Amazon, you've got Amazon Redshift, you've got Snowflake, you've got dozens up MPP sequel databases. And so the whole playing field shifts. And not only that, Amazon has instrumented their, in that particular case, their application, to be more of a more managed service, so there's a whole lot less for admins to do. And you take that on sort of, if you look at the slides, you take every step in that pipeline. And when you put it on a different cloud, it's got different competitors. And even if you take the same step in a pipeline, let's say Spark on HDFS to do your ETL, and your analysis, and your shaping of data, and even some of the machine learning, you put that on Azure and on Amazon, it's actually on different storage foundation. So even if you're using the same component, it's different. There's a lot of complexity and a lot of trade off that you got to make. >> Is that a problem for customers? >> Yes, because all of a sudden, they have to evaluate what those trade offs are. They have to evaluate the trade off between specialization. Do I use the best to breed thing on one platform. And if I do, it's not compatible with what I might be running on prem. >> That'll slow a lot of things down. I can tell you right now, people want to have the same code base on all environments, and then just have the same seamless operational role. OK, that's a great point, George. Thanks for sharing that. The second point here is harmonizing and simplifying management across hybrid clouds. Again, back to your point. You set that up beautifully. Great example, open source innovation hits a roadblock. And the roadblock is incompatible components in multiple clouds. That's a problem. It's a management nightmare. How do harmonization about hybrid cloud work? >> You couldn't have asked it better. Let me put it up in terms of an X Y chart where on the x-axis, you have the components of an analytic pipeline. Ingest, process, analyze, predict, serve. But then on the y-axis, this is for an admin, not a developer. These are just some of the tasks they have to worry about. Data governance, performance monitoring, scheduling and orchestration, availability and recovery, that whole list. Now, if you have a different product for each step in that pipeline, and each product has a different way of handling all those admin tasks, you're basically taking all the unique activities on the y-axis, multiplying it by all the unique products on the x-axis, and you have overwhelming complexity, even if these are managed services on the cloud. Here now you've got several trade offs. Do I use the specialized products that you would call best to breed? Do I try and do end to end integration so I get simplification across the pipeline? Or do I use products that I had on-prem, like you were saying, so that I have seamless compatibility? Or do I use the cloud vendors? That's a tough trade off. There's another similar one for developers. Again, on the y-axis, for all the things that a developer would have to deal with, not all of them, just a sample. The data model and the data itself, how to address it, the programing model, the persistence. So on that y-axis, you multiply all those different things you have to master for each product. And then on the x-axis, all the different products and the pipeline. And you have that same trade off, again. >> Complexity is off the charts. >> Right. And you can trade end to end integration to simplify the complexity, but we don't really have products that are fully fleshed out and mature that stretch from one end of the pipeline to the other, so that's a challenge. Alright. Let's talk about another way of looking at management. This was looking at the administrators and the developers. Now, we're getting better and better software for monitoring performance and operations, and trying to diagnose root cause when something goes wrong and then remediate it. There's two real approaches. One is you go really deep, but on a narrow part of your application and infrastructure landscape. And that narrow part might be, you know, your analytic pipeline, your big data. The broad approach is to get end to end visibility across Edge with your IOT devices, across on-prem, perhaps even across multiple clouds. That's the breadth approach, end to end visibility. Now, there's a trade off here too as in all technology choices. When you go deep, you have bounded visibility, but that bounded visibility allows you to understand exactly what is in that set of services, how they fit together, how they work. Because the vendor, knowing that they're only giving you management of your big data pipeline, they can train their models, their machine learning models, so that whenever something goes wrong, they know exactly what caused it and they can filter out all the false positives, the scattered errors that can confuse administrators. Whereas if you want breadth, you want to see end to end your entire landscape so that you can do capacity planning and see if there was an error way upstream, something might be triggered way downstream or a bunch of things downstream. So the best way to understand this is how much knowledge do you have of all the pieces work together, and how much knowledge you have of all the pieces, the software pieces fit together. >> This is actually an interesting point. So if I kind of connect the dots for you here is the bounded root cause analysis that we see a lot of machine learning, that's where the automation is. >> George: Yeah. >> The unbounded, the breadth, that's where the data volume is. But they can work together, that's what you're saying. >> Yes. And actually, I hadn't even got to that, so thanks for taking it out. >> John: Did I jump ahead on that one? (laughing) >> No, no, you teed it out. (laughing) Because ultimately-- >> Well a lot of people want to know where it's going to be automated away. All the undifferentiated labored and scale can be automated. >> Well, when you talk about them working together. So for the deep depth first, there's a small company called Unravel Data that sort of modeled eight million jobs or workloads of big data workloads from high tech companies, so they know how all that fits