>>Okay. We're back here, live in New York city for big data week. This is siliconangle.tvs, exclusive coverage of Hadoop world strata plus Hadoop world big event, a big data week. And we just wrote a blog post on siliconangle.com calling this the south by Southwest for data geeks and, and, um, it's my prediction that this is going to turn into a, quite the geek Fest. Uh, obviously the crowd here is enormous packed and an amazing event. And, uh, we're excited. This is siliconangle.com. I'm the founder John ferry. I'm joined by cohost update >>Volante of Wiki bond.org, where people go for free research and peers collaborate to solve problems. And we're here with Jack Norris. Who's the vice president of market marketing at map are a company that we've been tracking for quite some time. Jack, welcome back to the cube. Thank you, Dave. I'm going to hand it to you. You know, we met quite a while ago now. It was well over a year ago and we were pushing at you guys and saying, well, you know, open source and nice look, we're solving problems for customers. We got the right model. We think, you know, this is, this is our strategy. We're sticking to it. Watch what happens. And like I said, I have to hand it to you. You guys are really have some great traction in the market and you're doing what you said. And so congratulations on that. I know you've got a lot more work to do, but >>Yeah, and actually the, the topic of openness is when it's, it's pretty interesting. Um, and, uh, you know, if you look at the different options out there, all of them are combining open source with some proprietary. Uh, now in the case of some distributions, it's very small, like an ODBC driver with a proprietary, um, driver. Um, but I think it represents that that any solution combining to make it more open is, is important. So what we've done is make innovations, but what we've made those innovations we've opened up and provided API. It's like NFS for standard access, like rest, like, uh, ODBC drivers, et cetera. >>So, so it's a spectrum. I mean, actually we were at Oracle open world a few weeks ago and you listen to Larry Ellison, talk about the Oracle public cloud mix of actually a very strong case that it's open. You can move data, it's all Java. So it's all about standards. Yeah. And, uh, yeah, it from an opposite, but it was really all about the business value. That's, that's what the bottom line is. So, uh, we had your CEO, John Schroeder on yesterday. Uh, John and I both were very impressed with, um, essentially what he described as your philosophy of we, we not as a product when we have, we have customers when we announce that product and, um, you know, that's impressive, >>Is that what he was also given some good feedback that startup entrepreneurs out there who are obviously a lot of action going on with the startup community. And he's basically said the same thing, get customers. Yeah. And that's it, that's all and use your tech, but don't be so locked into the tech, get the cutters, understand the needs and then deliver that. So you guys have done great. And, uh, I want to talk about the, the show here. Okay. Because, uh, you guys are, um, have a big booth and big presence here at the show. What, what did you guys are learning? I'll say how's the positioning, how's the new news hitting. Give us a quick update. So, >>Uh, a lot of news, uh, first started, uh, on Tuesday where we announced the M seven edition. And, uh, yeah, I brought a demo here for me, uh, for you all. Uh, because the, the big thing about M seven is what we don't have. So, uh, w we're not demoing Regents servers, we're not demoing compactions, uh, we're not demoing a lot of, uh, manual administration, uh, administrative tasks. So what that really means is that we took this stack. And if you look at HBase HBase today has about half of dupe users, uh, adopting HBase. So it's a lot of momentum in the market, uh, and, you know, use for everything from real-time analytics to kind of lightweight LTP processing. But it's an infrastructure that sits on top of a JVM that stores it's data in the Hadoop distributed file system that sits on a JVM that stores its data in a Linux file system that writes to disk. >>And so a lot of the complexity is that stack. And so as an administrator, you have to worry about how data gets permit, uh, uh, you know, kind of basically written across that. And you've got region servers to keep up, uh, when you're doing kind of rights, you have things called compactions, which increased response time. So it's, uh, it's a complex environment and we've spent quite a bit of time in, in collapsing that infrastructure and with the M seven edition, you've got files and tables together in the same layer writing directly to disc. So there's no region servers, uh, there's no compactions to deal with. There's no pre splitting of tables and trying to do manual merges. It just makes it much, much simpler. >>Let's talk about some of your customers in terms of, um, the profile of these guys are, uh, I'm assuming and correct me if I'm wrong, that you're not selling to the tire kickers. You're selling to the guys who actually have some experience with, with a dupe and have run into some of the limitations and you come in and say, Hey, we can solve some of those problems. Is that, is that, is that right? Can you talk about that a little bit >>Characterization? I think part of it is when you're in the evaluation process and when you first hear about Hadoop, it's kind of like the Gartner hype curve, right. And, uh, you know, this stuff, it does everything. And of course you got data protection, cause you've got things replicated across the cluster. And, uh, of course you've got scalability because you can just add nodes and so forth. Well, once you start using it, you realize that yes, I've got data replicated across the cluster, but if I accidentally delete something or if I've got some corruption that's replicated across the cluster too. So things like snapshots are really important. So you can return to, you know, what was it, five minutes before, uh, you know, performance where you can get the most out of your hardware, um, you know, ease of administration where I can cut this up into, into logical volumes and, and have policies at that whole level instead of at an individual file. >>So there's a, there's a bunch of features that really resonate with users after they've had some experience. And those tend to be our, um, you know, our, our kind of key customers. There's a, there's another phase two, which is when you're testing Hadoop, you're looking at, what's possible with this platform. What, what type of analytics can I do when you go into production? Now, all of a sudden you're looking at how does this fit in with my SLS? How does this fit in with my data protection, uh, policies, you know, how do I integrate with my different data sources? And can I leverage existing code? You know, we had one customer, um, you know, a large kind of a systems integrator for the federal government. They have a million lines of code that they were told to rewrite, to run with other distributions that they could use just out of the box with Matt BARR. >>So, um, let's talk about some of those customers. Can you name some names and get >>Sure. So, um, actually I'll, I'll, I'll talk with, uh, we had a keynote today and, uh, we had this beautiful customer video. They've had to cut because of times it's running in our booth and it's screaming on our website. And I think we've got to, uh, actually some of the bumper here, we kind of inserted. So, um, but I want to shout out to those because they ended up in the cutting room floor running it here. Yeah. So one was Rubicon project and, um, they're, they're an interesting company. They're a real-time advertising platform at auction network. They recently passed a Google in terms of number one ad reach as mentioned by comScore, uh, and a lot of press on that. Um, I particularly liked the headline that mentioned those three companies because it was measured by comScore and comScore's customer to map our customer. And Google's a key partner. >>And, uh, yesterday we announced a world record for the Hadoop pterosaur running on, running on Google. So, um, M seven for Rubicon, it allows them to address and replace different point solutions that were running alongside of Hadoop. And, uh, you know, it simplifies their, their potentially simplifies their architecture because now they have more things done with a single platform, increases performance, simplifies administration. Um, another customer is ancestry.com who, uh, you know, maybe you've seen their ads or heard, uh, some of their radio shots. Um, they're they do a tremendous amount of, of data processing to help family services and genealogy and figure out, you know, family backgrounds. One of the things they do is, is DNA testing. Uh, so for an internet service to do that, advanced technology is pretty impressive. And, uh, you know, you send them it's $99, I believe, and they'll send you a DNA kit spit in the tube, you send it back and then they process that and match and give you insights into your family background. So for them simplifying HBase meant additional performance, so they could do matches faster and really simplified administration. Uh, so, you know, and, and Melinda Graham's words, uh, you know, it's simpler because they're just not there. Those, those components >>Jack, I want to ask you about enterprise grade had duped because, um, um, and then, uh, Ted Dunning, because he was, he was mentioned by Tim SDS on his keynote speech. So, so you have some rockstars stars in the company. I was in his management team. We had your CEO when we've interviewed MC Sri vis and Google IO, and we were on a panel together. So as to know your team solid team, uh, so let's talk about, uh, Ted in a minute, but I want to ask you about the enterprise grade Hadoop conversation. What does that mean now? I mean, obviously you guys were very successful at first. Again, we were skeptics at first, but now your traction and your performance has proven this is a market for that kind of platform. What does that mean now in this, uh, at this event today, as this is evolving as Hadoop ecosystem is not just Hadoop anymore. It's other things. Yeah, >>There's, there's, there's three dimensions to enterprise grade. Um, the first is, is ease of use and ease of use from an administrator standpoint, how easy does it integrate into an existing environment? How easy does it, does it fit into my, my it policies? You know, do you run in a lights out data center? Does the Hadoop distribution fit into that? So that's, that's one whole dimension. Um, a key to that is, is, you know, complete NFS support. So it functions like, uh, you know, like standard storage. Uh, a second dimension is undependability reliability. So it's not just, you know, do you have a checkbox ha feature it's do you have automated stateful fail over? Do you have self healing? Can you handle multiple, uh, failures and, and, you know, automated recovery. So, you know, in a lights out data center, can you actually go there once a week? Uh, and then just, you know, replace drives. And a great example of that is one of our customers had a test cluster with, with Matt BARR. It was a POC went on and did other things. They had a power field, they came back a week later and the cluster was up and running and they hadn't done any manual tasks there. And they were, they were just blown away to the recovery process for the other distributions, a long laundry list of, >>So I've got to ask you, I got to ask you this, the third >>One, what's the third one, third one is performance and performance is, is, you know, kind of Ross' speed. It's also, how do you leverage the infrastructure? Can you take advantage of, of the network infrastructure, multiple Knicks? Can you take advantage of heterogeneous hardware? Can you mix and match for different workloads? And it's really about sharing a cluster for different use cases and, and different users. And there's a lot of features there. It's not just raw >>The existing it infrastructure policies that whole, the whole, what happens when something goes wrong. Can you automate that? And then, >>And it's easy to be dependable, fast, and speed the same thing, making HBase, uh, easy, dependable, fast with themselves. >>So the talk of the show right now, he had the keynote this morning is that map. Our marketing has dropped the big data term and going with data Kozum. Is that true? Is that true? So, Joe, Hellerstein just had a tweet, Joe, um, famous, uh, Cal Berkeley professor, computer science professor now is CEO of a startup. Um, what's the industry trifecta they're doing, and he had a good couple of epic tweets this week. So shout out to Joe Hellerstein, but Joel Hellison's tweet that says map our marketing has decided to drop the term big data and go with data Kozum with a shout out to George Gilder. So I'm kind of like middle intellectual kind of humor. So w w w what's what's your response to that? Is it true? What's happening? What is your, the embargo, the VP of marketing? >>Well, if you look at the big data term, I think, you know, there's a lot of big data washing going on where, um, you know, architectures that have been out there for 30 years or, you know, all about big data. Uh, so I think there's a, uh, there's the need for a more descriptive term. Um, the, the purpose of data Kozum was not to try to coin something or try to, you know, change a big data label. It was just to get people to take a step back and think, and to realize that we are in a massive paradigm shift. And, you know, with a shout out to George Gilder, acknowledging, you know, he recognized what the impact of, of making available compute, uh, meant he recognized with Telekom what bandwidth would mean. And if you look at the combination of we've got all this, this, uh, compute efficiency and bandwidth, now data them is, is basically taking those resources and unleashing it and changing the way we do things. >>And, um, I think, I think one of the ways to look at that is the new things that will be possible. And there's been a lot of focus on, you know, SQL interfaces on top of, of Hadoop, which are important. But I think some of the more interesting use cases are taking this machine J generated data that's being produced very, very rapidly and having automated operational analytics that can respond in a very fast time to change how you do business, either, how you're communicating with customers, um, how you're responding to two different, uh, uh, risk factors in the environment for fraud, et cetera, or, uh, just increasing and improving, um, uh, your response time to kind of cost events. We met earlier called >>Actionable insight. Then he said, assigning intent, you be able to respond. It's interesting that you talk about that George Gilder, cause we like to kind of riff and get into the concept abstract concepts, but he also was very big in supply side economics. And so if you look at the business value conversation, one of things we pointed out, uh, yesterday and this morning, so opening, um, review was, you know, the, the top conversations, insight and analytics, you know, as a killer app right now, the app market has not developed. And that's why we like companies like continuity and what you guys are doing under the hood is being worked on right at many levels, performance units of those three things, but analytics is a no brainer insight, but the other one's business value. So when you look at that kind of data, Kozum, I can see where you're going with that. >>Um, and that's kind of what people want, because it's not so much like I'm Republican because he's Republican George Gilder and he bought American spectator. Everyone knows that. So, so obviously he's a Republican, but politics aside, the business side of what big data is implementing is massive. Now that I guess that's a Republican concept. Um, but not really. I mean, businesses is, is, uh, all parties. So relative to data caused them. I mean, no one talks about e-business anymore. We talking to IBM at the IBM conference and they were saying, Hey, that was a great marketing campaign, but no one says, Hey, uh, you and eat business today. So we think that big data is going to have the same effect, which is, Hey, are you, do you have big data? No, it's just assumed. Yeah. So that's what you're basically trying to establish that it's not just about big. >>Yeah. Let me give you one small example, um, from a business value standpoint and, uh, Ted Dunning, you mentioned Ted earlier, chief application architect, um, and one of the coauthors of, of, uh, the book hoot, which deals with machine learning, uh, he dealt with one of our large financial services, uh, companies, and, uh, you know, one of the techniques on Hadoop is, is clustering, uh, you know, K nearest neighbors, uh, you know, different algorithms. And they looked at a particular process and they sped up that process by 30,000 times. So there's a blog post, uh, that's on our website. You can find out additional information on that. And I, >>There's one >>Point on this one point, but I think, you know, to your point about business value and you know, what does data Kozum really mean? That's an incredible speed up, uh, in terms of, of performance and it changes how companies can react in real time. It changes how they can do pattern recognition. And Google did a really interesting paper called the unreasonable effectiveness of data. And in there they say simple algorithms on big data, on massive amounts of data, beat a complex model every time. And so I think what we'll see is a movement away from data sampling and trying to do an 80 20 to looking at all your data and identifying where are the exceptions that we want to increase because there, you know, revenue exceptions or that we want to address because it's a cost or a fraud. >>Well, that's what I, I would give a shout out to, uh, to the guys that digital reasoning Tim asked he's plugged, uh, Ted. It was idolized him in terms of his work. Obviously his work is awesome, but two, he brought up this concept of understanding gap and he showed an interesting chart in his keynote, which was the date explosion, you know, it's up and, you know, straight up, right. It's massive amount of data, 64% unstructured by his calculation. Then he showed out a flat line called attention. So as data's been exploding over time, going up attention mean user attention is flat with some uptick maybe, but so users and humans, they can't expand their mind fast enough. So machine learning technologies have to bridge that gap. That's analytics, that's insight. >>Yeah. There's a big conversation now going on about more data, better models, people trying to squint through some of the comments that Google made and say, all right, does that mean we just throw out >>The models and data trumps algorithms, data >>Trumps algorithms, but the question I have is do you think, and your customer is talking about, okay, well now they have more data. Can I actually develop better algorithms that are simpler? And is it a virtuous cycle? >>Yeah, it's I, I think, I mean, uh, there are there's, there are a lot of debate here, a lot of information, but I think one of the, one of the interesting things is given that compute cycles, given the, you know, kind of that compute efficiency that we have and given the bandwidth, you can take a model and then iterate very quickly on it and kind of arrive at, at insight. And in the past, it was just that amount of data in that amount of time to process. Okay. That could take you 40 days to get to the point where you can do now in hours. Right. >>Right. So, I mean, the great example is fraud detection, right? So we used the sample six months later, Hey, your credit card might've been hacked. And now it's, you know, you got a phone call, you know, or you can't use your credit card or whatever it is. And so, uh, but there's still a lot of use cases where, you know, whether is an example where modeling and better modeling would be very helpful. Uh, excellent. So, um, so Dana custom, are you planning other marketing initiatives around that? Or is this sort of tongue in cheek fun? Throw it out there. A little red meat into the chum in the waters is, >>You know, what really motivated us was, um, you know, the cubes here talking, you know, for the whole day, what could we possibly do to help give them a topic of conversation? >>Okay. Data cosmos. Now of course, we found that on our proprietary HBase tools, Jack Norris, thanks for coming in. We appreciate your support. You guys have been great. We've been following you and continue to follow. You've been a great support of the cube. Want to thank you personally, while we're here. Uh, Matt BARR has been generous underwriter supportive of our great independent editorial. We want to recognize you guys, thanks for your support. And we continue to look forward to watching you guys grow and kick ass. So thanks for all your support. And we'll be right back with our next guest after this short break. >>Thank you. >>10 years ago, the video news business believed the internet was a fat. The science is settled. We all know the internet is here to stay bubbles and busts come and go. But the industry deserves a news team that goes the distance coming up on social angle are some interesting new metrics for measuring the worth of a customer on the web. What zinc every morning, we're on the air to bring you the most up-to-date information on the tech industry with scrutiny on releases of the day and news of industry-wide trends. We're here daily with breaking analysis, from the best minds in the business. Join me, Kristin Filetti daily at the news desk on Silicon angle TV, your reference point for tech innovation 18 months.

