(gentle music) >> From the Cube Studios in Palo Alto and Boston, connecting with thought leaders all around the world. This is theCube Conversation. >> Hi everybody, this is Dave Vellante, we're back at Cube on Cloud, and with me is Muddu Sudhakar. He's a long time alum of theCube, a technologist and executive, a serial entrepreneur and an investor. Welcome my friend, good to see you. >> Good to see you, Dave. Pleasure to be with you. Happy elections, I guess. >> Yeah, yeah. So I wanted to start, this work from home, pivot's been amazing, and you've seen the enterprise collaboration explode. I wrote a piece a couple months ago, looking at valuations of various companies, right around the snowflake IPO, I want to ask you about that, but I was looking at the valuations of various companies, at Spotify, and Shopify, and of course Zoom was there. And I was looking at just simple revenue multiples, and I said, geez, Zoom actually looks, might look undervalued, which is crazy, right? And of course the stock went up after that, and you see teams, Microsoft Teams, and Microsoft doing a great job across the board, we've written about that, you're seeing Webex is exploding, I mean, what do you make of this whole enterprise collaboration play? >> No, I think the look there is a trend here, right? So I think this probably trend started before COVID, but COVID is going to probably accelerate this whole digital transformation, right? People are going to work remotely a lot more, not everybody's going to come back to the offices even after COVID, so I think this whole collaboration through Slack, and Zoom, and Microsoft Teams and Webex, it's going to be the new game now, right? Both the video, audio and chat solutions, that's really going to help people like eyeballs. You're not going to spend time on all four of them, right? It's like everyday from a consumer side, you're going to spend time on your Gmail, Facebook, maybe Twitter, maybe Instagram, so like in the consumer side, on your personal life, you have something on the enterprise. The eyeballs are going to be in these platforms. >> Yeah. Well. >> But we're not going to take everything. >> Well, So you are right, there's a permanence to this, and I got a lot of ground to cover with you. And I always like our conversations mood because you tell it like it is, I'm going to stay on that work from home pivot. You know a lot about security, but you've seen three big trends, like mega trends in security, Endpoint, Identity Access Management, and Cloud Security, you're seeing this in the stock prices of companies like CrowdStrike, Zscaler, Okta- >> Right >> Sailpoint- >> Right, I mean, they exploded, as a result of the pandemic, and I think I'm inferring from your comment that you see that as permanent, but that's a real challenge from a security standpoint. What's the impact of Cloud there? >> No, it isn't impact but look, first is all the services required to be Cloud, right? See, the whole ideas for it to collaborate and do these things. So you cannot be running an application, like you can't be running conference and SharePoint oN-Prem, and try to on a Zoom and MS teams. So that's why, if you look at Microsoft is very clever, they went with Office 365, SharePoint 365, now they have MS Teams, so I think that Cloud is going to drive all these workloads that you have been talking about a lot, right? You and John have been saying this for years now. The eruption of Cloud and SAS services are the vehicle to drive this next-generation collaboration. >> You know what's so cool? So Cloud obviously is the topic, I wonder how you look at the last 10 years of Cloud, and maybe we could project forward, I mean the big three Cloud vendors, they're running it like $20 billion a quarter, and they're growing collectively, 35, 40% clips, so we're really approaching a hundred billion dollars for these three. And you hear stats like only 20% of the workloads are in the public Cloud, so it feels like we're just getting started. How do you look at the impact of Cloud on the market, as you say, the last 10 years, and what do you expect going forward? >> No, I think it's very fascinating, right? So I remember when theCube, you guys are talking about 10 years back, now it's been what? More than 10 years, 15 years, since AWS came out with their first S3 service back in 2006. >> Right. >> Right? so I think look, Cloud is going to accelerate even more further. The areas is going to accelerate is for different reasons. I think now you're seeing the initial days, it's all about startups, initial workloads, Dev test and QA test, now you're talking about real production workloads are moving towards Cloud, right? Initially it was backup, we really didn't care for backup they really put there. Now you're going to have Cloud health primary services, your primary storage will be there, it's not going to be an EMC, It's not going to be a NetApp storage, right? So workloads are going to shift from the business applications, and these business applications, will be running on the Cloud, and I'll make another prediction, make customer service and support. Customer service and support, again, we should be running on the Cloud. You're not want to run the thing on a Dell server, or an IBM server, or an HP server, with your own hosted environment. That model is not because there's no economies of scale. So to your point, what will drive Cloud for the next 10 years, will be economies of scale. Where can you take the cost? How can I save money? If you don't move to the Cloud, you won't save money. So all those workloads are going to go to the Cloud are people who really want to save, like global gradual custom, right? If you stay on the ASP model, a hosted, you're not going to save your costs, your costs will constantly go up from a SaaS perspective. >> So that doesn't bode well for all the On-prem guys, and you hear a lot of the vendors that don't own a Cloud that talk about repatriation, but the numbers don't support that. So what do those guys do? I mean, they're talking multi-Cloud, of course they're talking hybrid, that's IBM's big play, how do you see it? >> I think, look, see there, to me, multi-Cloud makes sense, right? You don't want one vendor that you never want to get, so having Amazon, Microsoft, Google, it gives them a multi-Cloud. Even hybrid Cloud does make sense, right? There'll be some workloads. It's like, we are still running On-prem environment, we still have mainframe, so it's never going to be a hundred percent, but I would say the majority, your question is, can we get to 60, 70, 80% workers in the next 10 years? I think you will. I think by 2025, more than 78% of the Cloud Migration by the next five years, 70% of workload for enterprise will be on the Cloud. The remaining 25, maybe Hybrid, maybe On-prem, but I get panics, really doesn't matter. You have saved and part of your business is running on the Cloud. That's your cost saving, that's where you'll see the economies of scale, and that's where all the growth will happen. >> So square the circle for me, because again, you hear the stat on the IDC stat, IBM Ginni Rometty puts it out there a lot that only 20% of the workloads are in the public Cloud, everything else is On-prem, but it's not a zero sum game, right? I mean the Cloud native stuff is growing like crazy, the On-prem stuff is flat to down, so what's going to happen? When you talk about 70% of the workloads will be in the Cloud, do you see those mission critical apps and moving into the car, I mean the insurance companies going to put their claims apps in the Cloud, or the financial services companies going to put their mission critical workloads in the Cloud, or they just going to develop new stuff that's Cloud native that is sort of interacts with the On-prem. How do you see that playing out? >> Yeah, no, I think absolutely, I think a very good question. So two things will happen. I think if you take an enterprise, right? Most businesses what they'll do is the workloads that they should not be running On-prem, they'll move it up. So obviously things like take, as I said, I use the word SharePoint, right? SharePoint and conference, all the knowledge stuff is still running on people's data centers. There's no reason. I understand, I've seen statistics that 70, 80% of the On-prem for SharePoint will move to SharePoint on the Cloud. So Microsoft is going to make tons of money on that, right? Same thing, databases, right? Whether it's CQL server, whether there is Oracle database, things that you are running as a database, as a Cloud, we move to the Cloud. Whether that is posted in Oracle Cloud, or you're running Oracle or Mongo DB, or Dynamo DB on AWS or SQL server Microsoft, that's going to happen. Then what you're talking about is really the App concept, the applications themselves, the App server. Is the App server is going to run On-prem, how much it's going to laureate outside? There may be a hybrid Cloud, like for example, Kafka. I may use a Purse running on a Kafka as a service, or I may be using Elasticsearch for my indexing on AWS or Google Cloud, but I may be running my App locally. So there'll be some hybrid place, but what I would say is for every application, 75% of your Comprende will be on the Cloud. So think of it like the Dev. So even for the On-prem app, you're not going to be a 100 percent On-prem. The competent, the billing materials will move to the Cloud, your Purse, your storage, because if you put it On-prem, you need to add all this, you need to have all the whole things to buy it and hire the people, so that's what is going to happen. So from a competent perspective, 70% of your bill of materials will move to the Cloud, even for an On-prem application. >> So, Of course, the susification of the industry in the last decade and in my three favorite companies last decade, you've worked for two of them, Tableau, ServiceNow, and Splunk. I want to ask you about those, but I'm interested in the potential disruption there. I mean, you've got these SAS companies, Salesforce of course is another one, but they can't get started in 1999. What do you see happening with those? I mean, we're basically building these sort of large SAS, platforms, now. Do you think that the Cloud native world that developers can come at this from an angle where they can disrupt those companies, or are they too entrenched? I mean, look at service now, I mean, I don't know, $80 billion market capital where they are, they bigger than Workday, I mean, just amazing how much they've grown and you feel like, okay, nothing can stop them, but there's always disruption in this industry, what are your thoughts on that. >> Not very good with, I think there'll be disrupted. So to me actually to your point, ServiceNow is now close to a 100 billion now, 95 billion market coverage, crazy. So from evaluation perspective, so I think the reason they'll be disrupted is that the SAS vendors that you talked about, ServiceNow, and all this plan, most of these services, they're truly not a multi-tenant or what do you call the Cloud Native. And that is the Accenture. So because of that, they will not be able to pass the savings back to the enterprises. So the cost economics, the economics that the Cloud provides because of the multi tenancy ability will not. The second reason there'll be disrupted is AI. So far, we talked about Cloud, but AI is the core. So it's not really Cloud Native, Dave, I look at the AI in a two-piece. AI is going to change, see all the SAS vendors were created 20 years back, if you remember, was an operator typing it, I don't respond administered we'll type a Splunk query. I don't need a human to type a query anymore, system will actually find it, that's what the whole security game has changed, right? So what's going to happen is if you believe in that, that AI, your score will disrupt all the SAS vendors, so one angle SAS is going to have is a Cloud. That's where you make the Cloud will take up because a SAS application will be Cloudified. Being SAS is not Cloud, right? Second thing is SAS will be also, I call it, will be AI-fied. So AI and machine learning will be trying to drive at the core so that I don't need that many licenses. I don't need that many humans. I don't need that many administrators to manage, I call them the tuners. Once you get a driverless car, you don't need a thousand tuners to tune your Tesla, or Google Waymo car. So the same philosophy will happen is your Dev Apps, your administrators, your service management, people that you need for service now, and these products, Zendesk with AI, will tremendously will disrupt. >> So you're saying, okay, so yeah, I was going to ask you, won't the SAS vendors, won't they be able to just put, inject AI into their platforms, and I guess I'm inferring saying, yeah, but a lot of the problems that they're solving, are going to go away because of AI, is that right? And automation and RPA and things of that nature, is that right? >> Yes and no. So I'll tell you what, sorry, you have asked a very good question, let's answer, let me rephrase that question. What you're saying is, "Why can't the existing SAS vendors do the AI?" >> Yes, right. >> Right, >> And there's a reason they can't do it is their pricing model is by number of seats. So I'm not going to come to Dave, and say, come on, come pay me less money. It's the same reason why a board and general lover build an electric car. They're selling 10 million gasoline cars. There's no incentive for me, I'm not going to do any AI, I'm going to put, I'm not going to come to you and say, hey, buy me a hundred less license next year from it. So that is one reason why AI, even though these guys do any AI, it's going to be just so I call it, they're going to, what do you call it, a whitewash, kind of like you put some paint brush on it, trying to show you some AI you did from a marketing dynamics. But at the core, if you really implement the AI with you take the driver out, how are you going to change the pricing model? And being a public company, you got to take a hit on the pricing model and the price, and it's going to have a stocking part. So that, to your earlier question, will somebody disrupt them? The person who is going to disrupt them, will disrupt them on the pricing model. >> Right. So I want to ask you about that, because we saw a Snowflake, and it's IPO, we were able to pour through its S-1, and they have a different pricing model. It's a true Cloud consumption model, Whereas of course, most SAS companies, they're going to lock you in for at least one year term, maybe more, and then, you buy the license, you got to pay X. If you, don't use it, you still got to pay for it. Snowflake's different, actually they have a different problem, that people are using it too much and the sea is driving the CFO crazy because the bill is going up and up and up, but to me, that's the right model, It's just like the Amazon model, if you can justify it, so how do you see the pricing, that consumption model is actually, you're seeing some of the On-prem guys at HPE, Dell, they're doing as a service. They're kind of taking a page out of the last decade SAS model, so I think pricing is a real tricky one, isn't it? >> No, you nailed it, you nailed it. So I think the way in which the Snowflake there, how the disruptors are data warehouse, that disrupted the open source vendors too. Snowflake distributed, imagine the playbook, you disrupted something as the $ 0, right? It's an open source with Cloudera, Hortonworks, Mapper, that whole big data that you want me to, or that market is this, that disrupting data warehouses like Netezza, Teradata, and the charging more money, they're making more money and disrupting at $0, because the pricing models by consumption that you talked about. CMT is going to happen in the service now, Zen Desk, well, 'cause their pricing one is by number of seats. People are going to say, "How are my users are going to ask?" right? If you're an employee help desk, you're back to your original health collaborative. I may be on Slack, I could be on zoom, I'll maybe on MS Teams, I'm going to ask by using usage model on Slack, tools by employees to service now is the pricing model that people want to pay for. The more my employees use it, the more value I get. But I don't want to pay by number of seats, so the vendor, who's going to figure that out, and that's where I look, if you know me, I'm right over as I started, that's what I've tried to push that model look, I love that because that's the core of how you want to change the new game. >> I agree. I say, kill me with that problem, I mean, some people are trying to make it a criticism, but you hit on the point. If you pay more, it's only because you're getting more value out of it. So I wanted to flip the switch here a little bit and take a customer angle. Something that you've been on all sides. And I want to talk a little bit about strategies, you've been a strategist, I guess, once a strategist, always a strategist. How should organizations be thinking about their approach to Cloud, it's cost different for different industries, but, back when the cube started, financial services Cloud was a four-letter word. But of course the age of company is going to matter, but what's the framework for figuring out your Cloud strategy to get to your 70% and really take advantage of the economics? Should I be Mono Cloud, Multi-Cloud, Multi-vendor, what would you advise? >> Yeah, no, I mean, I mean, I actually call it the tech stack. Actually you and John taught me that what was the tech stack, like the lamp stack, I think there is a new Cloud stack needs to come, and that I think the bottomline there should be... First of all, anything with storage should be in the Cloud. I mean, if you want to start, whether you are, financial, doesn't matter, there's no way. I come from cybersecurity side, I've seen it. Your attackers will be more with insiders than being on the Cloud, so storage has to be in the Cloud then come compute, Kubernetes. If you really want to use containers and Kubernetes, it has to be in the public Cloud, leverage that have the computer on their databases. That's where it can be like if your data is so strong, maybe run it On-prem, maybe have it on a hosted model for when it comes to database, but there you have a choice between hybrid Cloud and public Cloud choice. Then on top when it comes to App, the app itself, you can run locally or anywhere, the App and database. Now the areas that you really want to go after to migrate is look at anything that's an enterprise workload that you don't need people to manage it. You want your own team to move up in the career. You don't want thousand people looking at... you don't want to have a, for example, IT administrators to call central people to the people to manage your compute storage. That workload should be more, right? You already saw Sierra moved out to Salesforce. We saw collaboration already moved out. Zoom is not running locally. You already saw SharePoint with knowledge management mode up, right? With a box, drawbacks, you name anything. The next global mode is a SAS workloads, right? I think Workday service running there, but work data will go into the Cloud. I bet at some point Zendesk, ServiceNow, then either they put it on the public Cloud, or they have to create a product and public Cloud. To your point, these public Cloud vendors are at $2 trillion market cap. They're they're bigger than the... I call them nation States. >> Yeah, >> So I'm servicing though. I mean, there's a 2 trillion market gap between Amazon and Azure, I'm not going to compete with them. So I want to take this workload to run it there. So all these vendors, if you see that's where Shandra from Adobe is pushing this right, Adobe, Workday, Anaplan, all the SAS vendors we'll move them into the public Cloud within these vendors. So those workloads need to move out, right? So that all those things will start, then you'll start migrating, but I call your procurement. That's where the RPA comes in. The other thing that we didn't talk about, back to your first question, what is the next 10 years of Cloud will be RPA? That third piece to Cloud is RPA because if you have your systems On-prem, I can't automate them. I have to do a VPN into your house there and then try to automate your systems, or your procurement, et cetera. So all these RPA vendors are still running On-prem, most of them, whether it's UI path automation anywhere. So the Cloud should be where the brain should be. That's what I call them like the octopus analogy, the brain is in the Cloud, the tentacles are everywhere, they should manage it. But if my tentacles have to do a VPN with your house to manage it, I'm always will have failures. So if you look at the why RPA did not have the growth, like the Snowflake, like the Cloud, because they are running it On-prem, most of them still. 80% of the RP revenue is On-prem, running On-prem, that needs to be called clarified. So AI, RPA and the SAS, are the three reasons Cloud will take off. >> Awesome. Thank you for that. Now I want to flip the switch again. You're an investor or a multi-tool player here, but so if you're, let's say you're an ecosystem player, and you're kind of looking at the landscape as you're in an investor, of course you've invested in the Cloud, because the Cloud is where it's at, but you got to be careful as an ecosystem player to pick a spot that both provides growth, but allows you to have a moat as, I mean, that's why I'm really curious to see how Snowflake's going to compete because they're competing with AWS, Microsoft, and Google, unlike, Frank, when he was at service now, he was competing with BMC and with on-prem and he crushed it, but the competitors are much more capable here, but it seems like they've got, maybe they've got a moat with MultiCloud, and that whole data sharing thing, we'll see. But, what about that? Where are the opportunities? Where's that white space? And I know there's a lot of white space, but what's the framework to look at, from an investor standpoint, or even a CEO standpoint, where you want to put place your bets. >> No, very good question, so look, I did something. We talk as an investor in the board with many companies, right? So one thing that says as an investor, if you come back and say, I want to create a next generation Docker or a computer, there's no way nobody's going to invest. So that we can motor off, even if you want to do object storage or a block storage, I mean, I've been an investor board member of so many storage companies, there's no way as an industry, I'll write a check for a compute or storage, right? If you want to create a next generation network, like either NetSuite, or restart Juniper, Cisco, there is no way. But if you come back and say, I want to create a next generation Viper for remote working environments, where AI is at the core, I'm interested in that, right? So if you look at how the packets are dropped, there's no intelligence in either not switching today. The packets come, I do it. The intelligence is not built into the network with AI level. So if somebody comes with an AI, what good is all this NVD, our GPS, et cetera, if you cannot do wire speed, packet inspection, looking at the content and then route the traffic. If I see if it's a video package, but in UN Boston, there's high interview day of they should be loading our package faster, because you are a premium ISP. That intelligence has not gone there. So you will see, and that will be a bad people will happen in the network, switching, et cetera, right? So that is still an angle. But if you work and it comes to platform services, remember when I was at Pivotal and VMware, all models was my boss, that would, yes, as a platform, service is a game already won by the Cloud guys. >> Right. (indistinct) >> Silicon Valley Investors, I don't think you want to invest in past services, right? I mean, you might come with some lecture edition database to do some updates, there could be some game, let's say we want to do a time series database, or some metrics database, there's always some small angle, but the opportunity to go create a national database there it's very few. So I'm kind of eliminating all the black spaces, right? >> Yeah. >> We have the white spaces that comes in is the SAS level. Now to your point, if I'm Amazon, I'm going to compete with Snowflake, I have Redshift. So this is where at some point, these Cloud platforms, I call them aircraft carriers. They're not going to stay on the aircraft carriers, they're going to own the land as well. So they're going to move up to the SAS space. The question is you want to create a SAS service like CRM. They are not going to create a CRM like service, they may not create a sales force and service now, but if you're going to add a data warehouse, I can very well see Azure, Google, and AWS, going to create something to compute a Snowflake. Why would I not? It's so close to my database and data warehouse, I already have Redshift. So that's going to be nightlights, same reason, If you look at Netflix, you have a Netflix and you have Amazon prime. Netflix runs on Amazon, but you have Amazon prime. So you have the same model, you have Snowflake, and you'll have Redshift. The both will help each other, there'll be a... What do you call it? Coexistence will happen. But if you really want to invest, you want to invest in SAS companies. You do not want to be investing in a compliment players. You don't want to a feature. >> Yeah, that's great, I appreciate that perspective. And I wonder, so obviously Microsoft play in SAS, Google's got G suite. And I wonder if people often ask the Andy Jassy, you're going to move up the stack, you got to be an application, a SAS vendor, and you never say never with Atavist, But I wonder, and we were talking to Jerry Chen about this, years ago on theCube, and his angle was that Amazon will play, but they'll play through developers. They'll enable developers, and they'll participate, they'll take their, lick off the cone. So it's going to be interesting to see how directly Amazon plays, but at some point you got Tam expansion, you got to play in that space. >> Yeah, I'll give you an example of knowing, I got acquired by a couple of times by EMC. So I learned a lot from Joe Tucci and Paul Merage over the years. see Paul and Joe, what they did is to look at how 20 years, and they are very close to Boston in your area, Joe, what games did is they used to sell storage, but you know what he did, he went and bought the Apps to drive them. He bought like Legato, he bought Documentum, he bought Captiva, if you remember how he acquired all these companies as a services, he bought VMware to drive that. So I think the good angle that Microsoft has is, I'm a SAS player, I have dynamics, I have CRM, I have SharePoint, I have Collaboration, I have Office 365, MS Teams for users, and then I have the platform as Azure. So I think if I'm Amazon, (indistinct). I got to own the apps so that I can drive this workforce on my platform. >> Interesting. >> Just going to developers, like I know Jerry Chan, he was my peer a BMF. I don't think just literally to developers and that model works in open source, but the open source game is pretty much gone, and not too many companies made money. >> Well, >> Most companies pretty much gone. >> Yeah, he's right. Red hats not bad idea. But it's very interesting what you're saying there. And so, hey, its why Oracle wants to have Tiktok, running on their platform, right? I mean, it's going to. (laughing) It's going to drive that further integration. I wanted to ask you something, you were talking about, you wouldn't invest in storage or compute, but I wonder, and you mentioned some commentary about GPU's. Of course the videos has been going crazy, but they're now saying, okay, how do we expand our Team, they make the acquisition of arm, et cetera. What about this DPU thing, if you follow that, that data processing unit where they're like hyper dis-aggregation and then they reaggregate, and as an offload and really to drive data centric workloads. Have you looked at that at all? >> I did, I think, and that's a good angle. So I think, look, it's like, it goes through it. I don't know if you remember in your career, we have seen it. I used to get Silicon graphics. I saw the first graphic GPU, right? That time GPU was more graphic processor unit, >> Right, yeah, work stations. >> So then become NPUs at work processing units, right? There was a TCP/IP office offloading, if you remember right, there was like vector processing unit. So I think every once in a while the industry, recreated this separate unit, as a co-processor to the main CPU, because main CPU's inefficient, and it makes sense. And then Google created TPU's and then we have the new world of the media GPU's, now we have DPS all these are good, but what's happening is, all these are driving for machine learning, AI for the training period there. Training period Sometimes it's so long with the workloads, if you can cut down, it makes sense. >> Yeah. >> Because, but the question is, these aren't so specialized in nature. I can't use it for everything. >> Yup. >> I want Ideally, algorithms to be paralyzed, I want the training to be paralyzed, I want so having deep use and GPS are important, I think where I want to see them as more, the algorithm, there should be more investment from the NVIDIA's and these guys, taking the algorithm to be highly paralyzed them. (indistinct) And I think that still has not happened in industry yet. >> All right, so we're pretty much out of time, but what are you doing these days? Where are you spending your time, are you still in Stealth, give us a little glimpse. >> Yeah, no, I'm out of the Stealth, I'm actually the CEO of Aisera now, Aisera, obviously I invested with them, but I'm the CEO of Aisero. It's funded by Menlo ventures, Norwest, True, along with Khosla ventures and Ram Shriram is a big investor. Robin's on the board of Google, so these guys, look, we are going out to the collaboration game. How do you automate customer service and support for employees and then users, right? In this whole game, we talked about the Zoom, Slack and MS Teams, that's what I'm spending time, I want to create next generation service now. >> Fantastic. Muddu, I always love having you on you, pull punches, you tell it like it is, that you're a great visionary technologist. Thanks so much for coming on theCube, and participating in our program. >> Dave, it's always a pleasure speaking to you sir. Thank you. >> Okay. Keep it right there, there's more coming from Cuba and Cloud right after this break. (slow music)

(gentle music) >> From the Cube Studios in Palo Alto and Boston, connecting with thought leaders all around the world. This is theCube Conversation. >> Hi everybody, this is Dave Vellante, we're back at Cube on Cloud, and with me is Muddu Sudhakar. He's a long time alum of theCube, a technologist and executive, a serial entrepreneur and an investor. Welcome my friend, good to see you. >> Good to see you, Dave. Pleasure to be with you. Happy elections, I guess. >> Yeah, yeah. So I wanted to start, this work from home, pivot's been amazing, and you've seen the enterprise collaboration explode. I wrote a piece a couple months ago, looking at valuations of various companies, right around the snowflake IPO, I want to ask you about that, but I was looking at the valuations of various companies, at Spotify, and Shopify, and of course Zoom was there. And I was looking at just simple revenue multiples, and I said, geez, Zoom actually looks, might look undervalued, which is crazy, right? And of course the stock went up after that, and you see teams, Microsoft Teams, and Microsoft doing a great job across the board, we've written about that, you're seeing Webex is exploding, I mean, what do you make of this whole enterprise collaboration play? >> No, I think the look there is a trend here, right? So I think this probably trend started before COVID, but COVID is going to probably accelerate this whole digital transformation, right? People are going to work remotely a lot more, not everybody's going to come back to the offices even after COVID, so I think this whole collaboration through Slack, and Zoom, and Microsoft Teams and Webex, it's going to be the new game now, right? Both the video, audio and chat solutions, that's really going to help people like eyeballs. You're not going to spend time on all four of them, right? It's like everyday from a consumer side, you're going to spend time on your Gmail, Facebook, maybe Twitter, maybe Instagram, so like in the consumer side, on your personal life, you have something on the enterprise. The eyeballs are going to be in these platforms. >> Yeah. Well. >> But we're not going to take everything. >> Well, So you are right, there's a permanence to this, and I got a lot of ground to cover with you. And I always like our conversations mood because you tell it like it is, I'm going to stay on that work from home pivot. You know a lot about security, but you've seen three big trends, like mega trends in security, Endpoint, Identity Access Management, and Cloud Security, you're seeing this in the stock prices of companies like CrowdStrike, Zscaler, Okta- >> Right >> Sailpoint- >> Right, I mean, they exploded, as a result of the pandemic, and I think I'm inferring from your comment that you see that as permanent, but that's a real challenge from a security standpoint. What's the impact of Cloud there? >> No, it isn't impact but look, first is all the services required to be Cloud, right? See, the whole ideas for it to collaborate and do these things. So you cannot be running an application, like you can't be running conference and SharePoint oN-Prem, and try to on a Zoom and MS teams. So that's why, if you look at Microsoft is very clever, they went with Office 365, SharePoint 365, now they have MS Teams, so I think that Cloud is going to drive all these workloads that you have been talking about a lot, right? You and John have been saying this for years now. The eruption of Cloud and SAS services are the vehicle to drive this next-generation collaboration. >> You know what's so cool? So Cloud obviously is the topic, I wonder how you look at the last 10 years of Cloud, and maybe we could project forward, I mean the big three Cloud vendors, they're running it like $20 billion a quarter, and they're growing collectively, 35, 40% clips, so we're really approaching a hundred billion dollars for these three. And you hear stats like only 20% of the workloads are in the public Cloud, so it feels like we're just getting started. How do you look at the impact of Cloud on the market, as you say, the last 10 years, and what do you expect going forward? >> No, I think it's very fascinating, right? So I remember when theCube, you guys are talking about 10 years back, now it's been what? More than 10 years, 15 years, since AWS came out with their first S3 service back in 2006. >> Right. >> Right? so I think look, Cloud is going to accelerate even more further. The areas is going to accelerate is for different reasons. I think now you're seeing the initial days, it's all about startups, initial workloads, Dev test and QA test, now you're talking about real production workloads are moving towards Cloud, right? Initially it was backup, we really didn't care for backup they really put there. Now you're going to have Cloud health primary services, your primary storage will be there, it's not going to be an EMC, It's not going to be a ETAP storage, right? So workloads are going to shift from the business applications, and this business App again, will be running on the Cloud, and I'll make another prediction, make customer service and support. Customer service and support, again, we should be running on the Cloud. You're not want to run the thing on a Dell server, or an IBM server, or an HP server, with your own hosted environment. That model is not because there's no economies of scale. So to your point, what will drive Cloud for the next 10 years, will be economies of scale. Where can you take the cost? How can I save money? If you don't move to the Cloud, you won't save money. So all those workloads are going to go to the Cloud are people who really want to save, like global gradual custom, right? If you stay on the ASP model, a hosted, you're not going to save your costs, your costs will constantly go up from a SAS perspective. >> So that doesn't bode well for all the On-prem guys, and you hear a lot of the vendors that don't own a Cloud that talk about repatriation, but the numbers don't support that. So what do those guys do? I mean, they're talking multi-Cloud, of course they're talking hybrid, that's IBM's big play, how do you see it? >> I think, look, see there, to me, multi-Cloud makes sense, right? You don't want one vendor that you never want to get, so having Amazon, Microsoft, Google, it gives them a multi-Cloud. Even hybrid Cloud does make sense, right? There'll be some workloads. It's like, we are still running On-prem environment, we still have mainframe, so it's never going to be a hundred percent, but I would say the majority, your question is, can we get to 60, 70, 80% workers in the next 10 years? I think you will. I think by 2025, more than 78% of the Cloud Migration by the next five years, 70% of workload for enterprise will be on the Cloud. The remaining 25, maybe Hybrid, maybe On-prem, but I get panics, really doesn't matter. You have saved and part of your business is running on the Cloud. That's your cost saving, that's where you'll see the economies of scale, and that's where all the growth will happen. >> So square the circle for me, because again, you hear the stat on the IDC stat, IBM Ginni Rometty puts it out there a lot that only 20% of the workloads are in the public Cloud, everything else is On-prem, but it's not a zero sum game, right? I mean the Cloud native stuff is growing like crazy, the On-prem stuff is flat to down, so what's going to happen? When you talk about 70% of the workloads will be in the Cloud, do you see those mission critical apps and moving into the car, I mean the insurance companies going to put their claims apps in the Cloud, or the financial services companies going to put their mission critical workloads in the Cloud, or they just going to develop new stuff that's Cloud native that is sort of interacts with the On-prem. How do you see that playing out? >> Yeah, no, I think absolutely, I think a very good question. So two things will happen. I think if you take an enterprise, right? Most businesses what they'll do is the workloads that they should not be running On-prem, they'll move it up. So obviously things like take, as I said, I use the word SharePoint, right? SharePoint and conference, all the knowledge stuff is still running on people's data centers. There's no reason. I understand, I've seen statistics that 70, 80% of the On-prem for SharePoint will move to SharePoint on the Cloud. So Microsoft is going to make tons of money on that, right? Same thing, databases, right? Whether it's CQL server, whether there is Oracle database, things that you are running as a database, as a Cloud, we move to the Cloud. Whether that is posted in Oracle Cloud, or you're running Oracle or Mongo DB, or Dynamo DB on AWS or SQL server Microsoft, that's going to happen. Then what you're talking about is really the App concept, the applications themselves, the App server. Is the App server is going to run On-prem, how much it's going to laureate outside? There may be a hybrid Cloud, like for example, Kafka. I may use a Purse running on a Kafka as a service, or I may be using Elasticsearch for my indexing on AWS or Google Cloud, but I may be running my App locally. So there'll be some hybrid place, but what I would say is for every application, 75% of your Comprende will be on the Cloud. So think of it like the Dev. So even for the On-prem app, you're not going to be a 100 percent On-prem. The competent, the billing materials will move to the Cloud, your Purse, your storage, because if you put it On-prem, you need to add all this, you need to have all the whole things to buy it and hire the people, so that's what is going to happen. So from a competent perspective, 70% of your bill of materials will move to the Cloud, even for an On-prem application. >> So, Of course, the susification of the industry in the last decade and in my three favorite companies last decade, you've worked for two of them, Tableau, ServiceNow, and Splunk. I want to ask you about those, but I'm interested in the potential disruption there. I mean, you've got these SAS companies, Salesforce of