together and they can tell you when something goes wrong exactly what goes wrong and how to remediate it. So take something like Rocana or Splunk, they look end to end. The interesting thing that you brought up is at some point, that end to end product is going to be like a data warehouse and the depth products are going to sit on top of it. So you'll have all the contextual data of your end to end landscape, but you'll have the deep knowledge of how things work and what goes wrong sitting on it. >> So just before we jump to the machine learning question which I want to ask you, what you're saying is the industry is evolving to almost looking like a data warehouse model, but in a completely different way. >> Yeah. Think of it as, another cue. (laughing) >> John: That's what I do, George. I help you out with the cues. (laughing) No, but I mean the data warehouse, everyone knows what that was. A huge industry, created a lot of value, but then the world got rocked by unstructured data. And then their bounded, if you will, view has got democratized. So creative destruction happened which is another word for new entrants came in and incumbents got rattled. But now it's kind of going back to what looks like a data warheouse, but it's completely distributed around. >> Yes. And I was going to do one of my movie references, but-- >> No, don't do it. Save us the judge. >> If you look at this starting in the upper right, that's the data lake where you're collecting all the data and it's for search, it's exploratory. As you get more structure, you get to the descriptive place where you can build dashboards to monitor what's going on. And you get really deep, that's when you have the machine learning. >> Well, the machine learning is hitting the low hanging fruit, and that's where I want to get to next to move it along. Sourcing machine learning capability, let's discuss that. >> OK, alright. Just to set contacts before we get there, notice that when you do end to end visibility, you're really seeing across a broad landscape. And when I'm showing my public cloud big data, that would be depth first just for that component. But you would do breadth first, you could do like a Rocana or a Splunk that then sees across everything. The point I wanted to make was when you said we're reverting back to data warehouses and revisiting that dream again, the management applications started out as saying we know how to look inside machine data and tell you what's going on with your landscape. It turns out that machine data and business operations data, your application data, are really becoming one and the same. So what used to be a transaction, there was one transaction. And that, when you summarized them, that went into the data warehouse. Then we had with systems of engagement, you had about 100 interaction events that you tracked or sort of stored for everything business transaction. And then when we went out to the big data world, it's so resource intensive that we actually had 1,000 to 10,000 infrastructure events for every business transaction. So that's why the data volumes have grown so much and why we had to go back first to data lake, and then curate it to the warehouse. >> Classic innovation story, great. Machine learning. Sourcing machine learning capabilities 'cause that's where the rubber starts hitting the road. You're starting to see clear skies when it comes to where machine learning is starting fit in. Sourcing machine learning capabilities. >> You know, even though we sort of didn't really rehearse this, you're helping cue me on perfectly. Let me make the assertion that with machine learning, we have the same shortage of really trained data scientists that we had when we were trying to stand up Hadoop clusters and do big data analytics. We did not have enough administrators because these were open source components built from essentially different projects, and putting them all together required a huge amount of skills. Data science requires, really, knowledge of algorithms that even really sophisticated programmers will tell you, "Jeez, now I need a PhD "to really understand how this stuff works." So the shortage, that means we're not going to get a lot of hand-built machine learning applications for a while. >> John: In a lot of libraries out there right now, you see TensorFlow from Google. Big traction with that application. >> George: But for PhDs, for PhDs. My contention is-- >> John: Well developers too, you could argue developers, but I'm just putting it out there. >> George: I will get to that, actually. A slide just on that. Let me do this one first because my contention is the first big application, widespread application of machine learning, is going to be the depth first management because it comes with a model built in of how all the big data workloads, services, and infrastructure fit together and work together. And if you look at how the machine learning model operates, when it knows something goes wrong, let's say an analytic job takes 17 hours and then just falls over and crashes, the model can actually look at the data layout and say we have way too much on one node, and it can change the settings and change the layout or the data because it knows how all the stuff works. The point about this is the vendor. In this particular example, Unravel Data, they built into their model an understanding of how to keep a big data workload running as opposed to telling the customer, "You have to program it." So that fits into the question you were just asking which is where do you get this talent. When you were talking about like TensorFlow, and Cafe, and Torch, and MXnet, those are all like assembly language. Yes, those are the most powerful places you could go to program machine learning. But the number of people is inversely proportional to the power of those. >> John: Yeah, those are like really unique specialty people. High, you know, the top guys. >> George: Lab coats, rocket scientists. >> John: Well yeah, just high end tier one coders, tier one brains coding away, AI gurus. This is not your working developer. >> George: But if you go up two levels. So go up one level is Amazon machine learning, Spark