okay we're back live in new york city for hadoop world 2011 john furrier its founder SiliconANGLE calm and we have a special walk-in guest tomorrow and allah the vp of engineering co founder of Cloudera who's going to be on at two thirty eastern time on the cube to go more in depth but since we saw her in the hallway we had a quick spot wanted to grab him in here this is the cube our flagship telecast where we go out to the event atop the smartest people and i'm here with my co-host i'm dave vellante Wikibon door welcome back you're a longtime cube alum so appreciate you coming back on and doing a quick drive by here thanks for the nice welcome so you know we go talk to the smart people in the room you're one of the smartest guys that I know and we've been friends for years and it was your my tweet heard around the world by you to find space and we've been sharing the office space at Cloudera a year didn't have you I meant to have you we're going to be trying to find space because you're expanding so fast we have to get in a new home sorry about that but I wanted to really thank you personally appear on live you've enabled SiliconANGLE Wikibon to we figured it out early because of you I mean we had our nose sniffing around the big data area before it's called big data but when we met talked we've been tracking the social web and really it's exploded in an amazing way and I'm just really thankful because I've been had a front-row seat in the trenches with you guys and and it's been amazing so I want to thank you're welcome and that's great to have you on board and so so you you've been evangelizing in the trenches at Yahoo you were a ir a textile partners announcing the hundred million dollar fund which is all great news today but you've been the real spark get cloudy air is one of the 10 others one of them but I know one of the main sparks a co-founder a lots of ginger cuz I'm Rebecca and my co-founder from facebook I mean we both we said this before like we saw the future like an hour companies we saw the future where everybody is gonna go next and now Jeff's gonna be on as well he's now taking this whole date of science thing art yep building out a team you gotta drilled that down with him what do you what do you think about all this I mean like right now how do you feel personally emotionally and looking at the marketplace share with us your yeah I'm very emotional today actually yeah lots of the good news is you heard about the funding news yes million dollars for startups but no but the 14 oh yeah yeah it is more most actually the news was supposed to come out today came out a bit earlier sir day but yeah I'm very very emotional because of that it's a very Testament from very big name investor's of how well we were doing and recognition of how big this wave really is also the hundred million fun from Excel that's also a huge testament and lots of hopefully lots of new innovations or startups will come out of that so I'm very emotional about that but also overwhelmed by the by the the size of this event and how many people are really gravitating towards the technology which shows how much work we still have to do going forward it was very very August of a great a bit scared a bit scared Michaels is a great CEO on stage they're great guy we love Mike just really he's geeky and he's pragmatic Jerry strategist and you got Kirk who's the operator yeah but he showed a slide up at his keynote that showed the evolution of Hadoop yes the core Hadoop and then he showed ya year-by-year and now we got that columns extending and you got new new components coming out take us through that that progression just go back a few years in and walk us through why is this going on so fast and what are the what's the what's the community doing and just yeah and what happened in 2008 it doesn't need was one mr. yeah when we when we started so I mean first 2008 when we started and what he was believing us back then that hey this thing is going to be big like we had the belief because we saw it happen firsthand but many folks were dismissive and no no no this this big data thing is a fat and nobody will care about it and look and behold today it's obviously proving not to be the case in terms of the maturity of the of the platform you're absolutely right i mean the slide that Mike showed should but only thirty percent of the contributions happening today are in the Hadoop core layer and and and and the overall kind of vision there is very system very similar to the operating system right except what this really is it's a data operating system right it's how to operate large amounts of data in a big data center so sorry it's like an operating system for many machines as opposed to Linux which does not bring system for a single machine right so Hadoop when it came out Hadoop is only the colonel it's only that inner layers which if you look at any opening system like windows or linux and so on the core functionality is two things storing files and running applications on top of these files that's what windows does that's what linux does that was loop does at the heart but then to really get an opening system to work you need many ancillary components around it that really make it functional you need libraries in it applications in eat integration IO devices etc etc and that's really what's happening in the hadoop world so started with the core OS layer which is Hadoop HDFS for storage MapReduce for computation but then now all of these other things are showing around that core kernel to really make it a fully functional extensible data opening system I which made a little replay button but let's just put the paws on that because this is kind of an important point in folks out there there's a lot of different and a lot of people and metaphors are used in this business so it's the Linux I want to be it's just like Red Hat right yeah we kind of use that term the business model is talk a little bit about that we just mentioned you know not like Linux just unpack that a little bit deeper for us what's the difference you mentioned Linux is can you replay what you just said that was really so I was actually talking about the similarity the similarity and then i can and then i can talk about the difference the similarity is the heart of Hadoop is a system for storing files which is sdfs and a system for running applications on top of these files which is MapReduce the heart of Linux is the same thing assistant for storing files which is a txt for and a system for scheduling applications on top of these files that's the same heart of Windows and so on the difference though so that's the similarity I got a difference is Linux is made to run on a single note right and when this is made to run on a single note Hadoop is really made to run on many many notes so hadoo bicester cares about taking a data center of servers a rack of servers or a data center of servers and having them look like one big massive mainframe built out of commodity hardware that can store arbitrary amounts of data and run any type of hence the new components like the hives of the world so now so now these new components coming up like high for example I've makes it easier to write queries for Hadoop it's it's a sequel language for writing queries on top of Hadoop so you don't have to go and write it in MapReduce which we call that assembly language of Hadoop so if you write it and MapReduce you will get the most flexibility you will get the most performance but only if you know what you're doing very similar when you do machine code if you do machine cool assembly you will able do anything but you can also shoot yourself in the foot sunbelt is that right the same thing with MapReduce right when you use hive hive abstracts that out for you so your rights equal and then hive takes care of doing all of the plumbing work to get that compulsion to map it is for you so that's hive HBase for example is a very nice system that augments a dupe makes it low latency and makes it makes it support update and insert and delete transactions which are HDFS does not support out of the box so small like a database it's more like my sequel yeah the energy of my sequel to Linux is very similar to hbase to HDFS and what's your take on were from you know your founders had on now yeah on the business model similarities and differences with with redhead yes so actually they are different I mean that the sonority the similarity stops at open source we are both open source right in the sense that the core system is open source is available out there you can look at the source code again the and so on the difference is with redhead red that actually has a license on their bits so there's the source code and then there's the bits so when Red Hat compiles the source code and two bits these bits you cannot deploy them without having a red hat license with us is very different is now we have the source code which is Apache is all in the patchy we compile the source code into a bunch of bits which is our distribution called cdh these bits are one hundred percent open-source 103 can deploy them use them you don't have to face anything the only reason why you would come back and pay us is for Cloudera enterprise which is really when you go operational when become operational a mission-critical cloud enterprise gives you two things first it gives you a proprietary management suite that we built and it's very unique to us nobody in the market has anything close to what we have right now that makes it easier for you to deploy configure monitor provision do capacity planning security management etc for a loop nobody else has anything close what we have right now for that management's that is unique to cloud area and not part of a patchy open source yes it's not part of the vet's office you only get that as a subscriber to cloud era we do have a free version of that that's available for download and it can run up to 15 hours just for you to get up and running quickly yeah and it's really very simple has a very simple installer like you should be able to go fire off that software and say install Hadoop these are one of my servers and would take care of everything else for you it's like having these installers you know when windows came out in the beginning and he had this nice progress bar and you can install applications very easily imagine that now for a cluster of servers right that's ready what this is the other reason why people subscribe to the cloud enterprise in addition to getting this management suite is getting our support services right and support is necessary for any software even if it's free even for hardware think if I give you a free airplane right now just comment just give it here you go here is an airplane right you can run this airplane make money from passengers you still need somebody to maintain their plane for you right you can still go higher your mechanics maybe we'd have a tweetup bummer you can hire your own mechanics to maintain that airplane but we tell you like if you subscribe with us as the mechanics for your airplane the support you will get with us will be way better than anything else and economics of it also would be way better than having your own stuff for doing the maintenance for that airplane okay final question and we got a one-minute because we slid you in real quick we're going to come back for folks armor is going to come back at two-thirty so come back its eastern time and we'll have a more in-depth conversation but just share with the folks watching your view of what's going on in the patchy and you know there's all these kind of weird you know Fudd being thrown around that clutter is not this and that and you guys clearly the leader we talked with Kirk about that we don't need to go into that but just surely this what's going on what's the real deal happening with Apache the code and you have a unique offering which I mean the real deal and I advise people to go look at this blog post that our CEO wrote called by Michaelson road called the community effect and the real deal is there is a very big healthy community developing the source code for Hadoop the core system which is actually fsm MapReduce and all the components around around that core system we at Cloudera employ a very large engineering organization and tactile engineering relation is bigger than many of these other companies in the space that's our engineering is bigger if you look at the whole company itself is much much bigger than any of these other players so we we do a lot of contributions and to the core system and to the projects around it however we are part of the community and we're definitely doing this with the community it's not just a clowder thing for the core platform so that that's the real deal all right yeah so here we are armor that co-founder congratulations great funding hundred L from accel partners who invested in you guys congratulations you're part of the community we all know that just kind of clarifying that for the record and you have a unique differentiator management suite and the enterprise stuff and say expand the experience experience yeah I think a huge differentiation we have is we have been doing this for three years I had over everybody else we have the experience across all the industries that matter so when you come to us we know how to do this in the finance industry in the retail industry and the health industry and the government so that that's something also that so I'll just for the audience out there arm is coming back at two third you're gonna go deeper in today's the highly decorated or a general because there is there a leak oh and thanks for the small extra info he's in the uniform to the cloud era logo yes sir affecting some of those for us to someday great so what you see you again love love our great great friend

Yeah, I'm Aala, They're the co-founder back to back. This is the cube silicon angle.com, Silicon angle dot TV's production of the cube, our flagship telecasts. We go out to the event. That was a great conversation. I was really just, just cool. I could have, we could have probably hit on a few more things, obviously well read. Awesome. Co-founder of Cloudera a. You were, you did a good job teaming up with that co-founder, huh? Not bad on the cube, huh? He's not bad on the cube, isn't he? He, >>He reads the internet. >>That's what I'm saying. >>Anything is going on. >>He's a cube star, you know, And >>Technology. Jeff knows it. Yeah. >>We, we tell you, I'm smarter just by being in Cloudera all those years. And I actually was following what he was saying, Sad and didn't dust my brain. So, Okay, so you're back. So we were talking earlier with Michaels and about the relational database thing. So I kind of pick that up where we left off with you around, you know, he was really excited. It's like, you know, hey, we saw that relational database movement happen. He was part of that. Yeah, yeah. That generation. And then, but things were happening or kind of happening the same way in a similar way, still early. So I was trying to really peg with him, how early are we, like, so, you know, as the curve, you know, this is 1400, it's not the Javit Center yet. Maybe the Duke world, you know, next year might be at the Javit Center, 35,000 just don't go to Vegas. So I'm trying to figure out where we are on that curve. Yeah. And we on the upwards slope, you know, down here, not even hitting that, >>I think, I think, I think we're moving up quicker than previous waves. And actually if you, if you look for example, Oracle, I think it took them 15, 20 years until they, they really became a mature company, VM VMware, which started about, what, 12, 13 years ago. It took them about maybe eight years to, to be a big company, met your company, and I'm hoping we're gonna do it in five. So a couple more years. >>Highly accelerated. >>Yes. But yeah, we see, I mean, I'm, I'm, I've been surprised by the growth. I have been, Right? I've been told, warned about enterprise software and, and that it takes long for production to take place. >>But the consumerization trend is really changing that. I mean, it seems to be that, yeah, the enterprises always last. Why the shorter >>Cycle? I think the shorter cycle is coming from having the, the, the, the right solution for the right problem at the right time. I think that's a big part of it. So luck definitely is a big part of this. Now, in terms of why this is changing compared to a couple of dec decades ago, why the adoption is changing compared to a couple of decades ago. I, I think that's coming just because of how quickly the technology itself, the underlying hardware is evolving. So right now, the fact that you can buy a single server and it has eight cores to 16 cores has 12 hards to terabytes. Each is, is something that's just pushing the, the, the, the limits what you can do with the existing systems and hence making it more likely for new systems to disrupt them. >>Yeah. We can talk about a lot. It's very easy for people to actually start a, a big data >>Project. >>Yes. For >>Example. Yes. And the hardest part is, okay, what, what do I really, what problem do I need to solve? How am I gonna, how am I gonna monetize it? Right? Those are the hard parts. It's not the, not the underlying >>Technology. Yes, Yes, that's true. That's true. I mean, >>You're saying, eh, you're saying >>Because, because I'm seeing both so much. I'm, I'm seeing both. I'm seeing both. And like, I'm seeing cases where you're right. There's some companies that was like, Oh, this Hadoop thing is so cool. What problem can I solve with it? And I see other companies, like, I have this huge problem and, and, and they don't know that HA exists. It's so, And once they know, they just jump on it right away. It's like, we know when you have a headache and you're searching for the medicine in Espin. Wow. It >>Works. I was talking to Jeff Hiba before he came on stage and, and I didn't even get to it cuz we were so on a nice riff there. Right. Bunch of like a musicians playing the guitar together. But like he, we talked about the it and and dynamics and he said something that I thoughts right. On money and SAP is talking the same thing and said they're going to the lines of business. Yes. Because it is the gatekeeper that's, it's like selling mini computers to a mainframe selling client servers from a mini computer team. Yeah. >>There's not, we're seeing, we're seeing both as well. So more likely the, the former one meaning, meaning that yes, line of business and departments, they adopt the technology and then it comes in and they see there's already these five different departments having it and they think, okay, now we need to formalize this across the organization. >>So what happens then? What are you seeing out there? Like when that happens, that mean people get their hands on, Hey, we got a problem to solve. Yeah. Is that what it comes down to? Well, Hadoop exist. Go get Hadoop. Oh yeah. They plop it in there and I what does it do? They, >>So they pop it into their, in their own installation or on the, on the cloud and they show that this actually is working and solving the problem for them. Yeah. And when that happens, it's a very, it's a very easy adoption from there on because they just go tell it, We need this right now because it's solving this problem and it's gonna make, make us much >>More money moving it right in. Yes. No problems. >>Is is that another reason why the cycle's compressed? I mean, you know, you think client server, there was a lot of resistance from it and now it's more much, Same thing with mobile. I mean mobile is flipped, right? I mean, so okay, bring it in. We gotta deal with it. Yep. I would think the same thing. We, we have a data problem. Let's turn it into an >>Opportunity. Yeah. In my, and it goes back to what I said earlier, the right solution for the right problem at the right time. Like when they, when you have larger amounts of unstructured data, there isn't anything else out there that can even touch what had, can >>Do. So Amar, I need to just change gears here a minute. The gaming stuff. So we have, we we're featured on justin.tv right now on the front page. Oh wow. But the numbers aren't coming in because there's a competing stream of a recently released Modern Warfare three feature. Yes. Yes. So >>I was looking for, we >>Have to compete with Modern Warfare three. So can you, can we talk about Modern Warfare three for a minute and share the folks what you think of the current version, if any, if you played it. Yeah. So >>Unfortunately I'm waiting to get back home. I don't have my Xbox with me here. >>A little like a, I'm talking about >>My lines and business. >>Boom. Water warfares like a Christmas >>Tree here. Sorry. You know, I love, I'm a big gamer. I'm a big video gamer at Cloudera. We have every Thursday at five 30 end office, we, we play Call of of Beauty version four, which is modern world form one actually. And I challenge, I challenge people out there to come challenge our team. Just ping me on Twitter and we'll, we'll do a Cloudera versus >>Let's, let's, let's reframe that. Let team out. There am Abalas company. This is the geeks that invent the future. Jeff Haer Baer at Facebook now at Cloudera. Hammerer leading the charge. These guys are at gamers. So all the young gamers out there am are saying they're gonna challenge you. At which version? >>Modern Warfare one. >>Modern Warfare one. Yes. How do they fire in? Can you set up an >>External We'll >>We'll figure it out. We'll figure it out. Okay. >>Yeah. Just p me on Twitter and We'll, >>We can carry it live actually we can stream that. Yeah, >>That'd be great. >>Great. >>Yeah. So I'll tell you some of our best Hadooop committers and Hadoop developers pitch >>A picture. Modern Warfare >>Three going now Model Warfare three. Very excited about the game. I saw the, the trailers for it looks, graphics look just amazing. Graphics are amazing. I love the Sirius since the first one that came out. And I'm looking forward to getting back home to playing the game. >>I can't play, my son won't let me play. I'm such a fumbler with the Hub. I'm a keyboard controller. I can't work the Xbox controller. Oh, I have a coordination problem my age and I'm just a gluts and like, like Dad, sorry, Charity's over. I can I play with my friends? You the box. But I'm around big gamer. >>But, but in terms of, I mean, something I wanted to bring up is how to link up gaming with big data and analysis and so on. So like, I, I'm a big gamer. I love playing games, but at the same time, whenever I play games, I feel a little bit guilty because it's kind of like wasted time. So it's like, I mean, yeah, it's fun and I'm getting lots of enjoyment on it makes my life much more cheerful. But still, how can we harness all of this, all of these hours that gamers spend playing a game like Modern Warfare three, How can we, how can we collect instrument, all of the data that's coming from that and coming up, for example, with something useful with predicted. >>This is exactly, this is exactly the kind of application that's mainstream is gaming. Yeah. Yeah. Danny at Riot G is telling me, we saw him at Oracle Open World. He's up there for the Java one. He said that they, they don't really have a big data platform and their business is about understanding user behavior rep tons of data about user playing time, who they're playing with. Yeah, Yeah. How they want us to get into currency trading, You know, >>Buy, I can't, I can't mention the names, but some of the biggest giving companies out there are using Hadoop right now. And, and depending on CDH for doing exactly that kind of thing, creating >>A good user experience >>Today, they're doing it for the purpose of enhancing the user experience and improving retention. So they do track everything. Like every single bullet, you fire everything in best Ball Head, you get everything home run, you do. And, and, and in, in a three >>Type of game consecutive headshot, you get >>Everything, everything is being Yeah. Headshot you get and so on. But, but as you said, they are using that information today to sell more products and, and, and retain their users. Now what I'm suggesting is that how can you harness that energy for the good as well? I mean for making money, money is good and everything, but how can you harness that for doing something useful so that all of this entertainment time is also actually productive time as well. I think that'd be a holy grail in this, in this environment if we >>Can achieve that. Yeah. It used to be that corn used to be the telegraph of the future of about, of applications, but gaming really is, if you look at gaming, you know, you get the headset on. It's a collaborative environment. Oh yeah. You got unified communications. >>Yeah. And you see our teenager kids, how, how many hours they spend on these things. >>You got play as a play environments, very social collaborative. Yeah. You know, some say, you know, we we're saying, what I'm saying is that that's the, that's the future work environment with Skype evolving. We're our multiplayer game's called our job. Right? Yeah. You know, so I'm big on gaming. So all the gamers out there, a has challenged you. Yeah. Got a big data example. What else are we seeing? So let's talk about the, the software. So we, one of the things you were talking about that I really liked, you were going down the list. So on Mike's slide he had all the new features. So around the core, can you just go down the core and rattle off your version of what, what it means and what it is. So you start off with say H Base, we talked about that already. What are the other ones that are out there? >>So the projects that we have right there, >>The projects that are around those tools that are being built. Cause >>Yeah, so the foundational, the foundational one as we mentioned before, is sdfs for storage map use for processing. Yeah. And then the, the immediate layer above that is how to make MAP reduce easier for the masses. So how can, not everybody knows how to learn map, use Java, everybody knows sql, right? So, so one of the most successful projects right now that has the highest attach rate, meaning people usually when they install had do installed as well is Hive. So Hive takes sequel and so Jeff Harm Becker, my co-founder, when he was at Facebook, his team built the Hive system. Essentially Hive takes sql so you don't have to learn a new language, you already know sql. And then converts that into MAP use for you. That not only expands the developer base for how many people can use adu, but also makes it easier to integrate Hadoop through all DBC and JDBC integrated with BI tools like MicroStrategy and Tableau and Informatica, et cetera, et cetera. >>You mentioned R too. You mentioned R Program R >>As well. Yeah, R is one of our best partnerships. We're very, very happy with them. So that's, that's one of the very key projects is Hive assisted project to Hive ISS called Pig. A pig Latin is a language that ya invented that you have to learn the language. It's very easy, it's very easy to learn compared to map produce. But once you learn it, you can, you can specify very deep data pipelines, right? SQL is good for queries. It's not good for data pipelines because it becomes very convoluted. It becomes very hard for the, the human brain to understand it. So Pig is much more natural to the human. It's more like Pearl very similar to scripting kind of languages. So with Peggy can write very, very long data pipelines, again, very successful projects doing very, very well. Another key project is Edge Base, like you said. So Edge Base allows you to do low latencies. So you can do very, very quick lookups and also allows you to do transactions. So you can do updates in inserts and deletes. So one of the talks here that had World we try to recommend people watch when the videos come out is the Talk by Jonathan Gray from Facebook. And he talked about how they use Edge Base, >>Jonathan, something on here in the Cube later. Yeah. So >>Drill him on that. So they use Edge Base now for many, many things within Facebook. They have a big team now committed to building an improving edge base with us and with the community at large. And they're using it for doing their online messaging system. The live mail system in Facebook is powered by Edge Base right now. Again, Pro and eBay, The Casini project, they gave a keynote earlier today at the conference as well is using Edge Base as well. So Edge Base is definitely one of the projects that's growing very, very quickly right now within the Hudu system. Another key project that Jeff alluded to earlier when he was on here is Flum. So Flume is very instrumental because you have this nice system had, but Hadoop is useless unless you have data inside it. So how do you get the data inside do? >>So Flum essentially is this very nice framework for having these agents all over your infrastructure, inside your web servers, inside your application servers, inside your mobile devices, your network equipment that collects all of that data and then reliably and, and materializes it inside Hado. So Flum does that. Another good project is Uzi, so many of them, I dunno how, how long you want me to keep going here, But, but Uzi is great. Uzi is a workflow processing system. So Uzi allows you to define a series of jobs. Some of them in Pig, some of them in Hive, some of them in map use. You can define a series of them and then link them to each other and say, only start this job when these other jobs, two jobs finish because I'm waiting for the input from them before I can kick off and so on. >>So Uzi is a very nice framework that will will do that. We'll manage the whole graph of jobs for you and retry things when they fail, et cetera, et cetera. Another good project is where W H I R R and where allows you to very easily start ADU cluster on top of Amazon. Easy two on top of Rackspace, virtualized environ. It's more for kicking off, it's for kicking off Hadoop instances or edge based instances on any virtual infrastructure. Okay. VMware, vCloud. So that it supports all of the major vCloud, sorry, all of the me, all of the major virtualized infrastructure systems out there, Eucalyptus as well, and so on. So that's where W H I R R ARU is another key project. It's one, it's duck cutting's main kind of project right now. Don of that gut cutting came on stage with you guys has, So Aru ARO is a project about how do we encode with our files, the schema of these files, right? >>Because when you open up a text file and you don't know how to what the columns mean and how to pars it, it becomes very hard to work for it. So ARU allows you to do that much more easily. It's also useful for doing rrp. We call rtc remove procedure calls for having different services talk to each other. ARO is very useful for that as well. And the list keeps going on and on Maha. Yeah. Which we just, thanks for me for reminding me of my house. We just added Maha very recently actually. What is that >>Adam? I'm not >>Familiar with it. So Maha is a data mining library. So MAHA takes some of the most popular data mining algorithms for doing clustering and regression and statistical modeling and implements them using the map map with use model. >>They have, they have machine learning in it too or Yes, yes. So that's the machine learning. >>So, So yes. Stay vector to machines and so on. >>What Scoop? >>So Scoop, you know, all of them. Thanks for feeding me all the names. >>The ones I don't understand, >>But there's so many of them, right? I can't even remember all of them. So Scoop actually is a very interesting project, is short for SQL to Hadoop, hence the name Scoop, right? So SQ from SQL and Oops from Hadoop and also means Scoop as in scooping up stuff when you scoop up ice cream. Yeah. And the idea for Scoop is to make it easy to move data between relational systems like Oracle metadata and it is a vertical and so on and Hadoop. So you can very simply say, Scoop the name of the table inside the relation system, the name of the file inside Hadoop. And the, the table will be copied over to the file and Vice and Versa can say Scoop the name of the file in Hadoop, the name of the table over there, it'll move the table over there. So it's a connectivity tool between the relational world and the Hadoop world. >>Great, great tutorial. >>And all of these are Apache projects. They're all projects built. >>It's not part of your, your unique proprietary. >>Yes. But >>These are things that you've been contributing >>To, We're contributing to the whole ecosystem. Yes. >>And you understand very well. Yes. And >>And contribute to your knowledge of the marketplace >>And Absolutely. We collaborate with the, with the community on creating these projects. We employ committers and founders for many of these projects. Like Duck Cutting, the founder of He works in Cloudera, the founder for that UIE project. He works at Calera for zookeeper works at Calera. So we have a number of them on stuff >>Work. So we had Aroon from Horton Works. Yes. And and it was really good because I tell you, I walk away from that conversation and I gotta say for the folks out there, there really isn't a war going on in Apache. There isn't. And >>Apache, there isn't. I mean isn't but would be honest. Like, and in the developer community, we are friends, we're working together. We want to achieve the, there's >>No war. It's all Kumbaya. Everyone understands the rising tide floats, all boats are all playing nice in the same box. Yes. It's just a competitive landscape in Horton. Works >>In the business, >>Business business, competitive business, PR and >>Pr. We're trying to be friendly, as friendly as we can. >>Yeah, no, I mean they're, they're, they're hying it up. But he was like, he was cool. Like, Hey, you know, we know each other. Yes. We all know each other and we're just gonna offer free Yes. And charge with support. And so are they. And that's okay. And they got other things going on. Yes. But he brought up the question. He said they're, they're launching a management console. So I said, Tyler's got a significant lead. He kind of didn't really answer the question. So the question is, that's your core bread and butter, That's your yes >>And no. Yes and no. I mean if you look at, if you look at Cloudera Enterprise, and I mentioned this earlier and when we talked in the morning, it has two main things in it. Cloudera Enterprise has the management suite, but it also has the, the the the support and maintenance that we provide to our customers and all the experience that we have in our team part That subscription. Yes. For a description. And I, I wanna stress the point that the fact that I built a sports car doesn't mean that I'm good at running that sports car. The driver of the car usually is much better at driving the car than the guy who built the car, right? So yes, we have many people on staff that are helping build had, but we have many more people on stuff that helped run Hado at large scale, at at financial indu, financial industry, retail industry, telecom industry, media industry, health industry, et cetera, et cetera. So that's very, very important for our customer. All that experience that we bring in on how to run the system technically Yeah. Within these verticals. >>But their strategies clear. We're gonna create an open source project within Apache for a management consult. Yes. And we sell support too. Yes. So there'll be a free alternative to management. >>So we have to see, But I mean we look at the product, I mean our products, >>It's gotta come down to product differentiation. >>Our product has been in the market for two years, so they just started building their products. It's >>Alpha, It's just Alpha. The >>Product is Alpha in Alpha right now. Yeah. Okay. >>Well the Apache products, it is >>Apache, right? Yeah. The Apache project is out. So we'll see how it does it compare to ours. But I think ours is way, way ahead of anything else out there. Yeah. Essentially people to try that for themselves and >>See essentially, John, when I asked Arro why does the world need Hortonwork? You know, eventually the answer we got was, well it's free. It needs to be more open. Had needs to be more open. >>No, there's, >>It's going to be, That's not really the reason why Warton >>Works. >>No, they want, they want to go make money. >>Exactly. We wasn't >>Gonna say them you >>When I kept pushing and pushing and that's ultimately the closest we can get cuz you >>Just listens. Not gonna >>12 open source projects. Yes. >>I >>Mean, yeah, yeah. You can't get much more open. Yeah. Look >>At management >>Consult, but Airs not shooting on all those. I mean, I mean not only we are No, no, not >>No, no, we absolutely >>Are. No, you are contributing. You're not. But that's not all your projects. There's other people >>Involved. Yeah, we didn't start, we didn't start all of these projects. Yeah, that's >>True. You contributing heavily to all of them. >>Yes, we >>Are. And that's clear. Todd Lipkin said that, you know, he contributed his first patch to HPAC in 2008. Yes. So I mean, you go back through the ranks >>Of your people and Todd now is a committer on Edge base is a committer on had itself. So on a number >>Of you clearly the lead and, and you know, and, but >>There is a concern. But we, we've heard it and I wanna just ask you No, no. So there's a concern that if I build processes around a proprietary management console, Yes. I'm gonna end up being locked into that proprietary management CNA all over again. Now this is so far from ca Yes. >>Right. >>But that's a concern that some people have expressed. And, and, and I think one of the reasons why Port Works is getting so much attention. So Yes. >>Talk about that. It's, it's a very good, it's a very good observation to make. Actually, >>There there is two separate things here. There's the platform where all the data sets and then there's this management parcel beside the platform. Now why did we make the management console why the cloud didn't make the management console? Because it makes our job for supporting the customers much more achievable. When a customer calls in and says, We have a problem, help us fix this problem. When they go to our management console, there is a button they click that gives us a dump of the state, of the cluster. And that's what allows us to very quickly debug what's going on. And within minutes tell them you need to do this and you to do that. Yeah. Without that we just can't offer the support services. There's >>Real value there. >>Yes. So, so now a year from, But, but, but you have to keep in mind that the, the underlying platform is completely open source and free CBH is completely a hundred percent open source, a hundred percent free, a hundred percent Apache. So a year from now, when it comes time to renew with us, if the customer is not happy with our management suite is not happy with our support data, they can, they can go to work >>And works. People are afraid >>Of all they can go to ibm. >>The data, you can take the data that >>You don't even need to take the data. You're not gonna move the data. It's the same system, the same software. Every, everything in CDH is Apache. Right? We're not putting anything in cdh, which is not Apache. So a year from now, if you're not happy with our service to you and the value that we're providing, you can switch. There is no lock in. There is no lock. And >>Your, your argument would be the switching costs to >>The only lock in is happiness. The only lock in is which >>Happiness inspection customer delay. Which by, by the way, we just wrote a piece about those wars and we said the risk of lockin is low. We made that statement. We've got some heat for it. Yes. And >>This is sort of at scale though. What the, what the people are saying, they're throwing the tomatoes is saying if this is, again, in theory at scale, the customers are so comfortable with that, the console that they don't switch. Now my argument was >>Yes, but that means they're happy with it. That means they're satisfied and happy >>With it. >>And it's more economical for them than going and hiding people full-time on stuff. Yeah. >>So you're, you're always on check as, as long as the customer doesn't feel like Oracle. >>Yeah. See that's different. Oracle is very, Oracle >>Is like different, right? Yeah. Here it's like Cisco routers, they get nested into the environment, provide value. That's just good competitive product strategy. Yes. If it they're happy. Yeah. It's >>Called open washing with >>Oracle, >>I mean our number one core attribute on the company, the number one value for us is customer satisfaction. Keeping our people Yeah. Our customers happy with the service that we provide. >>So differentiate in the product. Yes. Keep the commanding lead. That's the strategist. That's the, that's what's happening. That's your goal. Yes. >>That's what's happening. >>Absolutely. Okay. Co-founder of Cloudera, Always a pleasure to have you on the cube. We really appreciate all the hospitality over the beer and a half. And wanna personally thank you for letting us sit in your office and we'll miss you >>And we'll miss you too. We'll >>See you at the, the Cube events off Swing by, thanks for coming on the cube and great to see you and congratulations on all your success. >>Thank >>You. And thanks for the review on Modern Warfare three. Yeah, yeah. >>Love me again. If there any gaming stuff, you know, I.