course is another one, but they can't get started in 1999. What do you see happening with those? I mean, we're basically building these sort of large SAS, platforms, now. Do you think that the Cloud native world that developers can come at this from an angle where they can disrupt those companies, or are they too entrenched? I mean, look at service now, I mean, I don't know, $80 billion market capital where they are, they bigger than Workday, I mean, just amazing how much they've grown and you feel like, okay, nothing can stop them, but there's always disruption in this industry, what are your thoughts on that. >> Not very good with, I think there'll be disrupted. So to me actually to your point, ServiceNow is now close to a 100 billion now, 95 billion market coverage, crazy. So from evaluation perspective, so I think the reason they'll be disrupted is that the SAS vendors that you talked about, ServiceNow, and all this plan, most of these services, they're truly not a multi-tenant or what do you call the Cloud Native. And that is the Accenture. So because of that, they will not be able to pass the savings back to the enterprises. So the cost economics, the economics that the Cloud provides because of the multi tenancy ability will not. The second reason there'll be disrupted is AI. So far, we talked about Cloud, but AI is the core. So it's not really Cloud Native, Dave, I look at the AI in a two-piece. AI is going to change, see all the SAS vendors were created 20 years back, if you remember, was an operator typing it, I don't respond administered we'll type a Splunk query. I don't need a human to type a query anymore, system will actually find it, that's what the whole security game has changed, right? So what's going to happen is if you believe in that, that AI, your score will disrupt all the SAS vendors, so one angle SAS is going to have is a Cloud. That's where you make the Cloud will take up because a SAS application will be Cloudified. Being SAS is not Cloud, right? Second thing is SAS will be also, I call it, will be AI-fied. So AI and machine learning will be trying to drive at the core so that I don't need that many licenses. I don't need that many humans. I don't need that many administrators to manage, I call them the tuners. Once you get a driverless car, you don't need a thousand tuners to tune your Tesla, or Google Waymo car. So the same philosophy will happen is your Dev Apps, your administrators, your service management, people that you need for service now, and these products, Zendesk with AI, will tremendously will disrupt. >> So you're saying, okay, so yeah, I was going to ask you, won't the SAS vendors, won't they be able to just put, inject AI into their platforms, and I guess I'm inferring saying, yeah, but a lot of the problems that they're solving, are going to go away because of AI, is that right? And automation and RPA and things of that nature, is that right? >> Yes and no. So I'll tell you what, sorry, you have asked a very good question, let's answer, let me rephrase that question. What you're saying is, "Why can't the existing SAS vendors do the AI?" >> Yes, right. >> Right, >> And there's a reason they can't do it is their pricing model is by number of seats. So I'm not going to come to Dave, and say, come on, come pay me less money. It's the same reason why a board and general lover build an electric car. They're selling 10 million gasoline cars. There's no incentive for me, I'm not going to do any AI, I'm going to put, I'm not going to come to you and say, hey, buy me a hundred less license next year from it. So that is one reason why AI, even though these guys do any AI, it's going to be just so I call it, they're going to, what do you call it, a whitewash, kind of like you put some paint brush on it, trying to show you some AI you did from a marketing dynamics. But at the core, if you really implement the AI with you take the driver out, how are you going to change the pricing model? And being a public company, you got to take a hit on the pricing model and the price, and it's going to have a stocking part. So that, to your earlier question, will somebody disrupt them? The person who is going to disrupt them, will disrupt them on the pricing model. >> Right. So I want to ask you about that, because we saw a Snowflake, and it's IPO, we were able to pour through its S-1, and they have a different pricing model. It's a true Cloud consumption model, Whereas of course, most SAS companies, they're going to lock you in for at least one year term, maybe more, and then, you buy the license, you got to pay X. If you, don't use it, you still got to pay for it. Snowflake's different, actually they have a different problem, that people are using it too much and the sea is driving the CFO crazy because the bill is going up and up and up, but to me, that's the right model, It's just like the Amazon model, if you can justify it, so how do you see the pricing, that consumption model is actually, you're seeing some of the On-prem guys at HPE, Dell, they're doing as a service. They're kind of taking a page out of the last decade SAS model, so I think pricing is a real tricky one, isn't it? >> No, you nailed it, you nailed it. So I think the way in which the Snowflake there, how the disruptors are data warehouse, that disrupted the open source vendors too. Snowflake distributed, imagine the playbook, you disrupted something as the $ 0, right? It's an open source with Cloudera, Hortonworks, Mapper, that whole big data that you want me to, or that market is this, that disrupting data warehouses like Netezza, Teradata, and the charging more money, they're making more money and disrupting at $0, because the pricing models by consumption that you talked about. CMT is going to happen in the service now, Zen Desk, well, 'cause their pricing one is by number of seats. People are going to say, "How are my users are going to ask?" right? If you're an employee help desk, you're back to your original health collaborative. I may be on Slack, I could be on zoom, I'll maybe on MS Teams, I'm going to ask by using usage model on Slack, tools by employees to service now is the pricing model that people want to pay for. The more my employees use it, the more value I get. But I don't want to pay by number of seats, so the vendor, who's going to figure that out, and that's where I look, if you know me, I'm right over as I started, that's what I've tried to push that model look, I love that because that's the core of how you want to change the new game. >> I agree. I say, kill me with that problem, I mean, some people are trying to make it a criticism, but you hit on the point. If you pay more, it's only because you're getting more value out of it. So I wanted to flip the switch here a little bit and take a customer angle. Something that you've been on all sides. And I want to talk a little bit about strategies, you've been a strategist, I guess, once a strategist, always a strategist. How should organizations be thinking about their approach to Cloud, it's cost different for different industries, but, back when the cube started, financial services Cloud was a four-letter word. But of course the age of company is going to matter, but what's the framework for figuring out your Cloud strategy to get to your 70% and really take advantage of the economics? Should I be Mono Cloud, Multi-Cloud, Multi-vendor, what would you advise? >> Yeah, no, I mean, I mean, I actually call it the tech stack. Actually you and John taught me that what was the tech stack, like the lamp stack, I think there is a new Cloud stack needs to come, and that I think the bottomline there should be... First of all, anything with storage should be in the Cloud. I mean, if you want to start, whether you are, financial, doesn't matter, there's no way. I come from cybersecurity side, I've seen it. Your attackers will be more with insiders than being on the Cloud, so storage has to be in the Cloud and encompass compute whoever it is. If you really want to use containers and Kubernetes, it has to be in the public Cloud, leverage that have the computer on their databases. That's where it can be like if your data is so strong, maybe run it On-prem, maybe have it on a hosted model for when it comes to database, but there you have a choice between hybrid Cloud and public Cloud choice. Then on top when it comes to App, the app itself, you can run locally or anywhere, the App and database. Now the areas that you really want to go after to migrate is look at anything that's an enterprise workload that you don't need people to manage it. You want your own team to move up in the career. You don't want thousand people looking at... you don't want to have a, for example, IT administrators to call central people to the people to manage your compute storage. That workload should be more, right? You already saw Sierra moved out to Salesforce. We saw collaboration already moved out. Zoom is not running locally. You already saw SharePoint with knowledge management mode up, right? With a box, drawbacks, you name anything. The next global mode is a SAS workloads, right? I think Workday service running there, but work data will go into the Cloud. I bet at some point Zendesk, ServiceNow, then either they put it on the public Cloud, or they have to create a product and public Cloud. To your point, these public Cloud vendors are at $2 trillion market cap. They're they're bigger than the... I call them nation States. >> Yeah, >> So I'm servicing though. I mean, there's a 2 trillion market gap between Amazon and Azure, I'm not going to compete with them. So I want to take this workload to run it there. So all these vendors, if you see that's where Shandra from Adobe is pushing this right, Adobe, Workday, Anaplan, all the SAS vendors we'll move them into the public Cloud within these vendors. So those workloads need to move out, right? So that all those things will start, then you'll start migrating, but I call your procurement. That's where the RPA comes in. The other thing that we didn't talk about, back to your first question, what is the next 10 years of Cloud will be RPA? That third piece to Cloud is RPA because if you have your systems On-prem, I can't automate them. I have to do a VPN into your house there and then try to automate your systems, or your procurement, et cetera. So all these RPA vendors are still running On-prem, most of them, whether it's UI path automation anywhere. So the Cloud should be where the brain should be. That's what I call them like the octopus analogy, the brain is in the Cloud, the tentacles are everywhere, they should manage it. But if my tentacles have to do a VPN with your house to manage it, I'm always will have failures. So if you look at the why RPA did not have the growth, like the Snowflake, like the Cloud, because they are running it On-prem, most of them still. 80% of the RP revenue is On-prem, running On-prem, that needs to be called clarified. So AI, RPA and the SAS, are the three reasons Cloud will take off. >> Awesome. Thank you for that. Now I want to flip the switch again. You're an investor or a multi-tool player here, but so if you're, let's say you're an ecosystem player, and you're kind of looking at the landscape as you're in an investor, of course you've invested in the Cloud, because the Cloud is where it's at, but you got to be careful as an ecosystem player to pick a spot that both provides growth, but allows you to have a moat as, I mean, that's why I'm really curious to see how Snowflake's going to compete because they're competing with AWS, Microsoft, and Google, unlike, Frank, when he was at service now, he was competing with BMC and with on-prem and he crushed it, but the competitors are much more capable here, but it seems like they've got, maybe they've got a moat with MultiCloud, and that whole data sharing thing, we'll see. But, what about that? Where are the opportunities? Where's that white space? And I know there's a lot of white space, but what's the framework to look at, from an investor standpoint, or even a CEO standpoint, where you want to put place your bets. >> No, very good question, so look, I did something. We talk as an investor in the board with many companies, right? So one thing that says as an investor, if you come back and say, I want to create a next generation Docker or a computer, there's no way nobody's going to invest. So that we can motor off, even if you want to do object storage or a block storage, I mean, I've been an investor board member of so many storage companies, there's no way as an industry, I'll write a check for a compute or storage, right? If you want to create a next generation network, like either NetSuite, or restart Juniper, Cisco, there is no way. But if you come back and say, I want to create a next generation Viper for remote working environments, where AI is at the core, I'm interested in that, right? So if you look at how the packets are dropped, there's no intelligence in either not switching today. The packets come, I do it. The intelligence is not built into the network with AI level. So if somebody comes with an AI, what good is all this NVD, our GPS, et cetera, if you cannot do wire speed, packet inspection, looking at the content and then route the traffic. If I see if it's a video package, but in UN Boston, there's high interview day of they should be loading our package faster, because you are a premium ISP. That intelligence has not gone there. So you will see, and that will be a bad people will happen in the network, switching, et cetera, right? So that is still an angle. But if you work and it comes to platform services, remember when I was at Pivotal and VMware, all models was my boss, that would, yes, as a platform, service is a game already won by the Cloud guys. >> Right. (indistinct) >> Silicon Valley Investors, I don't think you want to invest in past services, right? I mean, you might come with some lecture edition database to do some updates, there could be some game, let's say we want to do a time series database, or some metrics database, there's always some small angle, but the opportunity to go create a national database there it's very few. So I'm kind of eliminating all the black spaces, right? >> Yeah. >> We have the white spaces that comes in is the SAS level. Now to your point, if I'm Amazon, I'm going to compete with Snowflake, I have Redshift. So this is where at some point, these Cloud platforms, I call them aircraft carriers. They're not going to stay on the aircraft carriers, they're going to own the land as well. So they're going to move up to the SAS space. The question is you want to create a SAS service like CRM. They are not going to create a CRM like service, they may not create a sales force and service now, but if you're going to add a data warehouse, I can very well see Azure, Google, and AWS, going to create something to compute a Snowflake. Why would I not? It's so close to my database and data warehouse, I already have Redshift. So that's going to be nightlights, same reason, If you look at Netflix, you have a Netflix and you have Amazon prime. Netflix runs on Amazon, but you have Amazon prime. So you have the same model, you have Snowflake, and you'll have Redshift. The both will help each other, there'll be a... What do you call it? Coexistence will happen. But if you really want to invest, you want to invest in SAS companies. You do not want to be investing in a compliment players. You don't want to a feature. >> Yeah, that's great, I appreciate that perspective. And I wonder, so obviously Microsoft play in SAS, Google's got G suite. And I wonder if people often ask the Andy Jassy, you're going to move up the stack, you got to be an application, a SAS vendor, and you never say never with Atavist, But I wonder, and we were talking to Jerry Chen about this, years ago on theCube, and his angle was that Amazon will play, but they'll play through developers. They'll enable developers, and they'll participate, they'll take their, lick off the cone. So it's going to be interesting to see how directly Amazon plays, but at some point you got Tam expansion, you got to play in that space. >> Yeah, I'll give you an example of knowing, I got acquired by a couple of times by EMC. So I learned a lot from Joe Tucci and Paul Merage over the years. see Paul and Joe, what they did is to look at how 20 years, and they are very close to Boston in your area, Joe, what games did is they used to sell storage, but you know what he did, he went and bought the Apps to drive them. He bought like Legato, he bought Documentum, he bought Captiva, if you remember how he acquired all these companies as a services, he bought VMware to drive that. So I think the good angle that Microsoft has is, I'm a SAS player, I have dynamics, I have CRM, I have SharePoint, I have Collaboration, I have Office 365, MS Teams for users, and then I have the platform as Azure. So I think if I'm Amazon, (indistinct). I got to own the apps so that I can drive this workforce on my platform. >> Interesting. >> Just going to developers, like I know Jerry Chan, he was my peer a BMF. I don't think just literally to developers and that model works in open source, but the open source game is pretty much gone, and not too many companies made money. >> Well, >> Most companies pretty much gone. >> Yeah, he's right. Red hats not bad idea. But it's very interesting what you're saying there. And so, hey, its why Oracle wants to have Tiktok, running on their platform, right? I mean, it's going to. (laughing) It's going to drive that further integration. I wanted to ask you something, you were talking about, you wouldn't invest in storage or compute, but I wonder, and you mentioned some commentary about GPU's. Of course the videos has been going crazy, but they're now saying, okay, how do we expand our Team, they make the acquisition of arm, et cetera. What about this DPU thing, if you follow that, that data processing unit where they're like hyper dis-aggregation and then they reaggregate, and as an offload and really to drive data centric workloads. Have you looked at that at all? >> I did, I think, and that's a good angle. So I think, look, it's like, it goes through it. I don't know if you remember in your career, we have seen it. I used to get Silicon graphics. I saw the first graphic GPU, right? That time GPU was more graphic processor unit, >> Right, yeah, work stations. >> So then become NPUs at work processing units, right? There was a TCP/IP office offloading, if you remember right, there was like vector processing unit. So I think every once in a while the industry, recreated this separate unit, as a co-processor to the main CPU, because main CPU's inefficient, and it makes sense. And then Google created TPU's and then we have the new world of the media GPU's, now we have DPS all these are good, but what's happening is, all these are driving for machine learning, AI for the training period there. Training period Sometimes it's so long with the workloads, if you can cut down, it makes sense. >> Yeah. >> Because, but the question is, these aren't so specialized in nature. I can't use it for everything. >> Yup. >> I want Ideally, algorithms to be paralyzed, I want the training to be paralyzed, I want so having deep use and GPS are important, I think where I want to see them as more, the algorithm, there should be more investment from the NVIDIA's and these guys, taking the algorithm to be highly paralyzed them. (indistinct) And I think that still has not happened in industry yet. >> All right, so we're pretty much out of time, but what are you doing these days? Where are you spending your time, are you still in Stealth, give us a little glimpse. >> Yeah, no, I'm out of the Stealth, I'm actually the CEO of Aisera now, Aisera, obviously I invested with them, but I'm the CEO of Aisero. It's funded by Menlo ventures, Norwest, True, along with Khosla ventures and Ram Shriram is a big investor. Robin's on the board of Google, so these guys, look, we are going out to the collaboration game. How do you automate customer service and support for employees and then users, right? In this whole game, we talked about the Zoom, Slack and MS Teams, that's what I'm spending time, I want to create next generation service now. >> Fantastic. Muddu, I always love having you on you, pull punches, you tell it like it is, that you're a great visionary technologist. Thanks so much for coming on theCube, and participating in our program. >> Dave, it's always a pleasure speaking to you sir. Thank you. >> Okay. Keep it right there, there's more coming from Cuba and Cloud right after this break. (slow music)

>> From theCUBE Studios in Palo Alto in Boston, bringing you data driven insights from theCUBE and ETR. This is breaking analysis with Dave Vellante. >> In June of this year, Snowflake filed a confidential document suggesting that it would do an IPO. Now of course, everybody knows about it, found out about it and it had a $20 billion valuation. So, many in the community and the investment community and so forth are excited about this IPO. It could be the hottest one of the year, and we're getting a number of questions from investors and practitioners and the entire Wiki bond, ETR and CUBE community. So, welcome everybody. This is Dave Vellante. This is "CUBE Insights" powered by ETR. In this breaking analysis, we're going to unpack five critical questions around Snowflake's IPO or pending IPO. And with me to discuss that is Erik Bradley. He's the Chief Engagement Strategists at ETR and he's also the Managing Director of VENN. Erik, thanks for coming on and great to see you as always. >> Great to see you too. Always enjoy being on the show. Thank you. >> Now for those of you don't know Erik, VENN is a roundtable that he hosts and he brings in CIOs, IT practitioners, CSOs, data experts and they have an open and frank conversation, but it's private to ETR clients. But they know who the individual is, what their role is, what their title is, et cetera and it's a kind of an ask me anything. And I participated in one of them this past week. Outstanding. And we're going to share with you some of that. But let's bring up the agenda slide if we can here. And these are really some of the questions that we're getting from investors and others in the community. There's really five areas that we want to address. The first is what's happening in this enterprise data warehouse marketplace? The second thing is kind of a one area. What about the legacy EDW players like Oracle and Teradata and Netezza? The third question we get a lot is can Snowflake compete with the big cloud players? Amazon, Google, Microsoft. I mean they're right there in the heart, in the thick of things there. And then what about that multi-cloud strategy? Is that viable? How much of a differentiator is that? And then we get a lot of questions on the TAM. Meaning the total available market. How big is that market? Does it justify the valuation for Snowflake? Now, Erik, you've been doing this now. You've run a couple VENNs, you've been following this, you've done some other work that you've done with Eagle Alpha. What's your, just your initial sort of takeaway from all this work that you've been doing. >> Yeah, sure. So my first take on Snowflake was about two and a half years ago. I actually hosted them for one of my VENN interviews and my initial thought was impressed. So impressed. They were talking at the time about their ability to kind of make ease of use of a multi-cloud strategy. At the time although I was impressed, I did not expect the growth and the hyper growth that we have seen now. But, looking at the company in its current iteration, I understand where the hype is coming from. I mean, it's 12 and a half billion private valuation in the last round. The least confidential IPO (laughs) anyone's ever seen (Dave laughs) with a 15 to $20 billion valuation coming out, which is more than Teradata, Margo and Cloudera combined. It's a great question. So obviously the success to this point is warranted, but we need to see what they're going to be able to do next. So I think the agenda you laid out is a great one and I'm looking forward to getting into some of those details. >> So let's start with what's happening in the marketplace and let's pull up a slide that I very much love to use. It's the classic X-Y. On the vertical axis here we show net score. And remember folks, net score is an indicator of spending momentum. ETR every quarter does like a clockwork survey where they're asking people, "Essentially are you spending more or less?" They subtract the less from the more and comes up with a net score. It's more complicated than, but like NPS, it's a very simple and reliable methodology. That's the vertical axis. And the horizontal axis is what's called market share. Market share is the pervasiveness within the data set. So it's calculated by the number of mentions of the vendor divided by the number of mentions within that sector. And what we're showing here is the EDW sector. And we've pulled out a few companies that I want to talk about. So the big three, obviously Microsoft, AWS and Google. And you can see Microsoft has a huge presence far to the right. AWS, very, very strong. A lot of Redshift in there. And then they're pretty high on the vertical axis. And then Google, not as much share, but very solid in that. Close to 60% net score. And then you can see above all of them from a vertical standpoint is Snowflake with a 77.5% net score. You can see them in the upper right there in the green. One of the highest Erik in the entire data set. So, let's start with some sort of initial comments on the big guys and Snowflakes. Your thoughts? >> Sure. Just first of all to comment on the data, what we're showing there is just the data warehousing sector, but Snowflake's actual net score is that high amongst the entire universe that we follow. Their data strength is unprecedented and we have forward-looking spending intention. So this bodes very well for them. Now, what you did say very accurately is there's a difference between their spending intentions on a net revenue level compared to AWS, Microsoft. There no one's saying that this is an apples-to-apples comparison when it comes to actual revenue. So we have to be very cognizant of that. There is domination (laughs) quite frankly from AWS and from Azure. And Snowflake is a necessary component for them not only to help facilitate a multi-cloud, but look what's happening right now in the US Congress, right? We have these tech leaders being grilled on their actual dominance. And one of the main concerns they have is the amount of data that they're collecting. So I think the environment is right to have another player like this. I think Snowflake really has a lot of longevity and our data is supporting that. And the commentary that we hear from our end users, the people that take the survey are supporting that as well. >> Okay, and then let's stay on this X-Y slide for a moment. I want to just pull out a couple of other comments here, because one of the questions we're asking is Whither, the legacy EDW players. So we've got in here, IBM, Oracle, you can see Teradata and then Hortonworks and MapR. We're going to talk a little bit about Hortonworks 'cause it's now Cloudera. We're going to talk a little bit about Hadoop and some of the data lakes. So you can see there they don't have nearly the net score momentum. Oracle obviously has a huge install base and is investing quite frankly in R&D and do an Exadata and it has its own cloud. So, it's got a lock on it's customers and if it keeps investing and adding value, it's not going away. IBM with Netezza, there's really been some questions around their commitment to that base. And I know that a lot of the folks in the VENNs that we've talked to Erik have said, "Well, we're replacing Netezza." Frank Slootman has been very vocal about going after Teradata. And then we're going to talk a little bit about the Hadoop space. But, can you summarize for us your thoughts in your research and the commentary from your community, what's going on with the legacy guys? Are these guys cooked? Can they hang on? What's your take? >> Sure. We focus on this quite a bit actually. So, I'm going to talk about it from the data perspective first, and then we'll go into some of the commentary and the panel. You even joined one yesterday. You know that it was touched upon. But, first on the data side, what we're noticing and capturing is a widening bifurcation between these cloud native and the legacy on-prem. It is undeniable. There is nothing that you can really refute. The data is concrete and it is getting worse. That gap is getting wider and wider and wider. Now, the one thing I will say is, nobody's going to rip out their legacy applications tomorrow. It takes years and years. So when you look at Teradata, right? Their market cap's only 2 billion, 2.3 billion. How much revenue growth do they need to stay where they are? Not much, right? No one's expecting them to grow 20%, which is what you're seeing on the left side of that screen. So when you look at the legacy versus the cloud native, there is very clear direction of what's happening. The one thing I would note from the data perspective is if you switched from net score or adoptions and you went to flat spending, you suddenly see Oracle and Teradata move over to that left a little bit, because again what I'm trying to say is I don't think they're going to catch up. No, but also don't think they're going away tomorrow. That these have large install bases, they have relationships. Now to kind of get into what you were saying about each particular one, IBM, they shut down Netezza. They shut it down and then they brought it back to life. How does that make you feel if you're the head of data architecture or you're DevOps and you're trying to build an application for a large company? I'm not going back to that. There's absolutely no way. Teradata on the other hand is known to be incredibly stable. They are known to just not fail. If you need to kind of re-architect or you do a migration, they work. Teradata also has a lot of compliance built in. So if you're a financials, if you have a regulated business or industry, there's still some data sets that you're not going to move up to the cloud. Whether it's a PII compliance or financial reasons, some of that stuff is still going to live on-prem. So Teradata is still has a very good niche. And from what we're hearing from our panels, then this is a direct quote if you don't mind me looking off screen for one second. But this is a great one. Basically said, "Teradata is the only one from the legacy camp who is putting up a fight and not giving up." Basically from a CIO perspective, the rest of them aren't an option anymore. But Teradata is still fighting and that's great to hear. They have their own data as a service offering and listen, they're a small market cap compared to these other companies we're talking about. But, to summarize, the data is very clear. There is a widening bifurcation between the two camps. I do not think legacy will catch up. I think all net new workloads are moving to data as a service, moving to cloud native, moving to hosted, but there are still going to be some existing legacy on-prem applications that will be supported with these older databases. And of those, Oracle and Teradata are still viable options. >> I totally agree with you and my colleague David Floyd is actually quite high on Teradata Vantage because he really does believe that a key component, we're going to talk about the TAM in a minute, but a key component of the TAM he believes must include the on-premises workloads. And Frank Slootman has been very clear, "We're not doing on-prem, we're not doing this halfway house." And so that's an opportunity for companies like Teradata, certainly Oracle I would put it in that camp is putting up a fight. Vertica is another one. They're very small, but another one that's sort of battling it out from the old NPP world. But that's great. Let's go into some of the specifics. Let's bring up here some of the specific commentary that we've curated here from the roundtables. I'm going to go through these and then ask you to comment. The first one is just, I mean, people are obviously very excited about Snowflake. It's easy to use, the whole thing zero to Snowflake in 90 minutes, but Snowflake is synonymous with cloud-native data warehousing. There are no equals. We heard that a lot from your VENN panelist. >> We certainly did. There was even more euphoria around Snowflake than I expected when we started hosting these series of data warehousing panels. And this particular gentleman that said that happens to be the global head of data architecture for a fortune 100 financials company. And you mentioned earlier that we did a report alongside Eagle Alpha. And we noticed that among fortune 100 companies that are also using the big three public cloud companies, Snowflake is growing market share faster than anyone else. They are positioned in a way where even if you're aligned with Azure, even if you're aligned with AWS, if you're a large company, they are gaining share right now. So that particular gentleman's comments was very interesting. He also made a comment that said, "Snowflake is the person who championed the idea that data warehousing is not dead yet. Use that old monthly Python line and you're not dead yet." And back in the day where the Hadoop came along and the data lakes turned into a data swamp and everyone said, "We don't need warehousing anymore." Well, that turned out to be a head fake, right? Hadoop was an interesting technology, but it's a complex technology. And it ended up not really working the way people want it. I think Snowflake came in at that point at an opportune time and said, "No, data warehousing isn't dead. We just have to separate the compute from the storage layer and look at what I can do. That increases flexibility, security. It gives you that ability to run across multi-cloud." So honestly the commentary has been nothing but positive. We can get into some of the commentary about people thinking that there's competition catching up to what they do, but there is no doubt that right now Snowflake is the name when it comes to data as a service. >> The other thing we heard a lot was ETL is going to get completely disrupted, you sort of embedded ETL. You heard one panelist say, "Well, it's interesting to see that guys like Informatica are talking about how fast they can run inside a Snowflake." But Snowflake is making that easy. That data prep is sort of part of the package. And so that does not bode well for ETL vendors. >> It does not, right? So ETL is a legacy of on-prem databases and even when Hadoop came along, it still needed that extra layer to kind of work with the data. But this is really, really disrupting them. Now the Snowflake's credit, they partner well. All the ETL players are partnered with Snowflake, they're trying to play nice with them, but the writings on the wall as more and more of this application and workloads move to the cloud, you don't need the ETL layer. Now, obviously that's going to affect their talent and Informatica the most. We had a recent comment that said, this was a CIO who basically said, "The most telling thing about the ETL players right now is every time you speak to them, all they talk about is how they work in a Snowflake architecture." That's their only metric that they talk about right now. And he said, "That's very telling." That he basically used it as it's their existential identity to be part of Snowflake. If they're not, they don't exist anymore. So it was interesting to have sort of a philosophical comment brought up in one of my roundtables. But that's how important playing nice and finding a niche within this new data as a service is for ETL, but to be quite honest, they might be going the same way of, "Okay, let's figure out our niche on these still the on-prem workloads that are still there." I think over time we might see them maybe as an M&A possibility, whether it's Snowflake or one of these new up and comers, kind of bring them in and sort of take some of the technology that's useful and layer it in. But as a large market cap, solo existing niche, I just don't know how long ETL is for this world. >> Now, yeah. I mean, you're right that if it wasn't for the marketing, they're not fighting fashion. But >> No. >> really there're some challenges there. Now, there were some contrarians in the panel and they signaled some potential icebergs ahead. And I guarantee you're going to see this in Snowflake's Red Herring when we actually get it. Like we're going to see all the risks. One of the comments, I'll mention the two and then we can talk about it. "Their engineering advantage will fade over time." Essentially we're saying that people are going to copycat and we've seen that. And the other point is, "Hey, we might see some similar things that happened to Hadoop." The public cloud players giving away these offerings at zero cost. Essentially marginal cost of adding another service is near zero. So the cloud players will use their heft to compete. Your thoughts? >> Yeah, first of all one of the reasons I love doing panels, right? Because we had three gentlemen on this panel that all had nothing but wonderful things to say. But you always get one. And this particular person is a CTO of a well known online public travel agency. We'll put it that way. And he said, "I'm going to be the contrarian here. I have seven different technologies from private companies that do the same thing that I'm evaluating." So that's the pressure from behind, right? The technology, they're going to catch up. Right now Snowflake has the best engineering which interestingly enough they took a lot of that engineering from IBM and Teradata if you actually go back and look at it, which was brought up in our panel as well. He said, "However, the engineering will catch up. They always do." Now from the other side they're getting squeezed because the big cloud players just say, "Hey, we can do this too. I can bundle it with all the other services I'm giving you and I can squeeze your pay. Pretty much give it a waive at the cost." So I do think that there is a very valid concern. When you come out with a $20 billion IPO evaluation, you need to warrant that. And when you see competitive pressures from both sides, from private emerging technologies and from the more dominant public cloud players, you're going to get squeezed there a little bit. And if pricing gets squeezed, it's going to be very, very important for Snowflake to continue to innovate. That comment you brought up about possibly being the next Cloudera was certainly the best sound bite that I got. And I'm going to use it as Clickbait in future articles, because I think everyone who starts looking to buy a Snowflake stock and they see that, they're going to need to take a look. But I would take that with a grain of salt. I don't think that's happening anytime soon, but what that particular CTO was referring to was if you don't innovate, the technology itself will become commoditized. And he believes that this technology will become commoditized. So therefore Snowflake has to continue to innovate. They have to find other layers to bring in. Whether that's through their massive war chest of cash they're about to have and M&A, whether that's them buying analytics company, whether that's them buying an ETL layer, finding a way to provide more value as they move forward is going to be very important for them to justify this valuation going forward. >> And I want to comment on that. The Cloudera, Hortonworks, MapRs, Hadoop, et cetera. I mean, there are dramatic differences obviously. I mean, that whole space was so hard, very difficult to stand up. You needed science project guys and lab coats to do it. It was very services intensive. As well companies like Cloudera had to fund all these open source projects and it really squeezed their R&D. I think Snowflake is much more focused and you mentioned some of the background of their engineers, of course Oracle guys as well. However, you will see Amazon's going to trot out a ton of customers using their RA3 managed storage and their flash. I think it's the DC two piece. They have a ton of action in the marketplace because it's just so easy. It's interesting one of the comments, you asked this yesterday, was with regard to separating compute from storage, which of course it's Snowflakes they basically invented it, it was one of their climbs to fame. The comment was what AWS has done to separate compute from storage for Redshift is largely a bolt on. Which I thought that was an interesting comment. I've had some other comments. My friend George Gilbert said, "Hey, despite claims to the contrary, AWS still hasn't separated storage from compute. What they have is really primitive." We got to dig into that some more, but you're seeing some data points that suggest there's copycatting going on. May not be as functional, but at the same time, Erik, like I was saying good enough is maybe good enough in this space. >> Yeah, and especially with the enterprise, right? You see what Microsoft has done. Their technology is not as good as all the niche players, but it's good enough and I already have a Microsoft license. So, (laughs) you know why am I going to move off of it. But I want to get back to the comment you mentioned too about that particular gentleman who made that comment about RedShift, their separation is really more of a bolt on than a true offering. It's interesting because I know who these people are behind the scenes and he has a very strong relationship with AWS. So it was interesting to me that in the panel yesterday he said he switched from Redshift to Snowflake because of that and some other functionality issues. So there is no doubt from the end users that are buying this. And he's again a fortune 100 financial organization. Not the same one we mentioned. That's a different one. But again, a fortune 100 well known financials organization. He switched from AWS to Snowflake. So there is no doubt that right now they have the technological lead. And when you look at our ETR data platform, we have that adoption reasoning slide that you show. When you look at the number one reason that people are adopting Snowflake is their feature set of technological lead. They have that lead now. They have to maintain it. Now, another thing to bring up on this to think about is when you have large data sets like this, and as we're moving forward, you need to have machine learning capabilities layered into it, right? So they need to make sure that they're playing nicely with that. And now you could go open source with the Apache suite, but Google is doing so well with BigQuery and so well with their machine learning aspects. And although they don't speak enterprise well, they don't sell to the enterprise well, that's changing. I think they're somebody to really keep an eye on because their machine learning capabilities that are layered into the BigQuery are impressive. Now, of course, Microsoft Azure has Databricks. They're layering that in, but this is an area where I think you're going to see maybe what's next. You have to have machine learning capabilities out of the box if you're going to do data as a service. Right now Snowflake doesn't really have that. Some of the other ones do. So I had one of my guest panelist basically say to me, because of that, they ended up going with Google BigQuery because he was able to run a machine learning algorithm within hours of getting set up. Within hours. And he said that that kind of capability out of the box is what people are going to have to use going forward. So that's another thing we should dive into a little bit more. >> Let's get into that right now. Let's bring up the next slide which shows net score. Remember this is spending momentum across the major cloud players and plus Snowflake. So you've got Snowflake on the left, Google, AWS and Microsoft. And it's showing three survey timeframes last October, April 20, which is right in the middle of the pandemic. And then the most recent survey which has just taken place this month in July. And you can see Snowflake very, very high scores. Actually improving from the last October survey. Google, lower net scores, but still very strong. Want to come back to that and pick up on your comments. AWS dipping a little bit. I think what's happening here, we saw this yesterday with AWS's results. 