machine learning. Go up another level, and I'm using Amazon as an example here. Amazon has a vision service called Recognition. They have a speech generation service, Natural Language. Those are developer ready. And when I say developer ready, I mean developer just uses an API, you know, passes in the data that comes out. He doesn't have to know how the model works. >> John: It's kind of like what DevOps was for cloud at the end of the day. This slide is completely accurate in my opinion. And we're at the early days and you're starting to see the platforms develop. It's the classic abstraction layer. Whoever can extract away the complexity as AI and machine learning grows is going to be the winning platform, no doubt about it. Amazon is showing some good moves there. >> George: And you know how they abstracted away. In traditional programming, it was just building higher and higher APIs, more accessible. In machine learning, you can't do that. You have to actually train the models which means you need data. So if you look at the big cloud vendors right now. So Google, Microsoft, Amazon, and IBM. Most of them, the first three, they have a lot of data from their B to C businesses. So you know, people talking to Echo, people talking to Google Assistant or Siri. That's where they get enough of their speech. >> John: So data equals power? >> George: Yes. >> By having data, you have the ingredients. And the more data that you have, the more data that you know about, the more data that has information around it, the more effective it can be to train machine learning algorithms. >> Yes. >> And the benefit comes back to the people who have the data. >> Yes. And so even though your capabilities get narrower, 'cause you could do anything on TensorFlow. >> John: Well, that's why Facebook is getting killed right now just to kind of change tangents. They have all this data and people are very unhappy, they just released that the Russians were targeting anti-semitic advertising, they enabled that. So it's hard to be a data platform and still provide user utility. This is what's going on. Whoever has the data has the power. It was a Frankenstein moment for Facebook. So there's that out there for everyone. How do companies do the right thing? >> And there's also the issue of customer intellectual property protection. As consumers, we're like you can take our voice, you can take all our speech to Siri or to Echo or whatever and get better at recognizing speech because we've given up control of that 'cause we want those services for free. >> Whoever can shift the data value to the users. >> George: To the developers. >> Or to the developers, or communities, better said, will win. >> OK. >> In my opinion, that's my opinion. >> For the most part, Amazon, Microsoft, and Google have similar data assets. For the most part, so far. IBM has something different which is they work closely with their industry customers and they build progressively. They're working with Mercedes, they're working with BMW. They'll work on the connected car, you know, the autonomous car, and they build out those models slowly. >> So George, this slide is really really interesting and I think this should be a roadmap for all customers to look at to try to peg where they are in the machine learning journey. But then the question comes in. They do the blocking and tackling, they have the foundational low level stuff done, they're building the models, they're understanding the mission, they have the right organizational mindset and personnel. Now, they want to orchestrate it and implement it into action. That's the final question. How do you orchestrate the distributed machine learning feedback and the data coherency? How do you get this thing scaling? How do these machines and the training happen so you have the breadth, and then you could bring the machine learning up the curve into the dashboard? >> OK. We've saved the best for last. It's not easy. When I show the chevrons, that's the analytic data pipeline. And imagine in the serve and predict at the very end, let's take an IOT app, a very sophisticated one. which would be an autonomous car. And it doesn't actually have to be an autonomous one, you could just be collected a lot of information off the car to do a better job insuring it, the insurance company. But the key then is you're collecting data on a fleet of cars, right? You're collecting data off each one, but you're also collecting then the fleet. And that, in the cloud, is where you keep improving your model of how the car works. You run simulations to figure out not just how to design better ones in the future, but how to tune and optimize the ones that are on the road now. That's number three. And then in four, you push that feedback back out to the cars on the road. And you have to manage, and this is tricky, you have to make sure that the models that you trained in step three are coherent, or the same, when you take out the fleet data and then you put the model for a particular instance of a car back out on the highway. >> George, this is a great example, and I think this slide really represents the modern analytical operational role in digital business. You can't look further than Tesla, this is essentially Tesla, and now all cars as a great example 'cause it's complex, it's an internet (mumbling) device, it's on the edge of the network, it's mobility, it's using 5G. It encapsulates everything that you are presenting, so I think this is example, is a great one, of the modern operational analytic applications that supports digital business. Thanks for joining this Wikibon conversaion. >> Thank you, John. >> George Gilbert, the analyst at Wikibon covering big data and the modern operational analytical system supporting digital business. It's data driven. The people with the data can train the machines that have the power. That's the mandate, that's the action item. I'm John Furrier with George Gilbert. Thanks for watching. (upbeat electronic music)