>>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.

(soft music) >> Hello, fellow hardware nerds and welcome back to Dallas Texas where we're reporting live from Supercomputing 2022. My name is Savannah Peterson, joined with the John Furrier on my left. >> Looking good today. >> Thank you, John, so are you. It's been a great show so far. >> We've had more hosts, more guests coming than ever before. >> I know. >> Amazing, super- >> We've got a whole thing going on. >> It's been a super computing performance. >> It, wow. And, we'll see how many times we can say super on this segment. Speaking of super things, I am in a very unique position right now. I am a flanked on both sides by people who have been doing content on theCUBE for 12 years. Yes, you heard me right, our next guest was on theCUBE 12 years ago, the third event, was that right, John? >> Man: First ever VM World. >> Yeah, the first ever VM World, third event theCUBE ever did. We are about to have a lot of fun. Please join me in welcoming Justin Emerson of Pure Storage. Justin, welcome back. >> It's a pleasure to be here. It's been too long, you never call, you don't write. (Savannah laughs) >> Great to see you. >> Yeah, likewise. >> How fun is this? Has the set evolved? Is everything looking good? >> I mean, I can barely remember what happened last week, so. (everyone laughs) >> Well, I remember lot's changed that VM world. You know, Paul Moritz was the CEO if you remember at that time. His actual vision actually happened but not the way, for VMware, but the industry, the cloud, he called the software mainframe. We were kind of riffing- >> It was quite the decade. >> Unbelievable where we are now, how we got here, but not where we're going to be. And you're with Pure Storage now which we've been, as you know, covering as well. Where's the connection into the supercomputing? Obviously storage performance, big part of this show. >> Right, right. >> What's the take? >> Well, I think, first of all it's great to be back at events in person. We were talking before we went on, and it's been so great to be back at live events now. It's been such a drought over the last several years, but yeah, yeah. So I'm very glad that we're doing in person events again. For Pure, this is an incredibly important show. You know, the product that I work with, with FlashBlade is you know, one of our key areas is specifically in this high performance computing, AI machine learning kind of space. And so we're really glad to be here. We've met a lot of customers, met a lot of other folks, had a lot of really great conversations. So it's been a really great show for me. And also just seeing all the really amazing stuff that's around here, I mean, if you want to find, you know, see what all the most cutting edge data center stuff that's going to be coming down the pipe, this is the place to do it. >> So one of the big themes of the show for us and probably, well, big theme of your life, is balancing power efficiency. You have a product in this category, Direct Flash. Can you tell us a little bit more about that? >> Yeah, so Pure as a storage company, right, what do we do differently from everybody else? And if I had to pick one thing, right, I would talk about, it's, you know, as the name implies, we're an all, we're purely flash, we're an all flash company. We've always been, don't plan to be anything else. And part of that innovation with Direct Flash is the idea of rather than treating a solid state disc as like a hard drive, right? Treat it as it actually is, treat it like who it really is and that's a very different kind of thing. And so Direct Flash is all about bringing native Flash interfaces to our product portfolio. And what's really exciting for me as a FlashBlade person, is now that's also part of our FlashBlade S portfolio, which just launched in June. And so the benefits of that are our myriad. But, you know, talking about efficiency, the biggest difference is that, you know, we can use like 90% less DRAM in our drives, which you know, everything uses, everything that you put in a drive uses power, it adds cost and all those things and so that really gives us an efficiency edge over everybody else and at a show like this, where, I mean, you walk the aisles and there's there's people doing liquid cooling and so much immersion stuff, and the reason they're doing that is because power is just increasing everywhere, right? So if you can figure out how do we use less power in some areas means you can shift that budget to other places. So if you can talk to a customer and say, well, if I could shrink your power budget for storage by two thirds or even, save you two-thirds of power, how many more accelerators, how many more CPUs, how much more work could you actually get done? So really exciting. >> I mean, less power consumption, more power and compute. >> Right. >> Kind of power center. So talk about the AI implications, where the use cases are. What are you seeing here? A lot of simulations, a lot of students, again, dorm room to the boardroom we've been saying here on theCUBE this is a great broad area, where's the action in the ML and the AI for you guys? >> So I think, not necessarily storage related but I think that right now there's this enormous explosion of custom silicon around AI machine learning which I as a, you said welcome hardware nerds at the beginning and I was like, ah, my people. >> We're all here, we're all here in Dallas. >> So wonderful. You know, as a hardware nerd we're talking about conferences, right? Who has ever attended hot chips and there's so much really amazing engineering work going on in the silicon space. It's probably the most exciting time for, CPU and accelerator, just innovation in, since the days before X 86 was the defacto standard, right? And you could go out and buy a different workstation with 16 different ISAs. That's really the most exciting thing, I walked past so many different places where you know, our booth is right next to Havana Labs with their gout accelerator, and they're doing this cute thing with one of the AI image generators in their booth, which is really cute. >> Woman: We're going to have to go check that out. >> Yeah, but that to me is like one of the more exciting things around like innovation at a, especially at a show like this where it's all about how do we move forward, the state of the art. >> What's different now than just a few years ago in terms of what's opening up the creativity for people to look at things that they could do with some of the scale that's different now. >> Yeah well, I mean, every time the state of the art moves forward what it means is, is that the entry level gets better, right? So if the high end is going faster, that means that the mid-range is going faster, and that means the entry level is going faster. So every time it pushes the boundary forward, it's a rising tide that floats all boats. And so now, the kind of stuff that's possible to do, if you're a student in a dorm room or if you're an enterprise, the world, the possible just keeps expanding dramatically and expanding almost, you know, geometrically like the amount of data that we are, that we have, as a storage guy, I was coming back to data but the amount of data that we have and the amount of of compute that we have, and it's not just about the raw compute, but also the advances in all sorts of other things in terms of algorithms and transfer learning and all these other things. There's so much amazing work going on in this area and it's just kind of this Kay Green explosion of innovation in the area. >> I love that you touched on the user experience for the community, no matter the level that you're at. >> Yeah. >> And I, it's been something that's come up a lot here. Everyone wants to do more faster, always, but it's not just that, it's about making the experience and the point of entry into this industry more approachable and digestible for folks who may not be familiar, I mean we have every end of the ecosystem here, on the show floor, where does Pure Storage sit in the whole game? >> Right, so as a storage company, right? What AI is all about deriving insights from data, right? And so everyone remembers that magazine cover data's the new oil, right? And it's kind of like, okay, so what do you do with it? Well, how do you derive value from all of that data? And AI machine learning and all of this supercomputing stuff is about how do we take all this data? How do we innovate with it? And so if you want data to innovate with, you need storage. And so, you know, our philosophy is that how do we make the best storage platforms that we can using the best technology for our customers that enable them to do really amazing things with AI machine learning and we've got different products, but, you know at the show here, what we're specifically showing off is our new flashlight S product, which, you know, I know we've had Pure folks on theCUBE before talking about FlashBlade, but for viewers out there, FlashBlade is our our scale out unstructured data platform and AI and machine learning and supercomputing is all about unstructured data. It's about sensor data, it's about imaging, it's about, you know, photogrammetry, all this other kinds of amazing stuff. But, you got to land all that somewhere. You got to process that all somewhere. And so really high performance, high throughput, highly scalable storage solutions are really essential. It's an enabler for all of the amazing other kinds of engineering work that goes on at a place like Supercomputing. >> It's interesting you mentioned data's oil. Remember in 2010, that year, our first year of theCUBE, Hadoop World, Hadoop just started to come on the scene, which became, you know kind of went away and, but now you got, Spark and Databricks and Snowflake- >> Justin: And it didn't go away, it just changed, right? >> It just got refactored and right size, I think for what the people wanted it to be easy to use but there's more data coming. How is data driving innovation as you bring, as people see clearly the more data's coming? How is data driving innovation as you guys look at your products, your roadmap and your customer base? How is data driving innovation for your customers? >> Well, I think every customer who has been, you know collecting all of this data, right? Is trying to figure out, now what do I do with it? And a lot of times people collect data and then it will end up on, you know, lower slower tiers and then suddenly they want to do something with it. And it's like, well now what do I do, right? And so there's all these people that are reevaluating you know, we, when we developed FlashBlade we sort of made this bet that unstructured data was going to become the new tier one data. It used to be that we thought unstructured data, it was emails and home directories and all that stuff the kind of stuff that you didn't really need a really good DR plan on. It's like, ah, we could, now of course, as soon as email goes down, you realize how important email is. But, the perspectives that people had on- >> Yeah, exactly. (all laughing) >> The perspectives that people had on unstructured data and it's value to the business was very different and so now- >> Good bet, by the way. >> Yeah, thank you. So now unstructured data is considered, you know, where companies are going to derive their value from. So it's whether they use the data that they have to build better products whether it's they use the data they have to develop you know, improvements in processes. All those kinds of things are data driven. And so all of the new big advancements in industry and in business are all about how do I derive insights from data? And so machine learning and AI has something to do with that, but also, you know, it all comes back to having data that's available. And so, we're working very hard on building platforms that customers can use to enable all of this really- >> Yeah, it's interesting, Savannah, you know, the top three areas we're covering for reinventing all the hyperscale events is data. How does it drive innovation and then specialized solutions to make customers lives easier? >> Yeah. >> It's become a big category. How do you compose stuff and then obviously compute, more and more compute and services to make the performance goes. So those seem to be the three hot areas. So, okay, data's the new oil refineries. You've got good solutions. What specialized solutions do you see coming out because once people have all this data, they might have either large scale, maybe some edge use cases. Do you see specialized solutions emerging? I mean, obviously it's got DPU emerging which is great, but like, do you see anything else coming out at that people are- >> Like from a hardware standpoint. >> Or from a customer standpoint, making the customer's lives easier? So, I got a lot of data flowing in. >> Yeah. >> It's never stopping, it keeps powering in. >> Yeah. >> Are there things coming out that makes their life easier? Have you seen anything coming out? >> Yeah, I think where we are as an industry right now with all of this new technology is, we're really in this phase of the standards aren't quite there yet. Everybody is sort of like figuring out what works and what doesn't. You know, there was this big revolution in sort of software development, right? Where moving towards agile development and all that kind of stuff, right? The way people build software change fundamentally this is kind of like another wave like that. I like to tell people that AI and machine learning is just a different way of writing software. What is the output of a training scenario, right? It's a model and a model is just code. And so I think that as all of these different, parts of the business figure out how do we leverage these technologies, what it is, is it's a different way of writing software and it's not necessarily going to replace traditional software development, but it's going to augment it, it's going to let you do other interesting things and so, where are things going? I think we're going to continue to start coalescing around what are the right ways to do things. Right now we talk about, you know, ML Ops and how development and the frameworks and all of this innovation. There's so much innovation, which means that the industry is moving so quickly that it's hard to settle on things like standards and, or at least best practices you know, at the very least. And that the best practices are changing every three months. Are they really best practices right? So I think, right, I think that as we progress and coalesce around kind of what are the right ways to do things that's really going to make customers' lives easier. Because, you know, today, if you're a software developer you know, we build a lot of software at Pure Storage right? And if you have people and developers who are familiar with how the process, how the factory functions, then their skills become portable and it becomes easier to onboard people and AI is still nothing like that right now. It's just so, so fast moving and it's so- >> Wild West kind of. >> It's not standardized. It's not industrialized, right? And so the next big frontier in all of this amazing stuff is how do we industrialize this and really make it easy to implement for organizations? >> Oil refineries, industrial Revolution. I mean, it's on that same trajectory. >> Yeah. >> Yeah, absolutely. >> Or industrial revolution. (John laughs) >> Well, we've talked a lot about the chaos and sort of we are very much at this early stage stepping way back and this can be your personal not Pure Storage opinion if you want. >> Okay. >> What in HPC or AIML I guess it all falls under the same umbrella, has you most excited? >> Ooh. >> So I feel like you're someone who sees a lot of different things. You've got a lot of customers, you're out talking to people. >> I think that there is a lot of advancement in the area of natural language processing and I think that, you know, we're starting to take things just like natural language processing and then turning them into vision processing and all these other, you know, I think the, the most exciting thing for me about AI is that there are a lot of people who are, you are looking to use these kinds of technologies to make technology more inclusive. And so- >> I love it. >> You know the ability for us to do things like automate captioning or the ability to automate descriptive, audio descriptions of video streams or things like that. I think that those are really,, I think they're really great in terms of bringing the benefits of technology to more people in an automated way because the challenge has always been bandwidth of how much a human can do. And because they were so difficult to automate and what AI's really allowing us to do is build systems whether that's text to speech or whether that's translation, or whether that's captioning or all these other things. I think the way that AI interfaces with humans is really the most interesting part. And I think the benefits that it can bring there because there's a lot of talk about all of the things that it does that people don't like or that they, that people are concerned about. But I think it's important to think about all the really great things that maybe don't necessarily personally impact you, but to the person who's not cited or to the person who you know is hearing impaired. You know, that's an enormously valuable thing. And the fact that those are becoming easier to do they're becoming better, the quality is getting better. I think those are really important for everybody. >> I love that you brought that up. I think it's a really important note to close on and you know, there's always the kind of terminator, dark side that we obsess over but that's actually not the truth. I mean, when we think about even just captioning it's a tool we use on theCUBE. It's, you know, we see it on our Instagram stories and everything else that opens the door for so many more people to be able to learn. >> Right? >> And the more we all learn, like you said the water level rises together and everything is magical. Justin, it has been a pleasure to have you on board. Last question, any more bourbon tasting today? >> Not that I'm aware of, but if you want to come by I'm sure we can find something somewhere. (all laughing) >> That's the spirit, that is the spirit of an innovator right there. Justin, thank you so much for joining us from Pure Storage. John Furrier, always a pleasure to interview with you. >> I'm glad I can contribute. >> Hey, hey, that's the understatement of the century. >> It's good to be back. >> Yeah. >> Hopefully I'll see you guys in, I'll see you guys in 2034. >> No. (all laughing) No, you've got the Pure Accelerate conference. We'll be there. >> That's right. >> We'll be there. >> Yeah, we have our Pure Accelerate conference next year and- >> Great. >> Yeah. >> I love that, I mean, feel free to, you know, hype that. That's awesome. >> Great company, great runs, stayed true to the mission from day one, all Flash, continue to innovate congratulations. >> Yep, thank you so much, it's pleasure being here. >> It's a fun ride, you are a joy to talk to and it's clear you're just as excited as we are about hardware, so thanks a lot Justin. >> My pleasure. >> And thank all of you for tuning in to this wonderfully nerdy hardware edition of theCUBE live from Dallas, Texas, where we're at, Supercomputing, my name's Savannah Peterson and I hope you have a wonderful night. (soft music)

(light hearted music) >> Okay, welcome back to Supercloud '22. I'm John Furrier, host of theCUBE. We got Ali Ghodsi here, co-founder and CEO of Databricks. Ali, Great to see you. Thanks for spending your valuable time to come on and talk about Supercloud and the future of all the structural change that's happening in cloud computing. >> My pleasure, thanks for having me. >> Well, first of all, congratulations. We've been talking for many, many years, and I still go back to the video that we have in archive, you talking about cloud. And really, at the beginning of the big reboot, I called the post Hadoop, a revitalization of data. Congratulations, you've been cloud-first, now on multiple clouds. Congratulations to you and your team for achieving what looks like a billion dollars in annualized revenue as reported by the Wall Street Journal, so first, congratulations. >> Thank you so much, appreciate it. >> So I was talking to some young developers and I asked a random poll, what do you think about Databricks? Oh, we love those guys, they're AI and ML-native, and that's their advantage over the competition. So I pressed why. I don't think they knew why, but that's an interesting perspective. This idea of cloud native, AI/ML-native, ML Ops, this has been a big trend and it's continuing. This is a big part of how this change and this structural change is happening. How do you react to that? And how do you see Databricks evolving into this new Supercloud-like multi-cloud environment? >> Yeah, look, I think it's a continuum. It starts with having data, but they want to clean it, you know, and they want to get insights out of it. But then, eventually, you'd like to start asking questions, doing reports, maybe ask questions about what was my revenue yesterday, last week, but soon you want to start using the crystal ball, predictive technology. Okay, but what will my revenue be next week? Next quarter? Who's going to churn? And if you can finally automate that completely so that you can act on the predictions, right? So this credit card that got swiped, the AI thinks it's fraud, we're going to deny it. That's when you get real value. So we're trying to help all these organizations move through this data AI maturity curve, all the way to that, the prescriptive, automated AI machine learning. That's when you get real competitive advantage. And you know, we saw that with the fans, right? I mean, Google wouldn't be here today if it wasn't for AI. You know, we'd be using AltaVista or something. We want to help all organizations to be able to leverage data and AI that way that the fans did. >> One of the things we're looking at with supercloud and why we call it supercloud versus other things like multi-cloud is that today a lot of the successful companies have started in the cloud have been successful, but have realized and even enterprises who have gotten by accident, and maybe have done nothing with cloud have just some cloud projects on multiple clouds. So, people have multiple cloud operational things going on but it hasn't necessarily been a strategy per se. It's been more of kind of a default reaction to things but the ones that are innovating have been successful in one native cloud because the use cases that drove that got scale got value, and then they're making that super by bringing it on premise, putting in a modern data stack, for the modern application development, and kind of dealing with the things that you guys are in the middle of with data bricks is that, that is where the action is, and they don't want to go, lose the trajectory in all the economies of scale. So we're seeing another structural change where the evolutionary nature of the cloud has solved a bunch of use cases, but now other use cases are emerging that's on premises and edge that have been driven by applications because of the developer boom, that's happening. You guys are in the middle of it. What is happening with this structural change? Are people looking for the modern data stack? Are they looking for more AI? What's the, what's your perspective on this supercloud kind of position? >> Look, it started with not AR on multiple clouds, right? So multi-cloud has been a thing. It became a thing 70, 80% of our customers when you ask them, they're more than one cloud. But then soon to start realizing that, hey, you know, if I'm on multiple clouds, this data stuff is hard enough as it is. Do I want to redo it again and again with different proprietary technologies, on each of the clouds. And that's when I started thinking about let's standardize this, let's figure out a way which just works across them. That's where I think open source comes in, becomes really important. Hey, can we leverage open standards because then we can make it work in these different environments, as we said so that we can actually go super, as you said, that's one. The second thing is, can we simplify it? You know, and I think today, the data landscape is complicated. Conceptually it's simple. You have data which is essentially customer data that you have, maybe employee data. And you want to get some kind of insights from that. But how you do that is very complicated. You have to buy data warehouse, hire data analysts. You have to buy, store stuff in the Delta Lake you know, get your data engineers. If you want streaming real time thing that's another complete different set of technologies you have to buy. And then you have to stitch all these together, and you have to do again and again on every cloud. So they just want simplification. So that's why we're big believers in this Delta Lakehouse concept. Which is an open standard to simplifying this data stack and help people to just get value out of their data in any environment. So they can do that in this sort of supercloud as you call it. >> You know, we've been talking about that in previous interviews, do the heavy lifting let them get the value. I have to ask you about how you see that going forward, Because if I'm a customer, I have a lot of operational challenges. Cause the developers are are kicking butt right now. We see that clearly. Open sources growing at, and continue to be great. But ops and security teams they really care about this stuff. And most companies don't want to spin up multiple ops teams to deal with different stacks. This is one big problem that I think that's leading into the multi-cloud viability. How do you guys deal with that? How do you talk to customers when they say, I want to have less complications on operations? >> Yeah, you're absolutely right. You know, it's easy for a developer to adopt all these technologies and new things are coming out all the time. The ops teams are the ones that have to make sure this works. Doing that in multiple different environments is super hard. especially when there's a proprietary stack in each environment that's different. So they just want standardization. They want open source, that's super important. We hear that all the time from them. They want open the source technologies. They believe in the communities around it. You know, they know that source code is open. So you can also see if there's issues with it. If there's security breaches, those kind of things that they can have a community around it. So they can actually leverage that. So they're the ones that are really pushing this, and we're seeing it across the board. You know, it starts first with the digital natives you know, the companies that are, but slowly it's also now percolating to the other organizations, we're hearing across the board. >> Where are we, Ali on the innovation strategies for customers? Where are they on the trajectory around how they're building out their teams? How are they looking at the open source? How are they extending the value proposition of Databricks, and data at scale, as they start to build out their teams and operations, because some are like kind of starting, crawl, walk, run, kind of vibe. Some are big companies, they're dealing with data all the time. Where are they in their journey? What's the core issues that they're solving? What are some of the use cases that you see that are most pressing in customer? >> Yeah, what I've seen, that's really exciting about this Delta Lakehouse concept is that we're now seeing a lot of use cases around real time. So real time fraud detection, real time stock ticker pricing, anyone that's doing trading, they want that to work real time. Lots of use cases around that. Lots of use cases around how do we in real time drive more engagement on our web assets if we're a media company, right? We have all these assets how do we get people to get engaged? Stay on our sites. Continue engaging with the material we have. Those are real time use cases. And the interesting thing is, they're real time. So, you know, it's really important that you that now you don't want to recommend someone, hey, you should go check out this restaurant if they just came from that restaurant, half an hour ago. So you want it to be real time, but B, that it's also all based on machine learning. These are a lot of this is trying to predict what you want to see, what you want to do, is it fraudulent? And that's also interesting because basically more and more machine learning is coming in. So that's super exciting to see, the combination of real time and machine learning on the Lakehouse. And finally, I would say the Lakehouse is really important for this because that's where the data is flowing in. If they have to take that data that's flowing into the lake and actually copy it into a separate warehouse, that delays the real time use cases. And then it can't hit those real time deadlines. So that's another catalyst for this Lakehouse pattern. >> Would that be an example of how the metrics are changing? Cause I've been looking at some people saying, well you can tell if someone's doing well there's a lot of data being transferred. And then I was saying, well, wait a minute. Data transfer costs money, right? And time. So this is interesting dynamic, in a way you don't want to have a lot of movement, right? >> Yeah, movement actually decreases for a lot of these real time use cases. 