30% growth. Awesome. Slight miss on the revenue side for AWS, but look, I mean massive. And they're so exposed to so many industries. So some of their industries have been pretty hard hit. Microsoft pretty interesting. A little softness there. But one of the things I wanted to pick up on Erik, when you're talking about Google and BigQuery and it's ML out of the box was what we heard from a lot of the VENN participants. There's no question about it that Google technically I would say is one of Snowflake's biggest competitors because it's cloud native. Remember >> Yep. >> AWS did a license one time. License deal with PowerShell and had a sort of refactor the thing to be cloud native. And of course we know what's happening with Microsoft. They basically were on-prem and then they put stuff in the cloud and then all the updates happen in the cloud. And then they pushed to on-prem. But they have that what Frank Slootman calls that halfway house, but BigQuery no question technically is very, very solid. But again, you see Snowflake right now anyway outpacing these guys in terms of momentum. >> Snowflake is out outpacing everyone (laughs) across our entire survey universe. It really is impressive to see. And one of the things that they have going for them is they can connect all three. It's that multi-cloud ability, right? That portability that they bring to you is such an important piece for today's modern CIO as data architects. They don't want vendor lock-in. They are afraid of vendor lock-in. And this ability to make their data portable and to do that with ease and the flexibility that they offer is a huge advantage right now. However, I think you're a hundred percent right. Google has been so focused on the engineering side and never really focusing on the enterprise sales side. That is why they're playing catch up. I think they can catch up. They're bringing in some really important enterprise salespeople with experience. They're starting to learn how to talk to enterprise, how to sell, how to support. And nobody can really doubt their engineering. How many open sources have they given us, right? They invented Kubernetes and the entire container space. No one's really going to compete with them on that side if they learn how to sell it and support it. Yeah, right now they're behind. They're a distant third. Don't get me wrong. From a pure hosted ability, AWS is number one. Microsoft is yours. Sometimes it looks like it's number one, but you have to recognize that a lot of that is because of simply they're hosted 365. It's a SAS app. It's not a true cloud type of infrastructure as a service. But Google is a distant third, but their technology is really, really great. And their ability to catch up is there. And like you said, in the panels we were hearing a lot about their machine learning capability is right out of the box. And that's where this is going. What's the point of having this huge data if you're not going to be supporting it on new application architecture. And all of those applications require machine learning. >> Awesome. So we're. And I totally agree with what you're saying about Google. They just don't have it figured out how to sell the enterprise yet. And a hundred percent AWS has the best cloud. I mean, hands down. But a very, very competitive market as we heard yesterday in front of Congress. Now we're on the point about, can Snowflake compete with the big cloud players? I want to show one more data point. So let's bring up, this is the same chart as we showed before, but it's new adoptions. And this is really telling. >> Yeah. >> You can see Snowflake with 34% in the yellow, new adoptions, down yes from previous surveys, but still significantly higher than the other players. Interesting to see Google showing momentum on new adoptions, AWS down on new adoptions. And again, exposed to a lot of industries that have been hard hit. And Microsoft actually quite low on new adoption. So this is very impressive for Snowflake. And I want to talk about the multi-cloud strategy now Erik. This came up a lot. The VENN participants who are sort of fans of Snowflake said three things: It was really the flexibility, the security which is really interesting to me. And a lot of that had to do with the flexibility. The ability to easily set up roles and not have to waste a lot of time wrangling. And then the third was multi-cloud. And that was really something that came through heavily in the VENN. Didn't it? >> It really did. And again, I think it just comes down to, I don't think you can ever overstate how afraid these guys are of vendor lock-in. They can't have it. They don't want it. And it's best practice to make sure your sensitive information is being kind of spread out a little bit. We all know that people don't trust Bezos. So if you're in certain industries, you're not going to use AWS at all, right? So yeah, this ability to have your data portability through multi-cloud is the number one reason I think people start looking at Snowflake. And to go to your point about the adoptions, it's very telling and it bodes well for them going forward. Most of the things that we're seeing right now are net new workloads. So let's go again back to the legacy side that we were talking about, the Teradatas, IBMs, Oracles. They still have the monolithic applications and the data that needs to support that, right? Like an old ERP type of thing. But anyone who's now building a new application, bringing something new to market, it's all net new workloads. There is no net new workload that is going to go to SAP or IBM. It's not going to happen. The net new workloads are going to the cloud. And that's why when you switch from net score to adoption, you see Snowflake really stand out because this is about new adoption for net new workloads. And that's really where they're driving everything. So I would just say that as this continues, as data as a service continues, I think Snowflake's only going to gain more and more share for all the reasons you stated. Now get back to your comment about security. I was shocked by that. I really was. I did not expect these guys to say, "Oh, no. Snowflake enterprise security not a concern." So two panels ago, a gentleman from a fortune 100 financials said, "Listen, it's very difficult to get us to sign off on something for security. Snowflake is past it, it is enterprise ready, and we are going full steam ahead." Once they got that go ahead, there was no turning back. We gave it to our DevOps guys, we gave it to everyone and said, "Run with it." So, when a company that's big, I believe their fortune rank is 28. (laughs) So when a company that big says, "Yeah, you've got the green light. That we were okay with the internal compliance aspect, we're okay with the security aspect, this gives us multi-cloud portability, this gives us flexibility, ease of use." Honestly there's a really long runway ahead for Snowflake. >> Yeah, so the big question I have around the multi-cloud piece and I totally and I've been on record saying, "Look, if you're going looking for an agnostic multi-cloud, you're probably not going to go with the cloud vendor." (laughs) But I've also said that I think multi-cloud to date anyway has largely been a symptom as opposed to a strategy, but that's changing. But to your point about lock-in and also I think people are maybe looking at doing things across clouds, but I think that certainly it expands Snowflake's TAM and we're going to talk about that because they support multiple clouds and they're going to be the best at that. That's a mandate for them. The question I have is how much of complex joining are you going to be doing across clouds? And is that something that is just going to be too latency intensive? Is that really Snowflake's expertise? You're really trying to build that data layer. You're probably going to maybe use some kind of Postgres database for that. >> Right. >> I don't know. I need to dig into that, but that would be an opportunity from a TAM standpoint. I just don't know how real that is. >> Yeah, unfortunately I'm going to just be honest with this one. I don't think I have great expertise there and I wouldn't want to lead anyone a wrong direction. But from what I've heard from some of my VENN interview subjects, this is happening. So the data portability needs to be agnostic to the cloud. I do think that when you're saying, are there going to be real complex kind of workloads and applications? Yes, the answer is yes. And I think a lot of that has to do with some of the container architecture as well, right? If I can just pull data from one spot, spin it up for as long as I need and then just get rid of that container, that ethereal layer of compute. It doesn't matter where the cloud lies. It really doesn't. I do think that multi-cloud is the way of the future. I know that the container workloads right now in the enterprise are still very small. I've heard people say like, "Yeah, I'm kicking the tires. We got 5%." That's going to grow. And if Snowflake can make themselves an integral part of that, then yes. I think that's one of those things where, I remember the guy said, "Snowflake has to continue to innovate. They have to find a way to grow this TAM." This is an area where they can do so. I think you're right about that, but as far as my expertise, on this one I'm going to be honest with you and say, I don't want to answer incorrectly. So you and I need to dig in a little bit on this one. >> Yeah, as it relates to question four, what's the viability of Snowflake's multi-cloud strategy? I'll say unquestionably supporting multiple clouds, very viable. Whether or not portability across clouds, multi-cloud joins, et cetera, TBD. So we'll keep digging into that. The last thing I want to focus on here is the last question, does Snowflake's TAM justify its $20 billion valuation? And you think about the data pipeline. You go from data acquisition to data prep. I mean, that really is where Snowflake shines. And then of course there's analysis. You've got to bring in EMI or AI and ML tools. That's not Snowflake's strength. And then you're obviously preparing that, serving that up to the business, visualization. So there's potential adjacencies that they could get into that they may or may not decide to. But so we put together this next chart which is kind of the TAM expansion opportunity. And I just want to briefly go through it. We published this stuff so you can go and look at all the fine print, but it's kind of starts with the data lake disruption. You called it data swamp before. The Hadoop no schema on, right? Basically the ROI of Hadoop became reduction of investment as my friend Abby Meadow would say. But so they're kind of disrupting that data lake which really was a failure. And then really going after that enterprise data warehouse which is kind of I have it here as a 10 billion. It's actually bigger than that. It's probably more like a $20 billion market. I'll update this slide. And then really what Snowflake is trying to do is be data as a service. A data layer across data stores, across clouds, really make it easy to ingest and prepare data and then serve the business with insights. And then ultimately this huge TAM around automated decision making, real-time analytics, automated business processes. I mean, that is potentially an enormous market. We got a couple of hundred billion. I mean, just huge. Your thoughts on their TAM? >> I agree. I'm not worried about their TAM and one of the reasons why as I mentioned before, they are coming out with a whole lot of cash. (laughs) This is going to be a red hot IPO. They are going to have a lot of money to spend. And look at their management team. Who is leading the way? A very successful, wise, intelligent, acquisitive type of CEO. I think there is going to be M&A activity, and I believe that M&A activity is going to be 100% for the mindset of growing their TAM. The entire world is moving to data as a service. So let's take as a backdrop. I'm going to go back to the panel we did yesterday. The first question we asked was, there was an understanding or a theory that when the virus pandemic hit, people wouldn't be taking on any sort of net new architecture. They're like, "Okay, I have Teradata, I have IBM. Let's just make sure the lights are on. Let's stick with it." Every single person I've asked, they're just now eight different experts, said to us, "Oh, no. Oh, no, no." There is the virus pandemic, the shift from work from home. Everything we're seeing right now has only accelerated and advanced our data as a service strategy in the cloud. We are building for scale, adopting cloud for data initiatives. So, across the board they have a great backdrop. So that's going to only continue, right? This is very new. We're in the early innings of this. So for their TAM, that's great because that's the core of what they do. Now on top of it you mentioned the type of things about, yeah, right now they don't have great machine learning. That could easily be acquired and built in. Right now they don't have an analytics layer. I for one would love to see these guys talk to Alteryx. Alteryx is red hot. We're seeing great data and great feedback on them. If they could do that business intelligence, that analytics layer on top of it, the entire suite as a service, I mean, come on. (laughs) Their TAM is expanding in my opinion. >> Yeah, your point about their leadership is right on. And I interviewed Frank Slootman right in the heart of the pandemic >> So impressed. >> and he said, "I'm investing in engineering almost sight unseen. More circumspect around sales." But I will caution people. That a lot of people I think see what Slootman did with ServiceNow. And he came into ServiceNow. I have to tell you. It was they didn't have their unit economics right, they didn't have their sales model and marketing model. He cleaned that up. Took it from 120 million to 1.2 billion and really did an amazing job. People are looking for a repeat here. This is a totally different situation. ServiceNow drove a truck through BMCs install base and with IT help desk and then created this brilliant TAM expansion. Let's learn and expand model. This is much different here. And Slootman also told me that he's a situational CEO. He doesn't have a playbook. And so that's what is most impressive and interesting about this. He's now up against the biggest competitors in the world: AWS, Google and Microsoft and dozens of other smaller startups that have raised a lot of money. Look at the company like Yellowbrick. They've raised I don't know $180 million. They've got a great team. Google, IBM, et cetera. So it's going to be really, really fun to watch. I'm super excited, Erik, but I'll tell you the data right now suggest they've got a great tailwind and if they can continue to execute, this is going to be really fun to watch. >> Yeah, certainly. I mean, when you come out and you are as impressive as Snowflake is, you get a target on your back. There's no doubt about it, right? So we said that they basically created the data as a service. That's going to invite competition. There's no doubt about it. And Yellowbrick is one that came up in the panel yesterday about one of our CIOs were doing a proof of concept with them. We had about seven others mentioned as well that are startups that are in this space. However, none of them despite their great valuation and their great funding are going to have the kind of money and the market lead that Slootman is going to have which Snowflake has as this comes out. And what we're seeing in Congress right now with some antitrust scrutiny around the large data that's being collected by AWS as your Google, I'm not going to bet against this guy either. Right now I think he's got a lot of opportunity, there's a lot of additional layers and because he can basically develop this as a suite service, I think there's a lot of great opportunity ahead for this company. >> Yeah, and I guarantee that he understands well that customer acquisition cost and the lifetime value of the customer, the retention rates. Those are all things that he and Mike Scarpelli, his CFO learned at ServiceNow. Not learned, perfected. (Erik laughs) Well Erik, really great conversation, awesome data. It's always a pleasure having you on. Thank you so much, my friend. I really appreciate it. >> I appreciate talking to you too. We'll do it again soon. And stay safe everyone out there. >> All right, and thank you for watching everybody this episode of "CUBE Insights" powered by ETR. This is Dave Vellante, and we'll see you next time. (soft music)

(upbeat music) >> Narrator: It's theCUBE, covering the Virtual Vertica Big Data Conference 2020. Brought to you by Vertica. >> Dave Vellante: Hello everyone, and welcome to theCUBE's exclusive coverage of the Vertica Virtual Big Data Conference. You're watching theCUBE, the leader in digital event tech coverage. And we're broadcasting remotely from our studios in Palo Alto and Boston. And, we're pleased to be covering wall-to-wall this digital event. Now, as you know, originally BDC was scheduled this week at the new Encore Hotel and Casino in Boston. Their theme was "Win big with big data". Oh sorry, "Win big with data". That's right, got it. And, I know the community was really looking forward to that, you know, meet up. But look, we're making the best of it, given these uncertain times. We wish you and your families good health and safety. And this is the way that we're going to broadcast for the next several months. Now, we want to unpack Colin Mahony's keynote, but, before we do that, I want to give a little context on the market. First, theCUBE has covered every BDC since its inception, since the BDC's inception that is. It's a very intimate event, with a heavy emphasis on user content. Now, historically, the data engineers and DBAs in the Vertica community, they comprised the majority of the content at this event. And, that's going to be the same for this virtual, or digital, production. Now, theCUBE is going to be broadcasting for two days. What we're doing, is we're going to be concurrent with the Virtual BDC. We got practitioners that are coming on the show, DBAs, data engineers, database gurus, we got a security experts coming on, and really a great line up. And, of course, we'll also be hearing from Vertica Execs, Colin Mahony himself right of the keynote, folks from product marketing, partners, and a number of experts, including some from Micro Focus, which is the, of course, owner of Vertica. But I want to take a moment to share a little bit about the history of Vertica. The company, as you know, was founded by Michael Stonebraker. And, Verica started, really they started out as a SQL platform for analytics. It was the first, or at least one of the first, to really nail the MPP column store trend. Not only did Vertica have an early mover advantage in MPP, but the efficiency and scale of its software, relative to traditional DBMS, and also other MPP players, is underscored by the fact that Vertica, and the Vertica brand, really thrives to this day. But, I have to tell you, it wasn't without some pain. And, I'll talk a little bit about that, and really talk about how we got here today. So first, you know, you think about traditional transaction databases, like Oracle or IMBDB tour, or even enterprise data warehouse platforms like Teradata. They were simply not purpose-built for big data. Vertica was. Along with a whole bunch of other players, like Netezza, which was bought by IBM, Aster Data, which is now Teradata, Actian, ParAccel, which was the basis for Redshift, Amazon's Redshift, Greenplum was bought, in the early days, by EMC. And, these companies were really designed to run as massively parallel systems that smoked traditional RDBMS and EDW for particular analytic applications. You know, back in the big data days, I often joked that, like an NFL draft, there was run on MPP players, like when you see a run on polling guards. You know, once one goes, they all start to fall. And that's what you saw with the MPP columnar stores, IBM, EMC, and then HP getting into the game. So, it was like 2011, and Leo Apotheker, he was the new CEO of HP. Frankly, he has no clue, in my opinion, with what to do with Vertica, and totally missed one the biggest trends of the last decade, the data trend, the big data trend. HP picked up Vertica for a song, it wasn't disclosed, but my guess is that it was around 200 million. So, rather than build a bunch of smart tokens around Vertica, which I always call the diamond in the rough, Apotheker basically permanently altered HP for years. He kind of ruined HP, in my view, with a 12 billion dollar purchase of Autonomy, which turned out to be one of the biggest disasters in recent M&A history. HP was forced to spin merge, and ended up selling most of its software to Microsoft, Micro Focus. (laughs) Luckily, during its time at HP, CEO Meg Whitman, largely was distracted with what to do with the mess that she inherited form Apotheker. So, Vertica was left alone. Now, the upshot is Colin Mahony, who was then the GM of Vertica, and still is. By the way, he's really the CEO, and he just doesn't have the title, I actually think they should give that to him. But anyway, he's been at the helm the whole time. And Colin, as you'll see in our interview, is a rockstar, he's got technical and business jobs, people love him in the community. Vertica's culture is really engineering driven and they're all about data. Despite the fact that Vertica is a 15-year-old company, they've really kept pace, and not been polluted by legacy baggage. Vertica, early on, embraced Hadoop and the whole open-source movement. And that helped give it tailwinds. It leaned heavily into cloud, as we're going to talk about further this week. And they got a good story around machine intelligence and AI. So, whereas many traditional database players are really getting hurt, and some are getting killed, by cloud database providers, Vertica's actually doing a pretty good job of servicing its install base, and is in a reasonable position to compete for new workloads. On its last earnings call, the Micro Focus CFO, Stephen Murdoch, he said they're investing 70 to 80 million dollars in two key growth areas, security and Vertica. Now, Micro Focus is running its Suse play on these two parts of its business. What I mean by that, is they're investing and allowing them to be semi-autonomous, spending on R&D and go to market. And, they have no hardware agenda, unlike when Vertica was part of HP, or HPE, I guess HP, before the spin out. Now, let me come back to the big trend in the market today. And there's something going on around analytic databases in the cloud. You've got companies like Snowflake and AWS with Redshift, as we've reported numerous times, and they're doing quite well, they're gaining share, especially of new workloads that are merging, particularly in the cloud native space. They combine scalable compute, storage, and machine learning, and, importantly, they're allowing customers to scale, compute, and storage independent of each other. Why is that important? Because you don't have to buy storage every time you buy compute, or vice versa, in chunks. So, if you can scale them independently, you've got granularity. Vertica is keeping pace. In talking to customers, Vertica is leaning heavily into the cloud, supporting all the major cloud platforms, as we heard from Colin earlier today, adding Google. And, why my research shows that Vertica has some work to do in cloud and cloud native, to simplify the experience, it's more robust in motor stack, which supports many different environments, you know deep SQL, acid properties, and DNA that allows Vertica to compete with these cloud-native database suppliers. Now, Vertica might lose out in some of those native workloads. But, I have to say, my experience in talking with customers, if you're looking for a great MMP column store that scales and runs in the cloud, or on-prem, Vertica is in a very strong position. Vertica claims to be the only MPP columnar store to allow customers to scale, compute, and storage independently, both in the cloud and in hybrid environments on-prem, et cetera, cross clouds, as well. So, while Vertica may be at a disadvantage in a pure cloud native bake-off, it's more robust in motor stack, combined with its multi-cloud strategy, gives Vertica a compelling set of advantages. So, we heard a lot of this from Colin Mahony, who announced Vertica 10.0 in his keynote. He really emphasized Vertica's multi-cloud affinity, it's Eon Mode, which really allows that separation, or scaling of compute, independent of storage, both in the cloud and on-prem. Vertica 10, according to Mahony, is making big bets on in-database machine learning, he talked about that, AI, and along with some advanced regression techniques. He talked about PMML models, Python integration, which was actually something that they talked about doing with Uber and some other customers. Now, Mahony also stressed the trend toward object stores. And, Vertica now supports, let's see S3, with Eon, S3 Eon in Google Cloud, in addition to AWS, and then Pure and HDFS, as well, they all support Eon Mode. Mahony also stressed, as I mentioned earlier, a big commitment to on-prem and the whole cloud optionality thing. So 10.0, according to Colin Mahony, is all about really doubling down on these industry waves. As they say, enabling native PMML models, running them in Vertica, and really doing all the work that's required around ML and AI, they also announced support for TensorFlow. So, object store optionality is important, is what he talked about in Eon Mode, with the news of support for Google Cloud and, as well as HTFS. And finally, a big focus on deployment flexibility. Migration tools, which are a critical focus really on improving ease of use, and you hear this from a lot of customers. So, these are the critical aspects of Vertica 10.0, and an announcement that we're going to be unpacking all week, with some of the experts that I talked about. So, I'm going to close with this. My long-time co-host, John Furrier, and I have talked some time about this new cocktail of innovation. No longer is Moore's law the, really, mainspring of innovation. It's now about taking all these data troves, bringing machine learning and AI into that data to extract insights, and then operationalizing those insights at scale, leveraging cloud. And, one of the things I always look for from cloud is, if you've got a cloud play, you can attract innovation in the form of startups. It's part of the success equation, certainly for AWS, and I think it's one of the challenges for a lot of the legacy on-prem players. Vertica, I think, has done a pretty good job in this regard. And, you know, we're going to look this week for evidence of that innovation. One of the interviews that I'm personally excited about this week, is a new-ish company, I would consider them a startup, called Zebrium. What they're doing, is they're applying AI to do autonomous log monitoring for IT ops. And, I'm interviewing Larry Lancaster, who's their CEO, this week, and I'm going to press him on why he chose to run on Vertica and not a cloud database. This guy is a hardcore tech guru and I want to hear his opinion. Okay, so keep it right there, stay with us. We're all over the Vertica Virtual Big Data Conference, covering in-depth interviews and following all the news. So, theCUBE is going to be interviewing these folks, two days, wall-to-wall coverage, so keep it right there. We're going to be right back with our next guest, right after this short break. This is Dave Vellante and you're watching theCUBE. (upbeat music)

>> From the SiliconANGLE media office in Boston, Massachusetts, it's theCUBE. Now, here's your host, Dave Vellante. >> Hello everyone, and welcome to this CUBE Conversation. You know, theCUBE has been following the trends in the so-called big data space since 2010. And one of the things that we reported on for a number of years is the complexity involved in wrangling and making sense out of data. The allure of this idea of no schema on write and very low cost platforms like Hadoop became a data magnet. And for years, organizations would shove data into a data lake. And of course the joke was it was became a data swamp. And organizations really struggled to realize the promised return on their big data investments. Now, while the cloud certainly simplified infrastructure deployment, it really introduced a much more complex data environment and data pipeline, with dozens of APIs and a mind-boggling array of services that required highly skilled data engineers to properly ingest, shape, and prepare that data, so that it could be turned into insights. This became a real time suck for data pros, who spent 70 to 80% of their time wrestling data. A number of people saw the opportunity to solve this problem and automate the heavy lift of data, and simplify the process to adjust, synchronize, transform, and really prepare data for analysis. And one of the companies that is attacking this challenge is InfoWorks. And with me to talk about the evolving data landscape is Buno Pati, CEO of InfoWorks. Buno, great to see you, thanks for coming in. >> Well thank you Dave, thanks for having me here. >> You're welcome. I love that you're in Palo Alto, you come to MetroWest in Boston to see us (Buno laughs), that's great. Well welcome. So, you heard my narrative. We're 10 years plus into this big data theme and meme. What did we learn, what are some of the failures and successes that we can now build on, from your point of view? >> All right, so Dave, I'm going to start from the top, with why big data, all right? I think this big data movement really started with the realization by companies that they need to transform their customer experience and their operations, in order to compete effectively in this increasingly digital world, right? And in that context, they also realized very quickly that data was the key asset on which this transformation would be built. So given that, you look at this and say, "What is digital transformation really about?" It is about competing with digital disruption, or fending off digital disruption. And this has become, over time, an existential imperative. You cannot survive and be relevant in this world without leveraging data to compete with others who would otherwise disrupt your business. >> You know, let's stay on that for a minute, because when we started the whole big data, covering that big data space, you didn't really hear about digital transformation. That's sort of a more recent trend. So I got to ask you, what's the difference between a business and a digital business, in your view? >> That is the foundational question behind big data. So if you look at a digital native, there are many of them that you can name. These companies start by building a foundational platform on which they build their analytics and data programs. It gives them a tremendous amount of agility and the right framework within which to build a data-first strategy. A data-first strategy where business information is persistently collected and used at every level of the organization. Furthermore, they take this and they automate this process. Because if you want to collect all your data and leverage it at every part of the business, it needs to be a highly automated system, and it needs to be able to seamlessly traverse on-premise, cloud, hybrid, and multi-cloud environments. Now, let's look at a traditional business. In a traditional enterprise, there is no foundational platform. There are things like point tools for ETL, and data integration, and you can name a whole slew of other things, that need to be stitched together and somehow made to work to deliver data to the applications that consume. The strategy is not a data-first strategy. It is use case by use case. When there is a use case, people go and find the data, they gather the data, they transform that data, and eventually feed an application. A process that can take months to years, depending on the complexity of the project that they're trying. And they don't automate this. This is heavily dependent, as you pointed out, on engineering talent, highly skilled engineering talent that is scarce. And they have not seamlessly traversed the various clouds and on-premise environments, but rather fragmented those environments, where individual teams are focused on a single environment, building different applications, using different tools, and different infrastructure. >> So you're saying the digital native company puts data at the core. They organize around that data, as opposed to maybe around a bottling plant, or around people. And then they leverage that data for competitive advantage through a platform that's kind of table stakes. And then obviously there's cultural aspects and other skills that they need to develop, right? >> Yeah, they have an ability which traditional enterprises don't. Because of this choice of a data-first strategy with a foundational platform, they have the ability to rapidly launch analytics use cases and iterate all them. That is not possible in a traditional or legacy environment. >> So their speed to market and time to value is going to be much better than their competition. This gets into the risk of disruption. Sometimes we talk about cloud native and cloud naive. You could talk about digital native and digital naive. So it's hard for incumbents to fend off the disrupters, and then ultimately become disrupters themselves. But what are you seeing in terms of some of the trends where organizations are having success there? >> One of the key trends that we're seeing, or key attributes of companies that are seeing a lot of success, is when they have organized themselves around their data. Now, what do I mean by that? This is usually a high-level mandate coming down from the top of the company, where they're forming centralized groups to manage the data and make it available for the rest of the organization to use. There are a variety of names that are being used for this. People are calling it their data fabric. They're calling it data as a service, which is pretty descriptive of what it ends up being. And those are terms that are all sort of representing the same concept of a centralized environment and, ideally, a highly automated environment that serves the rest of the business with data. And the goal, ultimately, is to get any data at any time for any application. >> So, let's talk a little bit about the cloud. I mentioned up front that the cloud really simplified infrastructure deployment, but it really didn't solve this problem of, we talked about in terms of data wrangling. So, why didn't it solve that problem? And you got companies like Amazon and Google and Microsoft, who are very adept at data. They're some of these data-first companies. Why is it that the cloud sort of in and of itself has not been able to solve this problem? >> Okay, so when you say solve this problem, it sort of begs the question, what's the goal, right? And if I were to very simply state the goal, I would call it analytics agility. It is gaining agility with analytics. Companies are going from a traditional world, where they had to generate a handful of BI and other reporting type of dashboards in a year, to where they literally need to generate thousands of these things in a year, to run the business and compete with digital disruption. So agility is the goal. >> But wait, the cloud is all about agility, is it not? >> It is, when you talk about agility of compute and storage infrastructure. So, there are three layers to this problem. The first is, what is the compute and storage infrastructure? The cloud is wonderful in that sense. It gives you the ability to rapidly add new infrastructure and spin it down when it's not in use. That is a huge blessing, when you compare it to the six to nine months, or perhaps even longer, that it takes companies to order, install, and test hardware on premise, and then find that it's only partially used. The next layer on that is what is the operating system on which my data and analytics are going to be run? This is where Hadoop comes in. Now, Hadoop is inherently complex, but operating systems are complex things. And Spark falls in that category. Databricks has taken some of the complexity out of running Spark because of their sort of manage service type of offering. But there's still a missing layer, which leverages that infrastructure and that operating system to deliver this agility where users can access data that they need anywhere in the organization, without intensely deep knowledge of what that infrastructure is and what that operating system is doing underneath. >> So, in my up front narrative, I talked about the data pipeline a little bit. But I'm inferring from your comments on platform that it's more than just this sort of narrow data pipeline. There's a macro here. I wonder if you could talk about that a little bit. >> Yeah. So, the data pipeline is one piece of the puzzle. What needs to happen? Data needs to be ingested. It needs to be brought into these environments. It has to be kept fresh, because the source data is persistently changing. It needs to be organized and cataloged, so that people know what's there. And from there, pipelines can be created that ultimately generate data in a form that's consumable by the application. But even surrounding that, you need to be able to orchestrate all of this. Typical enterprise is a multi-cloud enterprise. 