>> Announcer: Live from Orlando, Florida, it's theCUBE. Covering ServiceNow Knowledge '17. Brought to you by ServiceNow. >> Welcome back to Orlando, everybody. This is theCUBE, the leader in live tech coverage, and we are covering three days wall to wall coverage of ServiceNow Knowledge 2017. I'm Dave Volante with my co-host Jeff Frick. When we first started doing Knowledge in 2013, you'd walk around the show floor, and the names that you'd see weren't the brand names. Well, Infosys is here and Gaurav Uniyal, who's the industry principal of North America for the practice lead at ITSM for the ServiceNow practice with Infosys, you're seeing the big SIs join the community and really start to add value. Gaurav, welcome to theCUBE, thanks so much. >> Thank you. >> How'd you guys get into this? Like you say, four or five years ago, you guys might have been kicking the can, and now, you're all in. What's the journey been like? >> Sure, sure. We have been a partner with ServiceNow for almost last eight years, and as I look back to the journey, I can categorize the journey into four parts. Initially we saw 2010 to 2012 is basically about ITSM, how do you get the foundation capabilities in? Once that was there, we saw for the next couple of years it was all about how do you integrate services together, the service integration management as a concept. The third wave we saw is where concepts like ITOM, mobility, there's a lot of focus on user experience. And now, here we are in 2017, and as we look at the trends, what we are anticipating for the next two to three years, on a very high level, there are three trends which we believe are going to shape the journey of ServiceNow. First one is AI, obviously, how do you bring in concepts of machine learning, chat bars, predictive analytics, and how would that help organization do things faster, more efficiently, and in a cost-optimizing manner? AI is definitely one. Second trend that we are seeing is now organizations are looking for solutions that are relevant to their business. Solutions which are specific to retail industry, to CBGs, to finance, to healthcare, and so on, so forth. We are seeing a lot of traction there. And third is the natural expansion of ServiceNow into newer areas like obviously CSM, HR and so on, so forth. These are the three trends on the high level that we see, AI, going vertical, and on going horizontal by expanding these capabilities. >> Big factor when you talk to customers is sometimes it's not simple to implement ServiceNow. They need a partner like yours, so where do you start? I mean, when we first started following ServiceNow, a lot of folks weren't adopting CMDB and going too hard on the service catalog. To take advantage of these trends, the AI and other things that you talked about, do they need to be there on the majority curve? I wonder if you could talk about that a little bit. >> Sure, sure. What we see is that obviously there are a set of foundational capabilities that are required. There's definitely a push required from the management to be able to drive the initiator. But more and more we are seeing our clients implementing the solution in a standardized manner. If I look back four or five years back, a lot of customization, everybody have their own processes. But when I talk with clients now, they're looking for something which is ready-made, which can be deployed in a very, very faster manner. >> Gaurav, why Infosys? Talk about what you bring to the table versus maybe some of the other suppliers out there, and what do you consider your sweet spot? >> I think I would, a couple of things. One is Infosys we do a lot of work outside of ServiceNow. We have our practices for cloud, we have practices for HR, and so on, so forth. One thing that have been to our table is the domain expertise. If you're implementing HR, it requires not only ServiceNow skills, but as well as domain skills to be able to configure the processes. That's one differentiator that we have. The second differentiator we have is delivering ServiceNow as a service, so clients are also looking for turnkey projects where one render can bring in the platform, bring in consulting, implementation services, and also be able to manage the platform end-to-end, so that's the second thing. And third thing is basically being ahead of the curve. What we have done, we have invested last, I would say, last eight to 10 months in building a product that we brand as ESM Cafe, Enterprise Service Management Cafe, and it's what we call as a gold image of ServiceNow, and that helps you deploy ServiceNow faster and in efficient manner. >> So, Gaurav, what did you see eight years ago, 'cause clearly ServiceNow isn't where it is today, that gave you guys the confidence to make the investment? >> And before ServiceNow, we used to work with other products as well. What we saw new with ServiceNow was a huge focus on user experience. How do you make it easy for the users, how do you deploy an intuitive solution? And in our view, that has been the key, a focus on user experience, bring simplistic workflows, and be able to drive user behavior. >> Maybe some of those other domains, you mentioned HR, where else do you see Infosys as really strong? >> What we are seeing is ITOM is definitely one area that we are focusing on. HR, CSM, these are two big stack we have. And then, we are also focusing a lot on building vertical solutions. As I said, having specific solutions for retail industry, for our healthcare clients, or manufacturing clients. That has been a focus for us. >> We're out of time, Gaurav, but I'd like to leave you with the last word. Knowledge 2017, what does it mean to you, your customers, and Infosys and your presence here? Give us the bumper sticker. >> So I think, if I have to summarize everything in one word, I will say it's all about diversity. We see so many partners, so many clients, everybody they have their own perspective. But how do you bring in all that diverse experience and gel it together to be able to deliver the experience for the users? >> Great, well, Gaurav, thanks very much for coming on theCUBE, we appreciate it. >> Yep, it has been pleasure. >> Okay, well, keep it right there, everybody. We'll be back with our next guest right after this short break. This is theCUBE, we're live from ServiceNow Knowledge '17. Be right back. (electronic keyboard music)