'Cause what we saw in the past was that they would run a batch processing to process all the data. So once they process all the data. But actually if you look at the things that have changed since the data that we have yesterday it's actually not that much. So if you can actually incrementally process it in real time, you can actually reduce the cost of transfers and storage and processing. So that's actually a great point. That's also one of the main things that we're seeing with the use cases, the bill shrinks and the cost goes down, and they can process less. >> Yeah, and it'd be interesting to see how those KPIs evolve into industry metrics down the road around the supercloud of evolution. I got to ask you about the open source concept of data platforms. You guys have been a pioneer in there doing great work, kind of picking the baton off where the Hadoop World left off as Dave Vellante always points out. But if working across clouds is super important. How are you guys looking at the ability to work across the different clouds with data bricks? Are you going to build that abstraction yourself? Does data sharing and model sharing kind of come into play there? How do you see this data bricks capability across the clouds? >> Yeah, I mean, let me start by saying, we just we're big fans of open source. We think that open source is a force in software. That's going to continue for, decades, hundreds of years, and it's going to slowly replace all proprietary code in its way. We saw that, it could do that with the most advanced technology. Windows, you know proprietary operating system, very complicated, got replaced with Linux. So open source can pretty much do anything. And what we're seeing with the Delta Lakehouse is that slowly the open source community is building a replacement for the proprietary data warehouse, Delta Lake, machine learning, real time stack in open source. And we're excited to be part of it. For us, Delta Lake is a very important project that really helps you standardize how you layout your data in the cloud. And when it comes a really important protocol called data sharing, that enables you in a open way actually for the first time ever share large data sets between organizations, but it uses an open protocol. So the great thing about that is you don't need to be a Databricks customer. You don't need to even like Databricks, you just need to use this open source project and you can now securely share data sets between organizations across clouds. And it actually does so really efficiently just one copy of the data. So you don't have to copy it if you're within the same cloud. >> So you're playing the long game on open source. >> Absolutely. I mean, this is a force it's going to be there if if you deny it, before you know it there's going to be, something like Linux, that is going to be a threat to your propriety. >> I totally agree by the way. I was just talking to somebody the other day and they're like hey, the software industry someone made the comment, the software industry, the software industry is open source. There's no more software industry, it's called open source. It's integrations that become interesting. And I was looking at integrations now is really where the action is. And we had a panel with the Clouderati we called it, the people have been around for a long time. And it was called the innovator's dilemma. And one of the comments was it's the integrator's dilemma, not the innovator's dilemma. And this is a big part of this piece of supercloud. Can you share your thoughts on how cloud and integration need to be tightened up to really make it super? >> Actually that's a great point. I think the beauty of this is, look the ecosystem of data today is vast, there's this picture that someone puts together every year of all the different vendors and how they relate, and it gets bigger and bigger and messy and messier. So, we see customers use all kinds of different aspects of what's existing in the ecosystem and they want it to be integrated in whatever you're selling them. And that's where I think the power of open source comes in. Open source, you get integrations that people will do without you having to push it. So us, Databricks as a vendor, we don't have to go tell people please integrate with Databricks. The open source technology that we contribute to, automatically, people are integrating with it. Delta Lake has integrations with lots of different software out there and Databricks as a company doesn't have to push that. So I think open source is also another thing that really helps with the ecosystem integrations. Many of these companies in this data space actually have employees that are full-time dedicated to make sure make sure our software works well with Spark. Make sure our software works well with Delta and they contribute back to that community. And that's the way you get this sort of ecosystem to further sort of flourish. >> Well, I really appreciate your time. And I, my final question for you is, as we're kind of unpack and and kind of shape and frame supercloud for the future, how would you see a roadmap or architecture or outcome for companies that are going to clearly be in the cloud where it's open source is going to be dominating. Integrations has got to be seamless and frictionless. Abstraction layer make things super easy and take away the complexity. What is supercloud to them? What does the outcome look like? How would you define a supercloud environment for an enterprise? >> Yeah, for me, it's the simplification that you get where you standardize an open source. You get your data in one place, in one format in one standardized way, and then you can get your insights from it, without having to buy lots of different idiosyncratic proprietary software from different vendors. That's different in each environment. So it's this slow standardization that's happening. And I think it's going to happen faster than we think. And I think in a couple years it's going to be a requirement that, does your software work on all these different departments? Is it based on open source? Is it using this Delta Lake house pattern? And if it's not, I think they're going to demand it. >> Yeah, I feel like we're close to some sort of defacto standard coming and you guys are a big part of it, once that clicks in, it's going to highly accelerate in the open, and I think it's going to be super valuable. Ali, thank you so much for your time, and congratulations to you and your team. Like we've been following you guys since the beginning. Remember the early days and look how far it's come. And again, you guys are really making a big difference in making a super cool environment out there. Thanks for coming on sharing. >> Thank you so much John. >> Okay, this is supercloud 22. I'm John Furrier stay with more for more coverage and more commentary after this break. (light hearted 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. >> Snowflake Summit 2022 underscored that the ecosystem excitement which was once forming around Hadoop is being reborn, escalated and coalescing around Snowflake's data cloud. What was once seen as a simpler cloud data warehouse and good marketing with the data cloud is evolving rapidly with new workloads of vertical industry focus, data applications, monetization, and more. The question is, will the promise of data be fulfilled this time around, or is it same wine, new bottle? Hello, and welcome to this week's Wikibon CUBE Insights powered by ETR. In this "Breaking Analysis," we'll talk about the event, the announcements that Snowflake made that are of greatest interest, the major themes of the show, what was hype and what was real, the competition, and some concerns that remain in many parts of the ecosystem and pockets of customers. First let's look at the overall event. It was held at Caesars Forum. Not my favorite venue, but I'll tell you it was packed. Fire Marshall Full, as we sometimes say. Nearly 10,000 people attended the event. Here's Snowflake's CMO Denise Persson on theCUBE describing how this event has evolved. >> Yeah, two, three years ago, we were about 1800 people at a Hilton in San Francisco. We had about 40 partners attending. This week we're close to 10,000 attendees here. Almost 10,000 people online as well, and over over 200 partners here on the show floor. >> Now, those numbers from 2019 remind me of the early days of Hadoop World, which was put on by Cloudera but then Cloudera handed off the event to O'Reilly as this article that we've inserted, if you bring back that slide would say. The headline it almost got it right. Hadoop World was a failure, but it didn't have to be. Snowflake has filled the void created by O'Reilly when it first killed Hadoop World, and killed the name and then killed Strata. Now, ironically, the momentum and excitement from Hadoop's early days, it probably could have stayed with Cloudera but the beginning of the end was when they gave the conference over to O'Reilly. We can't imagine Frank Slootman handing the keys to the kingdom to a third party. Serious business was done at this event. I'm talking substantive deals. Salespeople from a host sponsor and the ecosystems that support these events, they love physical. They really don't like virtual because physical belly to belly means relationship building, pipeline, and deals. And that was blatantly obvious at this show. And in fairness, all theCUBE events that we've done year but this one was more vibrant because of its attendance and the action in the ecosystem. Ecosystem is a hallmark of a cloud company, and that's what Snowflake is. We asked Frank Slootman on theCUBE, was this ecosystem evolution by design or did Snowflake just kind of stumble into it? Here's what he said. >> Well, when you are a data clouding, you have data, people want to do things with that data. They don't want just run data operations, populate dashboards, run reports. Pretty soon they want to build applications and after they build applications, they want build businesses on it. So it goes on and on and on. So it drives your development to enable more and more functionality on that data cloud. Didn't start out that way, you know, we were very, very much focused on data operations. Then it becomes application development and then it becomes, hey, we're developing whole businesses on this platform. So similar to what happened to Facebook in many ways. >> So it sounds like it was maybe a little bit of both. The Facebook analogy is interesting because Facebook is a walled garden, as is Snowflake, but when you come into that garden, you have assurances that things are going to work in a very specific way because a set of standards and protocols is being enforced by a steward, i.e. Snowflake. This means things run better inside of Snowflake than if you try to do all the integration yourself. Now, maybe over time, an open source version of that will come out but if you wait for that, you're going to be left behind. That said, Snowflake has made moves to make its platform more accommodating to open source tooling in many of its announcements this week. Now, I'm not going to do a deep dive on the announcements. Matt Sulkins from Monte Carlo wrote a decent summary of the keynotes and a number of analysts like Sanjeev Mohan, Tony Bear and others are posting some deeper analysis on these innovations, and so we'll point to those. I'll say a few things though. Unistore extends the type of data that can live in the Snowflake data cloud. It's enabled by a new feature called hybrid tables, a new table type in Snowflake. One of the big knocks against Snowflake was it couldn't handle and transaction data. Several database companies are creating this notion of a hybrid where both analytic and transactional workloads can live in the same data store. Oracle's doing this for example, with MySQL HeatWave and there are many others. We saw Mongo earlier this month add an analytics capability to its transaction system. Mongo also added sequel, which was kind of interesting. Here's what Constellation Research analyst Doug Henschen said about Snowflake's moves into transaction data. Play the clip. >> Well with Unistore, they're reaching out and trying to bring transactional data in. Hey, don't limit this to analytical information and there's other ways to do that like CDC and streaming but they're very closely tying that again to that marketplace, with the idea of bring your data over here and you can monetize it. Don't just leave it in that transactional database. So another reach to a broader play across a big community that they're building. >> And you're also seeing Snowflake expand its workload types in its unique way and through Snowpark and its stream lit acquisition, enabling Python so that native apps can be built in the data cloud and benefit from all that structure and the features that Snowflake is built in. Hence that Facebook analogy, or maybe the App Store, the Apple App Store as I propose as well. Python support also widens the aperture for machine intelligence workloads. We asked Snowflake senior VP of product, Christian Kleinerman which announcements he thought were the most impactful. And despite the who's your favorite child nature of the question, he did answer. Here's what he said. >> I think the native applications is the one that looks like, eh, I don't know about it on the surface but he has the biggest potential to change everything. That's create an entire ecosystem of solutions for within a company or across companies that I don't know that we know what's possible. >> Snowflake also announced support for Apache Iceberg, which is a new open table format standard that's emerging. So you're seeing Snowflake respond to these concerns about its lack of openness, and they're building optionality into their cloud. They also showed some cost op optimization tools both from Snowflake itself and from the ecosystem, notably Capital One which launched a software business on top of Snowflake focused on optimizing cost and eventually the rollout data management capabilities, and all kinds of features that Snowflake announced that the show around governance, cross cloud, what we call super cloud, a new security workload, and they reemphasize their ability to read non-native on-prem data into Snowflake through partnerships with Dell and Pure and a lot more. Let's hear from some of the analysts that came on theCUBE this week at Snowflake Summit to see what they said about the announcements and their takeaways from the event. This is Dave Menninger, Sanjeev Mohan, and Tony Bear, roll the clip. >> Our research shows that the majority of organizations, the majority of people do not have access to analytics. And so a couple of the things they've announced I think address those or help to address those issues very directly. So Snowpark and support for Python and other languages is a way for organizations to embed analytics into different business processes. And so I think that'll be really beneficial to try and get analytics into more people's hands. And I also think that the native applications as part of the marketplace is another way to get applications into people's hands rather than just analytical tools. Because most people in the organization are not analysts. They're doing some line of business function. They're HR managers, they're marketing people, they're sales people, they're finance people, right? They're not sitting there mucking around in the data, they're doing a job and they need analytics in that job. >> Primarily, I think it is to contract this whole notion that once you move data into Snowflake, it's a proprietary format. So I think that's how it started but it's usually beneficial to the customers, to the users because now if you have large amount of data in paket files you can leave it on S3, but then you using the Apache Iceberg table format in Snowflake, you get all the benefits of Snowflake's optimizer. So for example, you get the micro partitioning, you get the metadata. And in a single query, you can join, you can do select from a Snowflake table union and select from an iceberg table and you can do store procedure, user defined function. So I think what they've done is extremely interesting. Iceberg by itself still does not have multi-table transactional capabilities. So if I'm running a workload, I might be touching 10 different tables. So if I use Apache Iceberg in a raw format, they don't have it, but Snowflake does. So the way I see it is Snowflake is adding more and more capabilities right into the database. So for example, they've gone ahead and added security and privacy. So you can now create policies and do even cell level masking, dynamic masking, but most organizations have more than Snowflake. So what we are starting to see all around here is that there's a whole series of data catalog companies, a bunch of companies that are doing dynamic data masking, security and governance, data observability which is not a space Snowflake has gone into. So there's a whole ecosystem of companies that is mushrooming. Although, you know, so they're using the native capabilities of Snowflake but they are at a level higher. So if you have a data lake and a cloud data warehouse and you have other like relational databases, you can run these cross platform capabilities in that layer. So that way, you know, Snowflake's done a great job of enabling that ecosystem. >> I think it's like the last mile, essentially. In other words, it's like, okay, you have folks that are basically that are very comfortable with Tableau but you do have developers who don't want to have to shell out to a separate tool. And so this is where Snowflake is essentially working to address that constituency. To Sanjeev's point, and I think part of it, this kind of plays into it is what makes this different from the Hadoop era is the fact that all these capabilities, you know, a lot of vendors are taking it very seriously to put this native. Now, obviously Snowflake acquired Streamlit. So we can expect that the Streamlit capabilities are going to be native. >> I want to share a little bit about the higher level thinking at Snowflake, here's a chart from Frank Slootman's keynote. It's his version of the modern data stack, if you will. Now, Snowflake of course, was built on the public cloud. If there were no AWS, there would be no Snowflake. Now, they're all about bringing data and live data and expanding the types of data, including structured, we just heard about that, unstructured, geospatial, and the list is going to continue on and on. Eventually I think it's going to bleed into the edge if we can figure out what to do with that edge data. Executing on new workloads is a big deal. They started with data sharing and they recently added security and they've essentially created a PaaS layer. We call it a SuperPaaS layer, if you will, to attract application developers. Snowflake has a developer-focused event coming up in November and they've extended the marketplace with 1300 native apps listings. And at the top, that's the holy grail, monetization. We always talk about building data products and we saw a lot of that at this event, very, very impressive and unique. Now here's the thing. There's a lot of talk in the press, in the Wall Street and the broader community about consumption-based pricing and concerns over Snowflake's visibility and its forecast and how analytics may be discretionary. But if you're a company building apps in Snowflake and monetizing like Capital One intends to do, and you're now selling in the marketplace, that is not discretionary, unless of course your costs are greater than your revenue for that service, in which case is going to fail anyway. But the point is we're entering a new error where data apps and data products are beginning to be built and Snowflake is attempting to make the data cloud the defacto place as to where you're going to build them. In our view they're well ahead in that journey. Okay, let's talk about some of the bigger themes that we heard at the event. Bringing apps to the data instead of moving the data to the apps, this was a constant refrain and one that certainly makes sense from a physics point of view. But having a single source of data that is discoverable, sharable and governed with increasingly robust ecosystem options, it doesn't have to be moved. Sometimes it may have to be moved if you're going across regions, but that's unique and a differentiator for Snowflake in our view. I mean, I'm yet to see a data ecosystem that is as rich and growing as fast as the Snowflake ecosystem. Monetization, we talked about that, industry clouds, financial services, healthcare, retail, and media, all front and center at the event. My understanding is that Frank Slootman was a major force behind this shift, this development and go to market focus on verticals. It's really an attempt, and he talked about this in his keynote to align with the customer mission ultimately align with their objectives which not surprisingly, are increasingly monetizing with data as a differentiating ingredient. We heard a ton about data mesh, there were numerous presentations about the topic. And I'll say this, if you map the seven pillars Snowflake talks about, Benoit Dageville talked about this in his keynote, but if you map those into Zhamak Dehghani's data mesh framework and the four principles, they align better than most of the data mesh washing that I've seen. The seven pillars, all data, all workloads, global architecture, self-managed, programmable, marketplace and governance. Those are the seven pillars that he talked about in his keynote. All data, well, maybe with hybrid tables that becomes more of a reality. Global architecture means the data is globally distributed. It's not necessarily physically in one place. Self-managed is key. Self-service infrastructure is one of Zhamak's four principles. And then inherent governance. Zhamak talks about computational, what I'll call automated governance, built in. And with all the talk about monetization, that aligns with the second principle which is data as product. So while it's not a pure hit and to its credit, by the way, Snowflake doesn't use data mesh in its messaging anymore. But by the way, its customers do, several customers talked about it. Geico, JPMC, and a number of other customers and partners are using the term and using it pretty closely to the concepts put forth by Zhamak Dehghani. But back to the point, they essentially, Snowflake that is, is building a proprietary system that substantially addresses some, if not many of the goals of data mesh. Okay, back to the list, supercloud, that's our term. We saw lots of examples of clouds on top of clouds that are architected to spin multiple clouds, not just run on individual clouds as separate services. And this includes Snowflake's data cloud itself but a number of ecosystem partners that are headed in a very similar direction. Snowflake still talks about data sharing but now it uses the term collaboration in its high level messaging, which is I think smart. Data sharing is kind of a geeky term. And also this is an attempt by Snowflake to differentiate from everyone else that's saying, hey, we do data sharing too. And finally Snowflake doesn't say data marketplace anymore. It's now marketplace, accounting for its application market. Okay, let's take a quick look at the competitive landscape via this ETR X-Y graph. Vertical access remembers net score or spending momentum and the x-axis is penetration, pervasiveness in the data center. That's what ETR calls overlap. Snowflake continues to lead on the vertical axis. They guide it conservatively last quarter, remember, so I wouldn't be surprised if that lofty height, even though it's well down from its earlier levels but I wouldn't be surprised if it ticks down again a bit in the July survey, which will be in the field shortly. Databricks is a key competitor obviously at a strong spending momentum, as you can see. We didn't draw it here but we usually draw that 40% line or red line at 40%, anything above that is considered elevated. So you can see Databricks is quite elevated. But it doesn't have the market presence of Snowflake. It didn't get to IPO during the bubble and it doesn't have nearly as deep and capable go-to market machinery. Now, they're getting better and they're getting some attention in the market, nonetheless. But as a private company, you just naturally, more people are aware of Snowflake. Some analysts, Tony Bear in particular, believe Mongo and Snowflake are on a bit of a collision course long term. I actually can see his point. You know, I mean, they're both platforms, they're both about data. It's long ways off, but you can see them sort of in a similar path. They talk about kind of similar aspirations and visions even though they're quite in different markets today but they're definitely participating in similar tam. The cloud players are probably the biggest or definitely the biggest partners and probably the biggest competitors to Snowflake. And then there's always Oracle. Doesn't have the spending velocity of the others but it's got strong market presence. It owns a cloud and it knows a thing about data and it definitely is a go-to market machine. Okay, we're going to end on some of the things that we heard in the ecosystem. 'Cause look, we've heard before how particular technology, enterprise data warehouse, data hubs, MDM, data lakes, Hadoop, et cetera. We're going to solve all of our data problems and of course they didn't. And in fact, sometimes they create more problems that allow vendors to push more incremental technology to solve the problems that they created. Like tools and platforms to clean up the no schema on right nature of data lakes or data swamps. But here are some of the things that I heard firsthand from some customers and partners. First thing is, they said to me that they're having a hard time keeping up sometimes with the pace of Snowflake. It reminds me of AWS in 2014, 2015 timeframe. You remember that fire hose of announcements which causes increased complexity for customers and partners. I talked to several customers that said, well, yeah this is all well and good but I still need skilled people to understand all these tools that I'm integrated in the ecosystem, the catalogs, the machine learning observability. A number of customers said, I just can't use one governance tool, I need multiple governance tools and a lot of other technologies as well, and they're concerned that that's going to drive up their cost and their complexity. I heard other concerns from the ecosystem that it used to be sort of clear as to where they could add value you know, when Snowflake was just a better data warehouse. But to point number one, they're either concerned that they'll be left behind or they're concerned that they'll be subsumed. Look, I mean, just like we tell AWS customers and partners, you got to move fast, you got to keep innovating. If you don't, you're going to be left. Either if your customer you're going to be left behind your competitor, or if you're a partner, somebody else is going to get there or AWS is going to solve the problem for you. Okay, and there were a number of skeptical practitioners, really thoughtful and experienced data pros that suggested that they've seen this movie before. That's hence the same wine, new bottle. Well, this time around I certainly hope not given all the energy and investment that is going into this ecosystem. And the fact is Snowflake is unquestionably making it easier to put data to work. They built on AWS so you didn't have to worry about provisioning, compute and storage and networking and scaling. Snowflake is optimizing its platform to take advantage of things like Graviton so you don't have to, and they're doing some of their own optimization tools. The ecosystem is building optimization tools so that's all good. And firm belief is the less expensive it is, the more data will get brought into the data cloud. And they're building a data platform on which their ecosystem can build and run data applications, aka data products without having to worry about all the hard work that needs to get done to make data discoverable, shareable, and governed. And unlike the last 10 years, you don't have to be a keeper and integrate all the animals in the Hadoop zoo. Okay, that's it for today, thanks for watching. Thanks to my colleague, Stephanie Chan who helps research "Breaking Analysis" topics. Sometimes Alex Myerson is on production and manages the podcasts. Kristin Martin and Cheryl Knight help get the word out on social and in our newsletters, and Rob Hof is our editor in chief over at Silicon, and Hailey does some wonderful editing, thanks to all. Remember, all these episodes are available as podcasts wherever you listen. All you got to do is search Breaking Analysis Podcasts. I publish each week on wikibon.com and siliconangle.com and you can email me at David.Vellante@siliconangle.com or DM me @DVellante. If you got something interesting, I'll respond. If you don't, I'm sorry I won't. Or comment on my LinkedIn post. Please check out etr.ai for the best survey data in the enterprise tech business. This is Dave Vellante for theCUBE Insights powered by ETR. Thanks for watching, and we'll see you next time. (upbeat music)