80% of all enterprises have more than one cloud that they're working on, and on-premise. So if you can't orchestrate all of this activity in the pipelines, and the data across these various environments, that's not a complete solution either. There's certainly no agility in that. Then there's governance, security, lineage. All of this has to be managed. It's not simply creation of the pipeline, but all these surrounding things that need to happen in order for analytics to run at-scale within enterprises. >> So the cloud sort of solved that layer one problem. And you certainly saw this in the, not early days, but sort of mid-days of Hadoop, where the cloud really became the place where people wanted to do a lot of their Hadoop workloads. And it was kind of ironic that guys like Hortonworks, and Cloudera and MapR really didn't have a strong cloud play. But now, it's sort of flipping back where, as you point out, everybody's multi-cloud. So you have to include a lot of these on-prem systems, whether it's your Oracle database or your ETL systems or your existing data warehouse, those are data feeds into the cloud, or the digital incumbent who wants to be a digital native. They can't just throw all that stuff away, right? So you're seeing an equilibrium there. >> An equilibrium between ... ? >> Yeah, between sort of what's in the cloud and what's on-prem. Let me ask it this way: If the cloud is not a panacea, is there an approach that does really solve the problem of different datasets, the need to ingest them from different clouds, on-prem, and bring them into a platform that can be analyzed and drive insights for an organization? >> Yeah, so I'm going to stay away from the word panacea, because I don't think there ever is really a panacea to any problem. >> That's good, that means we got a good roadmap for our business then. (both laugh) >> However, there is a solution. And the solution has to be guided by three principles. Number one, automation. If you do not automate, the dependence on skill talent is never going to go away. And that talent, as we all know, is very very scarce and hard to come by. The second thing is integration. So, what's different now? All of these capabilities that we just talked about, whether it's things like ETL, or cataloging, or ingesting, or keeping data fresh, or creating pipelines, all of this needs to be integrated together as a single solution. And that's been missing. Most of what we've seen is point tools. And the third is absolutely critical. For things to work in multi-cloud and hybrid environments, you need to introduce a layer of abstraction between the complexity of the underlying systems and the user of those systems. And the way to think about this, Dave, is to think about it much like a compiler. What does a compiler do, right? You don't have to worry about what Intel processor is underneath, what version of your operating system you're running on, what memory is in the system. Ultimately, you might-- >> As much as we love assembly code. >> As much as we love assembly code. Now, so take the analogy a little bit further, there was a time when we wrote assembly code because there was no compiler. So somebody had to sit back and say, "Hey, wouldn't it be nice if we abstracted away from this?" (both laugh) >> Okay, so this sort of sets up my next question, which is, is this why you guys started InfoWorks? Maybe you could talk a little bit about your why, and kind of where you fit. >> So, let me give you the history of InfoWorks. Because the vision of InfoWorks, believe it or not, came out of a rear view mirror. Looking backwards, not forwards. And then predicting the future in a different manner. So, Amar Arsikere is the founder of InfoWorks. And when I met him, he had just left Zynga, where he was the general manager of their gaming platform. What he told me was very very simple. He said he had been at Google at a time when Google was moving off of the legacy systems of, I believe it was Netezza, and Oracle, and a variety of things. And they had just created Bigtable, and they wanted to move and create a data warehouse on Bigtable. So he was given that job. And he led that team. And that, as you might imagine, was this massive project that required a high degree of automation to make it all come together. And he built that, and then he built a very similar system at Zynga, when he was there. These foundational platforms, going back to what I was talking about before digital days. When I met him, he said, "Look, looking back, "Google may have been the only company "that needed such a platform. "But looking forward, "I believe that everyone's going to need one." And that has, you know, absolute truth in it, and that's what we're seeing today. Where, after going through this exercise of trying to write machine code, or assembly code, or whatever we'd like to call it, down at the detailed, complex level of an operating system or infrastructure, people have realized, "Hey, I need something much more holistic. "I need to look at this from a enterprise-wide perspective. "And I need to eliminate all of this dependence on," kind of like the cloud plays a role because it eliminates some of the dependence, or the bottlenecks around hardware and infrastructure. "And ultimately gain a lot more agility "than I'm able to do with legacy methodology." So you were asking early on, what are the lessons learned from that first 10 years? And lot of technology goes through these types of cycles of hype and disillusionment, and we all know the curve. I think there are two key lessons. One is, just having a place to land your data doesn't solve your problem. That's the beginning of your problems. And the second is that legacy methodologies do not transfer into the future. You have to think differently. And looking to the digital natives as guides for how to think, when you're trying to compete with them is a wonderful perspective to take. >> But those legacy technologies, if you're an incumbent, you can't just rip 'em and throw 'em out and convert. You going to use them as feeders to your digital platform. So, presumably, you guys have products. You call this space Enterprise Data Ops and Orchestration, EDO2. Presumably you have products and a portfolio to support those higher layer challenges that we talked about, right? >> Yeah, so that's a really important question. No, you don't rip and replace stuff. These enterprises have been built over years of acquisitions and business systems. These are layers, one on top of another. So think about the introduction of ERP. By the way, ERP is a good analogy of to what happened, because those were point tools that were eventually combined into a single system called ERP. Well, these are point capabilities that are being combined into a single system for EDO2, or Enterprise Data Operations and Orchestration. The old systems do not go away. And we are seeing some companies wanting to move some of their workloads from old systems to new systems. But that's not the major trend. The major trend is that new things that get done, the things that give you holistic views of the company, and then analytics based on that holistic view, are all being done on the new platforms. So it's a layer on top. It's not a rip and replace of the layers underneath. What's in place stays in place. But for the layer on top, you need to think differently. You cannot use all the legacy methodologies and just say that's going to apply to the new platform or new system. >> Okay, so how do you engage with customers? Take a customer who's got, you know, on-prem, they've got legacy infrastructure, they don't want to get disrupted. They want to be a digital native. How do you help them? You know, what do I buy from you? >> Yeah, so our product is called DataFoundry. It is a EDO2 system. It is built on the three principles, founding principles, that I mentioned earlier. It is highly automated. It is integrated in all the capabilities that surround pipelines, perhaps. And ultimately, it's also abstracting. So we're able to very easily traverse one cloud to another, or on-premise to the cloud, or even back. There are some customers that are moving some workloads back from the cloud. Now, what's the benefit here? Well first of all, we lay down the foundation for digital transformation. And we enable these companies to consolidate and organize their data in these complex hybrid, cloud, multi-cloud environments. And then generate analytics use cases 10x faster with about tenth of the resource. And I'm happy to give you some examples on how that works. >> Please do. I mean, maybe you could share some customer examples? >> Yeah, absolutely. So, let me talk about Macy's. >> Okay. >> Macy's is a customer of ours. They've been a customer for about, I think about 14 months at this point in time. And they had built a number of systems to run their analytics, but then recognized what we're seeing other companies recognize. And that is, there's a lot of complexity there. And building it isn't the end game. Maintaining it is the real challenge, right? So even if you have a lot of talent available to you, maintaining what you built is a real challenge. So they came to us. And within a period of 12 months, I'll just give you some numbers that are just mind-blowing. They are currently running 165,000 jobs a month. Now, what's a job? A job is a ingestion job, or a synchronization job, or a transformation. They have launched 431 use cases over a period of 12 months. And you know what? They're just ramping. They will get to thousands. >> Scale. >> Yeah, scale. And they have ingested a lot of data, brought in a lot of DataSources. So to do that in a period of 12 months is unheard of. It does not happen. Why is it important for them? So what problem are they trying to solve? They're a retailer. They are being digitally disruptive like (chuckles) no one else. >> They have an Amazon war room-- >> Right. >> No doubt. >> And they have had to build themselves out as a omni-channel retailer now. They are online, they are also with brick and mortar stores. So you take a look at this. And the key to competing with digital disrupters is the customer experience. What is that experience? You're online, how does that meld with your in-store experience? What happens if I buy online and return something in a store? How does all this come together into a single unified experience for the consumer? And that's what they're chasing. So that was the first application that they came to us with. They said, "Look, let us go into a customer 360. "Let us understand the entirety "of that customer's interaction "and touchpoints with our business. "And having done so, we are in a position "to deliver a better experience." >> Now that's a data problem. I mean, different DataSources, and trying to understand 360, I mean, you got data all over the place. >> All over the place. (speaking simultaneously) And there's historical data, there's stuff coming in from, you know, what's online, what's in the store. And then they progress from there. I mean, they're not restricting it to customer experience and selling. They're looking at merchandising, and inventory, and fulfillment, and store operations. Simple problem. You order something online, where do I pull this from? A store or a warehouse? >> So this is, you know, big data 2.0, just to use a sort of silly term. But it's really taking advantage of all the investment. I've often said, you know, Hadoop, for all the criticism it gets, it did lower our cost of getting data into, you know, at least one virtual place. And it got us thinking about how to get insights out of data. And so, what you're describing is the ability to operationalize your data initiatives at scale. >> Yeah, you can absolutely get your insights off of Hadoop. And I know people have different opinions of Hadoop, given their experience. But what they don't have, what these customers have not achieved yet, most of them, is that agility, right? So, how easily can you get your insights off of Hadoop? Do I need to hire a boatload of consultants who are going to write code for me, and shovel data in, and create these pipelines, and so forth? Or can I do this with a click of a button, right? And that's the difference. That is truly the difference. The level of automation that you need, and the level of abstraction that you need, away from this complexity, has not been delivered. >> We did, in, it must have been 2011, I think, the very first big data market study from anybody in the world, and put it out on, you know, Wikibon, free research. And one of the findings was (chuckles) this is a huge services business. I mean, the professional service is where all the money was going to flow because it was so complicated. And that's kind of exactly what happened. But now we're entering, really it seems like a phase where you can scale, and operationalize, and really simplify, and really focus your attention on driving business value, versus making stuff work. >> You are absolutely correct. So I'll give you the numbers. 55% of this industry is services. About 30% is software, and the rest is hardware. Break it down that way. 55%. So what's going on? People will buy a big data system. Call it Hadoop, it could be something in the cloud, it could be Databricks. And then, this is welcome to the world of SIs. Because at this point, you need these SIs to write code and perform these services in order to get any kind of value out of that. And look, we have some dismal numbers that we're staring at. According to Gardner, only 17% of those who have invested in Hadoop have anything in production. This is after how many years? And you look at surveys from, well, pick your favorite. They all look the same. People have not been able to get the value out of this, because it is too hard. It is too complex and you need too many consultants (laughs) delivering services for you to make this happen. >> Well, what I like about your story, Buno, is you're not, I mean, a lot of the data companies have pivoted to AI. Sort of like, we have a joke, ya know, same wine, new bottle. But you're not talking about, I mean sure, machine intelligence, I'm sure, fits in here, but you're talking about really taking advantage of the investments that you've made in the last decade and helping incumbents become digital natives. That sounds like it's at least a part of your mission here. >> Not become digital natives, but rather compete with them. >> Yeah, right, right. >> Effectively, right? >> Yep, okay. >> So, yeah, that is absolutely what needs to get done. So let me talk for a moment about AI, all right? Way back when, there was another wave of AI in the late 80s. I was part of that, I was doing my PhD at the time. And that obviously went nowhere, because we didn't have any data, we didn't have enough compute power or connectivity. Pretty inert. So here it is again. Very little has changed. Except for we do have the data, we have the connectivity, and we have the compute power. But do we really? So what's AI without the data? Just A, right? There's nothing there. So what's missing, even for AI and ML to be, and I believe these are going to be powerful game changers. But for them to be effective, you need to provide data to it, and you need to be able to do so in a very agile way, so that you can iterate on ideas. No one knows exactly what AI solution is going to solve your problem or enhance your business. This is a process of experimentation. This is what a company like Google can do extraordinarily well, because of this foundational platform. They have this agility to keep iterating, and experimenting, and trying ideas. Because without trying them, you will not discover what works best. >> Yeah, I mean, for 50 years, this industry has marched to the cadence of Moore's Law, and that really was the engine of innovation. And today, it's about data, applying machine intelligence to that data. And the cloud brings, as you point out, agility and scale. That's kind of the new cocktail for innovation, isn't it? >> The cloud brings agility and scale to the infrastructure. >> In low risk, as you said, right? >> Yeah. >> Experimentation, fail fast, et cetera. >> But without an EDO2 type of system, that gives you a great degree of automation, you could spend six months to run one experiment with AI. >> Yeah, because-- >> In gathering data and feeding it to it. >> 'Cause if the answer is people and throwing people at the problem, then you're not going to scale. >> You're not going to scale, and you're never going to really leverage AI and ML capabilities. You need to be able to do that not in six months, in six days, right, or less. >> So let's talk about your company a little bit. Can you give us the status, you know, where you're at? As their newly minted CEO, what your sort of goals are, milestones that we should be watching in 2020 and beyond? >> Yeah, so newly minted CEO, I came in July of last year. This has been an extraordinary company. I started my journey with this company as an investor. And it was funded by actually two funds that I was associated with, first being Nexus Venture Partners, and then Centerview Capital, where I'm still a partner. And myself and my other two partners looked at the opportunity and what the company had been able to do. And in July of last year, I joined as CEO. My partner, David Dorman, who used to be CEO of AT&T, he joined as chairman. And my third partner, Ned Hooper, joined as President and Chief Operating Officer. Ned used to be the Chief Strategy Officer of Cisco. So we pushed pause on the funding, and that's about as all-in as a fund can get. >> Yeah, so you guys were operational experts that became investors, and said, "Okay, we're going to dive back in "and actually run the business." >> And here's why. So we obviously see a lot of companies as investors, as they go out and look for funding. There are three things that come together very rarely. One is a massive market opportunity combined with the second, which is the right product to serve that opportunity. But the third is pure luck, timing. (Dave chuckles) It's timing. And timing, you know, it's a very very challenging thing to try to predict. You can get lucky and get it right, but then again, it's luck. This had all three. It was the absolute perfect time. And it's largely because of what you described, the 10 years of time that had elapsed, where people had sort of run the experiment and were not going to get fooled again by how easy this supposed to be by just getting one piece or the other. They recognized that they need to take this holistic approach and deploy something as an enterprise-wide platform. >> Yeah, I mean, you talk about a large market, I don't even know how you do a TAM, what's the TAM? It's data. (laughs) You know, it's the data universe, which is just, you know, massive. So, I have to ask you a question as an investor. I think you've raised, what 50 million, is that right? >> We've raised 50 million. The last round was led by NEA. >> Right, okay. You got great investors, hefty amount. Although, you know, in this day and age, you know, you're seeing just outrageous amounts being raised. Software obviously is a capital efficient business, but today you need to raise a lot of money for promotion, right, to get your name out there. What's your thoughts on, as a Silicon Valley investor, as this wave, I mean, get it while you can, I guess. You know, we're in the 10th year of this boom market. But your thoughts? >> You're asking me to put on my other hat. (Dave laughs) I think companies have, in general, raised too much money at too high a value too fast. And there's a penalty for that. And the down round IPO, which has become fashionable these days, is one of those penalties. It's a clear indication. Markets are very rational, public markets are very rational. And the pricing in a public market, when it's significantly below the pricing of in a private market, is telling you something. So, we are a little old-fashioned in that sense. We believe that a company has to lay down the right foundation before it adds fuel to the mix and grows. You have to have evidence that the machinery that you build, whether it's for sales, or marketing, or other go-to-market activities, or even product development, is working. And if you do not see all of those signs, you're building a very fragile company. And adding fuel in that setting is like flooding the carburetor. You don't necessarily go faster. (laughs) You just-- >> Consume more. >> You consume more. So there's a little bit of, perhaps, old-fashioned discipline that we bring to the table. And you can argue against it. You can say, "Well, why don't you just raise a lot of money, "hire a lot of sales guys, and hope for the best?" >> See what sticks? (laughs) >> Yeah. We are fully expecting to build a large institution here. And I use that word carefully. And for that to happen, you need the right foundation down first. >> Well, that resonates with us east coast people. So, Buno, thanks very much for comin' on theCUBE and sharing with us your perspectives on the marketplace. And best of luck with InfoWorks. >> Thank you, Dave. This has been a pleasure. Thank you for having me here. >> All right, we'll be watching, thank you. And thank you for watching, everybody. This is Dave Vellante for theCUBE. We'll see ya next time. (upbeat music fades out)

from the silicon angle media office in Boston Massachusetts it's the queue now here's your host David on tape hi everybody welcome to this special cube in size powered by ET our enterprise Technology Research our partner who's got this database to solve the spending data and what we're gonna do is a braking analysis on the analytic database market we're seeing that cloud and cloud players are disrupting that marketplace and that marketplace really traditionally has been known as the enterprise data warehouse market so Alex if you wouldn't mind bringing up the first slide I want to talk about some of the trends in the traditional EDW market I almost don't like to use that term anymore because it's sort of a pejorative but let's look at it's a very large market it's about twenty billion dollars today growing it you know high single digits low double digits it's expected to be in the 30 to 35 billion dollar size by mid next decade now historically this is dominated by teradata who started this market really back in the 1980s with the first appliance the first converged appliance or coal with Exadata you know IBM I'll talk about IBM a little bit they bought a company called mateesah back in the day and they've basically this month just basically killed the t's and killed the brand Microsoft has entered the fray and so it's it's been a fairly large market but I say it's failed to really live up to the promises that we heard about in the late 90s early parts of the 2000 namely that you were going to be able to get a 360 degree view of your data and you're gonna have this flexible easy access to the data you know the reality is data warehouses were really expensive they were slow you had to go through a few experts to to get data it took a long time I'll tell you I've done a lot of research on this space and when you talked to the the data warehouse practitioners they would tell you we always had to chase the chips anytime Intel would come out with a new chip we forced it in there because we just didn't have the performance to really run the analytics as we need to it's took so long one practitioner described it as a snake swallowing a basketball so you've got all those data which is the sort of metaphor for the basketball just really practitioners had a hard time standing up infrastructure and what happened as a spate of new players came into the marketplace these these MPP players trying to disrupt the market you had Vertica who was eventually purchased by HP and then they sold them to Micro Focus greenplum was buy bought by EMC and really you know company is de-emphasized greenplum Netezza 1.7 billion dollar acquisition by IBM IBM just this month month killed the brand they're kind of you know refactoring everything par Excel was interesting was it was a company based on an open-source platform that Amazon AWS did a one-time license with and created a redshift it ever actually put a lot of innovation redshift this is really doing well well show you some data on that we've also at the time saw a major shift toward unstructured data and read much much greater emphasis on analytics it coincided with Hadoop which also disrupted the market economics I often joked it the ROI of a dupe was reduction on investment and so you saw all these data lakes being built and of course they turned into the data swamps and you had dozens of companies come into the database space which used to be rather boring but Mike Amazon with dynamodb s AP with HANA data stacks Redis Mongo you know snowflake is another one that I'm going to talk about in detail today so you're starting to see the blurring of lines between relational and non relational and what was was what once thought of is no sequel became not only sequel sequel became the killer app for Hadoop and so at any rate you saw this new class of data stores emerging and snowflake was one of the more interesting and and I want to share some of that data with you some of the spending intentions so over the last several weeks and months we've shared spending intentions from ETR enterprise technology research they're a company that that the manages of the spending data and has a panel of about 4,500 end-users they go out and do spending in tension surveys periodically so Alex if you bring up this survey data I want to show you this so this is spending intentions and and what it shows is that the public cloud vendors in snowflake who really is a database as a service offering so cloud like are really leading the pack here so the sector that I'm showing is the enterprise data warehouse and I've added in the the analytics business intelligence and Big Data section so what this chart shows is the vendor on the left-hand side and then this bar chart has colors the the red is we're leaving the platform the gray is our spending will be flat so this is from the July survey expect to expectations for the second half of 2019 so gray is flat the the dark green is increase and the lime green is we are a new customer coming on to the platform so if you take the the greens and subtract out the red and there's two Reds the dark red is leaving the lighter red is spending less so if you subtract the Reds from the greens you get what's called a net score so the higher the net score the better so you can see here the net score of snowflake is 81% so that very very high you can also see AWS in Microsoft a very high and Google so the cloud vendors of which I would consider a snowflake at cloud vendor like at the cloud model all kicking butt now look at Oracle look at the the incumbents Oracle IBM and Tara data Oracle and IBM are in the single digits for a net score and the Terra data is in a negative 10% so that's obviously not a good sign for those guys so you're seeing share gains from the cloud company snowflake AWS Microsoft and Google at the expense of certainly of teradata but likely IBM and Oracle Oracle's little for animal they got Exadata and they're putting a lot of investments in there maybe talk about that a little bit more now you see on the right hand side this black says shared accounts so the N in this survey this July survey that ETR did is a thousand sixty eight so of a thousand sixty eight customers each er is asking them okay what's your spending going to be on enterprise data warehouse and analytics big data platforms and you can see the number of accounts out of that thousand sixty eight that are being cited so snowflake only had 52 and I'll show you some other data from from past surveys AWS 319 Microsoft the big you know whale here trillion dollar valuation 851 going down the line you see Oracle a number you know very large number and in Tara data and IBM pretty large as well certainly enough to get statistically valid results so takeaway here is snowflake you know very very strong and the other cloud vendors the hyper scale is AWS Microsoft and Google and their data stores doing very well in the marketplace and challenging the incumbents now the next slide that I want to show you is a time series for selected suppliers that can only show five on this chart but it's the spending intentions again in that EDW and analytics bi big data segment and it shows the spending intentions from January 17 survey all the way through July 19 so you can see the the period the periods that ETR takes this the snapshots and again the latest July survey is over a thousand n the other ones are very very large too so you can see here at the very top snowflake is that yellow line and they just showed up in the January 19 a survey and so you're seeing now actually you go back one yeah January 19 survey and then you see them in July you see the net score is the July next net score that I'm showing that's 35 that's the number of accounts out of the corpus of data that snowflake had in the survey back in January and now it's up to 52 you can see they lead the packet just in terms of the spending intention in terms of mentions AWS and Microsoft also up there very strong you see big gap down to Oracle and Terra data I didn't show I BM didn't show Google Google actually would be quite high to just around where Microsoft is but you can see the pressure that the cloud is placing on the incumbents so what are the incumbents going to do about it well certainly you're gonna see you know in the case of Oracle spending a lot of money trying to maybe rethink the the architecture refactor the architecture Oracle open worlds coming up shortly I'm sure you're gonna see a lot of new announcements around Exadata they're putting a lot of wood behind the the exadata arrow so you know we'll keep in touch with that and stay tuned but you can see again the big takeaways here is that cloud guys are really disrupting the traditional edw marketplace alright let's talk a little bit about snowflakes so I'm gonna highlight those guys and maybe give a little bit of inside baseball here but what you need to know about snowflakes so I've put some some points here just some quick points on the slide Alex if you want to bring that up very fast-growing cloud and SAS based data warehousing player growing that couple hundred percent annually their annual recurring revenue very high these guys are getting ready to do an IPO talk about that a little bit they were founded in 2012 and it kind of came out of stealth and hiding in 2014 after bringing Bob Moog Leon from Microsoft as the CEO it was really the background on these guys is they're three engineers from Oracle will probably bored out of their mind like you know what we got this great idea why should we give it to Oracle let's go pop out and start a company and that NIN's and as such they started a snowflake they really are disrupting the incumbents they've raised over 900 million dollars in venture and they've got almost a four billion dollar valuation last May they brought on Frank salute Minh and this is really a pivot point I think for the company and they're getting ready to do an IPO so and so let's talk a little bit about that in a moment but before we do that I want to bring up just this really simple picture of Alex if you if you'd bring this this slide up this block diagram it's like a kindergarten so that you know people like you know I can even understand it but basically the innovation around the snowflake architecture was that they they separated their claim is that they separated the storage from the compute and they've got this other layer called cloud services so let me talk about that for a minute snowflake fundamentally rethought the architecture of the data warehouse to really try to take advantage of the cloud so traditionally enterprise data warehouses are static you've got infrastructure that kind of dictates what you can do with the data warehouse and you got to predict you know your peak needs and you bring in a bunch of storage and compute and you say okay here's the infrastructure and this is what I got it's static if your workload grows or some new compliance regulation comes out or some new data set has to be analyzed well this is what you got you you got your infrastructure and yeah you can add to it in chunks of compute and storage together or you can forklift out and put in new infrastructure or you can chase more chips as I said it's that snake swallowing a basketball was not pretty so very static situation and you have to over provision whereas the cloud is all about you know pay buy the drink and it's about elasticity and on demand resources you got cheap storage and cheap compute and you can just pay for it as you use it so the innovation from snowflake was to separate the compute from storage so that you could independently scale those and decoupling those in a way that allowed you to sort of tune the knobs oh I need more compute dial it up I need more storage dial it up or dial it down and pay for only what you need now another nuance here is traditionally the computing and data warehousing happens on one cluster so you got contention for the resources of that cluster what snowflake does is you can spin up a warehouse on the fly you can size it up you can size it down based on the needs of the workload so that workload is what dictates the infrastructure also in snowflakes architecture you can access the same data from many many different houses so you got again that three layers that I'm showing you the storage the compute and the cloud services so let me go through some examples so you can really better understand this so you've got storage data you got customer data you got you know order data you got log files you might have parts data you know what's an inventory kind of thing and you want to build warehouses based on that data you might have marketing a warehouse you might have a sales warehouse you might have a finance warehouse maybe there's a supply chain warehouse so again by separating the compute from that sort of virtualized compute from the from the storage layer you can access any data leave the data where it is and I'll talk about this in more and bring the compute to the data so this is what in part the cloud layer does they've got security and governance they got data warehouse management in that cloud layer and and resource optimization but the key in in my opinion is this metadata management I think that's part of snowflakes secret sauce is the ability to leave data where it is and have the smarts and the algorithms to really efficiently bring the compute to the data so that you're not moving data around if you think about how traditional data warehouses work you put all the data into a central location so you can you know operate on it well that data movement takes a long long time it's very very complicated so that's part of the secret sauce is knowing what data lives where and efficiently bringing that compute to the data this dramatically improves performance it's a game changer and it's much much less expensive now when I come back to Frank's Luqman this is somebody that I've is a career that I've followed I've known had him on the cube of a number of times I first met Frank Sloot when he was at data domain he took that company took it public and then sold it originally NetApp made a bid for the company EMC Joe Tucci in the defensive play said no we're not gonna let Ned afgan it there was a little auction he ended up selling the company for I think two and a half billion dollars sloop and came in he helped clean up the the data protection business of EMC and then left did a stint as a VC and then took over service now when snoop and took over ServiceNow and a lot of people know this the ServiceNow is the the shiny toy on Wall Street today service that was a mess when saluteth took it over it's about 100 120 million dollar company he and his team took it to 1.2 billion dramatically increased the the valuation and one of the ways they did that was by thinking about the Tam and expanding that Tim that's part of a CEOs job as Tam expansion Steuben is also a great operational guy and he brought in an amazing team to do that I'll talk a little bit about that team effect uh well he just brought in Mike Scarpelli he was the CFO was the CFO of ServiceNow brought him in to run finance for snowflake so you've seen that playbook emerge you know be interesting Beth white was the CMO at data domain she was the CMO at ServiceNow helped take that company she's an amazing resource she kind of you know and in retirement she's young but she's kind of in retirement doing some advisory roles wonder if slooping will bring her back I wonder if Dan Magee who was ServiceNow is operational you know guru wonder if he'll come out of retirement how about Dave Schneider who runs the sales team at at ServiceNow well he you know be be lord over we'll see the kinds of things that Sluman looks for just in my view of observing his playbook over the years he looks for great product he looks for a big market he looks for disruption and he looks for off-the-chart ROI so his sales teams can go in and really make a strong business case to disrupt the existing legacy players so I one of the things I said that snoopin looks for is a large market so let's look at this market and this is the thing that people missed around ServiceNow and to credit Pat myself and David for in the back you know we saw the Tam potential of ServiceNow is to be many many tens of billions you know Gartner when they when ServiceNow first came out said hey helpdesk it's a small market couple billion dollars we saw the potential to transform not only IT operations but go beyond helpdesk change management at cetera IT Service Management into lines of business and we wrote a piece on wiki Vaughn back then it's showing the potential Tam and we think something similar could happen here so the market today let's call 20 billion growing to 30 Billy big first of all but a lot of players in here what if so one of the things that we see snowflake potentially being able to do with its architecture and its vision is able to bring enterprise search you know to the marketplace 80% of the data that's out there today sits behind firewalls it's not searchable by Google what if you could unlock that data and access it in query at anytime anywhere put the power in the hands of the line of business users to do that maybe think Google search for enterprises but with provenance and security and governance and compliance and the ability to run analytics for a line of business users it's think of it as citizens data analytics we think that tam could be 70 plus billion dollars so just think about that in terms of how this company might this company snowflake might go to market you by the time they do their IPO you know it could be they could be you know three four five hundred billion dollar company so we'll see we'll keep an eye on that now because the markets so big this is not like the ITSM the the market that ServiceNow was going after they crushed BMC HP was there but really not paying attention to it IBM had a product it had all these products that were old legacy products they weren't designed for the cloud and so you know ServiceNow was able to really crush that market and caught everybody by surprise and just really blew it out there's a similar dynamic here in that these guys are disrupting the legacy players with a cloud like model but at the same time so the Amazon with redshift so is Microsoft with its analytics platform you know teradata is trying to figure it out they you know they've got an inertia of a large install base but it's a big on-prem install base I think they struggle a little bit but their their advantages they've got customers locked in or go with exudate is very interesting Oracle has burned the boats and in gone to cloud first in Oracle mark my words is is reacting everything for the cloud now you can say Oh Oracle they're old school they're old guard that's fine but one of the things about Oracle and Larry Ellison they spend money on R&D they're very very heavy investor in Rd and and I think that you know you can see the exadata as it's actually been a very successful product they will react attacked exadata believe you me to to bring compute to the data they understand you can't just move all this the InfiniBand is not gonna solve their problem in terms of moving data around their architecture so you know watch Oracle you've got other competitors like Google who shows up well in the ETR survey so they got bigquery and BigTable and you got a you know a lot of other players here you know guys like data stacks are in there and you've got you've got Amazon with dynamo DB you've got couch base you've got all kinds of database players that are sort of blurring the lines as I said between sequel no sequel but the real takeaway here from the ETR data is you've got cloud again is winning it's driving the discussion and the spending discussion with an IT watch this company snowflake they're gonna do an IPO I guarantee it hopefully they will see if they'll get in before the booth before the market turns down but we've seen this play by Frank Sluman before and his team and and and the spending data shows that this company is hot you see them all over Silicon Valley you're seeing them show up in the in the spending data so we'll keep an eye on this it's an exciting market database market used to be kind of boring now it's red-hot so there you have it folks thanks for listening is a Dave Volante cube insights we'll see you next time