(upbeat music) >> Hello, welcome to this CUBE conversation I'm John Furrier your host here in theCUBE in Palo Alto, California, featuring Cribl a hot startup taking over the enterprise when it comes to data pipelining, and we have a CUBE alumni who's the co-founder and CEO, Clint Sharp. Clint, great to see you again, you've been on theCUBE, you were on in 2013, great to see you, congratulations on the company that you co-founded, and leading as the chief executive officer over $200 million in funding, doing this really strong in the enterprise, congratulations thanks for joining us. >> Hey, thanks John it's really great to be back. >> You know, remember our first conversation the big data wave coming in, Hadoop World 2010, now the cloud comes in, and really the cloud native really takes data to a whole nother level. You've seeing the old data architectures being replaced with cloud scale. So the data landscape is interesting. You know, Data as Code you're hearing that term, data engineering teams are out there, data is everywhere, it's now part of how developers and companies are getting value whether it's real time, or coming out of data lakes, data is more pervasive than ever. Observability is a hot area, there's a zillion companies doing it, what are you guys doing? Where do you fit in the data landscape? >> Yeah, so what I say is that Cribl and our products and we solve the problem for our customers of the fundamental tension between data growth and budget. And so if you look at IDCs data data's growing at a 25%, CAGR, you're going to have two and a half times the amount of data in five years that you have today, and I talk to a lot of CIOs, I talk to a lot of CISOs, and the thing that I hear repeatedly is my budget is not growing at a 25% CAGR so fundamentally, how do I resolve this tension? We sell very specifically into the observability in security markets, we sell to technology professionals who are operating, you know, observability in security platforms like Splunk, or Elasticsearch, or Datadog, Exabeam, like these types of platforms they're moving, protocols like syslog, they're moving, they have lots of agents deployed on every endpoint and they're trying to figure out how to get the right data to the right place, and fundamentally you know, control cost. And we do that through our product called Stream which is what we call an observability pipeline. It allows you to take all this data, manipulate it in the stream and get it to the right place and fundamentally be able to connect all those things that maybe weren't originally intended to be connected. >> So I want to get into that new architecture if you don't mind, but let me first ask you on the problem space that you're in. So cloud native obviously instrumentating, instrumenting everything is a key thing. You mentioned data got all these tools, is the problem that there's been a sprawl of things being instrumented and they have to bring it together, or it's too costly to run all these point solutions and get it to work? What's the problem space that you're in? >> So I think customers have always been forced to make trade offs John. So the, hey I have volumes and volumes and volumes of data that's relevant to securing my enterprise, that's relevant to observing and understanding the behavior of my applications but there's never been an approach that allows me to really onboard all of that data. And so where we're coming at is giving them the tools to be able to, you know, filter out noise and waste, to be able to, you know, aggregate this high fidelity telemetry data. There's a lot of growing changes, you talk about cloud native, but digital transformation, you know, the pandemic itself and remote work all these are driving significantly greater data volumes, and vendors unsurprisingly haven't really been all that aligned to giving customers the tools in order to reshape that data, to filter out noise and waste because, you know, for many of them they're incentivized to get as much data into their platform as possible, whether that's aligned to the customer's interests or not. And so we saw an opportunity to come out and fundamentally as a customers-first company give them the tools that they need, in order to take back control of their data. >> I remember those conversations even going back six years ago the whole cloud scale, horizontally scalable applications, you're starting to see data now being stuck in the silos now to have high, good data you have to be observable, which means you got to be addressable. So you now have to have a horizontal data plane if you will. But then you get to the question of, okay, what data do I need at the right time? So is the Data as Code, data engineering discipline changing what new architectures are needed? What changes in the mind of the customer once they realize that they need this new way to pipe data and route data around, or make it available for certain applications? What are the key new changes? >> Yeah, so I think one of the things that we've been seeing in addition to the advent of the observability pipeline that allows you to connect all the things, is also the advent of an observability lake as well. Which is allowing people to store massively greater quantities of data, and also different types of data. So data that might not traditionally fit into a data warehouse, or might not traditionally fit into a data lake architecture, things like deployment artifacts, or things like packet captures. These are binary types of data that, you know, it's not designed to work in a database but yet they want to be able to ask questions like, hey, during the Log4Shell vulnerability, one of all my deployment artifacts actually had Log4j in it in an affected version. These are hard questions to answer in today's enterprise. Or they might need to go back to full fidelity packet capture data to try to understand that, you know, a malicious actor's movement throughout the enterprise. And we're not seeing, you know, we're seeing vendors who have great log indexing engines, and great time series databases, but really what people are looking for is the ability to store massive quantities of data, five times, 10 times more data than they're storing today, and they're doing that in places like AWSS3, or in Azure Blob Storage, and we're just now starting to see the advent of technologies we can help them query that data, and technologies that are generally more specifically focused at the type of persona that we sell to which is a security professional, or an IT professional who's trying to understand the behaviors of their applications, and we also find that, you know, general-purpose data processing technologies are great for the enterprise, but they're not working for the people who are running the enterprise, and that's why you're starting to see the concepts like observability pipelines and observability lakes emerge, because they're targeted at these people who have a very unique set of problems that are not being solved by the general-purpose data processing engines. >> It's interesting as you see the evolution of more data volume, more data gravity, then you have these specialty things that need to be engineered for the business. So sounds like observability lake and pipelining of the data, the data pipelining, or stream you call it, these are new things that they bolt into the architecture, right? Because they have business reasons to do it. What's driving that? Sounds like security is one of them. Are there others that are driving this behavior? >> Yeah, I mean it's the need to be able to observe applications and observe end-user behavior at a fine-grain detail. So, I mean I often use examples of like bank teller applications, or perhaps, you know, the app that you're using to, you know, I'm going to be flying in a couple of days. I'll be using their app to understand whether my flight's on time. Am I getting a good experience in that particular application? Answering the question of is Clint getting a good experience requires massive quantities of data, and your application and your service, you know, I'm going to sit there and look at, you know, American Airlines which I'm flying on Thursday, I'm going to be judging them based on off of my experience. I don't care what the average user's experience is I care what my experience is. And if I call them up and I say, hey, and especially for the enterprise usually this is much more for, you know, in-house applications and things like that. They call up their IT department and say, hey, this application is not working well, I don't know what's going on with it, and they can't answer the question of what was my individual experience, they're living with, you know, data that they can afford to store today. And so I think that's why you're starting to see the advent of these new architectures is because digital is so absolutely critical to every company's customer experience, that they're needing to be able to answer questions about an individual user's experience which requires significantly greater volumes of data, and because of significantly greater volumes of data, that requires entirely new approaches to aggregating that data, bringing the data in, and storing that data. >> Talk to me about enabling customer choice when it comes around controlling their data. You mentioned that before we came on camera that you guys are known for choice. How do you enable customer choice and control over their data? >> So I think one of the biggest problems I've seen in the industry over the last couple of decades is that vendors come to customers with hugely valuable products that make their lives better but it also requires them to maintain a relationship with that vendor in order to be able to continue to ask questions of that data. And so customers don't get a lot of optionality in these relationships. They sign multi-year agreements, they look to try to start another, they want to go try out another vendor, they want to add new technologies into their stack, and in order to do that they're often left with a choice of well, do I roll out like get another agent, do I go touch 10,000 computers, or a 100,000 computers in order to onboard this data? And what we have been able to offer them is the ability to reuse their existing deployed footprints of agents and their existing data collection technologies, to be able to use multiple tools and use the right tool for the right job, and really give them that choice, and not only give them the choice once, but with the concepts of things like the observability lake and replay, they can go back in time and say, you know what? I wanted to rehydrate all this data into a new tool, I'm no longer locked in to the way one vendor stores this, I can store this data in open formats and that's one of the coolest things about the observability late concept is that customers are no longer locked in to any particular vendor, the data is stored in open formats and so that gives them the choice to be able to go back later and choose any vendor, because they may want to do some AI or ML on that type of data and do some model training. They may want to be able to forward that data to a new cloud data warehouse, or try a different vendor for log search or a different vendor for time series data. And we're really giving them the choice and the tools to do that in a way in which was simply not possible before. >> You know you are bring up a point that's a big part of the upcoming AWS startup series Data as Code, the data engineering role has become so important and the word engineering is a key word in that, but there's not a lot of them, right? So like how many data engineers are there on the planet, and hopefully more will come in, come from these great programs in computer science but you got to engineer something but you're talking about developing on data, you're talking about doing replays and rehydrating, this is developing. So Data as Code is now a reality, how do you see Data as Code evolving from your perspective? Because it implies DevOps, Infrastructure as Code was DevOps, if Data as Code then you got DataOps, AIOps has been around for a while, what is Data as Code? And what does that mean to you Clint? >> I think for our customers, one, it means a number of I think sort of after-effects that maybe they have not yet been considering. One you mentioned which is it's hard to acquire that talent. I think it is also increasingly more critical that people who were working in jobs that used to be purely operational, are now being forced to learn, you know, developer centric tooling, things like GET, things like CI/CD pipelines. And that means that there's a lot of education that's going to have to happen because the vast majority of the people who have been doing things in the old way from the last 10 to 20 years, you know, they're going to have to get retrained and retooled. And I think that one is that's a huge opportunity for people who have that skillset, and I think that they will find that their compensation will be directly correlated to their ability to have those types of skills, but it also represents a massive opportunity for people who can catch this wave and find themselves in a place where they're going to have a significantly better career and more options available to them. >> Yeah and I've been thinking about what you just said about your customer environment having all these different things like Datadog and other agents. Those people that rolled those out can still work there, they don't have to rip and replace and then get new training on the new multiyear enterprise service agreement that some other vendor will sell them. You come in and it sounds like you're saying, hey, stay as you are, use Cribl, we'll have some data engineering capabilities for you, is that right? Is that? >> Yup, you got it. And I think one of the things that's a little bit different about our product and our market John, from kind of general-purpose data processing is for our users they often, they're often responsible for many tools and data engineering is not their full-time job, it's actually something they just need to do now, and so we've really built tool that's designed for your average security professional, your average IT professional, yes, we can utilize the same kind of DataOps techniques that you've been talking about, CI/CD pipelines, GITOps, that sort of stuff, but you don't have to, and if you're really just already familiar with administering a Datadog or a Splunk, you can get started with our product really easily, and it is designed to be able to be approachable to anybody with that type of skillset. >> It's interesting you, when you're talking you've remind me of the big wave that was coming, it's still here, shift left meant security from the beginning. What do you do with data shift up, right, down? Like what do you, what does that mean? Because what you're getting at here is that if you're a developer, you have to deal with data but you don't have to be a data engineer but you can be, right? So we're getting in this new world. Security had that same problem. Had to wait for that group to do things, creating tension on the CI/CD pipelining, so the developers who are building apps had to wait. Now you got shift left, what is data, what's the equivalent of the data version of shift left? >> Yeah so we're actually doing this right now. We just announced a new product a week ago called Cribl Edge. And this is enabling us to move processing of this data rather than doing it centrally in the stream to actually push this processing out to the edge, and to utilize a lot of unused capacity that you're already paying AWS, or paying Azure for, or maybe in your own data center, and utilize that capacity to do the processing rather than having to centralize and aggregate all of this data. So I think we're going to see a really interesting, and left from our side is towards the origination point rather than anything else, and that allows us to really unlock a lot of unused capacity and continue to drive the kind of cost down to make more data addressable back to the original thing we talked about the tension between data growth, if we want to offer more capacity to people, if we want to be able to answer more questions, we need to be able to cost-effectively query a lot more data. >> You guys had great success in the enterprise with what you got going on. Obviously the funding is just the scoreboard for that. You got good growth, what are the use cases, or what's the customer look like that's working for you where you're winning, or maybe said differently what pain points are out there the customer might be feeling right now that Cribl could fit in and solve? How would you describe that ideal persona, or environment, or problem, that the customer may have that they say, man, Cribl's a perfect fit? >> Yeah, this is a person who's working on tooling. So they administer a Splunk, or an Elastic, or a Datadog, they may be in a network operations center, a security operation center, they are struggling to get data into their tools, they're always at capacity, their tools always at the redline, they really wish they could do more for the business. They're kind of tired of being this department of no where everybody comes to them and says, "hey, can I get this data in?" And they're like, "I wish, but you know, we're all out of capacity, and you know, we have, we wish we could help you but we frankly can't right now." We help them by routing that data to multiple locations, we help them control costs by eliminating noise and waste, and we've been very successful at that in, you know, logos, like, you know, like a Shutterfly, or a, blanking on names, but we've been very successful in the enterprise, that's not great, and we continue to be successful with major logos inside of government, inside of banking, telco, et cetera. >> So basically it used to be the old hyperscalers, the ones with the data full problem, now everyone's got the, they're full of data and they got to really expand capacity and have more agility and more engineering around contributions of the business sounds like that's what you guys are solving. >> Yup and hopefully we help them do a little bit more with less. And I think that's a key problem for our enterprises, is that there's always a limit on the number of human resources that they have available at their disposal, which is why we try to make the software as easy to use as possible, and make it as widely applicable to those IT and security professionals who are, you know, kind of your run-of-the-mill tools administrator, our product is very approachable for them. >> Clint great to see you on theCUBE here, thanks for coming on. Quick plug for the company, you guys looking for hiring, what's going on? Give a quick update, take 30 seconds to give a plug. >> Yeah, absolutely. We are absolutely hiring cribl.io/jobs, we need people in every function from sales, to marketing, to engineering, to back office, GNA, HR, et cetera. So please check out our job site. If you are interested it in learning more you can go to cribl.io. We've got some great online sandboxes there which will help you educate yourself on the product, our documentation is freely available, you can sign up for up to a terabyte a day on our cloud, go to cribl.cloud and sign up free today. The product's easily accessible, and if you'd like to speak with us we'd love to have you in our community, and you can join the community from cribl.io as well. >> All right, Clint Sharp co-founder and CEO of Cribl, thanks for coming to theCUBE. Great to see you, I'm John Furrier your host thanks for watching. (upbeat music)

>>from around the globe. It's the Cube presenting Cuban cloud brought to you by silicon angle in 2000 >>nine. Hal Varian, Google's chief economist, said that statisticians would be the sexiest job in the coming decade. The modern big data movement >>really >>took off later in the following year. After the Second Hadoop World, which was hosted by Claudette Cloudera in New York City. Jeff Ham Abakar famously declared to me and John further in the Cube that the best minds of his generation, we're trying to figure out how to get people to click on ads. And he said that sucks. The industry was abuzz with the realization that data was the new competitive weapon. Hadoop was heralded as the new data management paradigm. Now, what actually transpired Over the next 10 years on Lee, a small handful of companies could really master the complexities of big data and attract the data science talent really necessary to realize massive returns as well. Back then, Cloud was in the early stages of its adoption. When you think about it at the beginning of the last decade and as the years passed, Maurin Mawr data got moved to the cloud and the number of data sources absolutely exploded. Experimentation accelerated, as did the pace of change. Complexity just overwhelmed big data infrastructures and data teams, leading to a continuous stream of incremental technical improvements designed to try and keep pace things like data Lakes, data hubs, new open source projects, new tools which piled on even Mawr complexity. And as we reported, we believe what's needed is a comm pleat bit flip and how we approach data architectures. Our next guest is Jean Marc de Connie, who is the director of emerging technologies That thought works. John Mark is a software engineer, architect, thought leader and adviser to some of the world's most prominent enterprises. She's, in my view, one of the foremost advocates for rethinking and changing the way we create and manage data architectures. Favoring a decentralized over monolithic structure and elevating domain knowledge is a primary criterion. And how we organize so called big data teams and platforms. Chamakh. Welcome to the Cube. It's a pleasure to have you on the program. >>Hi, David. This wonderful to be here. >>Well, okay, so >>you're >>pretty outspoken about the need for a paradigm shift in how we manage our data and our platforms that scale. Why do you feel we need such a radical change? What's your thoughts there? >>Well, I think if you just look back over the last decades you gave us, you know, a summary of what happened since 2000 and 10. But if even if we go before then what we have done over the last few decades is basically repeating and, as you mentioned, incrementally improving how we've managed data based on a certain assumptions around. As you mentioned, centralization data has to be in one place so we can get value from it. But if you look at the parallel movement off our industry in general since the birth of Internet, we are actually moving towards decentralization. If we think today, like if this move data side, if he said the only way Web would work the only way we get access to you know various applications on the Web pages is to centralize it. We would laugh at that idea, but for some reason we don't. We don't question that when it comes to data, right? So I think it's time to embrace the complexity that comes with the growth of number of sources, the proliferation of sources and consumptions models, you know, embrace the distribution of sources of data that they're not just within one part of organization. They're not just within even bounds of organization there beyond the bounds of organization. And then look back and say Okay, if that's the trend off our industry in general, Um, given the fabric of computation and data that we put in, you know globally in place, then how the architecture and technology and organizational structure incentives need to move to embrace that complexity. And to me, that requires a paradigm shift, a full stack from how we organize our organizations, how we organize our teams, how we, you know, put a technology in place, um, to to look at it from a decentralized angle. >>Okay, so let's let's unpack that a little bit. I mean, you've spoken about and written that today's big architecture and you basically just mentioned that it's flawed, So I wanna bring up. I love your diagrams of a simple diagram, guys, if you could bring up ah, figure one. So on the left here we're adjusting data from the operational systems and other enterprise data sets and, of course, external data. We cleanse it, you know, you've gotta do the do the quality thing and then serve them up to the business. So So what's wrong with that picture that we just described and give granted? It's a simplified form. >>Yeah, quite a few things. So, yeah, I would flip the question may be back to you or the audience if we said that. You know, there are so many sources off the data on the Actually, the data comes from systems and from teams that are very diverse in terms off domains. Right? Domain. If if you just think about, I don't know retail, Uh, the the E Commerce versus Order Management versus customer This is a very diverse domains. The data comes from many different diverse domains. And then we expect to put them under the control off a centralized team, a centralized system. And I know that centralization. Probably if you zoom out, it's centralized. If you zoom in it z compartmentalized based on functions that we can talk about that and we assume that the centralized model will be served, you know, getting that data, making sense of it, cleansing and transforming it then to satisfy in need of very diverse set of consumers without really understanding the domains, because the teams responsible for it or not close to the source of the data. So there is a bit of it, um, cognitive gap and domain understanding Gap, um, you know, without really understanding of how the data is going to be used, I've talked to numerous. When we came to this, I came up with the idea. I talked to a lot of data teams globally just to see, you know, what are the pain points? How are they doing it? And one thing that was evident in all of those conversations that they actually didn't know after they built these pipelines and put the data in whether the data warehouse tables or like, they didn't know how the data was being used. But yet the responsible for making the data available for these diverse set of these cases, So s centralized system. A monolithic system often is a bottleneck. So what you find is, a lot of the teams are struggling with satisfying the needs of the consumers, the struggling with really understanding the data. The domain knowledge is lost there is a los off understanding and kind of in that in that transformation. Often, you know, we end up training machine learning models on data that is not really representative off the reality off the business. And then we put them to production and they don't work because the semantic and the same tax off the data gets lost within that translation. So we're struggling with finding people thio, you know, to manage a centralized system because there's still the technology is fairly, in my opinion, fairly low level and exposes the users of those technologies. I said, Let's say warehouse a lot off, you know, complexity. So in summary, I think it's a bottleneck is not gonna, you know, satisfy the pace of change, of pace, of innovation and the pace of, you know, availability of sources. Um, it's disconnected and fragmented, even though the centralizes disconnected and fragmented from where the data comes from and where the data gets used on is managed by, you know, a team off hyper specialized people that you know, they're struggling to understand the actual value of the data, the actual format of the data, so it's not gonna get us where our aspirations and ambitions need to be. >>Yes. So the big data platform is essentially I think you call it, uh, context agnostic. And so is data becomes, you know, more important, our lives. You've got all these new data sources, you know, injected into the system. Experimentation as we said it with the cloud becomes much, much easier. So one of the blockers that you've started, you just mentioned it is you've got these hyper specialized roles the data engineer, the quality engineer, data scientists and and the It's illusory. I mean, it's like an illusion. These guys air, they seemingly they're independent and in scale independently. But I think you've made the point that in fact, they can't that a change in the data source has an effect across the entire data lifecycle entire data pipeline. So maybe you could maybe you could add some color to why that's problematic for some of the organizations that you work with and maybe give some examples. >>Yeah, absolutely so in fact, that initially the hypothesis around that image came from a Siris of requests that we received from our both large scale and progressive clients and progressive in terms of their investment in data architectures. So this is where clients that they were there were larger scale. They had divers and reached out of domains. Some of them were big technology tech companies. Some of them were retail companies, big health care companies. So they had that diversity off the data and the number off. You know, the sources of the domains they had invested for quite a few years in, you know, generations. If they had multi generations of proprietary data warehouses on print that they were moving to cloud, they had moved to the barriers, you know, revisions of the Hadoop clusters and they were moving to the cloud. And they the challenges that they were facing were simply there were not like, if I want to just, like, you know, simplifying in one phrase, they were not getting value from the data that they were collecting. There were continuously struggling Thio shift the culture because there was so much friction between all of these three phases of both consumption of the data and transformation and making it available consumption from sources and then providing it and serving it to the consumer. So that whole process was full of friction. Everybody was unhappy. So its bottom line is that you're collecting all this data. There is delay. There is lack of trust in the data itself because the data is not representative of the reality has gone through a transformation. But people that didn't understand really what the data was got delayed on bond. So there is no trust. It's hard to get to the data. It's hard to create. Ultimately, it's hard to create value from the data, and people are working really hard and under a lot of pressure. But it's still, you know, struggling. So we often you know, our solutions like we are. You know, Technologies will often pointed to technology. So we go. Okay, This this version of you know, some some proprietary data warehouse we're using is not the right thing. We should go to the cloud, and that certainly will solve our problems. Right? Or warehouse wasn't a good one. Let's make a deal Lake version. So instead of you know, extracting and then transforming and loading into the little bits. And that transformation is that, you know, heavy process, because you fundamentally made an assumption using warehouses that if I transform this data into this multi dimensional, perfectly designed schema that then everybody can run whatever choir they want that's gonna solve. You know everybody's problem, but in reality it doesn't because you you are delayed and there is no universal model that serves everybody's need. Everybody that needs the divers data scientists necessarily don't don't like the perfectly modeled data. They're looking for both signals and the noise. So then, you know, we've We've just gone from, uh, et elles to let's say now to Lake, which is okay, let's move the transformation to the to the last mile. Let's just get load the data into, uh into the object stores into semi structured files and get the data. Scientists use it, but they're still struggling because the problems that we mentioned eso then with the solution. What is the solution? Well, next generation data platform, let's put it on the cloud, and we sell clients that actually had gone through, you know, a year or multiple years of migration to the cloud. But with it was great. 