>> Announcer: Live from San Jose. It's theCUBE, presenting Big Data Silicon Valley. Brought to you by SiliconANGLE Media, and it's ecosystem partners. (techno music) >> Welcome back to theCUBE, our continuing coverage on day one of our event, Big Data SV. I'm Lisa Martin with George Gilbert. We are down the street from the Strata Data Conference. We've got a great, a lot of cool stuff going on. You can see the cool set behind me. We are at Forager Tasting Room & Eatery. Come down and join us, be in our audience today. We have a cocktail event tonight, who doesn't want to join that? And we have a nice presentation tomorrow morning of our Wikibon's 2018 Big Data Forecast and Review. Joining us next is Matthew Baird the co-founder of AtScale. Matthew, welcome to theCUBE. >> Thanks for having me. Fantastic venue, by the way. >> Isn't it cool? >> This is very cool. >> Yeah, it is. So, talking about Big Data, you know, Gardner says, "85% of Big Data projects have failed." I often say failure is not a bad F word, because it can spawn the genesis of a lot of great business opportunities. Data lakes were big a few years ago, turned into swamps. AtScale has this vision of Data Lake 2.0, what is that? >> So, you're right. There have been a lot of failures, there's no doubt about it. And you're also right that is how we evolve, and we're a Silicon Valley based company. We don't give up when faced with these things. It's just another way to not do something. So, what we've seen and what we've learned through our customers is they need to have a solution that is integrated with all the technologies that they've adopted in the enterprise. And it's really about, if you're going to make a data lake, you're going to have data on there that is the crown jewels of your business. How are you going to get that in the hands of your constituents, so that they can analyze it, and they can use it to make decisions? And how can we, furthermore, do that in a way that supplies governance and auditability on top of it, so that we aren't just sending data out into the ether and not knowing where it goes? We have a lot of customers in the insurance, health insurance space, and with financial customers that the data absolutely must be managed. I think one of the biggest changes is around that integration with the current technologies. There's a lot of movement into the Cloud. The new data lake is kind of focused more on these large data stores, where it was HDFS with Hadoop. Now it's S3, Google's object storage, and Azure ADLS. Those are the sorts of things that are backing the new data lake I believe. >> So if we take these, where the Data Lake Store didn't have to be something that's a open source HDFS implementation, it could even be through just through a HDSF API. >> Matthew: Yeah, absolutely. >> What are some of the, how should we think about the data sources and feeds, for this repository, and then what is it on top that we need to put to make the data more consumable? >> Yeah, that's a good point. S3, Google Object Storage, and Azure, they all have a characteristic of, they are large stores. You can store as much as you want. They generally on the Clouds, and in the open source on-prem software for landing the data exists, for streaming the data and landing it, but the important thing there is it's cost-effective. S3 is a cost-effective storage system. HDFS is a mostly cost-effective storage system. You have to manage it, so it has a slightly higher cost, but the advice has been, get it to the place you're going to store it. Store it in a unified format. You get a halo effect when you have a unified format, and I think the industry is coalescing around... I'd probably say ParK's in the lead right now, but once ParK can be read by, let's take Amazon for instance, can be read by Athena, can be read by Redshift Spectrum, it can be read by their EMR, now you have this halo effect where your data's always there, always available to be consumed by a tool or a technology that can then deliver it to your end users. >> So when we talk about ParK, we're talking about columnar serialization format, >> Matthew: Yes. but there's more on top of that that needs to be layered, so that you can, as we were talking about earlier, combine the experience of a data warehouse, and the curated >> Absolutely data access where there's guard rails, >> Matthew: Yes >> and it's simple, versus sort of the wild west, but where I capture everything in a data lake. How do you bring those two together? >> Well, specifically for AtScale, we allow you to integrate multiple data access tools in AtScale, and then we use the appropriate tool to access the data for the use case. So let me give you an example, in the Amazon case, Redshift is wonderful for accessing interactive data, which BI users want, right? They want fast queries, sub-second queries. They don't want to pay to have all the raw data necessarily stored in Redshift 'cause that's pretty expensive. So they have this Redshift spectrum, it's sitting in S3, that's cost effective. So when we go and we read raw data to build these summary tables, to deliver the data fast, we can read from Spectrum, we can put it all together, drop it into Redshift, a much smaller volume of data, so it has faster characteristics for being accessed. And it delivers it to the user that way. We do that in Hadoop when we access via Hive for building aggregate tables, but Spark or Impala, is a much faster interactive engine, so we use those. As I step back and look at this, I think the Data Lake 2.0, from a technical perspective is about abstraction, and abstraction's sort of what separates us from the animals, right? It's a concept where we can pack a lot of sophistication and complexity behind an interface that allows people to just do what they want to do. You don't know how, or maybe you do know how a car engine works, I don't really, kind of, a little bit, but I do know how to press the gas pedal and steer. >> Right. >> I don't need to know these things, and I think the Data Lake 2.0 is about, well I don't need to know how Century, or Ranger, or Atlas, or any of these technologies work. I need to know that they're there, and when I access data, they're going to be applied to that data, and they're going to deliver me the stuff that I have access to and that I can see. >> So a couple things, it sounded like I was hearing abstraction, and you said really that's kind of the key, that sounds like a differentiator for AtScale, is giving customers that abstraction they need. But I'm also curious from a data value perspective, you talked about in Redshift from an expense perspective. Do you also help customers gain abstraction by helping them evaluate value of data and where they ought to keep it, and then you give them access to it? Or is that something that they need to do, kind of bring to the table? >> We don't really care, necessarily, about the source of the data, as long as it can be expressed in a way that can be accessed by whatever engine it is. Lift and shift is an example. There's a big move to move from Teradata or from Netezza into a Cloud-based offering. People want to lift it and shift it. It's the easiest way to do this. Same table definitions, but that's not optimized necessarily for the underlying data store. Take BigQuery for example, BigQuery's an amazing piece of technology. I think there's nothing like it out there in the market today, but if you really want BigQuery to be cost-effective, and perform and scale up to concurrency of... one of our customers is going to roll out about 8,000 users on this. You have to do things in BigQuery that are BigQuery-friendly. The data structures, the way that you store the data, repeated values, those sorts of things need to be taken into consideration when you build your schema out for consumption. With AtScale they don't need to think about that, they don't need to worry about it, we do it for them. They drop the schema in the same way that it exists on their current technology, and then behind the scenes, what we're doing is we're looking at signals, we're looking at queries, we're looking at all the different ways that people access the data naturally, and then we restructure those summary tables using algorithms and statistics, and I think people would broadly call it ML type approaches, to build out something that answers those questions, and adapts over time to new questions, and new use cases. So it's really about, imagine you had the best data engineering team in the world, in a box, they're never tired, they never stop, and they're always interacting with what the customers really want, which is "Now I want to look at the data this way". >> It's sounds actually like what your talking about is you have a whole set of sources, and targets, and you understand how they operate, but why I say you, I mean your software. And so that you can take data from wherever it's coming in, and then you apply, if it's machine learning or whatever other capabilities to learn from the access methods, how to optimize that data for that engine. >> Matthew: Exactly. >> And then the end users have an optimal experience and it's almost like the data migration service that Amazon has, it's like, you give us your Postgres or Oracle database, and we'll migrate it to the cloud. It sounds like you add a lot of intelligence to that process for decision support workloads. >> Yes. >> And figure out, so now you're going to... It's not Postgres to Postgres, but it might be Teradata to Redshift, or S3, that's going to be accessed by Athena or Redshift, and then let's put that in the right format. >> I think you sort of hit something that we've noticed is very powerful, which is if you can set up, and we've done this with a number of customers, if you can set up at the abstraction layer that is AtScale, on your on-prem data, literally in, say hours, you can move it into the Cloud, obviously you have to write the detail to move it into the Cloud, but once it's in the Cloud you take the same AtScale instance, you re-point it at that new data source, and it works. We've done that with multiple customers, and it's fast and effective, and it let's you actually try out things that you may not have the agility to do before because there's differences in how the SQL dialects work, there's differences in, potentially, how the schema might be built. >> So a couple things I'm interested in, I'm hearing two A-words, that abstraction that we've talked about a number of times, you also mention adaptability. So when you're talking with customers, what are some of the key business outcomes they need to drive, where adaptability and abstraction are concerned, in terms of like cost reduction, revenue generation. What are some of those see-swee business objectives that AtScale can help companies achieve? >> So looking at, say, a customer, a large retailer on the East Coast, everybody knows the stores, they're everywhere, they sell hardware. they have a 20-terabyte cube that they use for day-to-day revenue analytics. So they do period over period analysis. When they're looking at stores, they're looking at things like, we just tried out a new marketing approach... I was talking to somebody there last week about how they have these special stores where they completely redo one area and just see how that works. They have to be able to look at those analytics, and they run those for a short amount of time. So if you're window for getting data, refreshing data, building cubes, which in the old world could take a week, you know my co-founder at Yahoo, he had a week and a half build time. That data is now two weeks old, maybe three weeks old. There might be bugs in it-- >> And the relevance might be, pshh... >> And the relevance goes down, or you can't react as fast. I've been at companies where... Speed is so important these days, and the new companies that are grasping data aggressively, putting it somewhere where they can make decisions on it on a day-to-day basis, they're winning. And they're spending... I was at a company that was spending three million dollars on pay-per-click data, a month. If you can't get data everyday, you're on the wrong campaigns, and everything goes off the rails, and you only learn about it a week later, that's 25% of your spend, right there, gone. >> So the biggest thing, sorry George, it really sounds to me like what AtScale can facilitate for probably customers in any industry is the ability to truly make data-driven business decisions that can really directly affect revenue and profit. >> Yes, and in an agile format. So, you can build-- >> That's the third A; agile, adaptability, abstraction. >> There ya go, the three A's. (Lisa laughs) We had the three V's, now we have the three A's. >> Yes. >> The fact that you're building a curated model, so in retail the calendars are complex. I'm sure everybody that uses Tableau is good at analyzing data, but they might not know what your rules are around your financial calendar, or around the hierarchies of your product. There's a lot of things that happen where you want an enterprise group of data modelers to build it, bless it, and roll it out, but then you're a user, and you say, wait, you forgot x, y, and z, I don't want to wait a week, I don't want to wait two weeks, three weeks, a month, maybe more. I want that data to be available in the model an hour later 'cause that's what I get with Tableau today. And that's where we've taken the two approaches of enterprise analytics and self-service, and tried to create a scenario where you get the best of both worlds. >> So, we know that an implication of what you're telling us is that insights are perishable, and latency is becoming more and more critical. How do you plan to work with streaming data where you've got a historical archive, but you've got fresh data coming in? But fresh could mean a variety of things. Tell us what some of those scenarios look like. >> Absolutely, I think there's two approaches to this problem, and I'm seeing both used in practice, and I'm not exactly sure, although I have some theories on which one's going to win. In one case, you are streaming everything into, sort of a... like I talked about, this data lake, S3, and you're putting it in a format like ParK, and then people are accessing it. The other way is access the data where it is. Maybe it's already in, this is a common BI scenario, you have a big data store, and then you have a dimensional data store, like Oracle has your customers, Hadoop has machine data about those customers accessing on their mobile devices or something. If there was some way to access those data without having to move the Oracle stuff into the big data store, that's a Federation story that I think we've talked about in the Bay Area for a long time, or around the world for a long time. I think we're getting closer to understanding how we can do that in practice, and have it be tenable. You don't move the big data around, you move the small data around. For data coming in from outside sources it's probably a little bit more difficult, but it is kind of a degenerate version of the same story. I would say that streaming is gaining a lot of momentum, and with what we do, we're always mapping, because of the governance piece that we've built into the product, we're always mapping where did the data come from, where did it land, and how did we use it to build summary tables. So if we build five summary tables, 'cause we're answering different types of questions, we still need to know that it goes back to this piece of data, which has these security constraints, and these audit requirements, and we always track it back to that, and we always apply those to our derived data. So when you're accessing this automatically ETLed summary tables, it just works the way it is. So I think that there are two ways that this is going to expand and I'm excited about Federation because I think the time has come. I'm also excited about streaming. I think they can serve two different use cases, and I don't actually know what the answer will be, because I've seen both in customers, it's some of the biggest customers we have. >> Well Matthew thank you so much for stopping by, and four A's, AtScale can facilitate abstraction, adaptability, and agility. >> Yes. Hashtag four A's. >> There we go. I don't even want credit for that. (laughs) >> Oh wow, I'm going to get five more followers, I know it! (George laughs) >> There ya go! >> We want to thank you for watching theCUBE, I am Lisa Martin, we are live in San Jose, at our event Big Data SV, I'm with George Gilbert. Stick around, we'll be back with our next guest after a short break. (techno music)

>> Narrator: Live from New York, it's theCUBE. Covering machine learning everywhere. Build your ladder to AI. Brought to you by IBM. >> Welcome back to IBM's Machine Learning Everywhere. Build your ladder to AI, along with Dave Vellante, John Walls here, wrapping up here in New York City. Just about done with the programming here in Midtown. Dave, let's just take a step back. We've heard a lot, seen a lot, talked to a lot of folks today. First off, tell me, AI. We've heard some optimistic outlooks, some, I wouldn't say pessimistic, but some folks saying, "Eh, hold off." Not as daunting as some might think. So just your take on the artificial intelligence conversation we've heard so far today. >> I think generally, John, that people don't realize what's coming. I think the industry, in general, our industry, technology industry, the consumers of technology, the businesses that are out there, they're steeped in the past, that's what they know. They know what they've done, they know the history and they're looking at that as past equals prologue. Everybody knows that's not the case, but I think it's hard for people to envision what's coming, and what the potential of AI is. Having said that, Jennifer Shin is a near-term pessimist on the potential for AI, and rightly so. There are a lot of implementation challenges. But as we said at the open, I'm very convinced that we are now entering a new era. The Hadoop big data industry is going to pale in comparison to what we're seeing. And we're already seeing very clear glimpses of it. The obvious things are Airbnb and Uber, and the disruptions that are going on with Netflix and over-the-top programming, and how Google has changed advertising, and how Amazon is changing and has changed retail. But what you can see, and again, the best examples are Apple getting into financial services, moving into healthcare, trying to solve that problem. Amazon buying a grocer. The rumor that I heard about Amazon potentially buying Nordstrom, which my wife said is a horrible idea. (John laughs) But think about the fact that they can do that is a function of, that they are a digital-first company. Are built around data, and they can take those data models and they can apply it to different places. Who would have thought, for example, that Alexa would be so successful? That Siri is not so great? >> Alexa's become our best friend. >> And it came out of the blue. And it seems like Google has a pretty competitive piece there, but I can almost guarantee that doing this with our thumbs is not the way in which we're going to communicate in the future. It's going to be some kind of natural language interface that's going to rely on artificial intelligence and machine learning and the like. And so, I think it's hard for people to envision what's coming, other than fast forward where machines take over the world and Stephen Hawking and Elon Musk say, "Hey, we should be concerned." Maybe they're right, not in the next 10 years. >> You mentioned Jennifer, we were talking about her and the influencer panel, and we've heard from others as well, it's a combination of human intelligence and artificial intelligence. That combination's more powerful than just artificial intelligence, and so, there is a human component to this. So, for those who might be on the edge of their seat a little bit, or looking at this from a slightly more concerning perspective, maybe not the case. Maybe not necessary, is what you're thinking. >> I guess at the end of the day, the question is, "Is the world going to be a better place with all this AI? "Are we going to be more prosperous, more productive, "healthier, safer on the roads?" I am an optimist, I come down on the side of yes. I would not want to go back to the days where I didn't have GPS. That's worth it to me. >> Can you imagine, right? If you did that now, you go back five years, just five years from where we are now, back to where we were. Waze was nowhere, right? >> All the downside of these things, I feel is offset by that. And I do think it's incumbent upon the industry to try to deal with the problem, especially with young people, the blue light problem. >> John: The addictive issue. >> That's right. But I feel like those downsides are manageable, and the upsides are of enough value that society is going to continue to move forward. And I do think that humans and machines are going to continue to coexist, at least in the near- to mid- reasonable long-term. But the question is, "What can machines "do that humans can't do?" And "What can humans do that machines can't do?" And the answer to that changes every year. It's like I said earlier, not too long ago, machines couldn't climb stairs. They can now, robots can climb stairs. Can they negotiate? Can they identify cats? Who would've imagined that all these cats on the Internet would've led to facial recognition technology. It's improving very, very rapidly. So, I guess my point is that that is changing very rapidly, and there's no question it's going to have an impact on society and an impact on jobs, and all those other negative things that people talk about. To me, the key is, how do we embrace that and turn it into an opportunity? And it's about education, it's about creativity, it's about having multi-talented disciplines that you can tap. So we talked about this earlier, not just being an expert in marketing, but being an expert in marketing with digital as an understanding in your toolbox. So it's that two-tool star that I think is going to emerge. And maybe it's more than two tools. So that's how I see it shaping up. And the last thing is disruption, we talked a lot about disruption. I don't think there's any industry that's safe. Colin was saying, "Well, certain industries "that are highly regulated-" In some respects, I can see those taking longer. But I see those as the most ripe for disruption. Financial services, healthcare. Can't we solve the HIPAA challenge? We can't get access to our own healthcare information. Well, things like artificial intelligence and blockchain, we were talking off-camera about blockchain, those things, I think, can help solve the challenge of, maybe I can carry around my health profile, my medical records. I don't have access to them, it's hard to get them. So can things like artificial intelligence improve our lives? I think there's no question about it. >> What about, on the other side of the coin, if you will, the misuse concerns? There are a lot of great applications. There are a lot of great services. As you pointed out, a lot of positive, a lot of upside here. But as opportunities become available and technology develops, that you run the risk of somebody crossing the line for nefarious means. And there's a lot more at stake now because there's a lot more of us out there, if you will. So, how do you balance that? >> There's no question that's going to happen. And it has to be managed. But even if you could stop it, I would say you shouldn't because the benefits are going to outweigh the risks. And again, the question we asked the panelists, "How far can we take machines? "How far can we go?" That's question number one, number two is, "How far should we go?" We're not even close to the "should we go" yet. We're still on the, "How far can we go?" Jennifer was pointing out, I can't get my password reset 'cause I got to call somebody. That problem will be solved. >> So, you're saying it's more of a practical consideration now than an ethical one, right now? >> Right now. Moreso, and there's certainly still ethical considerations, don't get me wrong, but I see light at the end of the privacy tunnel, I see artificial intelligence as, well, analytics is helping us solve credit card fraud and things of that nature. Autonomous vehicles are just fascinating, right? Both culturally, we talked about that, you know, we learned how to drive a stick shift. (both laugh) It's a funny story you told me. >> Not going to worry about that anymore, right? >> But it was an exciting time in our lives, so there's a cultural downside of that. I don't know what the highway death toll number is, but it's enormous. If cell phones caused that many deaths, we wouldn't be using them. So that's a problem that I think things like artificial intelligence and machine intelligence can solve. And then the other big thing that we talked about is, I see a huge gap between traditional companies and these born-in-the-cloud, born-data-oriented companies. We talked about the top five companies by market cap. Microsoft, Amazon, Facebook, Alphabet, which is Google, who am I missing? >> John: Apple. >> Apple, right. And those are pretty much very much data companies. Apple's got the data from the phones, Google, we know where they get their data, et cetera, et cetera. Traditional companies, however, their data resides in silos. Jennifer talked about this, Craig, as well as Colin. Data resides in silos, it's hard to get to. It's a very human-driven business and the data is bolted on. With the companies that we just talked about, it's a data-driven business, and the humans have expertise to exploit that data, which is very important. So there's a giant skills gap in existing companies. There's data silos. The other thing we touched on this is, where does innovation come from? Innovation drives value drives disruption. So the innovation comes from data. He or she who has the best data wins. It comes from artificial intelligence, and the ability to apply artificial intelligence and machine learning. And I think something that we take for granted a lot, but it's cloud economics. And it's more than just, and somebody, one of the folks mentioned this on the interview, it's more than just putting stuff in the cloud. It's certainly managed services, that's part of it. But it's also economies of scale. It's marginal economics that are essentially zero. It's speed, it's low latency. It's, and again, global scale. You combine those things, data, artificial intelligence, and cloud economics, that's where the innovation is going to come from. And if you think about what Uber's done, what Airbnb have done, where Waze came from, they were picking and choosing from the best digital services out there, and then developing their own software from this, what I say my colleague Dave Misheloff calls this matrix. And, just to repeat, that matrix is, the vertical matrix is industries. The horizontal matrix are technology platforms, cloud, data, mobile, social, security, et cetera. They're building companies on top of that matrix. So, it's how you leverage the matrix is going to determine your future. Whether or not you get disrupted, whether your the disruptor or the disruptee. It's not just about, we talked about this at the open. Cloud, SaaS, mobile, social, big data. They're kind of yesterday's news. It's now new artificial intelligence, machine intelligence, deep learning, machine learning, cognitive. We're still trying to figure out the parlance. You could feel the changes coming. I think this matrix idea is very powerful, and how that gets leveraged in organizations ultimately will determine the levels of disruption. But every single industry is at risk. Because every single industry is going digital, digital allows you to traverse industries. We've said it many times today. Amazon went from bookseller to content producer to grocer- >> John: To grocer now, right? >> To maybe high-end retailer. Content company, Apple with Apple Pay and companies getting into healthcare, trying to solve healthcare problems. The future of warfare, you live in the Beltway. The future of warfare and cybersecurity are just coming together. One of the biggest issues I think we face as a country is we have fake news, we're seeing the weaponization of social media, as James Scott said on theCUBE. So, all these things are coming together that I think are going to make the last 10 years look tame. >> Let's just switch over to the currency of AI, data. And we've talked to, Sam Lightstone today was talking about the database querying that they've developed with the Plex product. Some fascinating capabilities now that make it a lot richer, a lot more meaningful, a lot more relevant. And that seems to be, really, an integral step to making that stuff come alive and really making it applicable to improving your business. Because they've come up with some fantastic new ways to squeeze data that's relevant out, and get it out to the user. >> Well, if you think about what I was saying earlier about data as a foundational core and human expertise around it, versus what most companies are, is human expertise with data bolted on or data in silos. What was interesting about Queryplex, I think they called it, is it essentially virtualizes the data. Well, what does that mean? That means i can have data in place, but I can have access to that data, I can democratize that data, make it accessible to people so that they can become data-driven, data is the core. Now, what I don't know, and I don't know enough, just heard about it today, I missed that announcement, I think they announced it a year ago. He mentioned DB2, he mentioned Netezza. Most of the world is not on DB2 and Netezza even though IBM customers are. I think they can get to Hadoop data stores and other data stores, I just don't know how wide that goes, what the standards look like. He joked about the standards as, the great thing about standards is- >> There are a lot of 'em. (laughs) >> There's always another one you can pick if this one fails. And he's right about that. So, that was very interesting. And so, this is again, the question, can traditional companies close that machine learning, machine intelligence, AI gap? Close being, close the gap that the big five have created. And even the small guys, small guys like Uber and Airbnb, and so forth, but even those guys are getting disrupted. The Airbnbs and the Ubers, right? Again, blockchain comes in and you say, "Why do I need a trusted third party called Uber? "Why can't I do this on the blockchain?" I predict you're going to see even those guys get disrupted. And I'll say something else, it's hard to imagine that a Google or a Facebook can be unseated. But I feel like we may be entering an era where this is their peak. Could be wrong, I'm an Apple customer. I don't know, I'm not as enthralled as I used to be. They got trillions in the bank. But is it possible that opensource and blockchain and the citizen developer, the weekend and nighttime developers, can actually attack that engine of growth for the last 10 years, 20 years, and really break that monopoly? The Internet has basically become an oligopoly where five companies, six companies, whatever, 10 companies kind of control things. Is it possible that opensource software, AI, cryptography, all this activity could challenge the status quo? Being in this business as long as I have, things never stay the same. Leaders come, leaders go. >> I just want to say, never say never. You don't know. >> So, it brings it back to IBM, which is interesting to me. It was funny, I was asking Rob Thomas a question about disruption, and I think he misinterpreted it. I think he was thinking that I was saying, "Hey, you're going to get disrupted by all these little guys." IBM's been getting disrupted for years. They know how to reinvent. A lot of people criticize IBM, how many quarters they haven't had growth, blah, blah, blah, but IBM's made some big, big bets on the future. People criticizing Watson, but it's going to be really interesting to see how all this investment that IBM has made is going to pay off. They were early on. People in the Valley like to say, "Well, the Facebooks, and even Amazon, "Google, they got the best AI. "IBM is not there with them." But think about what IBM is trying to do versus what Google is doing. They're very consumer-oriented, solving consumer problems. Consumers have really led the consumerization of IT, that's true, but none of those guys are trying to solve cancer. So IBM is talking about some big, hairy, audacious goals. And I'm not as pessimistic as some others you've seen in the trade press, it's popular to do. So, bringing it back to IBM, I saw IBM as trying to disrupt itself. The challenge IBM has, is it's got a lot of legacy software products that have purchased over the years. And it's got to figure out how to get through those. So, things like Queryplex allow them to create abstraction layers. Things like Bluemix allow them to bring together their hundreds and hundreds and hundreds of SaaS applications. That takes time, but I do see IBM making some big investments to disrupt themselves. They've got a huge analytics business. We've been covering them for quite some time now. They're a leader, if not the leader, in that business. So, their challenge is, "Okay, how do we now "apply all these technologies to help "our customers create innovation?" What I like about the IBM story is they're not out saying, "We're going to go disrupt industries." Silicon Valley has a bifurcated disruption agenda. On the one hand, they're trying to, cloud, and SaaS, and mobile, and social, very disruptive technologies. On the other hand, is Silicon Valley going to disrupt financial services, healthcare, government, education? I think they have plans to do so. Are they going to be able to execute that dual disruption agenda? Or are the consumers of AI and the doers of AI going to be the ones who actually do the disrupting? We'll see, I mean, Uber's obviously disrupted taxis, Silicon Valley company. Is that too much to ask Silicon Valley to do? That's going to be interesting to see. So, my point is, IBM is not trying to disrupt its customers' businesses, and it can point to Amazon trying to do that. Rather, it's saying, "We're going to enable you." So it could be really interesting to see what happens. You're down in DC, Jeff Bezos spent a lot of time there at the Washington Post. >> We just want the headquarters, that's all we want. We just want the headquarters. >> Well, to the point, if you've got such a growing company monopoly, maybe you should set up an HQ2 in DC. >> Three of the 20, right, for a DC base? >> Yeah, he was saying the other day that, maybe we should think about enhancing, he didn't call it social security, but the government, essentially, helping people plan for retirement and the like. I heard that and said, "Whoa, is he basically "telling us he's going to put us all out of jobs?" (both laugh) So, that, if I'm a customer of Amazon's, I'm kind of scary. So, one of the things they should absolutely do is spin out AWS, I think that helps solve that problem. But, back to IBM, Ginni Rometty was very clear at the World of Watson conference, the inaugural one, that we are not out trying to compete with our customers. I would think that resonates to a lot of people. >> Well, to be continued, right? Next month, back with IBM again? Right, three days? >> Yeah, I think third week in March. Monday, Tuesday, Wednesday, theCUBE's going to be there. Next week we're in the Bahamas. This week, actually. >> Not as a group taking vacation. Actually a working expedition. >> No, it's that blockchain conference. Actually, it's this week, what am I saying next week? >> Although I'm happy to volunteer to grip on that shoot, by the way. >> Flying out tomorrow, it's happening fast. >> Well, enjoyed this, always good to spend time with you. And good to spend time with you as well. So, you've been watching theCUBE, machine learning everywhere. Build your ladder to AI. Brought to you by IBM. Have a good one. (techno music)