18 months I've seen, you know, nine months migrations of the warehouse versus two year migrations of the various data sources to the clubhouse. But ultimately, the result is the same on satisfy frustrated data users, data providers, um, you know, with lack of ability to innovate quickly on relevant data and have have have an experience that they deserve toe have have a delightful experience off discovering and exploring data that they trust. And all of that was still a missed so something something else more fundamentally needed to change than just the technology. >>So then the linchpin to your scenario is this notion of context and you you pointed out you made the other observation that look, we've made our operational systems context aware. But our data platforms are not on bond like CRM system sales guys very comfortable with what's in the CRM system. They own the data. So let's talk about the answer that you and your colleagues are proposing. You're essentially flipping the architecture whereby those domain knowledge workers, the builders, if you will, of data products or data services there now, first class citizens in the data flow and they're injecting by design domain knowledge into the system. So So I wanna put up another one of your charts. Guys, bring up the figure to their, um it talks about, you know, convergence. You showed data distributed domain, dream and architecture. Er this self serve platform design and this notion of product thinking. So maybe you could explain why this approach is is so desirable, in your view, >>sure. The motivation and inspiration for the approach came from studying what has happened over the last few decades in operational systems. We had a very similar problem prior to micro services with monolithic systems, monolithic systems where you know the bottleneck. Um, the changes we needed to make was always, you know, our fellow Noto, how the architecture was centralized and we found a nice nation. I'm not saying this is the perfect way of decoupling a monolith, but it's a way that currently where we are in our journey to become data driven, um is a nice place to be, um, which is distribution or decomposition off your system as well as organization. I think when we whenever we talk about systems, we've got to talk about people and teams that's responsible for managing those systems. So the decomposition off the systems and the teams on the data around domains because that's how today we are decoupling our business, right? We're decoupling our businesses around domains, and that's a that's a good thing and that What does that do really for us? What it does? Is it localizes change to the bounded context of fact business. It creates clear boundary and interfaces and contracts between the rest of the universe of the organization on that particular team, so removes the friction that often we have for both managing the change and both serving data or capability. So it's the first principle of data meshes. Let's decouple this world off analytical data the same to mirror the same way we have to couple their systems and teams and business why data is any different. And the moment you do that, So you, the moment you bring the ownership to people who understands the data best, then you get questions that well, how is that any different from silence that's connected databases that we have today and nobody can get to the data? So then the rest of the principles is really to address all of the challenges that comes with this first principle of decomposition around domain Context on the second principle is well, we have to expect a certain level off quality and accountability and responsibility for the teams that provide the data. So let's bring product thinking and treating data as a product to the data that these teams now, um share and let's put accountability around. And we need a new set of incentives and metrics for domain teams to share the data. We need to have a new set off kind of quality metrics that define what it means for the data to be a product. And we can go through that conversation perhaps later eso then the second principle is okay. The teams now that are responsible, the domain teams responsible for the analytical data need to provide that data with a certain level of quality and assurance. Let's call that a product and bring products thinking to that. And then the next question you get asked off by C. E. O s or city or the people who build the infrastructure and, you know, spend the money. They said, Well, it's actually quite complex to manage big data, and now we're We want everybody, every independent team to manage the full stack of, you know, storage and computation and pipelines and, you know, access, control and all of that. And that's well, we have solved that problem in operational world. And that requires really a new level of platform thinking toe provide infrastructure and tooling to the domain teams to now be able to manage and serve their big data. And that I think that requires reimagining the world of our tooling and technology. But for now, let's just assume that we need a new level of abstraction to hide away ton of complexity that unnecessarily people get exposed to and that that's the third principle of creating Selves of infrastructure, um, to allow autonomous teams to build their domains. But then the last pillar, the last you know, fundamental pillar is okay. Once you distributed problem into a smaller problems that you found yourself with another set of problems, which is how I'm gonna connect this data, how I'm gonna you know, that the insights happens and emerges from the interconnection of the data domains right? It does not necessarily locked into one domain. So the concerns around interoperability and standardization and getting value as a result of composition and interconnection of these domains requires a new approach to governance. And we have to think about governance very differently based on a Federated model and based on a computational model. Like once we have this powerful self serve platform, we can computational e automate a lot of governance decisions. Um, that security decisions and policy decisions that applies to you know, this fabric of mesh not just a single domain or not in a centralized. Also, really. As you mentioned that the most important component of the emissions distribution of ownership and distribution of architecture and data the rest of them is to solve all the problems that come with that. >>So very powerful guys. We actually have a picture of what Jamaat just described. Bring up, bring up figure three, if you would tell me it. Essentially, you're advocating for the pushing of the pipeline and all its various functions into the lines of business and abstracting that complexity of the underlying infrastructure, which you kind of show here in this figure, data infrastructure is a platform down below. And you know what I love about this Jama is it to me, it underscores the data is not the new oil because I could put oil in my car I can put in my house, but I can't put the same court in both places. But I think you call it polyglot data, which is really different forms, batch or whatever. But the same data data doesn't follow the laws of scarcity. I can use the same data for many, many uses, and that's what this sort of graphic shows. And then you brought in the really important, you know, sticking problem, which is that you know the governance which is now not a command and control. It's it's Federated governance. So maybe you could add some thoughts on that. >>Sure, absolutely. It's one of those I think I keep referring to data much as a paradigm shift. And it's not just to make it sound ground and, you know, like, kind of ground and exciting or in court. And it's really because I want to point out, we need to question every moment when we make a decision around how we're going to design security or governance or modeling off the data, we need to reflect and go back and say, um, I applying some of my cognitive biases around how I have worked for the last 40 years, I have seen it work. Or do I do I really need to question. And we do need to question the way we have applied governance. I think at the end of the day, the rule of the data governance and objective remains the same. I mean, we all want quality data accessible to a diverse set of users. And these users now have different personas, like David, Personal data, analyst data, scientists, data application, Um, you know, user, very diverse personal. So at the end of the day, we want quality data accessible to them, um, trustworthy in in an easy consumable way. Um, however, how we get there looks very different in as you mentioned that the governance model in the old world has been very commander control, very centralized. Um, you know, they were responsible for quality. They were responsible for certification off the data, you know, applying making sure the data complies. But also such regulations Make sure you know, data gets discovered and made available in the world of the data mesh. Really. The job of the data governance as a function becomes finding that equilibrium between what decisions need to be um, you know, made and enforced globally. And what decisions need to be made locally so that we can have an interoperable measure. If data sets that can move fast and can change fast like it's really about instead of hardest, you know, kind of putting the putting those systems in a straitjacket of being constant and don't change, embrace, change and continuous change of landscape because that's that's just the reality we can't escape. So the role of governance really the governance model called Federated and Computational. And by that I mean, um, every domain needs to have a representative in the governance team. So the role of the data or domain data product owner who really were understand the data that domain really well but also wears that hacks of a product owner. It is an important role that had has to have a representation in the governance. So it's a federation off domains coming together, plus the SMEs and people have, you know, subject matter. Experts who understands the regulations in that environmental understands the data security concerns, but instead off trying to enforce and do this as a central team. They make decisions as what need to be standardized, what need to be enforced. And let's push that into that computational E and in an automated fashion into the into the camp platform itself. For example, instead of trying to do that, you know, be part of the data quality pipeline and inject ourselves as people in that process, let's actually, as a group, define what constitutes quality, like, how do we measure quality? And then let's automate that and let Z codify that into the platform so that every native products will have a C I City pipeline on as part of that pipeline. Those quality metrics gets validated and every day to product needs to publish those SLOC or service level objectives. So you know, whatever we choose as a measure of quality, maybe it's the, you know, the integrity of the data, the delay in the data, the liveliness of it, whatever the are the decisions that you're making, let's codify that. So it's, um, it's really, um, the role of the governance. The objectives of the governance team tried to satisfies the same, but how they do it. It is very, very different. I wrote a new article recently trying to explain the logical architecture that would emerge from applying these principles. And I put a kind of light table to compare and contrast the roll off the You know how we do governance today versus how we will do it differently to just give people a flavor of what does it mean to embrace the centralization? And what does it mean to embrace change and continuous change? Eso hopefully that that that could be helpful. >>Yes, very so many questions I haven't but the point you make it to data quality. Sometimes I feel like quality is the end game. Where is the end game? Should be how fast you could go from idea to monetization with the data service. What happens again? You sort of address this, but what happens to the underlying infrastructure? I mean, spinning a PC to S and S three buckets and my pie torches and tensor flows. And where does that that lives in the business? And who's responsible for that? >>Yeah, that's I'm glad you're asking this question. Maybe because, um, I truly believe we need to re imagine that world. I think there are many pieces that we can use Aziz utilities on foundational pieces, but I but I can see for myself a 5 to 7 year roadmap of building this new tooling. I think, in terms of the ownership, the question around ownership, if that would remains with the platform team, but and perhaps the domain agnostic, technology focused team right that there are providing instead of products themselves. And but the products are the users off those products are data product developers, right? Data domain teams that now have really high expectations in terms of low friction in terms of lead time to create a new data product. Eso We need a new set off tooling, and I think with the language needs to shift from, You know, I need a storage buckets. So I need a storage account. So I need a cluster to run my, you know, spark jobs, too. Here's the declaration of my data products. This is where the data for it will come from. This is the data that I want to serve. These are the policies that I need toe apply in terms of perhaps encryption or access control. Um, go make it happen. Platform, go provision, Everything that I mean so that as a data product developer. All I can focus on is the data itself, representation of semantic and representation of the syntax. And make sure that data meets the quality that I have that I have to assure and it's available. The rest of provisioning of everything that sits underneath will have to get taken care of by the platform. And that's what I mean by requires a re imagination and in fact, Andi, there will be a data platform team, the data platform teams that we set up for our clients. In fact, themselves have a favorite of complexity. Internally, they divide into multiple teams multiple planes, eso there would be a plane, as in a group of capabilities that satisfied that data product developer experience, there would be a set of capabilities that deal with those need a greatly underlying utilities. I call it at this point, utilities, because to me that the level of abstraction of the platform is to go higher than where it is. So what we call platform today are a set of utilities will be continuing to using will be continuing to using object storage, will continue using relation of databases and so on so there will be a plane and a group of people responsible for that. There will be a group of people responsible for capabilities that you know enable the mesh level functionality, for example, be able to correlate and connects. And query data from multiple knows. That's a measure level capability to be able to discover and explore the measure data products as a measure of capability. So it would be set of teams as part of platforms with a strong again platform product thinking embedded and product ownership embedded into that. To satisfy the experience of this now business oriented domain data team teams s way have a lot of work to do. >>I could go on. Unfortunately, we're out of time. But I guess my first I want to tell people there's two pieces that you put out so far. One is, uh, how to move beyond a monolithic data lake to a distributed data mesh. You guys should read that in a data mesh principles and logical architectures kind of part two. I guess my last question in the very limited time we have is our organization is ready for this. >>E think the desire is there I've bean overwhelmed with number off large and medium and small and private and public governments and federal, you know, organizations that reached out to us globally. I mean, it's not This is this is a global movement and I'm humbled by the response of the industry. I think they're the desire is there. The pains are really people acknowledge that something needs to change. Here s so that's the first step. I think that awareness isa spreading organizations. They're more and more becoming aware. In fact, many technology providers are reach out to us asking what you know, what shall we do? Because our clients are asking us, You know, people are already asking We need the data vision. We need the tooling to support. It s oh, that awareness is there In terms of the first step of being ready, However, the ingredients of a successful transformation requires top down and bottom up support. So it requires, you know, support from Chief Data Analytics officers or above the most successful clients that we have with data. Make sure the ones that you know the CEOs have made a statement that, you know, we want to change the experience of every single customer using data and we're going to do, we're going to commit to this. So the investment and support, you know, exists from top to all layers. The engineers are excited that maybe perhaps the traditional data teams are open to change. So there are a lot of ingredients. Substance to transformation is to come together. Um, are we really ready for it? I think I think the pioneers, perhaps the innovators. If you think about that innovation, careful. My doctors, probably pioneers and innovators and leaders. Doctors are making making move towards it. And hopefully, as the technology becomes more available, organizations that are less or in, you know, engineering oriented, they don't have the capability in house today, but they can buy it. They would come next. Maybe those are not the ones who aren't quite ready for it because the technology is not readily available. Requires, you know, internal investment today. >>I think you're right on. I think the leaders are gonna lead in hard, and they're gonna show us the path over the next several years. And I think the the end of this decade is gonna be defined a lot differently than the beginning. Jammeh. Thanks so much for coming in. The Cuban. Participate in the >>program. Pleasure head. >>Alright, Keep it right. Everybody went back right after this short break.

(bright music) >> In 2009, Hal Varian, Google's Chief Economist said that statisticians would be the sexiest job in the coming decade. The modern big data movement really took off later in the following year, after the second Hadoop World, which was hosted by Cloudera, in New York city. Jeff Hama Bachar, famously declared to me and John Furrie, in "theCUBE," that the best minds of his generation were trying to figure out how to get people to click on ads. And he said that sucks. The industry was abuzz with the realization that data was the new competitive weapon. Hadoop was heralded as the new data management paradigm. Now what actually transpired over the next 10 years was only a small handful of companies could really master the complexities of big data and attract the data science talent, really necessary to realize massive returns. As well, back then, cloud was in the early stages of its adoption. When you think about it at the beginning of the last decade, and as the years passed, more and more data got moved to the cloud, and the number of data sources absolutely exploded, experimentation accelerated, as did the pace of change. Complexity just overwhelmed big data infrastructures and data teams, leading to a continuous stream of incremental technical improvements designed to try and keep pace, things like data lakes, data hubs, new open source projects, new tools, which piled on even more complexity. And as we reported, we believe what's needed is a complete bit flip and how we approach data architectures. Our next guest is Zhamak Dehgani, who is the Director of Emerging Technologies at ThoughtWorks. Zhamak is a software engineer, architect, thought leader and advisor, to some of the world's most prominent enterprises. She's in my view, one of the foremost advocates for rethinking and changing the way we create and manage data architectures, favoring a decentralized over monolithic structure, and elevating domain knowledge as a primary criterion, and how we organize so-called big data teams and platforms. Zhamak, welcome to the cube, it's a pleasure to have you on the program. >> Hi David, it's wonderful to be here. >> Okay. So you're pretty outspoken about the need for a paradigm shift, in how we manage our data, and our platforms at scale. Why do you feel we need such a radical change? What's your thoughts there? >> Well, I think if you just look back over the last decades, you gave us a summary of what happened since 2010. But even if we got it before then, what we have done over the last few decades is basically repeating, and as you mentioned, incrementally improving how we manage data, based on certain assumptions around, as you mentioned, centralization. Data has to be in one place so we can get value from it. But if you look at the parallel movement of our industry in general, since the birth of internet, we are actually moving towards decentralization. If we think today, like if in this move data side, if we said, the only way web would work, the only way we get access to various applications on the web or pages is to centralize it, we would laugh at that idea, but for some reason, we don't question that when it comes to data, right? So I think it's time to embrace the complexity that comes with the growth of number of sources, the proliferation of sources and consumptions models, embrace the distribution of sources of data, that they're not just within one part of organization. They're not just within even bounds of organizations. They're beyond the bounds of organization, and then look back and say, okay, if that's the trend of our industry in general, given the fabric of compensation and data that we put in globally in place, then how the architecture and technology and organizational structure incentives need to move, to embrace that complexity. And to me, that requires a paradigm shift. A full stack from how we organize our organizations, how we organize our teams, how we put a technology in place to look at it from a decentralized angle. >> Okay, so let's unpack that a little bit. I mean, you've spoken about and written today's big architecture, and you've basically just mentioned that it's flawed. So I want to bring up, I love your diagrams, you have a simple diagram, guys if you could bring up figure one. So on the left here, we're adjusting data from the operational systems, and other enterprise data sets. And of course, external data, we cleanse it, you've got to do the quality thing, and then serve them up to the business. So what's wrong with that picture that we just described, and give granted it's a simplified form. >> Yeah. Quite a few things. So, and I would flip the question maybe back to you or the audience. If we said that there are so many sources of the data and actually data comes from systems and from teams that are very diverse in terms of domains, right? Domain. If you just think about, I don't know, retail, the E-Commerce versus auto management, versus customer. These are very diverse domains. The data comes from many different diverse domains, and then we expect to put them under the control of a centralized team, a centralized system. And I know that centralization probably, if you zoom out is centralized, if you zoom in it's compartmentalized based on functions, and we can talk about that. And we assume that the centralized model, will be getting that data, making sense of it, cleansing and transforming it, then to satisfy a need of very diverse set of consumers without really understanding the domains because the teams responsible for it are not close to the source of the data. So there is a bit of a cognitive gap and domain understanding gap, without really understanding how the data is going to be used. I've talked to numerous, when we came to this, I came up with the idea. I talked to a lot of data teams globally, just to see, what are the pain points? How are they doing it? And one thing that was evident in all of those conversations, that they actually didn't know, after they built these pipelines and put the data in, whether the data warehouse tables or linked, they didn't know how the data was being used. But yet they're responsible for making the data available for this diverse set of use cases. So essentially system and monolithic system, often is a bottleneck. So what you find is that a lot of the teams are struggling with satisfying the needs of the consumers, are struggling with really understanding the data, the domain knowledge is lost, there is a loss of understanding and kind of it in that transformation, often we end up training machine learning models on data, that is not really representative of the reality of the business, and then we put them to production and they don't work because the semantic and the syntax of the data gets lost within that translation. So, and we are struggling with finding people to manage a centralized system because still the technology's fairly, in my opinion, fairly low level and exposes the users of those technology sets and let's say they warehouse a lot of complexity. So in summary, I think it's a bottleneck, it's not going to satisfy the pace of change or pace of innovation, and the availability of sources. It's disconnected and fragmented, even though there's centralized, it's disconnected and fragmented from where the data comes from and where the data gets used, and is managed by a team of hyper specialized people, they're struggling to understand the actual value of the data, the actual format of the data. So it's not going to get us where our aspirations, our ambitions need to be. >> Yeah, so the big data platform is essentially, I think you call it context agnostic. And so as data becomes more important in our lives, you've got all these new data sources injected into the system, experimentation as we said, the cloud becomes much, much easier. So one of the blockers that you've cited and you just mentioned it, is you've got these hyper specialized roles, the data engineer, the quality engineer, data scientist. And it's a losery. I mean, it's like an illusion. These guys, they seemingly they're independent, and can scale independently, but I think you've made the point that in fact, they can't. That a change in a data source has an effect across the entire data life cycle, entire data pipeline. So maybe you could add some some color to why that's problematic for some of the organizations that you work with, and maybe give some examples. >> Yeah, absolutely. So in fact initially, the hypothesis around data mesh came from a series of requests that we received from our both large scale and progressive clients, and progressive in terms of their investment in data architecture. So these were clients that were larger scale, they had diverse and rich set of domain, some of them were big technology, tech companies, some of them were big retail companies, big healthcare companies. So they had that diversity of the data and a number of the sources of the domains. They had invested for quite a few years in generations, of they had multi-generations of PROPRICER data warehouses on prem that were moving to cloud. They had moved through the various revisions of the Hadoop clusters, and they were moving to that to cloud, and then the challenges that they were facing were simply... If I want to just simplify it in one phrase, they we're not getting value from the data that they were collecting. They were continuously struggling to shift the culture because there was so much friction between all of these three phases of both consumption of the data, then transformation and making it available. Consumption from sources and then providing it and serving it to the consumer. So that whole process was full of friction. Everybody was unhappy. So it's bottom line is that you're collecting all this data, there is delay, there is lack of trust in the data itself, because the data is not representative of the reality, it's gone through the transformation, but people that didn't understand really what the data was got delayed. And so there's no trust, it's hard to get to the data. Ultimately, it's hard to create value from the data, and people are working really hard and under a lot of pressure, but it's still struggling. So we often, our solutions, like we are... Technologies, we will often point out to technology. So we go. Okay, this version of some proprietary data warehouse we're using is not the right thing. We should go to the cloud and that certainly will solve our problem, right? Or warehouse wasn't a good one, let's make a data Lake version. So instead of extracting and then transforming and loading into the database, and that transformation is that heavy process because you fundamentally made an assumption using warehouses that if I transform this data into this multidimensional perfectly designed schema, that then everybody can draw on whatever query they want, that's going to solve everybody's problem. But in reality, it doesn't because you are delayed and there is no universal model that serves everybody's need, everybody needs are diverse. Data scientists necessarily don't like the perfectly modeled data, they're for both signals and the noise. So then we've just gone from ATLs to let's say now to Lake, which is... Okay, let's move the transformation to the last mile. Let's just get load the data into the object stores and sort of semi-structured files and get the data scientists use it, but they still struggling because of the problems that we mentioned. So then what is the solution? What is the solution? Well, next generation data platform. Let's put it on the cloud. And we saw clients that actually had gone through a year or multiple years of migration to the cloud but it was great, 18 months, I've seen nine months migrations of the warehouse versus two year migrations of various data sources to the cloud. But ultimately the result is the same, unsatisfied, frustrated data users, data providers with lack of ability to innovate quickly on relevant data and have an experience that they deserve to have, have a delightful experience of discovering and exploring data that they trust. And all of that was still amiss. So something else more fundamentally needed to change than just the technology. >> So the linchpin to your scenario is this notion of context. And you pointed out, you made the other observation that "Look we've made our operational systems context aware but our data platforms are not." And like CRM system sales guys are very comfortable with what's in the CRMs system. They own the data. So let's talk about the answer that you and your colleagues are proposing. You're essentially flipping the architecture whereby those domain knowledge workers, the builders if you will, of data products or data services, they are now first-class citizens in the data flow, and they're injecting by design domain knowledge into the system. So I want to put up another one of your charts guys, bring up the figure two there. It talks about convergence. She showed data distributed, domain driven architecture, the self-serve platform design, and this notion of product thinking. So maybe you could explain why this approach is so desirable in your view. >> Sure. The motivation and inspirations for that approach came from studying what has happened over the last few