>> Narrator: Live from New York, it's the Cube. Covering Machine Learning Everywhere: Build Your Ladder to AI. Brought to you by IBM. >> And welcome back here to New York City. We're at IBM's Machine Learning Everywhere: Build Your Ladder to AI, along with Dave Vellante, John Walls, and we're now joined by Sam Lightstone, who is an IBM fellow in analytics. And Sam, good morning. Thanks for joining us here once again on the Cube. >> Yeah, thanks a lot. Great to be back. >> Yeah, great. Yeah, good to have you here on kind of a moldy New York day here in late February. So we're talking, obviously data is the new norm, is what certainly, have heard a lot about here today and of late here from IBM. Talk to me about, in your terms, of just when you look at data and evolution and to where it's now become so central to what every enterprise is doing and must do. I mean, how do you do it? Give me a 30,000-foot level right now from your prism. >> Sure, I mean, from a super, if you just stand back, like way far back, and look at what data means to us today, it's really the thing that is separating companies one from the other. How much data do they have and can they make excellent use of it to achieve competitive advantage? And so many companies today are about data and only data. I mean, I'll give you some like really striking, disruptive examples of companies that are tremendously successful household names and it's all about the data. So the world's largest transportation company, or personal taxi, can't call it taxi, but (laughs) but, you know, Uber-- >> Yeah, right. >> Owns no cars, right? The world's largest accommodation company, Airbnb, owns no hotels, right? The world's largest distributor of motion pictures owns no movie theaters. So these companies are disrupting because they're focused on data, not on the material stuff. Material stuff is important, obviously. Somebody needs to own a car, somebody needs to own a way to view a motion picture, and so on. But data is what differentiates companies more than anything else today. And can they tap into the data, can they make sense of it for competitive advantage? And that's not only true for companies that are, you know, cloud companies. That's true for every company, whether you're a bricks and mortars organization or not. Now, one level of that data is to simply look at the data and ask questions of the data, the kinds of data that you already have in your mind. Generating reports, understanding who your customers are, and so on. That's sort of a fundamental level. But the deeper level, the exciting transformation that's going on right now, is the transformation from reporting and what we'll call business intelligence, the ability to take those reports and that insight on data and to visualize it in the way that human beings can understand it, and go much deeper into machine learning and AI, cognitive computing where we can start to learn from this data and learn at the pace of machines, and to drill into the data in a way that a human being cannot because we can't look at bajillions of bytes of data on our own, but machines can do that and they're very good at doing that. So it is a huge, that's one level. The other level is, there's so much more data now than there ever was because there's so many more devices that are now collecting data. And all of us, you know, every one of our phones is collecting data right now. Your cars are collecting data. I think there's something like 60 sensors on every car that rolls of the manufacturing line today. 60. So it's just a wild time and a very exciting time because there's so much untapped potential. And that's what we're here about today, you know. Machine learning, tapping into that unbelievable potential that's there in that data. >> So you're absolutely right on. I mean the data is foundational, or must be foundational in order to succeed in this sort of data-driven world. But it's not necessarily the center of the universe for a lot of companies. I mean, it is for the big data, you know, guys that we all know. You know, the top market cap companies. But so many organizations, they're sort of, human expertise is at the center of their universe, and data is sort of, oh yeah, bolt on, and like you say, reporting. >> Right. >> So how do they deal with that? Do they get one big giant DB2 instance and stuff all the data in there, and infuse it with MI? Is that even practical? How do they solve this problem? >> Yeah, that's a great question. And there's, again, there's a multi-layered answer to that. But let me start with the most, you know, one of the big changes, one of the massive shifts that's been going on over the last decade is the shift to cloud. And people think of the shift to cloud as, well, I don't have to own the server. Someone else will own the server. That's actually not the right way to look at it. I mean, that is one element of cloud computing, but it's not, for me, the most transformative. The big thing about the cloud is the introduction of fully-managed services. It's not just you don't own the server. You don't have to install, configure, or tune anything. Now that's directly related to the topic that you just raised, because people have expertise, domains of expertise in their business. Maybe you're a manufacturer and you have expertise in manufacturing. If you're a bank, you have expertise in banking. You may not be a high-tech expert. You may not have deep skills in tech. So one of the great elements of the cloud is that now you can use these fully managed services and you don't have to be a database expert anymore. You don't have to be an expert in tuning SQL or JSON, or yadda yadda. Someone else takes care of that for you, and that's the elegance of a fully managed service, not just that someone else has got the hardware, but they're taking care of all the complexity. And that's huge. The other thing that I would say is, you know, the companies that are really like the big data houses, they got lots of data, they've spent the last 20 years working so hard to converge their data into larger and larger data lakes. And some have been more successful than others. But everybody has found that that's quite hard to do. Data is coming in many places, in many different repositories, and trying to consolidate, you know, rip the data out, constantly ripping it out and replicating into some data lake where you, or data warehouse where you can do your analytics, is complicated. And it means in some ways you're multiplying your costs because you have the data in its original location and now you're copying it into yet another location. You've got to pay for that, too. So you're multiplying costs. So one of the things I'm very excited about at IBM is we've been working on this new technology that we've now branded it as IBM Queryplex. And that gives us the ability to query data across all of these myriad sources as if they are in one place. As if they are a single consolidated data lake, and make it all look like (snaps) one repository. And not only to the application appear as one repository, but actually tap into the processing power of every one of those data sources. So if you have 1,000 of them, we'll bring to bear the power 1,000 data sources and all that computing and all that memory on these analytics problems. >> Well, give me an example why that matters, of what would be a real-world application of that. >> Oh, sure, so there, you know, there's a couple of examples. I'll give you two extremes, two different extremes. One extreme would be what I'll call enterprise, enterprise data consolidation or virtualization, where you're a large institution and you have several of these repositories. Maybe you got some IBM repositories like DB2. Maybe you've got a little bit of Oracle and a little bit of SQL Server. Maybe you've got some open source stuff like Postgres or MySQL. You got a bunch of these and different departments use different things, and it develops over decades and to some extent you can't even control it, (laughs) right? And now you just want to get analytics on that. You just, what's this data telling me? And as long as all that data is sitting in these, you know, dozens or hundreds of different repositories, you can't tell, unless you copy it all out into a big data lake, which is expensive and complicated. So Queryplex will solve that problem. >> So it's sort of a virtual data store. >> Yeah, and one of the terms, many different terms that are used, but one of the terms that's used in the industry is data virtualization. So that would be a suitable terminology here as well. To make all that data in hundreds, thousands, even millions of possible data sources, appear as one thing, it has to tap into the processing power of all of them at once. Now, that's one extreme. Let's take another extreme, which is even more extreme, which is the IoT scenario, Internet of Things, right? Internet of Things. Imagine you've, have devices, you know, shipping containers and smart meters on buildings. You could literally have 100,000 of these or a million of these things. They're usually small; they don't usually have a lot of data on them. But they can store, usually, couple of months of data. And what's fascinating about that is that most analytics today are really on the most recent you know, 48 hours or four weeks, maybe. And that time is getting shorter and shorter, because people are doing analytics more regularly and they're interested in, just tell me what's going on recently. >> I got to geek out here, for a second. >> Please, well thanks for the warning. (laughs) >> And I know you know things, but I'm not a, I'm not a technical person, but I've been a molt. I've been around a long time. A lot of questions on data virtualization, but let me start with Queryplex. The name is really interesting to me. When I, and you're a database expert, so I'm going to tap your expertise. When I read the Google Spanner paper, I called up my colleague David Floyer, who's an ex-IBM, I said, "This is like global Sysplex. "It's a global distributed thing," And he goes, "Yeah, kind of." And I got very excited. And then my eyes started bleeding when I read the paper, but the name, Queryplex, is it a play on Sysplex? Is there-- >> It's actually, there's a long story. I don't think I can say the story on-air, but we, suffice it to say we wanted to get a name that was legally usable and also descriptive. >> Dave: Okay. >> And we went through literally hundreds and hundreds of permutations of words and we finally landed on Queryplex. But, you know, you mentioned Google Spanner. I probably should spend a moment to differentiate how what we're doing is-- >> Great, if you would. >> A different kind of thing. You know, on Google Spanner, you put data into Google Spanner. With Queryplex, you don't put data into it. >> Dave: Don't have to move it. >> You don't have to move it. You leave it where it is. You can have your data in DB2, you can have it in Oracle, you can have it in a flat file, you can have an Excel spreadsheet, and you know, think about that. An Excel spreadsheet, a collection of text files, comma delimited text files, SQL Server, Oracle, DB2, Netezza, all these things suddenly appear as one database. So that's the transformation. It's not about we'll take your data and copy it into our system, this is about leave your data where it is, and we're going to tap into your (snaps) existing systems for you and help you see them in a unified way. So it's a very different paradigm than what others have done. Part of the reason why we're so excited about it is we're, as far as we know, nobody else is really doing anything quite like this. >> And is that what gets people to the 21st century, basically, is that they have all these legacy systems and yet the conversion is much simpler, much more economical for them? >> Yeah, exactly. It's economical, it's fast. (snaps) You can deploy this in, you know, a very small amount of time. And we're here today talking about machine learning and it's a very good segue to point out in order to get to high-quality AI, you need to have a really strong foundation of an information architecture. And for the industry to show up, as some have done over the past decade, and keep telling people to re-architect their data infrastructure, keep modifying their databases and creating new databases and data lakes and warehouses, you know, it's just not realistic. And so we want to provide a different path. A path that says we're going to make it possible for you to have superb machine learning, cognitive computing, artificial intelligence, and you don't have to rebuild your information architecture. We're going to make it possible for you to leverage what you have and do something special. >> This is exciting. I wasn't aware of this capability. And we were talking earlier about the cloud and the managed service component of that as a major driver of lowering cost and complexity. There's another factor here, which is, we talked about moving data-- >> Right. >> And that's one of the most expensive components of any infrastructure. If I got to move data and the transmission costs and the latency, it's virtually impossible. Speed of light's still up. I know you guys are working on speed of light, but (Sam laughs) you'll eventually get there. >> Right. >> Maybe. But the other thing about cloud economics, and this relates to sort of Queryplex. There's this API economy. You've got virtually zero marginal costs. When you were talking, I was writing these down. You got global scale, it's never down, you've got this network effect working for you. Are you able to, are the standards there? Are you able to replicate those sort of cloud economics the APIs, the standards, that scale, even though you're not in control of this, there's not a single point of control? Can you explain sort of how that magic works? >> Yeah, well I think the API economy is for real and it's very important for us. And it's very important that, you know, we talk about API standards. There's a beautiful quote I once heard. The beautiful thing about standards is there's so many to choose from. (All laugh) And the reality is that, you know, you have standards that are official standards, and then you have the de facto standards because something just catches on and nobody blessed it. It just got popular. So that's a big part of what we're doing at IBM is being at the forefront of adopting the standards that matter. We made a big, a big investment in being Spark compatible, and, in fact, even with Queryplex. You can issue Spark SQL against Queryplex even though it's not a Spark engine, per se, but we make it look and feel like it can be Spark SQL. Another critical point here, when we talk about the API economy, and the speed of light, and movement to the cloud, and these topics you just raised, the friction of the Internet is an unbelievable friction. (John laughs) It's unbelievable. I mean, you know, when you go and watch a movie over the Internet, your home connection is just barely keeping up. I mean, you're pushing it, man. So a gigabyte, you know, a gigabyte an hour or something like that, right? Okay, and if you're a big company, maybe you have a fatter pipe. But not a lot fatter. I mean, not orders of, you're talking incredible friction. And what that means is that it is difficult for people, for companies, to en masse, move everything to the cloud. It's just not happening overnight. And, again, in the interest of doing the best possible service to our customers, that's why we've made it a fundamental element of our strategy in IBM to be a hybrid, what we call hybrid data management company, so that the APIs that we use on the cloud, they are compatible with the APIs that we use on premises. And whether that's software or private cloud. You've got software, you've got private cloud, you've got public cloud. And our APIs are going to be consistent across, and applications that you code for one will run on the other. And you can, that makes it a lot easier to migrate at your leisure when you're ready. >> Makes a lot of sense. That way you can bring cloud economics and the cloud operating model to your data, wherever the data exists. Listening to you speak, Sam, it reminds me, do you remember when Bob Metcalfe who I used to work with at IDG, predicted the collapse of the Internet? He predicted that year after year after year, in speech after speech, that it was so fragile, and you're bringing back that point of, guys, it's still, you know, a lot of friction. So that's very interesting, (laughs) as an architect. >> You think Bob's going to be happy that you brought up that he predicted the Internet was going to be its own demise? (Sam laughs) >> Well, he did it in-- >> I'm just saying. >> I'm staying out of it, man. >> He did it as a lightning rod. >> As a talking-- >> To get the industry to respond, and he had a big enough voice so he could do that. >> That it worked, right. But so I want to get back to Queryplex and the secret sauce. Somehow you're creating this data virtualization capability. What's the secret sauce behind it? >> Yeah, so I think, we're not the first to try, by the way. Actually this problem-- >> Hard problem. >> Of all these data sources all over the place, you try to make them look like one thing. People have been trying to figure out how to do that since like the '70s, okay, so, but-- >> Dave: Really hasn't worked. >> And it hasn't worked. And really, the reason why it hasn't worked is that there's been two fundamental strategies. One strategy is, you have a central coordinator that tries to speak to each of these data sources. So I've got, let's say, 10,000 data sources. I want to have one coordinator tap into each of them and have a dialogue. And what happens is that that coordinator, a server, an agent somewhere, becomes a network bottleneck. You were talking about the friction of the Internet. This is a great example of friction. One coordinator trying to speak to, you know, and collaborators becomes a point of friction. And it also becomes a point of friction not only in the Internet, but also in the computation, because he ends up doing too much of the work. There's too many things that cannot be done at the, at these edge repositories, aggregations, and joins, and so on. So all the aggregations and joins get done by this one sucker who can't keep up. >> Dave: The queue. >> Yeah, so there's a big queue, right. So that's one strategy that didn't work. The other strategy that people tried was sort of an end squared topology where every data source tries to speak to every other data source. And that doesn't scale as well. So what we've done in Queryplex is something that we think is unique and much more organic where we try to organize the universe or constellation of these data sources so that every data source speaks to a small number of peers but not a large number of peers. And that way no single source is a bottleneck, either in network or in computation. That's one trick. And the second trick is we've designed algorithms that can truly be distributed. So you can do joins in a distributed manner. You can do aggregation in a distributed manner. These are things, you know, when I say aggregation, I'm talking about simple things like a sum or an average or a median. These are super popular in, in analytic queries. Everybody wants to do a sum or an average or a median, right? But in the past, those things were hard to do in a distributed manner, getting all the participants in this universe to do some small incremental piece of the computation. So it's really these two things. Number one, this organic, dynamically forming constellation of devices. Dynamically forming a way that is latency aware. So if I'm a, if I represent a data source that's joining this universe or constellation, I'm going to try to find peers who I have a fast connection with. If all the universe of peers were out there, I'll try to find ones that are fast. And the second is having algorithms that we can all collaborate on. Those two things change the game. >> We're getting the two minute sign, and this is fascinating stuff. But so, how do you deal with the data consistency problem? You hear about eventual consistency and people using atomic clocks and-- Right, so Queryplex, you know, there's a reason we call it Queryplex not Dataplex. Queryplex is really a read-only operation. >> Dave: Oh, there you go. >> You've got all these-- >> Problem solved. (laughs) >> Problem solved. You've got all these data sources. They're already doing their, they already have data's coming in how it's coming in. >> Dave: Simple and brilliant. >> Right, and we're not changing any of that. All we're saying is, if you want to query them as one, you can query them as one. I should say a few words about the machine learning that we're doing here at the conference. We've talked about the importance of an information architecture and how that lays a foundation for machine learning. But one of the things that we're showing and demonstrating at the conference today, or at the showcase today, is how we're actually putting machine learning into the database. Create databases that learn and improve over time, learn from experience. In 1952, Arthur Samuel was a researcher at IBM who first, had one of the most fundamental breakthroughs in machine learning when he created a machine learning algorithm that will play checkers. And he programmed this checker playing game of his so it would learn over time. And then he had a great idea. He programmed it so it would play itself, thousands and thousands and thousands of times over, so it would actually learn from its own mistakes. And, you know, the evolution since then. Deep Blue playing chess and so on. The Watson Jeopardy game. We've seen tremendous potential in machine learning. We're putting into the database so databases can be smarter, faster, more consistent, and really just out of the box (snaps) performing. >> I'm glad you brought that up. I was going to ask you, because the legend Steve Mills once said to me, I had asked him a question about in-memory databases. He said ever databases have been around, in-memory databases have been around. But ML-infused databases are new. >> Sam: That's right, something totally new. >> Dave: Yeah, great. >> Well, you mentioned Deep Blue. Looking forward to having Garry Kasparov on a little bit later on here. And I know he's speaking as well. But fascinating stuff that you've covered here, Sam. We appreciate the time here. >> Thank you, thanks for having me. >> And wish you continued success, as well. >> Thank you very much. >> Sam Lightstone, IBM fellow joining us here live on the Cube. We're back with more here from New York City right after this. (electronic music)

>> Narrator: Live from Munich Germany, it's theCUBE, covering IBM, Fast Track Your Data. Brought to you by IBM. >> We're back. This is Dave Vellante with Jim Kobielus, and this is theCUBE, the leader in live tech coverage. We go out to the events. We extract the signal from the noise. We are here covering special presentation of IBM's Fast Track your Data, and we're in Munich Germany. It's been a day-long session. We started this morning with a panel discussion with five senior level data scientists that Jim and I hosted. Then we did CUBE interviews in the morning. We cut away to the main tent. Kate Silverton did a very choreographed scripted, but very well done, main keynote set of presentations. IBM made a couple of announcements today, and then we finished up theCUBE interviews. Jim and I are here to wrap. We're actually running on IBMgo.com. We're running live. Hilary Mason talking about what she's doing in data science, and also we got a session on GDPR. You got to log in to see those sessions. So go ahead to IBMgo.com, and you'll find those. Hit the schedule and go to the Hilary Mason and GDP our channels, and check that out, but we're going to wrap now. Jim two main announcements today. I hesitate to call them big announcements. I mean they were you know just kind of ... I think the word you used last night was perfunctory. You know I mean they're okay, but they're not game changing. So what did you mean? >> Well first of all, when you look at ... Though IBM is not calling this a signature event, it's essentially a signature event. They do these every June or so. You know in the past several years, the signature events have had like a one track theme, whether it be IBM announcing their investing deeply in Spark, or IBM announcing that they're focusing on investing in R as the core language for data science development. This year at this event in Munich, it's really a three track event, in terms of the broad themes, and I mean they're all important tracks, but none of them is like game-changing. Perhaps IBM doesn't intend them to be it seems like. One of which is obviously Europe. We're holding this in Munich. And a couple of things of importance to European customers, first and foremost GDPR. The deadline next year, in terms of compliance, is approaching. So sound the alarm as it were. And IBM has rolled out compliance or governance tools. Download and the go from the information catalog, governance catalog and so forth. Now announcing the consortium with Hortonworks to build governance on top of Apache Atlas, but also IBM announcing that they've opened up a DSX center in England and a machine-learning hub here in Germany, to help their European clients, in those countries especially, to get deeper down into data science and machine learning, in terms of developing those applicants. That's important for the audience, the regional audience here. The second track, which is also important, and I alluded to it. It's governance. In all of its manifestations you need a master catalog of all the assets for building and maintaining and controlling your data applications and your data science applications. The catalog, the consortium, the various offerings at IBM is announced and discussed in great detail. They've brought in customers and partners like Northern Trust, talk about the importance of governance, not just as a compliance mandate, but also the potential strategy for monetizing your data. That's important. Number three is what I call cloud native data applications and how the state of the art in developing data applications is moving towards containerized and orchestrated environments that involve things like Docker and Kubernetes. The IBM DB2 developer community edition. Been in the market for a few years. The latest version they announced today includes kubernetes support. Includes support for JSON. So it's geared towards new generation of cloud and data apps. What I'm getting at ... Those three core themes are Europe governance and cloud native data application development. Each of them is individually important, but none of them is game changer. And one last thing. Data science and machine learning, is one of the overarching envelope themes of this event. They've had Hilary Mason. A lot of discussion there. My sense I was a little bit disappointed because there wasn't any significant new announcements related to IBM evolving their machine learning portfolio into deep learning or artificial intelligence in an environment where their direct competitors like Microsoft and Google and Amazon are making a huge push in AI, in terms of their investments. There's a bit of a discussion, and Rob Thomas got to it this morning, about DSX. Working with power AI, the IBM platform, I would like to hear more going forward about IBM investments in these areas. So I thought it was an interesting bunch of announcements. I'll backtrack on perfunctory. I'll just say it was good that they had this for a lot of reasons, but like I said, none of these individual announcements is really changing the game. In fact like I said, I think I'm waiting for the fall, to see where IBM goes in terms of doing something that's actually differentiating and innovative. >> Well I think that the event itself is great. You've got a bunch of partners here, a bunch of customers. I mean it's active. IBM knows how to throw a party. They've always have. >> And the sessions are really individually awesome. I mean terms of what you learn. >> The content is very good. I would agree. The two announcements that were sort of you know DB2, sort of what I call community edition. Simpler, easier to download. Even Dave can download DB2. I really don't want to download DB2, but I could, and play with it I guess. You know I'm not database guy, but those of you out there that are, go check it out. And the other one was the sort of unified data governance. They tried to tie it in. I think they actually did a really good job of tying it into GDPR. We're going to hear over the next, you know 11 months, just a ton of GDPR readiness fear, uncertainty and doubt, from the vendor community, kind of like we heard with Y2K. We'll see what kind of impact GDPR has. I mean it looks like it's the real deal Jim. I mean it looks like you know this 4% of turnover penalty. The penalties are much more onerous than any other sort of you know, regulation that we've seen in the past, where you could just sort of fluff it off. Say yeah just pay the fine. I think you're going to see a lot of, well pay the lawyers to delay this thing and battle it. >> And one of our people in theCUBE that we interviewed, said it exactly right. It's like the GDPR is like the inverse of Y2K. In Y2K everybody was freaking out. It was actually nothing when it came down to it. Where nobody on the street is really buzzing. I mean the average person is not buzzing about GDPR, but it's hugely important. And like you said, I mean some serious penalties may be in the works for companies that are not complying, companies not just in Europe, but all around the world who do business with European customers. >> Right okay so now bring it back to sort of machine learning, deep learning. You basically said to Rob Thomas, I see machine learning here. I don't see a lot of the deep learning stuff quite yet. He said stay tuned. You know you were talking about TensorFlow and things like that. >> Yeah they supported that ... >> Explain. >> So Rob indicated that IBM very much, like with power AI and DSX, provides an open framework or toolkit for plugging in your, you the developers, preferred machine learning or deep learning toolkit of an open source nature. And there's a growing range of open source deep learning toolkits beyond you know TensorFlow, including Theano and MXNet and so forth, that IBM is supporting within the overall ESX framework, but also within the power AI framework. In other words they've got those capabilities. They're sort of burying that message under a bushel basket, at least in terms of this event. Also one of the things that ... I said this too Mena Scoyal. Watson data platform, which they launched last fall, very important product. Very important platform for collaboration among data science professionals, in terms of the machine learning development pipeline. I wish there was more about the Watson data platform here, about where they're taking it, what the customers are doing with it. Like I said a couple of times, I see Watson data platform as very much a DevOps tool for the new generation of developers that are building machine learning models directly into their applications. I'd like to see IBM, going forward turn Watson data platform into a true DevOps platform, in terms of continuous integration of machine learning and deep learning another statistical models. Continuous training, continuous deployment, iteration. I believe that's where they're going, or probably she will be going. I'd like to see more. I'm expecting more along those lines going forward. What I just described about DevOps for data science is a big theme that we're focusing on at Wikibon, in terms where the industry is going. >> Yeah, yeah. And I want to come back to that again, and get an update on what you're doing within your team, and talk about the research. Before we do that, I mean one of the things we talked about on theCUBE, in the early days of Hadoop is that the guys are going to make the money in this big data business of the practitioners. They're not going to see, you know these multi-hundred billion dollar valuations come out of the Hadoop world. And so far that prediction has held up well. It's the Airbnbs and the Ubers and the Spotifys and the Facebooks and the Googles, the practitioners who are applying big data, that are crushing it and making all the money. You see Amazon now buying Whole Foods. That in our view is a data play, but who's winning here, in either the vendor or the practitioner community? >> Who's winning are the startups with a hot new idea that's changing, that's disrupting some industry, or set of industries with machine learning, deep learning, big data, etc. For example everybody's, with bated breath, waiting for you know self-driving vehicles. And the ecosystem as it develops somebody's going to clean up. And one or more companies, companies we probably never heard of, leveraging everything we're describing here today, data science and containerized distributed applications that involve you know deep learning for you know image analysis and sensor analyst and so forth. Putting it all together in some new fabric that changes the way we live on this planet, but as you said the platforms themselves, whether they be Hadoop or Spark or TensorFlow, whatever, they're open source. You know and the fact is, by it's very nature, open source based solutions, in terms of profit margins on selling those, inexorably migrate to zero. So you're not going to make any money as a tool vendor, or a platform vendor. You got to make money ... If you're going to make money, you make money, for example from providing an ecosystem, within which innovation can happen. >> Okay we have a few minutes left. Let's talk about the research that you're working on. What's exciting you these days? >> Right, right. So I think a lot of people know I've been around the analyst space for a long long time. I've joined the SiliconANGLE Wikibon team just recently. I used to work for a very large solution provider, and what I do here for Wikibon is I focus on data science as the core of next generation application development. When I say next-generation application development, it's the development of AI, deep learning machine learning, and the deployment of those data-driven statistical assets into all manner of application. And you look at the hot stuff, like chatbots for example. Transforming the experience in e-commerce on mobile devices. Siri and Alexa and so forth. Hugely important. So what we're doing is we're focusing on AI and everything. We're focusing on containerization and building of AI micro-services and the ecosystem of the pipelines and the tools that allow you to do that. DevOps for data science, distributed training, federated training of statistical models, so forth. We are also very much focusing on the whole distributed containerized ecosystem, Docker, Kubernetes and so forth. Where that's going, in terms of changing the state of the art, in terms of application development. Focusing on the API economy. All of those things that you need to wrap around the payload of AI to deliver it into every ... >> So you're focused on that intersection between AI and the related topics and the developer. Who is winning in that developer community? Obviously Amazon's winning. You got Microsoft doing a good job there. Google, Apple, who else? I mean how's IBM doing for example? Maybe name some names. Who do you who impresses you in the developer community? But specifically let's start with IBM. How is IBM doing in that space? >> IBM's doing really well. IBM has been for quite a while, been very good about engaging with new generation of developers, using spark and R and Hadoop and so forth to build applications rapidly and deploy them rapidly into all manner of applications. So IBM has very much reached out to, in the last several years, the Millennials for whom all of this, these new tools, have been their core repertoire from the very start. And I think in many ways, like today like developer edition of the DB2 developer community edition is very much geared to that market. Saying you know to the cloud native application developer, take a second look at DB2. There's a lot in DB2 that you might bring into your next application development initiative, alongside your spark toolkit and so forth. So IBM has startup envy. They're a big old company. Been around more than a hundred years. And they're trying to, very much bootstrap and restart their brand in this new context, in the 21st century. I think they're making a good effort at doing it. In terms of community engagement, they have a really good community engagement program, all around the world, in terms of hackathons and developer days, you know meetups here and there. And they get lots of turnout and very loyal customers and IBM's got to broadest portfolio. >> So you still bleed a little bit of blue. So I got to squeeze it out of you now here. So let me push a little bit on what you're saying. So DB2 is the emphasis here, trying to position DB2 as appealing for developers, but why not some of the other you know acquisitions that they've made? I mean you don't hear that much about Cloudant, Dash TV, and things of that nature. You would think that that would be more appealing to some of the developer communities than DB2. Or am I mistaken? Is it IBM sort of going after the core, trying to evolve that core you know constituency? >> No they've done a lot of strategic acquisitions like Cloudant, and like they've acquired Agrath Databases and brought them into their platform. IBM has every type of database or file system that you might need for web or social or Internet of Things. And so with all of the development challenges, IBM has got a really high-quality, fit-the-purpose, best-of-breed platform, underlying data platform for it. They've got huge amounts of developers energized all around the world working on this platform. DB2, in the last several years they've taken all of their platforms, their legacy ... That's the wrong word. All their existing mature platforms, like DB2 and brought them into the IBM cloud. >> I think legacy is the right word. >> Yeah, yeah. >> These things have been around for 30 years. >> And they're not going away because they're field-proven and ... >> They are evolving. >> And customers have implemented them everywhere. And they're evolving. If you look at how IBM has evolved DB2 in the last several years into ... For example they responded to the challenge from SAP HANA. We brought BLU Acceleration technology in memory technology into DB2 to make it screamingly fast and so forth. IBM has done a really good job of turning around these product groups and the product architecture is making them cloud first. And then reaching out to a new generation of cloud application developers. Like I said today, things like DB2 developer community edition, it's just the next chapter in this ongoing saga of IBM turning itself around. Like I said, each of the individual announcements today is like okay that's interesting. I'm glad to see IBM showing progress. None of them is individually disruptive. I think the last week though, I think Hortonworks was disruptive in the sense that IBM recognized that BigInsights didn't really have a lot of traction in the Hadoop spaces, not as much as they would have wished. Hortonworks very much does, and IBM has cast its lot to work with HDP, but HDP and Hortonworks recognizes they haven't achieved any traction with data scientists, therefore DSX makes sense, as part of the Hortonworks portfolio. Likewise a big sequel makes perfect sense as the sequel front end to the HDP. I think the teaming of IBM and Hortonworks is propitious of further things that they'll be doing in the future, not just governance, but really putting together a broader cloud portfolio for the next generation of data scientists doing work in the cloud. >> Do you think Hortonworks is a legitimate acquisition target for IBM. >> Of course they are. >> Why would IBM ... You know educate us. Why would IBM want to acquire Hortonworks? What does that give IBM? Open source mojo, obviously. >> Yeah mojo. >> What else? >> Strong loyalty with the Hadoop market with developers. >> The developer angle would supercharge the developer angle, and maybe make it more relevant outside of some of those legacy systems. Is that it? >> Yeah, but also remember that Hortonworks came from Yahoo, the team that developed much of what became Hadoop. They've got an excellent team. Strategic team. So in many ways, you can look at Hortonworks as one part aqui-hire if they ever do that and one part really substantial and growing solution portfolio that in many ways is complementary to IBM. Hortonworks is really deep on the governance of Hadoop. IBM has gone there, but I think Hortonworks is even deeper, in terms of their their laser focus. >> Ecosystem expansion, and it actually really wouldn't be that expensive of an acquisition. I mean it's you know north of ... Maybe a billion dollars might get it done. >> Yeah. >> You know so would you pay a billion dollars for Hortonworks? >> Not out of my own pocket. >> No, I mean if you're IBM. You think that would deliver that kind of value? I mean you know how IBM thinks about about acquisitions. They're good at acquisitions. They look at the IRR. They have their formula. They blue-wash the companies and they generally do very well with acquisitions. Do you think Hortonworks would fit profile, that monetization profile? >> I wouldn't say that Hortonworks, in terms of monetization potential, would match say what IBM has achieved by acquiring the Netezza. >> Cognos. >> Or SPSS. I mean SPSS has been an extraordinarily successful ... >> Well the day IBM acquired SPSS they tripled the license fees. As a customer I know, ouch, it worked. It was incredibly successful. >> Well, yeah. Cognos was. Netezza was. And SPSS. Those three acquisitions in the last ten years have been extraordinarily pivotal and successful for IBM to build what they now have, which is really the most comprehensive portfolio of fit-to-purpose data platform. So in other words all those acquisitions prepared IBM to duke it out now with their primary competitors in this new field, which are Microsoft, who's newly resurgent, and Amazon Web Services. In other words, the two Seattle vendors, Seattle has come on strong, in a way that almost Seattle now in big data in the cloud is eclipsing Silicon Valley, in terms of where you know ... It's like the locus of innovation and really of customer adoption in the cloud space. >> Quite amazing. Well Google still hanging in there. >> Oh yeah. >> Alright, Jim. Really a pleasure working with you today. Thanks so much. Really appreciate it. >> Thanks for bringing me on your team. >> And Munich crew, you guys did a great job. Really well done. Chuck, Alex, Patrick wherever he is, and our great makeup lady. Thanks a lot. Everybody back home. We're out. This is Fast Track Your Data. Go to IBMgo.com for all the replays. Youtube.com/SiliconANGLE for all the shows. TheCUBE.net is where we tell you where theCUBE's going to be. Go to wikibon.com for all the research. Thanks for watching everybody. This is Dave Vellante with Jim Kobielus. We're out.