decades in operational systems. We had a very similar problem prior to microservices with monolithic systems. One of the things systems where the bottleneck, the changes we needed to make was always on vertical now to how the architecture was centralized. And we found a nice niche. And I'm not saying this is a perfect way of decoupling your monolith, but it's a way that currently where we are in our journey to become data driven, it is a nice place to be, which is distribution or a decomposition of your system as well as organization. I think whenever we talk about systems, we've got to talk about people and teams that are responsible for managing those systems. So the decomposition of the systems and the teams, and the data around domains. Because that's how today we are decoupling our business, right? We are decoupling our businesses around domains, and that's a good thing. And what does that do really for us? What it does is it localizes change to the bounded context of that business. It creates clear boundary and interfaces and contracts between the rest of the universe of the organization, and that particular team, so removes the friction that often we have for both managing the change, and both serving data or capability. So if the first principle of data meshes, let's decouple this world of analytical data the same to mirror. The same way we have decoupled our systems and teams, and business. Why data is any different. And the moment you do that, so the moment you bring the ownership to people who understands the data best, then you get questions that well, how is that any different from silos of disconnected databases that we have today and nobody can get to the data? So then the rest of the principles is really to address all of the challenges that comes with this first principle of decomposition around domain context. And the second principle is, well, we have to expect a certain level of quality and accountability, and responsibility for the teams that provide the data. So let's bring products thinking and treating data as a product, to the data that these teams now share, and let's put accountability around it. We need a new set of incentives and metrics for domain teams to share the data, we need to have a new set of kind of quality metrics that define what it means for the data to be a product, and we can go through that conversation perhaps later. So then the second principle is, okay, the teams now that are responsible, the domain teams responsible for their analytical data need to provide that data with a certain level of quality and assurance. Let's call that a product, and bring product thinking to that. And then the next question you get asked off at work by CIO or CTO is the people who build the infrastructure and spend the money. They say, well, "It's actually quite complex to manage big data, now where we want everybody, every independent team to manage the full stack of storage and computation and pipelines and access control and all of that." Well, we've solved that problem in operational world. And that requires really a new level of platform thinking to provide infrastructure and tooling to the domain teams to now be able to manage and serve their big data, and I think that requires re-imagining the world of our tooling and technology. But for now, let's just assume that we need a new level of abstraction to hide away a ton of complexity that unnecessarily people get exposed to. And that's the third principle of creating self-serve infrastructure to allow autonomous teams to build their domains. But then the last pillar, the last fundamental pillar is okay, once he distributed a problem into smaller problems that you found yourself with another set of problems, which is how I'm going to connect this data. The insights happens and emerges from the interconnection of the data domains, right? It's just not necessarily locked into one domain. So the concerns around interoperability and standardization and getting value as a result of composition and interconnection of these domains requires a new approach to governance. And we have to think about governance very differently based on a federated model. And based on a computational model. Like once we have this powerful self-serve platform, we can computationally automate a lot of covenants decisions and security decisions, and policy decisions, that applies to this fabric of mesh, not just a single domain or not in a centralized. So really, as you mentioned, the most important component of the data mesh is distribution of ownership and distribution of architecture in data, the rest of them is to solve all the problems that come with that. >> So, very powerful. And guys, we actually have a picture of what Zhamak just described. Bring up figure three, if you would. So I mean, essentially, you're advocating for the pushing of the pipeline and all its various functions into the lines of business and abstracting that complexity of the underlying infrastructure which you kind of show here in this figure, data infrastructure as a platform down below. And you know why I love about this, Zhamak, is, to me it underscores the data is not the new oil. Because I can put oil in my car, I can put it in my house but I can't put the same code in both places. But I think you call it polyglot data, which is really different forms, batch or whatever. But the same data doesn't follow the laws of scarcity. I can use the same data for many, many uses, and that's what this sort of graphic shows. And then you brought in the really important, sticking problem, which is that the governance which is now not a command and control, it's federated governance. So maybe you could add some thoughts on that. >> Sure, absolutely. It's one of those, I think I keep referring to data mesh as a paradigm shift, and it's not just to make it sound grand and like kind of grand and exciting or important, it's really because I want to point out, we need to question every moment when we make a decision around, how we're going to design security, or governance or modeling of the data. We need to reflect and go back and say, "Am I applying some of my cognitive biases around how I have worked for the last 40 years?" I've seen it work? Or "Do I do I really need to question?" And do need to question the way we have applied governance. I think at the end of the day, the role of the data governance and the objective remains the same. I mean, we all want quality data accessible to a diverse set of users and its users now know have different personas, like data persona, data analysts, data scientists, data application user. These are very diverse personas. So at the end of the day, we want quality data accessible to them, trustworthy in an easy consumable way. However, how we get there looks very different in as you mentioned that the governance model in the old world has been very command and control, very centralized. They were responsible for quality, they were responsible for certification of the data, applying and making sure the data complies with all sorts of regulations, make sure data gets discovered and made available. In the world of data mesh, really the job of the data governance as a function becomes finding the equilibrium between what decisions need to be made and enforced globally, and what decisions need to be made locally so that we can have an interoperable mesh of data sets that can move fast and can change fast. It's really about, instead of kind of putting those systems in a straight jacket of being constantly and don't change, embrace change, and continuous change of landscape because that's just the reality we can't escape. So the role of governance really, the modern governance model I called federated and computational. And by that I mean, every domain needs to have a representative in the governance team. So the role of the data or domain data product owner who really were understands that domain really well, but also wears that hats of the product owner. It's an important role that has to have a representation in the governance. So it's a federation of domains coming together. Plus the SMEs, and people have Subject Matter Experts who understand the regulations in that environment, who understands the data security concerns. But instead of trying to enforce and do this as a central team, they make decisions as what needs to be standardized. What needs to be enforced. And let's push that into that computationally and in an automated fashion into the platform itself, For example. Instead of trying to be part of the data quality pipeline and inject ourselves as people in that process, let's actually as a group, define what constitutes quality. How do we measure quality? And then let's automate that, and let's codify that into the platform, so that every day the products will have a CICD pipeline, and as part of that pipeline, law's quality metrics gets validated, and every day to product needs to publish those SLOs or Service Level Objectives, or whatever we choose as a measure of quality, maybe it's the integrity of the data, or the delay in the data, the liveliness of the data, whatever are the decisions that you're making. Let's codify that. So it's really the objectives of the governance team trying to satisfies the same, but how they do it, it's very, very different. And I wrote a new article recently, trying to explain the logical architecture that would emerge from applying these principles, and I put a kind of a light table to compare and contrast how we do governance today, versus how we'll do it differently, to just give people a flavor of what does it mean to embrace decentralization, and what does it mean to embrace change, and continuous change. So hopefully that could be helpful. >> Yes. There's so many questions I have. But the point you make it too on data quality, sometimes I feel like quality is the end game, Where the end game should be how fast you can go from idea to monetization with a data service. What happens again? And you've sort of addressed this, but what happens to the underlying infrastructure? I mean, spinning up EC2s and S3 buckets, and MyPytorches and TensorFlows. That lives in the business, and who's responding for that? >> Yeah, that's why I'm glad you're asking this question, David, because I truly believe we need to reimagine that world. I think there are many pieces that we can use as utilities are foundational pieces, but I can see for myself at five to seven year road map building this new tooling. I think in terms of the ownership, the question around ownership, that would remain with the platform team, but I don't perhaps a domain agnostic technology focused team, right? That there are providing a set of products themselves, but the users of those products are data product developers, right? Data domain teams that now have really high expectations, in terms of low friction, in terms of a lead time to create a new data products. So we need a new set of tooling and I think the language needs to shift from I need a storage bucket, or I need a storage account, to I need a cluster to run my spark jobs. Too, here's the declaration of my data products. This is where the data file will come from, this is a data that I want to serve, these are the policies that I need to apply in terms of perhaps encryption or access control, go make it happen platform, go provision everything that I need, so that as a data product developer, all I can focus on is the data itself. Representation of semantic and representation of the syntax, and make sure that data meets the quality that I have to assure and it's available. The rest of provisioning of everything that sits underneath will have to get taken care of by the platform. And that's what I mean by requires a reimagination. And there will be a data platform team. The data platform teams that we set up for our clients, in fact themselves have a fair bit of complexity internally, they divide into multiple teams, multiple planes. So there would be a plane, as in a group of capabilities that satisfied that data product developer experience. There would be a set of capabilities that deal with those nitty gritty underlying utilities, I call them (indistinct) utilities because to me, the level of abstraction of the platform needs to go higher than where it is. So what we call platform today are a set of utilities we'll be continuing to using. We'll be continuing to using object storage, we will continue to using relational databases and so on. So there will be a plane and a group of people responsible for that. There will be a group of people responsible for capabilities that enable the mesh level functionality, for example, be able to correlate and connect and query data from multiple nodes, that's a mesh level capability, to be able to discover and explore the mesh of data products, that's the mesh of capability. So it would be a set of teams as part of platform. So we use a strong, again, products thinking embedded in a product and ownership embedded into that to satisfy the experience of this now business oriented domain data teams. So we have a lot of work to do. >> I could go on, unfortunately, we're out of time, but I guess, first of all, I want to tell people there's two pieces that you've put out so far. One is how to move beyond a Monolithic Data Lake to a distributed data mesh. You guys should read that in the "Data Mesh Principles and Logical Architecture," is kind of part two. I guess my last question in the very limited time we have is are organizations ready for this? >> I think how the desire is there. I've been overwhelmed with the number of large and medium and small and private and public, and governments and federal organizations that reached out to us globally. I mean, this is a global movement and I'm humbled by the response of the industry. I think, the desire is there, the pains are real, people acknowledge that something needs to change here. So that's the first step. I think awareness is spreading, organizations are more and more becoming aware, in fact, many technology providers are reaching to us asking what shall we do because our clients are asking us, people are already asking, we need the data mesh and we need the tooling to support it. So that awareness is there in terms of the first step of being ready. However, the ingredients of a successful transformation requires top-down and bottom-up support. So it requires support from chief data analytics officers, all above, the most successful clients that we have with data mesh are the ones that, the CEOs have made a statement that, "We'd want to change the experience of every single customer using data, and we're going to commit to this." So the investment and support exists from top to all layers, the engineers are excited, the maybe perhaps the traditional data teams are open to change. So there are a lot of ingredients of transformations that come together. Are we really ready for it? I think the pioneers, perhaps, the innovators if you think about that innovation curve of adopters, probably pioneers and innovators and lead adopters are making moves towards it, and hopefully as the technology becomes more available, organizations that are less engineering oriented, they don't have the capability in-house today, but they can buy it, they would come next. Maybe those are not the ones who are quite ready for it because the technology is not readily available and requires internal investments to make. >> I think you're right on. I think the leaders are going to lean in hard and they're going to show us the path over the next several years. And I think that the end of this decade is going to be defined a lot differently than the beginning. Zhamak, thanks so much for coming to "theCUBE" and participating in the program. >> Thank you for hosting me, David. >> Pleasure having you. >> It's been wonderful. >> All right, keep it right there everybody, we'll be back right after this short break. (slow music)

(upbeat music) >> From our studios in the heart of Silicon Valley, Palo Alto, California, this is a CUBEConversation. >> Hello everyone, welcome to Palo Alto's CUBE studios. I'm John Furrier, host of theCUBE. We're here for a special CUBEConversation as part of our new brand of tech leader series as well as Extracting the Signal From the Noise. We're here with Gary Conway, the CMO of Automation Anywhere, a hot startup, heavily funded, attacking a whole new market segment, that's kind of changing the game of value in digital, obviously, RPA, robotic process automation, is the buzz word. It's actually real, it's happening, we're seeing a lot of success in companies there. It's changing the way business is operated, business is structured, and value is created. Gary, thanks for joining me. >> My pleasure. >> So we covered your event, Automation Anywhere. You guys are essentially doing very very well, heavily funded, growing like crazy. RPA is one of the fastest growing segments in this next generation digital culture. You're seeing a lot of companies coming out attacking this. What's your perspective, why is RPA so important, why is it so hot? >> It's a pretty simple reason, actually. You know, the truth of the matter is that companies are now, because of RPA, able to automate parts of their business processes, or entire processes that they were never able to automate before, and they can do it with RPA at a relatively little cost compared to a lot of other technologies out there, especially from the big ERP vendors. We say that, and we really believe this, and we're finding this to be true, that since the onset of automation about 30 years ago, from the big technology companies, only about 20% of the processes that businesses manage now are actually automated. The 80% of them that are not automated are pretty much done by human beings, you know. Millions of human beings employed to manage those back office processes. RPA is enabling companies to actually automate more of those processes than ever before. >> Before we get started, just quickly define, what is RPA for the folks that are learning for the first time, because we're now seeing the concept really penetrating mainstream right now. It's becoming, frankly, a topic that's being discussed across most of the largest enterprises and small business, what is RPA? >> So RPA means robotic process automation. So think of them as robots that are built as predesigned software bots that you can plug into any business process, and it'll automate a part of that process, or the entire process, by just plugging it in. It actually is capable of observing what human beings do, remembering what human beings do, and then repeating that again and again and again, only in a fraction of a second. That's the easiest way to think of it. >> So when I think of robots, I think of like a machine, you know, moving things around, from, like, manufacturing, whatnot. It's beyond that, it's not just robots, it's software as well, and this is the key in all this. >> It is software, I mean. >> It is software, it's not robots. >> RPA is only software. >> It's only software. >> I think most people, when they think of robotics, do think of, you know, mechanical robots used in manufacturing. That's not what RPA is. RPA is robotics that is only constructed with preconfigured software. >> I want to get your take on the impact to business and how leaders are adapting to this, but first I want to get to the mainstream topic that is trying to be figured out, and the classic one is technology's going to automate my jobs away, and the example that I use is retail. Most people go to retail, and they think, you know, whether it's a person out of college, or someone working in retail, that oh my god, a robot's going to show up and move stuff on the shelves, and eliminate those jobs. It's not so much robots, per se, it's Amazon that's going to impact in retail. We know what Amazon and Walmart has done to commerce. So that's already happening, retail's impacted. It's not so much that jobs are going away, they're just changing. That's our opinion. Can you share your opinion on the impact of software automation to jobs? >> We agree that jobs are not going away. They will change, but I always tell people when I'm asked this question that there's not been one technology that's ever been introduced that has actually done anything but create more jobs, and I always use the example of the PC. You know, I'm old enough to remember when the PC was introduced, the headlines were what will people do with all this additional time? You know, people were predicting a three day work week because of all the efficiencies that would be created by the PCs, and in fact the opposite has happened. Technology actually makes people more productive, and when they're more productive they're capable of doing more things. So with the automation of certain things that people happen to be doing now, those people are being upskilled, they are being redeployed to other jobs, as we've seen in the past, and actually, more jobs are being created. >> You know, we cover a lot of the Big Data space going back to 2010 when we first started theCUBE, at Hadoop World, which that kind of had its course, but ultimately Big Data, which became AI, you know the bank teller example, you know the ATM was going to kill the branches, when in reality there's been even more branch offices-- >> That's what we're seeing, yeah. >> than ever before. So again, I think the argument is pretty clear from the data and the trend, technology is actually helping create new jobs, but not the jobs maybe that there were once there. That seems to be the big debate, so we agree with you on that. Now we applied some of our, not RPA, but we had some technology that applied to all of our videos that we did with you at your event, and a couple things came out of the entity extraction. I want to share with you, I want to get your reaction. Business hubs, human versus machines, complex problems, digital colleagues, digital worker, new potential applications, digital native companies, supply chain, system integrators, labor platforms, AI assistance, inefficiencies, and machine learning. These are key words that really kind of point to the next generation. This is essentially the language of your company. What's your reaction to that? >> Well, I'm not sure it's the language of our company as much as it's the language that people are using to determine what role they will play in the future, and what role, how they will impact their businesses going into the future. So these are not our terms, these are terms that exist in the space right now as people try to determine for themselves the role they will play in defining the future and how they will use technology to make their businesses more efficient. >> And companies are using cloud, for instance, to kind of reshape. We had a big conversation yesterday around, you know, do I want to be in the business of managing data centers, or be in the business of managing my business with technology. These concepts are interesting from an industry standpoint. Business hubs. Good concept, I get that. Digital worker. This is the impact that you guys are enabling. What's the managerial leadership role as an executive or a worker in these new cultural shifts? Because, as this is being enabled, new value is being created. Digital is enabling that. How does someone manage all this? What do you guys see, how do you see that playing out? >> Look, I think that whenever things are changing, and things are changing dramatically in business today, the only way to manage it is a day at a time. You can't project yourself so far into the future that you trip over the things that are immediately facing you now. So my suggestion would always be to evaluate options every day, every week, and make decisions when it's the right time to make decisions for your business. But let's go back to one of the terms you described, digital worker. So a digital worker in our view is actually available in what we call our bot store, which is a bot that is actually preconfigured to have skillsets that you would require. So let's just say you need an order-to-cash person, person who understands that, and it's a part of an automated process. The idea is that you would be able to download a digital worker with similar skills, and plug that bot into your process, and it would begin to work with, I would say, the skillsets of somebody who understands the order-to-cash process. That's really what a digital worker is. Now imagine that, in the future, and that future is not that far away, where every human being will be working side by side with a digital worker, so that the human being can offload the repetitive things that a digital something could actually do for them, and that digital worker would take on the task-based stuff, freeing up the individual to use their creativity to create higher order value for the business. That's really what we mean by digital worker and the importance of a digital colleague, for example. >> I think that it's a profound statement, and I think this is one of the cultural shifts that I see that this next generation workforce and leaders have to get their arms around, and in watching folks in Washington, D.C., we've been covering a lot of the procurement changes going on in government and businesses. There's a leveling up going on in the IQ of organizations, because that is a profound statement. Now we saw that with DevOps in cloud. You know, you talk to tech people, if you're doing the repetitive task more than three times, automate it. You're getting at something a little bit different. You're not just automating, you're adding intelligence to it, so what I like about the process automation area, is it's not just an undifferentiated, heavy lifting, mundane task. Yes it is, but there's an era of machine learning, you're seeing intelligence being applied to it, so it's truly becoming an augmentation to a human. That's kind of what I hear you saying. Do you agree with that, and is that something that you guys see happening, and what does that actually mean for the enterprise? >> No, I do agree with it, and we are at various stages of that evolution. But like anything else in business, and in life, you don't just flip a switch and all of a sudden people migrate to that new model, that's not how life really works. We evolve to those things, and I think what we're seeing is a very fast evolution to exactly what you just described. >> I want to get your thoughts on operationalizing new technology. You know, obviously, being an entrepreneur, I've done a bunch of startups, and the startup ethos is come on a narrow entry, get a landing area, and then sequence to the broader market opportunity. There's a lot of entrepreneurial ethos involved in how to operationalize something new like RPA, because you can't just, you know, shut down the old and bring in the new, there's a method there. This is a challenge in any new technology. How do you guys see this playing out? Because you guys are on the front end, bringing real value to the table, but people might want to get more aspirational and then get the reality. How do you get into the point of going into someone and saying I love what you guys do, what's the playbook, what do I do next? This is the challenge, can you share your thoughts on how an executive or a business can operationalize these benefits? >> So we have a lot of customers, 1800 customers, unique customers, and 2800 entities around the world that are using the software now. And I think that each of them had one thing in common. They started in bite-sized chunks. They said we're going to try this, and what's happening with RPA, which is one of the reasons it's growing so fast, is that once you try it, once you implement a few bots to automate the things that you weren't able to automate before, it starts ramping like this, right? It has a very very fast ramp-up. So you realize some successes in the processes that you begin to automate that you've never automated before. And the more you do it, the more you learn from it. The more you learn from it, the more you want to do it, the more processes you identify that could be automated, and should be automated, and what starts happening in most companies is they start adopting much much faster once they understand the benefits of it. And the benefits to business is driving higher levels of efficiencies, and reducing costs dramatically. >> So the tie to value is fast. >> Right, the value is very fast, compared to-- >> And that's driving the ramp-up, to your point. >> And that's driving the map. >> The flywheel kicks in, you start with a process that's known, and you automate it, wow, that's good, do it again, do it again. >> Correct. Well, do it again, and do it with more processes, right? And the other unique thing about this technology is human beings, once they understand the advantages of automating things that other human beings may have to do manually, most of those people who have been doing them manually will say I want more of that. We should be automating this, we should be automating that, and it actually makes them much more productive, and it makes them feel as if they are delivering higher value to the business themselves, and what an amazing human dynamic that is. >> You know, I was talking to Dave Vellante about this, we were talking about the TAM, the total adjustment market, for RPA, we're like, I think it's just in the trillions because with digital, everything is connected, so you can measure everything. Everything is ultimately a supply chain, whether it's network effect for internet, whether it's, you know, some process with cryptocurrency, whether it's blockchain or a process with cybersecurity, digital is pretty much connected, it's pretty much a supply chain. Some of them are more formed than others. This seems to be the entry point that most people would go to. Do they go to the supply chains first, or, better yet, what's the use cases that you see as the low hanging fruit that people come in on and automate? Is it simple supply chain stuff that's known, or are they applying it as they grow to other areas? >> It's very broad, but the fastest adoption, especially beginning about two years ago, were from the companies in industries like banking, other financial services, insurance, healthcare, manufacturing, which is supply chain, as you rightly point out. Those businesses that tend to be earlier adopters of technology have also become earlier adopters of RPA. But what we're finding now is it's now, because of the results that these businesses have demonstrated, and because