>> Announcer: Live from San Jose, California. It's The Cube, covering Big Data Silicon Valley 2017. (upbeat electronic music) >> California, Silicon Valley, at the heart of the big data world, this is The Cube's coverage of Big Data Silicon Valley in conjunction with Strata Hadoop, well of course we've been here for multiple years, covering Hadoop World for now our eighth year, now that's Strata Hadoop but we do our own event, Big Data SV in New York City and Silicon Valley, SV NYC. I'm John Furrier, my cohost George Gilbert, analyst at Wikibon. Our next guest is Tendü Yogurtçu with Syncsort, general manager of the big data, did I get that right? >> Yes, you got it right. It's always a pleasure to be at The Cube. >> (laughs) I love your name. That's so hard for me to get, but I think I was close enough there. Welcome back. >> Thank you. >> Great to see you. You know, one of the things I'm excited about with Syncsort is we've been following you guys, we talk to you guys every year, and it just seems to be that every year, more and more announcements happen. You guys are unstoppable. You're like what Amazon does, just more and more announcements, but the theme seems to be integration. Give us the latest update. You had an update, you bought Trillium, you got a hit deal with Hortonworks, you got integrated with Spark, you got big news here, what's the news here this year? >> Sure. Thank you for having me. Yes, it's very exciting times at Syncsort and I've probably say that every time I appear because every time it's more exciting than the previous, which is great. We bought Trillium Software and Trillium Software has been leading data quality over a decade in many of the enterprises. It's very complimentary to our data integration, data management portfolio because we are helping our customers to access all of their enterprise data, not just the new emerging sources in the connected devices and mobile and streaming. Also leveraging reference data, my main frame legacy systems and the legacy enterprise data warehouse. While we are doing that, accessing data, data lake is now actually, in some cases, turning into data swamp. That was a term Dave Vellante used a couple of years back in one of the crowd chats and it's becoming real. So, data-- >> Real being the data swamps, data lakes are turning into swamps because they're not being leveraged properly? >> Exactly, exactly. Because it's about also having access to write data, and data quality is very complimentary because dream has had trusted right data, so to enterprise customers in the traditional environments, so now we are looking forward to bring that enterprise trust of the data quality into data lake. In terms of the data integration, data integration has been always very critical to any organization. It's even more critical now that the data is shifting gravity and the amount of data organizations have. What we have been delivering in very large enterprise production environments for the last three years is we are hearing our competitors making announcements in those areas very recently, which is a validation because we are already running in very large production environments. We are offering value by saying "Create your applications for integrating your data," whether it's in the cloud or originating on the cloud or origination on the main frames, whether it's on the legacy data warehouse, you can deploy the same exact application without any recompilations, without any changes on your standalone Windows laptop or in Hadoop MapReduce, or Spark in the cloud. So this design once and deploy anywhere is becoming more and more critical with data, it's originating in many different places and cloud is definitely one of them. Our data warehouse optimization solution with Hortonworks and AtScale, it's a special package to accelerate this adoption. It's basically helping organizations to offload the workload from the existing Teradata or Netezza data warehouse and deploying in Hadoop. We provide a single button to automatically map the metadata, create the metadata in Hive or on Hadoop and also make the data accessible in the new environment and AtScale provides fast BI on top of that. >> Wow, that's amazing. I want to ask you a question, because this is a theme, so I just did a tweetup just now while you were talking saying "the theme this year is cleaning up the data lakes, or data swamps, AKA data lakes. The other theme is integration. Can you just lay out your premise on how enterprises should be looking at integration now because it's the multi-vendor world, it's the multi-cloud world, multi-data type and source with metadata world. How do you advise customers that have the plethora of action coming at them. IOT, you've got cloud, you've got big data, I've got Hadoop here, I got Spark over here, what's the integration formula? >> First thing is identify your business use cases. What's your business's challenge, what's your business goals, and the challenge, because that should be the real driver. We assist in some organizations, they start with the intention "we would like to create a data lake" without having that very clear understanding, what is it that I'm trying to solve with this data lake? Data as a service is really becoming a theme across multiple organizations, whether it's on the enterprise side or on some of the online retail organizations, for example. As part of that data as a service, organizations really need to adopt tools that are going to enable them to take advantage of the technology stack. The technology stack is evolving very rapidly. The skill sets are rare, and skill sets are rare because you need to be kind of making adjustments. Am I hiring Ph.D students who can program Scala in the most optimized way, or should I hire Java developers, or should I hire Python developers, the names of the tools in the stack, Spark one versus Spark two APIs, change. It's really evolving very rapidly. >> It's hard to find Scala developers, I mean, you go outside Silicon Valley. >> Exactly. So you need to be, as an organization, ours advises that you really need to find tools that are going to fit those business use cases and provide a single software environment, that data integration might be happening on premise now, with some of the legacy enterprise data warehouse, and it might happen in a hybrid, on premise and cloud environment in the near future and perhaps completely in the cloud. >> So standard tools, tools that have some standard software behind it, so you don't get stuck in the personnel hiring problem. Some unique domain expertise that's hard to hire. >> Yes, skill set is one problem, the second problem is the fact that the applications needs to be recompiled because the stack is evolving and the APIs are not compatible with the previous version, so that's the maintenance cost to keep up with things, to be able to catch up with the new versions of the stack, that's another area that the tools really help, because you want to be able to develop the application and deploy it anywhere in any complete platform. >> So Tendü, if I hear you properly, what you're saying is integration sounds great on paper, it's important, but there's some hidden costs there, and that is the skill set and then there's the stack recompiling, I'm making sure. Okay, that's awesome. >> The tools help with that. >> Take a step back and zoom out and talk about Syncsort's positioning, because you guys have been changing with the stacks as well, I mean you guys have been doing very well with the announcements, you've been just coming on the market all the time. What is the current value proposition for Syncsort today? >> The current value proposition is really we have organizations to create the next generation modern data architecture by accessing and liberating all enterprise data and delivering that data at the right time and the right quality data. It's liberate, integrate, with integrity. That's our value proposition. How do we do that? We provide that single software environment. You can have batch legacy data and streaming data sources integrated in the same exact environment and it enables you to adapt to Spark 2 or Flink or whichever complete framework is going to help them. That has been our value proposition and it is proven in many production deployments. >> What's interesting to is the way you guys have approached the market. You've locked down the legacy, so you have, we talk about the main frame and well beyond that now, you guys have and understand the legacy, so you kind of lock that down, protect it, make it secure, it's security-wise, but you do that too, but making sure it works because it's still data there, because legacy systems are really critical in the hybrid. >> Main frame expertise and heritage that we have is a critical part of our offering. We will continue to focus on innovation on the main frame side as well as on the distributed. One of the announcements that we made since our last conversation was we have partnership with Compuware and we now bring in more data types about application failures, it's a Band-Aid data to Splunk for operational intelligence. We will continue to also support more delivery types, we have batch delivery, we have streaming delivery, and now replication into Hadoop has been a challenge so our focus is now replication from the B2 on mainframe and ISA on mainframe to Hadoop environments. That's what we will continue to focus on, mainframe, because we have heritage there and it's also part of big enterprise data lake. You cannot make sense of the customer data that you are getting from mobile if you don't reference the critical data sets that are on the mainframe. With the Trillium acquisition, it's very exciting because now we are at a kind of pivotal point in the market, we can bring that data validation, cleansing, and matching superior capabilities we have to the big data environments. One of the things-- >> So when you get in low latency, you guys do the whole low latency thing too? You bring it in fast? >> Yes, we bring it, that's our current value proposition and as we are accessing this data and integrating this part of the data lake, now we have capabilities with Trillium that we can profile that data, get statistics and start using machine learning to automate the data steward's job. Data stewards are still spending 75% of their time trying to clean the data. So if we can-- >> Lot of manual work labor there, and modeling too, by the way, the modeling and just the cleaning, cleaning and modeling kind of go hand in hand. >> Exactly. If we can automate any of these steps to drive the business rules automatically and provide right data on the data lake, that would be very valuable. This is what we are hearing from our customers as well. >> We've heard probably five years about the data lake as the center of gravity of big data, but we're hearing at least a bifurcation, maybe more, where now we want to take that data and apply it, operationalize it in making decisions with machine learning, predictive analytics, but at the same time we're trying to square this strange circle of data, the data lake where you didn't say up front what you wanted it to look like but now we want ever richer metadata to make sense out of it, a layer that you're putting on it, the data prep layer, and others are trying to put different metadata on top of it. What do you see that metadata layer looking like over the next three to five years? >> The governance is a very key topic and social organizations who are ahead of the game in the big data and who already established that data lake, data governance and even analytics governance becomes important. What we are delivering here with Trillium, we will have generally available by end of Q1. We are basically bringing business rules to the data. Instead of bringing data to business rules, we are taking the business rules and deploying them where the data exists. That will be key because of the data gravity you mentioned because the data might be in the Hadoop environment, there might be in a, like I said, enterprise data warehouse, and it might be originating in the cloud, and you don't want to move the data to the business rules. You want to move the business rules to where the data exists. Cloud is an area that we see more and more of our customers are moving forward. Two main use cases around our integration is one, because the data is originating in cloud, and the second one is archiving data to cloud, and we announced actually, tighter integration with cloud with our director earlier this week for this event, and that we have been in cloud deployments and we have actually an offering, an elastic MapReduce already and on AC too for couple of years now, and also on the Google cloud storage, but this announcement is primarily making deployments even easier by leveraging cloud director's elasticity for increasing and reducing the deployment. Now our customers will also take advantage of integration jobs from that elasticity. >> Tendü, it's great to have you on The Cube because you have an engineering mind but you're also now general manager of the business, and your business is changing. You're in the center of the action, so I want to get your expertise and insight into enterprise readiness concept and we saw last week at Google Cloud 2017, you know, Google going down the path of being enterprise ready, or taking steps, I don't think they're fully ready, but they're certainly serious about the cloud on the enterprise, and that's clear from Diane Green, who knows the enterprise. It sparked the conversation last week, around what does enterprise readiness mean for cloud players, because there's so many details in between the lines, if you will, of what products are, that integration, certification, SLAs. What's your take on the notion of cloud readiness? Vizaviz, Google and others that are bringing cloud compute, a lot of resources, with an IOT market that's now booming, big data evolving very, very fast, lot of realtime, lot of analytics, lot of innovation happening. What's the enterprise picture look like from a readiness standpoint? How do these guys get ready? >> From a big picture, for enterprise there are couple of things that these cannot be afterthought. Security, metadata lineage is part of data governance, and being able to have flexibility in the architecture, that they will not be kind of recreating the jobs that they might have all the way to deployed and on premise environments, right? To be able to have the same application running from on premise to cloud will be critical because it gives flexibility for adaptation in the enterprise. Enterprise may have some MapReduce jobs running on premise with the Spark jobs on cloud because they are really doing some predictive analytics, graph analytics on those, they want to be able to kind of have that flexible architecture where we hear this concept of a hybrid environment. You don't want to be deploying a completely different product in the cloud and redo your jobs. That flexibility of architecture, flexibility-- >> So having different code bases in the cloud versus on prem requires two jobs to do the same thing. >> Two jobs for maintaining, two jobs for standardizing, and two different skill sets of people potentially. So security, governance, and being able to access easily and have applications move in between environments will be very critical. >> So seamless integration between clouds and on prem first, and then potentially multi-cloud. That's table stakes in your mind. >> They are absolutely table stakes. A lot of vendors are trying to focus on that, definitely Hadoop vendors are also focusing on that. Also, one of the things, like when people talk about governance, the requirements are changing. We have been talking about single view and customer 360 for a while now, right? Do we have it right yet? The enrichment is becoming a key. With Trillium we made the recent announcement, the precise enriching, it's not just the address that you want to deliver and make sure that address should be correct, it's also the email address, and the phone number, is it mobile number, is it landline? It's enriched data sets that we have to be really dealing, and there's a lot of opportunity, and we are really excited because data quality, discovery and integration are coming together and we have a good-- >> Well Tendü, thank you for joining us, and congratulations as Syncsort broadens their scope to being a modern data platform solution provider for companies, congratulations. >> Thank you. >> Thanks for coming. >> Thank you for having me. >> This is The Cube here live in Silicon Valley and San Jose, I'm John Furrier, George Gilbert, you're watching our coverage of Big Data Silicon Valley in conjunction with Strata Hadoop. This is Silicon Angles, The Cube, we'll be right back with more live coverage. We've got two days of wall to wall coverage with experts and pros talking about big data, the transformations here inside The Cube. We'll be right back. (upbeat electronic music)

>> Announcer: Live from Las Vegas! It's theCUBE. Covering Edge 2016. Brought to you by IBM. Here's your host, Dave Vellante. >> Welcome back to IBM Edge everybody. This is theCUBE's fifth year covering IBM Edge. We were at the inaugural Edge five years ago in Orlando. Marc Linster is here and he's joined by Lenley Hensarling. Marc is the Senior Vice President of Product Development. And Lenley is the Senior Vice President of Product Management and Strategy at EDB, Enterprise Database. Gentlemen, welcome to theCUBE. Thanks for coming on. >> Male Voice: Thank you. >> Okay, who wants to start. Enterprise Database, tell us about the company and what you guys are all about. >> Well the company has been around for little over 10 years now. And our job is really to give companies the ability to use Postgres as the platform for their digital business. So think about this, Postgres is a great open source database. Great capabilities for transactional management of data. But also multi-model data management. So think about standard SQL data but think also about document oriented, think about key-value pair. Think about GIS. So a great capability that is very, very robust. Has been around for quite a few years. And is really ready to allow companies to build on them for the new digital business but also to migrate off their existing commercial databases that are too expensive. >> What's the history of Postgres? Can you sort of educate me on that? >> Sort of the same roots back with System R, where DB2 came from, Oracle came from. So Berkeley, that's where the whole thing started out. Postgres is really the successor to Ingres. >> Dave: Umhmm. >> And then it turned into PostgreSQL. And it has been licensed under open source license, the Postgres license since 1996. And it's a very, very vibrant open source community that has been driving forward for many years now. And our view is the best available relational and multi-model database today. >> It's the mainspring of relational database management systems essentially >> Marc: Yeah. >> is what you're saying. And Lindley, from a product standpoint, how do you productize that, open source. >> Open source really, companies that have a distribution of open source for database and operating system, whatever the open source company most people are acquainted with, is Red Hat and Linux right. And so, we do the same thing that they do but for Postgres database. We take the distribution, we add testing, we add some other functionality around it so you can run Postgres responsively as Marc likes to say. So high availability, capability, fail-over management, replication, a backup solution. And instead of leaving it as an exercise for a customer, who wants to use open source, we test all this together. And then we validate it and we give them a complete package with documentation and services that they can access to help them be successful it. >> So if Michael Stonebraker were sitting right here, I say Michael, what do you think about Postgres? I'd say I had to start Vertica because we needed a new way. Yet, sort of PostgreSQL, is the killer remains the killer platform in the industry, doesn't it? >> Male Voice: Umhmm. Why is that? It's interesting when you talk to guys like Stonebraker, it's sort of dogma almost. But yet, customers, talk with their wallet. >> And it is, >> He did a very, very nice job of architecting it. It is a database that is extensible. The reason we add the first JSONB or document oriented implementation in the relational database space is because it was designed to make it easy to add new capabilities, new datatypes, new indexes, et cetera, into the same transactional model. That's why we have JSONB. That's why we have PostGIS. That's why we have key-value pair. So it was really well architected. And when you think about who else, not just Vertica has taken this engine >> Dave: Yeah. >> It is in Netezza, it is in a bunch of other. >> Dave: Master Data. >> Lenley: Greenplum. >> Greenplum yes. So it's a really robust architecture. Very, very nicely designed. It just does the job and it does it really well. Which is, what you want a database to do, right. It's not that exciting but it's really stable. It really works. The data is still there tomorrow. That's what really the requirements are. >> And to translate a little bit, Marc mentioned PostGIS, which is geo spacial capability for the Postgres database. And so we distribute that along with Postgres and test it so that you know it works. And he mentioned H-Store, so that's how you can actually store internet of things data really well into Postgres. And we talk about SQL, noSQL databases, so they're document databases. And the ability to have personalization at the same level you can in a document oriented database but in a structured SQL database are the kinds of things that have been added to Postgres over the years. Again, it's because of the basic architecture that Stonebraker put in place as an object relational database. >> It's so interesting to look at the history of database. Talk about Stonebraker, he's been on a number of times. It's just fascinating to listen to one of the fathers of this industry. But 10 years ago, database was like such a boring topic. And now it's exploded. Now you got Amazon going after Oracle. Oracle fighting the good fight. So many noSQL databases coming in. SQL becoming the killer big data app if you will. >> Male Voice: Umhmm. >> Why all of a sudden did database get so interesting? >> What happened was, application models changed. Led by Facebook, led by Amazon and Google. They said, let's refactor the applications and let's refactor the way we handle storage. >> Dave: Umhmm. >> And that led to the rise of the polyglot of databases is what a lot of people are saying. You have fit for purpose solutions and you may have three or four or five of them in your overall architecture. One thing about Postgres is, we're able to, because of the datatypes support that Marc mentioned, fit into that well. We don't try and do everything so if somebody says, I'm going to use Mongo for data capture, or I'm going to use Cassandra for capturing my internet of things data. We have what we call foreign data wrappers in the Postgres world. We call them just Enterprise DB Adapters but to Mongo, to Casandra, to Hadoop and can do bidirectional data there and just keep that data at rest over there in the other world. But be able to project relational schema onto it. We can push our data into those. We've got a great use case we've been talking about with a customer who had over a petabyte of data. And in the past what you do is, you'd go buy an expensive archiving solution and add that to it. Now, you just use Hadoop distributed file system. Push the data off there as it ages and have a foreign data wrapper that allows you to still query that data when it's out of your basic operational dataset. And move forward. >> Can I call that a connector or? >> Lenley: Yeah, a connector, that's not a bad idea. >> And it's interesting because If you guys remember Hadapt, probably. [Male Voices] Yeah. Yes. >> They came out, they were the connector killer. >> Male Voice: Umhmm. >> And it failed. >> Male Voice: Yeah. >> Seems like connectors are just fine. >> Male Voice: Yeah. >> And one of the really interesting things is, we call it data federation right. With philosophy here is, leave the data where it is. There are some data that should live in Hadoop or Cassandra. If I'm doing an e-commerce site with transactions and click streams, well, the click streams really should live in Hadoop. That the night natural place for them. The transactions should be in a transactional database. With the foreign data wrapper, I can run queries without moving the data, that will allow me to say, well, before you bought the brown teddy bear, which pages did you look at? >> Dave: Yeah. >> And I can do that integrated system and I can do a fit for purpose architecture. And that's what we think is really exciting. >> And that's fundamental to this new sort of programming or application models. >> Male Voice: That's right. >> The one that you were talking about is moving five megabytes of code to a petabyte of data. As opposed to moving data which we know has gravity and speed of light issues and so forth. >> Thank you for that little brief education. Appreciate it. So let's get into your business now, your relationship with IBM. What customers are doing. You mentioned IoT data so talk more about your business and your relationship with IBM and what you guys are doing for customers. >> There are a couple of things. We mentioned Oracle. And there are all the new databases. And then there's your, dare we say, legacy, proprietary databases as well. And people are looking to become more efficient in how they spend. We've done another thing with Postgres. We've added Oracle compatibility in terms of datatypes. So we support all the datatypes that Oracle does. And we support PL/SQL, they're sort of variant of stored procedure language. And implemented a lot of the packages that they have as well. So we can migrate workloads from Oracle over into an open source based solution. And give a lot cost effectiveness options to customers. >> Dave: Steal. This is a way that I can sort of have Oracle licensed database licensed and maintenance avoidance. >> Lenley: Yes. Yeah. >> Where possible, right. >> Where it makes sense. Where it makes sense. >> Obvious my quorum, I keep, but let's face it, the number one cost component of a TCO analysis of an Oracle customer is the database license and maintenance cost. >> Male Voice: That's right. >> It's not the people. One of the few examples I can think of where that's the case. There's always the people cost. [Male Voice] That's right, that's right. IT is very labor intensive. But for an Oracle customer, it's the database license. Cuz they license by Core. >> Male Voice: Yup. Cores are going through the roof. >> Male Voice: That's right. It's been great for Oracle's business. Although, wouldn't you agree, Oracle sees the writing on the wall that the SAS is really sort of the new control point for the industry. You see the acquisition of NetSuite and competition with Workday >> Male Voice: Yup. >> and the like. >> But the database remains the heart of the business. >> And really it's movement to the cloud, both private cloud and public cloud. And so we've been doing work there. We've had public cloud database as a service solution on Amazon for, what, [Marc] Four years. >> Four years, Marc. And have gained a lot experience with that. And were running that sort of running a retail, you can license the database and we'll provision it there. And so what we've done recently is change our perspective and said, let's put this into hands of customers. And let them standup their own database as a service. But also do it in a way that they can choose what workload should go to Amazon and what workload might go to their private cloud, built on open stack. And be able to arbitrage that if you will. Because they now have a way to provision the databases and make a choice about where to put it. >> So that's a bring your own license model that you just talked about? >> Bring your own license model or >> Are you in the Marketplace and, >> We're in the Marketplace in Amazon, where we can supply it that way. But customers have shown a preference for bring your own license. They want to make the best enterprise deal they can with a vendor like us or whomever else. And then have control over it. >> Amazon obviously wants you to be in the Marketplace. I won't even mention but I talked to some CEOs of database companies and they say, you know, we're in the Marketplace but we get in the Marketplace, next thing you know, Amazon is pushing them towards DynamoDB or you know. >> Male Voice: That's right, that's right. >> Now Amazon's come out with Aurora and Oracle migration and you know the intent to go after that business. Amazon's moving up the stack and you got to be careful. >> They are. But the thing about Amazon is that, they're a pure play in the cloud company. >> Dave: Yup. >> And all of the data shows that it's like a mix, it's going to be a hybrid cloud. Half the company in this world [Dave] Not Angie Jassie's data >> Eighty percent of the people in the cloud are going to be on-prem, still continuing their journey through virtualization. >> Dave: Yeah, that's right. >> Let along going to the cloud. But we want to be something that let's them put what they want in the public cloud and let's them manage on the private cloud in the same manner. So they can provision databases with a few clicks. Just like they do on Amazon. But do it in their data center. >> You doing that with Softlayer as well or not yet? >> Lenley: Not yet. >> Marc: Not yet. >> We've built this provisioning capability ourselves. And it came out of the work we did putting up databases on Amazon. >> So what are you guys doing here at Edge. Edge is kind of infrastructure show. Database is infrastructure. >> We're talking about our work with Power. >> Power is a big partner for us. Power is I think very, very interesting for our database customers. Because of the much higher clock speeds and the capabilities that the Power processor has. When I'm looking at Power, I get more oomph out of a single core which really for a database customer is very, very interesting. Because all databases are licensed by Core. >> Dave: Right. >> So it's a much better deal for the customer. And specifically for Postgres, Postgres scales very well with higher clock speeds. So by having, let's say, by growing performance, not by adding more cores but by making the individual cores faster, that plays very, very well to the Postgres capabilities. >> Okay, so you are a Power partner, part of that ecosystem that IBM is appealing to to grow the OpenPOWER base. And what kind of workloads are you seeing your customers demand and where you're having success? >> Across the board. Database is mostly infrastructure capabilities so there's a lot of interest that we're seeing that, for all kinds of applications really. >> What's the typical Power customer look like these days? You got some Oracle, you got some DB2, you guys are running on there, what's the mix? Paint the picture for us. >> I think the typical Power customer is the typical enterprise company. And, [Dave] Little bit of everything. >> It's a little bit of everything. But one of the key things is that, people are also looking at what they've got and the skills they have in place. You were talking about people cost right. [Dave] Yeah. >> And their understanding of management. Their understanding of how to manage the relationship with the vendor even. And then saying, look, how can I move into the new world of digital transformation and start my own private cloud options and things like that in an efficient way. That makes efficient use of hardware I have in place and has a growth curve and new hardware that's coming out that fits my workloads. >> Dave: Umhmm. >> And the profiles that Marc was talking about. >> And also the resources. Which is very interesting when we look at these new digital applications with Postgres. Because you can do so much in Postgres from geographic information systems to document oriented to key-value. But you can do that with your existing developers through existing DBAs. They don't need to go to school to learn a new database. And that's also a very, very, interesting capability. So you can use your existing team to do new stuff. [Male Voice] Yup. >> What's happening in IoT, what problems are you solving there and where's the limit? >> Sensor data collection. >> Lenley: Yeah. Real interesting because sensor data tends to come in all different forms. We have customer who collects temperature sensor, temperature data. But the sensors are all sending different data packets. So because we can do document oriented or key-value, we can easily accommodate that. In the old days with the relational model, I had to do all kinds of tricks to sort of stuff all that into a relational table. My table would be almost empty at the end because I'd have to add columns for every vendor et cetera. Here, now I can use put all that into the same format and provide it for analysis. So that's a real interesting capability. >> And it's interesting too because we've got really strong geo spacial data support. And the intersection of that, with IoT is a big deal. They track your iPhone, they know where we are. They know what's going on. That's sensor data. They know which lights in which building, which you know, louvers that are controlling HVAC are malfunctioning or not. They want to know specifically where it is, not just what the sensor is. And some of that stuff moves around. And it gets replaced in a new place in the building and such. So we're well setup to handle those types of workloads. >> What's interesting, when IBM bought the weather company, [Lenley] Yeah. >> And they thought okay great, they're getting all these data scientists and weather data, that's cool. They can monetize that but it's an IoT play, isn't it? [Male Voice] Right. Right. >> Talk about sensor. >> It's reference data. It's reference data for other company specific IoT plays. To have a broader set of sensors out there in their region and understand what's happening with weather and things. And then play that against what their experience is, managing new building or manufacturing processes, everything. >> So what's the engagement model. I'm a customer, I want to do business with you. How do I do it, how do I engage? >> Well, a lot of our businesses direct with us. Others through partners. And then a lot of customers come to us because they want to get off legacy systems. But really, what they do is, once they understand the database and the capabilities, they say, okay yeah, you can do the Oracle stuff. But what I'm really going to do with you is my new things. Because that's really exciting and it helps me kind of put a lid on the commercial license growth. So maybe I'm not going to get off it, but I will stop growing it. So I will start doing my new stuff on Postgres. Whenever I modernize something, Postgres is going to be my database of choice. If I already open up an application with its whole stack, this is one of the changes I'm going to make. And then the database as service, is very, very interesting. So these four entry vectors and what happens is, quite a few customers after a short time when they started with project or applications, they end up making Postgres as one of their database standards. Not the only one. But they make it one of the database standards so it gets into the catalog and every new project then has to consider Postgres. >> It's interesting, there's a space created as Microsoft sort of put all their wood behind the era of becoming a competitor to high end Oracle. And with this last release, they probably are on there, arguable. But they've also raised their prices too. And they've made the solution more complex. So there's this space that was vacated for like a ton of workloads and Postgres fits in there just about perfectly. We see enterprise after enterprise come to us with a sheet that says, now we're going to get some of this noSQL stuff. We're going to keep Oracle or DB2 over here for these really high end things. Run my financials, run my sales order processing, my manufacturing. And then we got this space in here. We got a slot for relational database and we want to go open source. Because of the cost savings. Because of other factors. It's ability to grow and not be bound to, hey, what if the vendor decides they're going to go for a new cooler thing and make me upgrade. >> Dave: Right. >> And I want to stay there and know that there's still being an investment made. And so there's a vibrant community around it. And it just fits that slot perfectly. >> You got to pay for that digital transformation and all these IoT initiates. You can't just keep pouring [Male Voice] Somehow. >> down to database licenses. [Male Voice] That's right. >> Tell me, we have to leave it there. >> Thanks very much >> Male Voice: Alright. >> for coming to theCUBE. >> Thanks so much. >> We appreciate the time. You welcome. [Male Voice] Enjoy it. Keep it right there buddy. We'll be right back with our next guest. This is theCUBE. We're live from IBM Edge 2016, be right back. (upbeat music)

>>Ed, welcome to the Cube. All right, Thanks guys. Good >>To see you. Thanks. Good to see you as well, >>John. Okay. Ed runs Biz dev for Cloudera, Industry veteran, worked at VMware. Ed, gotten to know you the past year. You guys have been doing great. What a difference one year makes, right? I mean, absolutely. Tell us, just let's start it off with what's happened in a year. I mean, you know, here at Hadoop World Cloudera, the ecosystem. Just give us your view of your perspective of what a difference one year makes. >>I think more than double is probably the, the fastest answer I could give you, which is, I mean, even looking around at the conference, it's, it itself is literally double from what it was last year. But in terms of the number of partners that have entered the market and really decided to work with, with Cloudera, but also in general, just the, the, the, the scope and size of the ecosystem itself, investors from every angle. You've got companies really well-branded marquee companies like Oracle coming into the mix and saying, Hey, Hadoop is the, is the real deal and we need to invest here. Marquee companies like IBM and EMC also doing the same. And of course, you know, as a result, you know, lots and lots of customer interest in the technology. And Cloudera's been fortunate to have been in the market early and really made the right investments with the right team. And so we're able to serve a lot of those customer needs. So it's been really, it's been a fantastic year for the company. >>So we had a great day yesterday with Cloudera. We had Kirk on, we had AER on twice, who by the way went viral with his modern warfare review, but we had Jeff Harmar Baer on, so we had pretty much the brain trust, Mike and Michaelson. Yep. The brain trust, the Cloudera. So we talked about the risk factors for Cloudera. Obviously you guys are number one, you've been kind of had untouchable lead and then all of a sudden boom competition. So Mike talked about that. So the strategy and the product side, they addressed, you're on the, the biz dev side, so you know, when you were number one, everyone wants to stand next to you and your phone rings off the hook from tier one partners all the way down to anyone's just getting in the business. Who wants a big data strategy on the execution. Now, what are you guys doing right now to, to continue your lead on the, on the sales marketing biz dev? I mean, I know you get the partner program, but what's your strategy for Phil, how to continue >>In that lead? The, the beautiful thing is honestly, our strategy hasn't changed at all. And I know that might sound counterintuitive, but we started off with a, a really crisp vision. And we want, what we wanna do is create a very attractive platform for partners. And, and, you know, one of the core, you know, sort of corporate strategy, Edix for Quadera is a recognition that the end of the day, the platform itself, Hado is an input into a solution. And Quadra is not likely to deliver the complete solution to market. Instead, it's going to be companies like Dell, for example, or it's going to be companies on the, on the ISV side like Informatica, which you're gonna deliver not only a base platform, but also the, the, the, the BI or analytics or data integration technologies on top. And as a result, what we've done is we've really focused in on creating a very attractive platform to vendors to build on. >>And one of the, I think one of the biggest misconceptions that I'm excited about that, you know, we are now having an opportunity to correct and that's a result, frankly, of the additional competitive dynamic. And I think the, the Wiki bond team pointed that out rather pointedly in their most recent articles. But is, is the sort of the lack of understanding around what CDH is and also the, some of the other investments that we're making to create a truly attractive platform for vendors to build on. And you know, I mean, I think you, you may have familiarity with exactly what CDH is, but for the sake of the audience here, what I'd like to do is say, say, first off, you know, first and foremost this is a hundred percent free in Apache license open source. But more importantly, it is everything that we build on the platform, meaning it's completely full featured. >>We put all of that out in the open. There's no turbo version of Hadoop that we've got hiding in the closet for our, our four pay customers. We're absolutely making investment. But I think, you know, when you think about it from the vendor perspective, and that's my bias. So I always think about, I treat all of the potential partners as really my customer. And when you think about it from that perspective, the things that matter most to vendors, number one, transparency. They need to understand exactly what our business model is, where we plan to make money and where we plan, don't make money. They need to know what we're really good at developing and what we're not so good at developing. And sort of where we draw the, the boundaries around that investment. I think, you know, a testament to that, for example, is tomorrow we're hosting a partner summit. >>So after this event, there are gonna be over 60 individuals, but they max two per per vendor. So we're gonna have over 35 vendors attending this event. And what they're gonna hear from is our entire management team is as deeply as we can and as open as we can. And you know, it, it's, it's, it's funny, you know, I think I saw this article in Forbes the other day about Cloudera. It was this, the title of the article was something like Spies Like Us. And it it, and it, what it highlighted was that some, some competitor of Cloudera had actually hired a, a, a competitive intelligence agency to go on and, and try to engage with, you know, and, and try to learn more about Cloudera. And so they went on to Cora, which we have a lot of active engineers on Cora. And they, you know, they went out and they asked a bunch of product related questions to our to, to someone on Cora. And our engineers immediately responded and they started being very transparent, completely open to what, what they're building and why they're building it. And the article basically summarized to say, Hey, you know what, you know, clearly some people aren't all that sophisticated in figuring out, you know, who they're talking to. And it's really important to do that. And they got the absolute wrong conclusion. Our engineers are actually encouraged and in fact rewarded for being extremely transparent in the market because we believe that it's transparency will ultimately allow us to be that platform vendor. >>And that's what attracts me. Jeff Hummer Bucker, who's active on core as well, he's recruiting there too. So you guys are out engaging the community. Yeah. So just let me just review, cuz this is cool that you're addressing this because Hortonworks and others, and I'll say the name Hortonworks has been pumping up the PR and creating a lot of noise around open and kind of Depositioning Cloudera. So you guys are completely open, a hundred percent Hadoop, open source, everything you build in, in every way, in every way. You have engineers building core, you've got tools and all the other stuff is being built in Cloudera then contributing into the community. >>Actually it's the other way around. We build it and the community@apache.org. So all of our technology is built@apache.org. It's, it's developed there. It's, it's, it's initially shared there. And then we have another team inside our company that pulls down bits from apache.org and then assembles them and integrates them. So it's really, it's a really key thing. And there's no, we do, we have no bits that we don't develop@apache.org that are part of cdh. So there, I mean there can be no mistake that everything that that is in CDH is everything we got. >>So CDH is free. >>It is free >>And every it's open source. It's open you >>Charge enterprise edition. That's the only thing that's different you guys charge >>Yeah. Which is your management console, right. >>Management >>Suite and all kinds of >>The tools. And that's not free and that's not open source. That's correct. Just to be clear. Yep. But so AER took us yesterday through, I don't know, half a dozen probably open source projects and then the one is the, the management console. And that's what you charge for, that's where you're gonna make money? >>Yeah. We, we manufacture, essentially we manufacture two products, but we sell one. So we manufacture the Quadera distribution, including Apache Duke, that's free. It's free. And then we all in open source and built it Apache and, and really heavily tested and well documented and, and, and well integrated. And then we also manufacture quadera Enterprise, which includes support and indemnities and warranties for that full featured CDH product and also includes the Quadra management suite. And >>That's a subscription. >>And that's a subscription. And so customers can, can run cdh, they can then buy and license Cloudera Enterprise and then someday if they decide they don't need Cloud Air Enterprise for whatever reason, if they're, if their team are scripting wizards and they've decided that they, you know, they don't need the extra opportunity for being able to track all of the things that Cloudier Enterprise allows 'em to, they can step off of cloud enterprise and continue to use full feature to do as they see >>Fit. So take an example of one of your partners that you announced this week. NetApp NetApp's gonna package your cdh CDH and the subscription Correct. To their, their customers. And then they're gonna let their channel either, you know, they'll pre bule it or do a reference architecture, you'll get paid for that subscription that's bundled. That's correct. Will make money off of its filers. Yes. And the customer gets a package solution. >>Exactly. Right. And in fact, that's another important thing that you know, is probably worth discussing, which is our go to market model. I don't know if you guys had a chance to talk with anyone yesterday on that, but I'm responsible for our channel strategy and one of the key things that we've agreed to as a, as a company is that we really are gonna go to market through channel partners. Yeah. >>We covered sgi, that was a great announcement. >>Yep, a >>Hundred percent >>As, as close as we can get. Okay. I mean that is our, he's >>Still doing the direct deals. You still have that belly to belly sales force because it's still early, right? So there's a mix of direct and indirects, not a pure >>Indirect, but as, and that's only, that's only as we're able to, until we're able to ramp up our partners fully, in which case we really want our, the current team that is working belly to belly to really support our partners. >>So all so VMware like, but I I wanted to ask >>You VMware, like NetApp, like very similar. >>Yes. Very, very NetApp. Like NetApp probably 75%, you know. Exactly. What are the similarities and differences with VMware in, in the ecosystem? You know it well, >>I do know it well. Yeah. I spent several years working at VMware and you know, I think, I mean the first and most obvious difference is that when you think, when I think about platform software in general, you know, there are a few different flavors of platform. One of the things that makes Hadoop very unique, very unique relative to other platforms is that it, not only is it Apache license, but it really is, it's dependent upon other external innovators to, to create the entire full value of the ecosystem. So, or, or you know, of the solution, right? So unlike for example, so like, let's take a platform like everyone's familiar with like Apple iTunes, right? What happens is Apple creates the platform and they put it kind of in the middle on top of and behind the scenes is the innovator, the app builder, he builds it, he publishes it on Apple, and then Apple controls all access to the >>Customer. Yep. >>That's not adu, right? Right. Let's take VMware or Red Hat for example. So in that case, they publish a platform they own and control the, the absolute structure and boundaries of what that platform is. And then on top of that application vendors build and then they deliver to the, the customer. But you know, at the end of the day, the, you know, the relationship really is, you know, from that external innovator straight down, and there's no, there's, you know, there's no way for them to really modify the platform. And you take kadu, which is a hundred percent Apache licensed to open source, and you really, you really open up the opportunity for vendors to take ADU as an input into their system and then deliver it straight to their customers or for customers themselves to say, I want straight up vanilla Hadoop, I'm gonna go this way and I'm gonna add on my own be app of applications. So you're, we're seeing all sorts of variants right now in the market. We're seeing software as a service being delivered that's based on Hadoop. There was a great announcement a few weeks ago from a company named Tidemark, previously known as Per Ferry, and they're taking all of cdh. They're, but they're, the customer doesn't know that they're, and what they're doing is they're delivering software as a, as a service based on adu. >>Yeah. So I mean, you know, we are psyched that you're clearing this up because obviously we're seeing, we saw all that stuff, but I really think that indirect strategy as a home run, I'm said it when we talked about the SGI thing, and it's accelerates you guys, you enable, but you know, channels is an interesting business. I mean the, you have to have pure transparency as you mentioned, but they need comp, people need confidence and, and they don't, they worry about competition. So channel conflict is always the big issue, right? Right. Is Cloudera gonna compete with us? So talk that, talk us through that, that strategy. So obviously the market's growing, new solutions are coming around the corner, These guys wanna make money. I mean channel, it's all about, you know, what have you done for me today? >>Right. That, that is exactly right. And you know what, that's, that's why we decided on the channel strategy specifically around our product is because we recognize that each and every single potential channel partner of ours can actually innovate themselves on top of and create differentiation. And we're not an obstacle to that process. So we provide our platform as an input and we're capable of managing that platform, but ultimately creating differentiation is all in the hands of our partners and we're there to help, but it gives them wide latitudes. So take for example, the differences between Dell and NetApp solution, they are very different reference architectures leveraging the exact same platform. >>Yeah. And they have to make money. I mean, the money making side of it is, you know, people have kind of, don't really talk about that, but, you know, channel partners loyalty is all about who can help them make cash. Right. Right. Exactly. What are you hearing there in terms of the ecosystem? Has the channels Bess and the partnerships or the more as size, what's the profile of your, of your partners? I mean, can you give us the breakdown of Sure. We have what you look like from Dell. We know Dell and NetApp, but they're gear guys. But, >>So a big part of our strategy is to work with IHVs and then Ihv resellers. So you're talking about companies like Dell, like sgi, like NetApp, for example, independent hardware manufacturers. Another part of our strategy though, and a key, a key requirement from our customers is to work with a whole variety of ISVs, particularly in the data management space. So you've got really marquee companies in the database space like IBM's Netezza or Terradata. You've got in companies like Informatica and Talent, you've got companies on the BI side, like Micro Strategy and Tableau. These kinds of technologies are currently in play at our customers that have made substantial investments. And ultimately they want to be able to continue to leverage them with the data platform, whichever data platform that they end up choosing. So we invest considerably there. A big part of that has been our Qera Connect partner program. >>It's an opportunity for us to help the customer to understand which technologies work and work well with, with our platform. It's also an opportunity for us to engage directly and assist the vendor. So one of the things that we created as part of that program is first off, immediate and absolute discounted access to any part of our training. Second, lots of free information, access to our world class knowledge base, access to our support team, direct access to our support team. The, the vendors also get access to a developer portal that would created specifically for them. So if, if you think about it this way, Hadoop gets built@apache.org, but solutions don't get built@apache.org. Right? So what we're really trying to help our vendors do is be able to develop their solutions by having real clear visibility to the API level points of Hadoop. They're not necessarily interested in, in trying to figure out how, how MR two works or, or contributing code to that. >>But they absolutely are interested in figuring out how to run and execute their software on top of a do. So when I think about the things that matter to create an attractive platform, and at the end of the day, that's what we're really trying to do, first and foremost is transparency, right? Second really ultimately is really clear visibility to the APIs and the documentation of that platform so that there's no ambiguity that the, the vendor, this is the user in this case, it's building a solution, can absolutely absorb all of that content really cleanly. And then ultimately, you know, I think it's customers, right? Users of the technology. And I think our download numbers are, they're, they're, there's something we're proud of. >>We, we are, we're hearing good feedback. I mean, the feedback we hear from folks is, yeah, I love how they take away the complexity of handling versions and whatnot. So, you know, I think totally is a great way, The CDH is a great bundle. You know, the questions that we have for you is what are you hearing about the other products, the ones you're actually selling? Does that create the lock in? So that's something that we asked Elmer directly, you know, is that the, is that the lock in and what happens when the deployments get so big? You know, >>I mean, the way, I >>Don't really see an issue there, but that's what people are afraid of. I mean, that's kind of the, it's more of fear. I mean, some people can use that fear and, and >>Play against. I think, I think what we've seen in other markets is that management tools are ultimately interchangeable. And the only way that we're gonna retain a customer is by out innovating the competition on the management side, the lock in, the lock in component, as you will, is not really part of our business model. It's very difficult to achieve with an Apache licensed platform and a management suite that sits on outside of that, that licensed artifact. So ultimately, if we don't owe innovate, we're gonna lose. So we're working on the innovation and that's, >>How's the hiring go? Oh, go ahead. >>I, I had a, I wanted to come back to that. You mentioned download numbers. Can you share the numbers >>With the others? I can't, I can't share them publicly, but what I can say is that they've been on an incredible trajectory. Okay. That, and what we've seen is month to month growth rates, every single month we continue to see really significant growth rates. >>And then I, I had a follow up question on, you talked about the, the partner program. How do you manage all those partners? How do you prioritize them? I mean, the, the hardware vendors, it's pretty easy. There's a few big whales, but the, the ISVs, they're, I mean, your phone, like John said, must be ringing off the hook. How do you juggle that and, and can you do it better than VMware, for example? >>Well, we do it, we handle the, the influx of partner interest in two ways. One, we've been relatively structured with the Quadra Connect partner program, and we make real investments there. So we have dedicated folks that are there to help. We have our engineering team that is actually feeding inputs, and we're, we're leveraging some of the same resources that we provide to our customers and feeding those directly to our partners as well. So that's one way that we handle it. But the other way, frankly, is, I mean, customers help here having access to and, and a real customer population, they help you set priorities pretty quickly. And so we're able to understand what we track in inside of our systems, which, which technologies our customers use. So we know, for example, what percentage of our customer base has has SaaS installed, and we'd like to use that with a, do we know which percentage of our customer base is currently running on Red Hat and which is not. So having core visibility, that helps us to prioritize. >>How about incentives? I mean, obviously channel businesses as, like I said, very fickle people, you know, you know the channel business, I spent, you know, almost a decade in, in HP's channel organization and you know, you have to provide soft dollars. There's a lot of kind of blocking and tackling. You guys are clearly building out that tier one with the SGIs of the world and other vendors, and then get the partner connect program for kinda everyone else who's gonna grow up into a tier one. Yeah. Training, soft dollars incentives. You guys have that going yet, or is the >>Roadmap? We do. And in fact, you know, in addition to the sort of more wide publicized relationships you see with companies like Dell and Cloudera, we're actually building a very successful network of independent ours. And the VAs in general. What we do is we prioritize and select ours based on the top level relationships that we have, because that really helps them to hone in. They've got validation from, for, for example, someone that sells resells. SGI is an organization that now is heard really loud and clear from sgi the, the specific platform configurations that they're gonna represent to their customers, and they ultimately wanna represent them directly. And how we make investments is we're, I mean, the investments we're making ultimately in our sales org, I'm gonna lose the word direct from that conversation because our sales org is being built to help our partners succeed. And I think that's where you're, >>The end game is to go completely indirect and have all your support go into managing that channel. What, what's the mix of revenue generation from your partners? Obviously as a, you know, with sgi they have pre-built channels that you're funneling in, you got NetApp and they're wrapping their products and services around it. How much is services and how much is a solution specifically? Do you have any visibility or a feel for that at this >>Point? I mean, services relative to, You mean for Cloudera particularly, or for our >>Partner? No, for the, for the part. I mean, if I'm a partner, I'm like, Hey, okay, I'm gonna use cdh. I'm on bundles. I don't mind paying you a wholesale if I'm gonna be able to throw off more cash on, you know, deployment and cloud and services, et cetera. And or if I'm a product manufacturer, a product, a solution I fund you in. I need to have that step >>Up a absolutely great question. So depending upon the partner we're dealing with, they like to either monetize or generate their revenue in different ways. So for example, NetApp, NetApp is a company that has very limited services, and their, their focus is a business is really on delivering hardware and software configured together. And they, they rely heavily on a services channel to fulfill, you take in, in contrast to a company like, for example, Dell, which has a very successful services business and really is excited about having service offerings around Hadoop. So it depends upon the company. But when we talk about our VAR channel in particular, one of the things that's a, in an internal acronym, but I'll share it publicly here. We, we call our, our supervisors and what makes them super and why, why we've selected the, the, the organizations that we are selecting right now to be our bar is that they not only can fulfill orders for hardware and software, particularly data management or infrastructure software, but they also have a services team on hand because we recognize that there is a services opportunity with every Hadoop deployment. And we want our partners to have that. So as an organization, we're structuring our, our services staff to facilitate and enable our partners not to be sold >>Directly. Okay. So that's the follow up that I had tomorrow when the partners ask, Okay, what do you want to be when you're really growing up? Is it services, is it software? >>Is it Carter is a software company, Crewing through, >>Oh, er we kind of got ett, well, he didn't say it, but we said it's a operating system. Yeah. >>So given that, so given that, I mean, you can make money on services, right? People need services. Okay, great. >>And partners will make that money for >>Us. And, and you know, early on you, you had to do some of that and you're, you've been very clear about where it's going. It's hard to make money in software when you're given all the software away for free. Well, >>We're not giving all >>The software. I know you've got that piece now, but, but here's my question. As ADU goes into the enterprise, which is clearly doing, is that that whole bundling, like what you're doing with NetApp is that really ultimately how you're gonna start to, to monetize and, and successfully monetize your software, >>Is by pushing it through >>Yeah. Packaging and that bundling that solution, in other words, our enterprise customer is gonna be more receptive to that solution package than say the, the fridge that has been using Hadoop for the last >>Two or three years. I think there's no question about it. If you, if you look at what Quadra Enterprise does, I don't know if, if you've had a chance to attend any of the sessions, maybe where Quadra Enterprise is, is currently being demonstrated. >>We just had Alex Williams as about on the air. Did a review, >>Okays >>Been going good and impressed with it? >>Yeah, there's no question about it. And I, I don't, and Alex probably hasn't seen the new version that, you know, our team is working on and it's, you know, quietly working on in the background. Incredible, incredible developments in, And that's really a function of when you have direct access to so many customers and you're getting so much input and feedback and they're the kinds of access to the kinds of customers we ultimately wanna serve. So real enterprises, what you get is really fast innovation from a really talented team that knows to do well. I mean, we are years ahead on the management side. Absolutely. Years ahead. And you know, I, so I was a guy who worked at VMware for several years, and I can tell you that while the hypervisor itself was, was a core component to VMware success, the monetization strategy was very squarely around vCenter. Yeah. Yes. Out. And we're not ignorant to that. Yeah. >>You can learn a lot from your VMware experience cause absolutely. The, the market changed significantly. And, you know, >>There were free hypervisors available all of a sudden. VMware itself had a free hypervisor. We had, we had VMware server and we had also our VMware player products, right? And those were all free. And they were very good technology. They were the best available in the market for free. And they were better, in my opinion, they were better than anything else. Open or not. No, our time >>Too, since still >>Are, they were, they, they were, they were superior products in every way. But yet how VMware was successful was recognizing that in the interest of running a production environment with an sola, you need management software. And they've also built the best management software. And there's no question that we understand that strategy and >>A phenomenal ecosystem. I mean, there's the >>Similarities, right? They did. And you, and the, and the ecosystem was in, in large part predicated on transparency act, very clear access to the APIs and a willingness to help partners be successful with those APIs. And ultimately drawing a very tight box about what the company wanted to do and didn't want to do. >>I mean, look, you're not, you're not gonna lose friends when you make people money. That's my philosophy, right? I agree. So when you're in that business where you can come in and enable a channel and have options on your growth strategy, which you do, I mean, you can say, Okay, bundling, I can go, you know, I can have this sold direct, or at least as long as you've got the options, you can grow with that market. So, you know, again, the, it's a money making opportunity for the partnerships, but there's >>More than that, right? Because you mentioned Apple, iTunes, Oracle's another example. And the way you make money with Apple and the way you make money with Oracle is different than the way you make money with VMware and presumably Cloudera. >>Yeah, I mean, our strategy is, if you make this base platform easier to install, more reliable, and you make it ultimately, you know, really rock solid from an integration standpoint, more people are gonna use it. So what happens when more people use it? First thing that happens is more solu, it's out there. So it's more solutions get built. When more solutions get built, then you see more clusters get developed. When more clusters are out there, they start to move into production. And then they, they need an sla when they need an sla, Cloudera and Enterprise gets purchased. But along that path, when those solutions got built, guess what else happened? More cloud units got sold, more servers got sold, more networking. Gear got sold, more services got created. You get, you get ultimately more operating systems got sold, more databases, got data into them, more BI clients got created. The ecosystem is deep and rich, and a lot of people stand to make money hop >>In people. The water's great. >>What about, what about support? Okay, so, you know, the other guys are saying, We're just gonna make money on support. I mean support, You guys still are doing support, right? I mean, you're selling >>Support. There's no question. Quad Enterprise contains two things, right? The management suite and support this is, this is not uncomplicated technology and having a world class support team is of value and customers do want to pay for that value. But we, we believe that support in and of itself is not enough. And that ultimately, when you wanna deliver an sla, being able to call when you have a problem is the wrong approach. You want to be proactive and understand the problem well in advance of it actually occurring. That's really important. When, for example, if you're a customer, a lot of our customers have a data pipeline that >>They, they're building out basically. I mean they're, it's, it's new and emerging. So they're building out, It's not just support. They need other tools. >>Yeah. And it building out I think is an understatement for some, where some of our customers are. I mean, when you have a thousand node cluster that you're operating Yeah, Yeah. To, that's mission critical to your business. I don't think that's building out anymore. I think that's an investment in a technology that's mission critical. And what you wanna see when you have a mission critical technology is you wanna know early and often when a problem may emerge. Not, Oh, oh my gosh, we have a problem now I need to go, you know, phone a friend, phone a friend is, is kind of a last resort. We offer that. But what we really do is, and that's the, that's the beau, That's why we don't decouple our support from our management suite. It's not about phone a friend. It's about understanding the operation of your cluster the entire way through 24. >>And the other op the other thing that people don't talk about in the support is that with open source, a lot of support gets handled in the community as well. So like That's right. So in a way, you're already pre cannibalized with the community >>By us and by others. Absolutely. But you, you'll never see to that Forbes article I referenced earlier. You will never, you will not see our, our engineers are not trained to withhold information and under any circumstances to anyone free or paying. Yeah. This is about getting, You >>Don't wanna hold back your business. I mean, you have nothing to hide. It's open rights. >>Open source. It's open. And we're here to help. We're here to help. Whether you're paying us or not, >>This is value to that anticipatory >>Remediation. Yeah. That's what you're packaging and clearing up the air. Great. Great cube guest, you're awesome on the cube. Gonna have you more on because great to get the info out there. Really impressed with the channel strategy. Love the love the growth strategy, the cloud air. You guys are really impressive. I'm really, really impressed to see that you guys got everything pumping on all cylinders, Kirk, and you are cranking out on the business execution. We're in the team playing this chest mask open. Perfect. So great. Congratulations. Great. Thanks. You guys just in the financing. >>Oh, thank you as >>Well. Hey, Ed from Cloudera, clearing it up here inside the cube. We're gonna take a quick break and we'll be right back with more video. >>Thanks guys. All right.