digital native competitors are actually coming into the space and threatening what are sometimes referred to as legacy businesses, businesses are not delaying the investments they're making so that they can actually become more competitive, and when you think about that, it's not just the efficiencies that these technologies like RPA drive, but it's the ability to make businesses acutely more competitive than they've ever been before. >> That's a great angle, competitive strategy has always been one of those things where, you know, the cloud native world or digital native world was like oh yeah, pick one feature, innovate, and you can go beat an incumbent. The incumbent now has leverage in the marketplace, whether it's physical presence or other assets. Using RPA gives them a way to level up, so to speak. >> Level up, for sure. So let's just take something we're all familiar with, right? You can now go on your phone, and you can have a car at your house to take you somewhere in about four minutes in most cities, right? If you have an issue, you can solve that issue on your phone as well. You don't have to call anybody, you just solve it on your phone. These ride share companies have made it so simple, it's almost as if there's no such thing anymore as a front office or a back office. Digital native companies have brought those things together, and now there's one office. So that immediacy is what legacy companies are actually competing against, and if those companies don't adopt this kind of automation to make more efficient those processes and narrow the gap between customer facing and back office, they won't be able to compete. >> Yeah, they can turn a liability into an advantage, with software. Big big bullish on the software, I think the competitive landscape also is interesting, I'd like your thoughts on. There seems to be a battlefield, at least from my perspective, my opinion is that, okay, RPA software is out there, it's going to grow really fast. The competitive battle will be around intelligence. How do you guys view the competitive levers? How do you guys compete, what's the advantage? Is it intelligence, is it being more intelligent, is it more operational, what's the advantage you guys see vis-a-vis the competition? >> Yeah, so we're actually seeing a sort of a bringing together of technology, what we have considered to be strictly technology, and what's being described broadly now as artificial intelligence. Artificial intelligence is still evolving. Everybody has his own definition of what it really is, but what we're seeing, and I think in other sectors we're seeing the same thing, is now the merging of things that have truly been technology with things that are perceived to be artificial intelligence, and they're beginning to come together. What that will look like five years from now, nobody knows. What it'll look like 10 years from now, no one can even conceive of, but we're seeing that dynamic in place now, and this is the beginning. >> It's a great wave, excited to have you on and share your insights, Gary. It's great stuff you guys are doing over there at Automation Anywhere, love the, we love this wave, I think it's going to be relevant. My final question for you, though, is little bit different. You know, you're at a cocktail party, you're at a friend's house, you're at a confab, and you see people that aren't in the business, and they're like Gary, I need to get, I need to be more competitive. What do I do, what is this RPA thing, how do I change my culture, how do I get my people and my process aligned with software, what's the playbook, what's your advice? >> So what I would say is, get started as quickly as possible, because if you delay too long, you will be left behind. So that's would be my first bit of advice. The other, it would be to start slowly. Learn as quickly as you can. Don't worry about automating things that are hard to automate, go to the things that are easy to automate. Companies find that when they address those things first, they're actually able to drive more success faster, and then they will look for more and more opportunities based on what they've learned and the success that they've derived, and that's what happens to create this ramp effect, where it becomes almost viral-like. Where you have one process that works great, you automate that, you automate another one, you automate five more, 10 more, and before you know it, believe it or not, we have customers that are implementing more than 3000 bots over the last year and a half, and that's how they started. >> Get rid of the mundane work, you've got happy people, HR is happy, you've got more revenue coming in, you're more competitive as a business, this is a good value proposition. It's an easy sale. >> Nothing's easy, but it has a huge appeal. >> Gary, thanks so much for coming on and sharing your insights around RPA, appreciate it and congratulations on your success. >> Thank you. >> This is CUBEConversation, and I'm John Furrier here in Palo Alto, thanks for watching. (upbeat music)

>> Live from Las Vegas. It's the queue covering Adobe Summit twenty nineteen brought to you by Adobe. >> Hey, welcome back, everyone. Cube live coverage here in Las Vegas for Adobe sum of twenty nineteen. I'm John for which have Frick. Where he with a cube alumni that had job for three years. And you'LL Bhambri, Vice president of Platform Engineering at Adobe. Great to see you. Thanks for coming by. >> Thank you. >> Let's talk. Engineering. That was your line on the keynote. Great Kino today, by the way, super impressed with content. I'm washing that slides you're presenting, like were to cloud company. I'm failing my Amazon reinvent here. You guys built a really cool platform. Take us through. This was your mission. That's true. So take us through your journey. So how'd we get here? How did you get this beautiful platform? >> So, you know, we've been at it for a few years, and as you know, we've seen CEOs and see emos late. That their focus is to really deliver, you know, delightful experiences to their customers. And not just once, but throughout the journey off the customer. Right? Delight your customer. Every step of the way is what you'LL hear from Adobe from our customers. And we are really helping them to do that. And obviously, in order to do that, there is on, as you well know, that data is behind everything to do with experiences as well. There is a lot ofthe interaction of data and bringing it all together to really understand that holistic view of the customer is super important. And, you know, as you've been this realist, you know, the holistic view of the customer. It's not that you just ended once, and you forget about it, right? You have to build this in real time because the interactions that customers are having with brands are to wear through mobile devices to the apse that they're using off the those brands. And the businesses have to understand that whole journey off the customers and understand what their preferences are. Write what? You know what they like, what they don't like and be able to keeping like that context really during the journey. Whether they're coming to their Web site for the first time are they are repeat, customers be able to give them the right experience at every touch point. And that's where you need all of this data, which is a lot of data. So so you know, We've been on this big data journey on me personally, even, you know, for a long time. But the scale that I've seen here I had not seen before >> our IBM conscious when you weren't IBM prior from Hadoop World, you had your eye on this big data trend. Now, at Adobe, when you have really data coming in with apple cases out in the market place to put a platform together. Hard task. But I want to ask you specific question around that. Looking at the architecture slide you have and analytics cloud and add Cloud a marketing cloud in the commerce cloud. They all have Marcus that they have to address and be highly effective as almost appear placed in alone. But now, integrating across each other now with the journey that you guys were put together is difficult. I know that from a computer science background. How does how did you guys look at that? Architecturally, what were some of the guiding principles around building that? Because you don't want to compromise the capabilities of those functional elements. So you decompose and I get that. How did you put it all together? What was the key guiding principle around. >> Yeah, so that's a really good question, because I mean, Adobe has bean delivering applications, right? Like you said, whether it's around analytics, our marketing cloud or advertising. And now we obviously just acquired the commerce cloud on DH. When you look at the common stuff around all of this, it's data, right? Data being captured, two different channels, data that needs to be curated, you know, having a common data dictionary so that, you know, things mean the same on DH, even though they're captured two different channels. So gathering this data curating this data, organizing it for that holistic view of the customer organizing it so that you can do B I, and reporting on that data is all something that we pull together in the platform there. Now it becomes that whether it is you're doing analytics on this right, which could be a B I and the putting all your doing I and Melander is to do your next best action. All your targeting these customers with personalized content. You're doing it on that single version of the truth, which is the real time customer profile that powers all of these different clouds. So that it's not like when you do reporting you have one view ofthe a customer. But when you're trying to show them personalized content, half the view is lost because the data was siloed. So we've gone past all of that. There's no data silos now, right? >> Real time customer profile is literally being updated all the time. That's the key in great, exciting part about it is a curious >> kind of philosophically. And execution is like you've been in this space for a long time, and one of the jokes I left shares, you know, we used to make decisions based on a sampling of something that happened in the past. Now you know, we can make decisions based on all of the data that's happening now, but at the same time, your challenges, that source's heir changing all the time. The speed of the input is changing all the time, and the expected return on your reaction is shortening all the time. So from from just a date, a professional and I'm sure it's super exciting and super scary to move that paradigm shift to you got to deliver the right thing right now >> and you know, one of the key things field is that as all of this data was being gathered, right, obviously this data has to be gathered with these events are occurring. So if you look at glands, their customers are global. They are transacting browsing, whether it's on where mobile devices with that land globally around the world. That means data has to be collected from these globally distributed edges. And it has to be brought in processed in real time pending that profile. And as the data keeps coming, the profile is updated right? And and you can't have stained a dying, they're right, because otherwise, you know you are action ing based on something that happened five minutes ago. You know how we've seen that you buy something and you're still getting ads off that same product that you buy even a day or two days late? >> Already bought ten anymore. Ten. >> So that's because that bland has a stale profile off you, right? But if they had the real time customer profile, then there's no way that they would be delivering our action ing based on that stale information. So just like the data was being gathered from edges even when we have to deliver the experiences right. This is where edge computing comes into the picture, right? So we are also taking. So when you look at the whole architecture of the platform, yes, it's based on the cloud and you know it's a big data stack. It's completely assassin offering. But there is also a big edge computing part of the platform, which is where all the hard data is collected. Process and action and to your point, trade, like as we build, say, predictive models on Ex Best action on the data that's on the cloud. The scoring off the models has to happen on the edges where the events are crying. So this is a complicated engineering problem. But that's why I guess we love it. >> Big smile. So the data is critical. So about how adobes changed over the past few years because you guys did clown. I heard the nuance. I heard that keynote, you know, reading through the names of the lines. Is that it? It's hard to get data right at the beginning. Yeah, get cloud right now. You got data rights. Take us through that point because this is where I think the key to success is how to make that data work. Because if you're gonna have open AP eyes and open data integrity, that data right database, it's a time Siri's aircraft dated. A lot of different applications might choose certain technology. Yes, you have to deal with that. How, how important is the texture on that? >> So So that's why that's a great question that, you know, from a platform standpoint, our goal is that we have to be able to answer the questions with the right laden see or speed as well as relevancy, right? So when we talk really time, it's about it's Leighton sees. You know, when you talk to engineers, they only talk agency. But it's not that right. It's needn't see and relevancy. So in order to depending on. Like if it's more like B I r. Reporting kind off questions or queries, you need to organize the data certainly for, you know, single lookups off customers, right? You have to organize the data differently, and that's where our I'd be comes into the picture that how do we partition and organize this data to meet the needs ofthe both operational as well as the more, you know, like analytical kind ofthe workloads. So we support both and to your point, also that, you know, then we need a sequel database where there's no sequel database are a graft database. I mean, those are choices we make, but on top, they're providing FBI's. So we're abstracting all of that from the user. And you know how where we direct question, that's all R ight, but their applications are not going to break because they're writing to the FBI's. So as technologies advance underneath, we make those choices, but again so that they're getting the right agency and relevancy. >> So in the cloud game, we used to talk about this when you when you're on the Cuban way, an IBM the devil's movement was full tilt and they use the term infrastructure is code. Uh, so you're kind of getting out. I want to get your reaction to this Is that if applications and workloads are the use, cases are gonna determine the date of structures, data architecture and Leighton see relevance equation isn't. Then there's a new kind of infrastructures code emerging. Is that data as code? So, or maybe it's this should that workloads dictate what type of data diversity and Leighton see relevance is needed Or is that come from the network again? The question is, workloads are kind of in charge, I guess. What? I'm trying to get out. So >> I Yeah, I would say that, you know, as a platform, you have to support all of these workloads, right? So which means that from an architecture standpoint, we have to make sure that whether it's analytical, kindof a question or workload like B. I reporting whether it is, you know, more like an operational kind ofthe question around, You know that you want to just do a quick question around. You know, what did this customer by or what John's action happened? The underneath data structures and databases we have to pick the right ones so that way are able to support both >> the expectations, the expected yes, the expectations of the workload. >> It is. >> You're running commerce. Leighton Seon Relevance. Low latent. She's going to be in the milliseconds or >> gut ache >> and relevance. Gus, have a high bar there, too. Analytics query for a B. I tool might be, if every second so again, this is a huge Delta in terms of capabilities, and I think that will happen on the flies hard. Yes. How do you guys do that was sauce. >> Yeah, so that's That's the, you know, underlying technology that you know the way we are bending, that is, so that you can support both of those and wait with the customers were sticking to that. They wants equal access to the data they're getting. That's equal access now, depending on the kind ofthe queries, whether they, Paula's B I and reporting are more like transactional kind of things in nature. That's the that. Those are the right technical choices that we're making behind the scenes so that the user, those on our lab print right, because they can really focus on the insights that they're getting and really making decisions based on that inside and not get caught into how to bend all of these different pieces so that they can support both of these work clothes. The other thing is that you know a lot off the time that has Bean spent an I T. Has Bean to figure out all of this so that the CEO can support the line of business like the CMO now by, you know, Adobe taking. Get off this all this. It's heavy lifting. That idea had to do. I think that, you know it will be able to meet the requirements of the line of business much faster. And there's going to be, you know, the agility that is needed to support the business. I think that's really our goal in how we support the CEOs so that they don't worry about all this technology, all the data management, how to collect all this data from globally distributed edges. I mean, that's the partnership that we are, you know, bending with the CEOs so that we help them in their journey off, really helping their line of business deliver the best experiences >> on Jewel. Great to see you having so much fun, Toby. Thank you. What's it like there? Tell us, what's it like working in a job? You got a platform? Certainly. There's a lot of hard problems to solve. So you got that on the engineering side, tell us what the cultures like they're >> doing is a fantastic company. I mean, I just love every bit every every minute that I spend here is fantastic. It's, you know, great people open culture open to new ideas on DH. You know, I guess, uh, >> all the >> creative cloud you know has got the straight of it. Eve itches in fused in people. So it's just it's it's just being a blast and and, you know, people recognize them. Barton's off how data is so critical to delivering those delightful experiences, and it's very rewarding to just see how focused everybody is in the company to really help businesses delight their customers. So it's zygo >> system is great, but the developer ecosystem What's your reaction to that of the >> I mean Adobe Io is I don't know. I feel, you know, Yeah, So that's so if you think of all the creators that work with Adobe products and build their applications, I mean, the ecosystem is very rich. So combined creatives on the data and I t I mean >> so we should call the marketing native like cloud native accomplice of developers, developers. It's coming together >> on DH because >> cats living together I mean, this is >> called wait. Call them that experience maker's late. So we are really bringing experience makers, developers, data, scientists all together >> It's a whole new level for a >> whole new level. It's thanks >> for coming on. Sharing the insights. Cube coverage live here, and it will be some in Las Vegas. I'm John for your jefe. Rick, Stay with us. We're here for two days. We're in day one of wall to wall coverage at Adobe Summit. We write back.

>> Live from New York. It's theCUBE. Covering theCUBE New York City, 2018. Brought to you by SiliconANGLE Media, and it's ecosystem partners. >> I'm John Furrier with Peter Burris, our next two guests are Jim Franklin with Dell EMC Director of Product Management Anant Chintamaneni, who is the Vice President of Products at BlueData. Welcome to theCUBE, good to see you. >> Thanks, John. >> Thank you. >> Thanks for coming on. >> I've been following BlueData since the founding. Great company, and the founders are great. Great teams, so thanks for coming on and sharing what's going on, I appreciate it. >> It's a pleasure, thanks for the opportunity. >> So Jim, talk about the Dell relationship with BlueData. What are you guys doing? You have the Dell-ready solutions. How is that related now, because you've seen this industry with us over the years morph. It's really now about, the set-up days are over, it's about proof points. >> That's right. >> AI and machine learning are driving the signal, which is saying, 'We need results'. There's action on the developer's side, there's action on the deployment, people want ROI, that's the main focus. >> That's right. That's right, and we've seen this journey happen from the new batch processing days, and we're seeing that customer base mature and come along, so the reason why we partnered with BlueData is, you have to have those softwares, you have to have the contenders. They have to have the algorithms, and things like that, in order to make this real. So it's been a great partnership with BlueData, it's dated back actually a little farther back than some may realize, all the way to 2015, believe it or not, when we used to incorporate BlueData with Isilon. So it's been actually a pretty positive partnership. >> Now we've talked with you guys in the past, you guys were on the cutting edge, this was back when Docker containers were fashionable, but now containers have become so proliferated out there, it's not just Docker, containerization has been the wave. Now, Kubernetes on top of it is really bringing in the orchestration. This is really making the storage and the network so much more valuable with workloads, whether respective workloads, and AI is a part of that. How do you guys navigate those waters now? What's the BlueData update, how are you guys taking advantage of that big wave? >> I think, great observation, re-embrace Docker containers, even before actually Docker was even formed as a company by that time, and Kubernetes was just getting launched, so we saw the value of Docker containers very early on, in terms of being able to obviously provide the agility, elasticity, but also, from a packaging of applications perspective, as we all know it's a very dynamic environment, and today, I think we are very happy to know that, with Kubernetes being a household name now, especially a tech company, so the way we're navigating this is, we have a turnkey product, which has containerization, and then now we are taking our value proposition of big data and AI and lifecycle management and bringing it to Kubernetes with an open source project that we launched called Cube Director under our umbrella. So, we're all about bringing stateful applications like Hadoop, AI, ML to the community and to our customer base, which is some of the largest financial services in health care customers. >> So the container revolution has certainly groped developers, and developers have always had a history of chasing after the next cool technology, and for good reason, it's not like just chasing after... Developers tend not to just chase after the shiny thing, they chased after the most productive thing, and they start using it, and they start learning about it, and they make themselves valuable, and they build more valuable applications as a result. But there's this interesting meshing of creators, makers, in the software world, between the development community and the data science community. How are data scientists, who you must be spending a fair amount of time with, starting to adopt containers, what are they looking at? Are they even aware of this, as you try to help these communities come together? >> We absolutely talk to the data scientists and they're the drivers of determining what applications they want to consume for the different news cases. But, at the end of the day, the person who has to deliver these applications, you know data scientists care about time to value, getting the environment quickly all prepared so they can access the right data sets. So, in many ways, most of our customers, many of them are unaware that there's actually containers under the hood. >> So this is the data scientists. >> The data scientists, but the actual administrators and the system administrators were making these tools available, are using containers as a way to accelerate the way they package the software, which has a whole bunch of dependent libraries, and there's a lot of complexity our there. So they're simplifying all that and providing the environment as quickly as possible. >> And in so doing, making sure that whatever workloads are put together, can scaled, can be combined differently and recombined differently, based on requirements of the data scientists. So the data scientist sees the tool... >> Yeah. >> The tool is manifest as, in concert with some of these new container related technologies, and then the whole CICD process supports the data scientist >> The other thing to think about though, is that this also allows freedom of choice, and we were discussing off camera before, these developers want to pick out what they want to pick out what they want to work with, they don't want to have to be locked in. So with containers, you can also speed that deployment but give them freedom to choose the tools that make them best productive. That'll make them much happier, and probably much more efficient. >> So there's a separation under the data science tools, and the developer tools, but they end up all supporting the same basic objective. So how does the infrastructure play in this, because the challenge of big data for the last five years as John and I both know, is that a lot of people conflated. The outcome of data science, the outcome of big data, with the process of standing up clusters, and lining up Hadoop, and if they failed on the infrastructure, they said it was a failure overall. So how you making the infrastructure really simple, and line up with this time of value? >> Well, the reality is, we all need food and water. IT still needs server and storage in order to work. But at the end of the day, the abstraction has to be there just like VMware in the early days, clouds, containers with BlueData is just another way to create a layer of abstraction. But this one is in the context of what the data scientist is trying to get done, and that's the key to why we partnered with BlueData and why we delivered big data as a service. >> So at that point, what's the update from Dell EMC and Dell, in particular, Analytics? Obviously you guys work with a lot of customers, have challenges, how are you solving those problems? What are those problems? Because we know there's some AI rumors, big Dell event coming up, there's rumors of a lot of AI involved, I'm speculating there's going to be probably a new kind of hardware device and software. What's the state of the analytics today? >> I think a lot of the customers we talked about, they were born in that batch processing, that Hadoop space we just talked about. I think they largely got that right, they've largely got that figured out, but now we're seeing proliferation of AI tools, proliferation of sandbox environments, and you're psyched to see a little bit of silo behavior happening, so what we're trying to do is that IT shop is trying to dispatch those environments, dispatch with some speed, with some agility. They want to have it at the right economic model as well, so we're trying to strike a better balance, say 'Hey, I've invested in all this infrastructure already, I need to modernize it, and that I also need to offer it up in a way that data scientists can consume it'. Oh, by the way, we're starting to see them start to hire more and more of these data scientists. Well, you don't want your data scientists, this very expensive, intelligent resource, sitting there doing data mining, data cleansing, detail offloads, we want them actually doing modeling and analytics. So we find that a lot of times right now as you're doing an operational change, the operational mindset as you're starting to hire these very expensive people to do this very good work, at the corest of the data, but they need to get productive in the way that you hired them to be productive. >> So what is this ready solution, can you just explain what that is? Is it a program, is it a hardware, is it a solution? What is the ready solution? >> Generally speaking, what we do as a division is we look for value workloads, just generally speaking, not necessarily in batch processing, or AI, or applications, and we try and create an environment that solves that customer challenge, typically they're very complex, SAP, Oracle Database, it's AI, my goodness. Very difficult. >> Variety of tools, using hives, no sequel, all this stuff's going on. >> Cassandra, you've got Tensorflow, so we try fit together a set of knowledge experts, that's the key, the intellectual property of our engineers, and their deep knowledge expertise in a certain area. So for AI, we have a sight of them back at the shop, they're in the lab, and this is what they do, and they're serving up these models, they're putting data through its paces, they're doing the work of a data scientist. They are data scientists. >> And so this is where BlueData comes in. You guys are part of this abstraction layer in the ready solutions. Offering? Is that how it works? >> Yeah, we are the software that enables the self-service experience, the multitenancy, that the consumers of the ready solution would want in terms of being able to onboard multiple different groups of users, lines of business, so you could have a user that wants to run basic spark, cluster, spark jobs, or you could have another user group that's using Tensorflow, or accelerated by a special type of CPU or GPU, and so you can have them all on the same infrastructure. >> One of the things Peter and I were talking about, Dave Vellante, who was here, he's at another event right now getting some content but, one of the things we observed was, we saw this awhile ago so it's not new to us but certainly we're seeing the impact at this event. Hadoop World, there's now called Strata Data NYC, is that we hear words like Kubernetes, and Multi Cloud, and Istio for the first time. At this event. This is the impact of the Cloud. The Cloud has essentially leveled the Hadoop World, certainly there's some Hadoop activity going on there, people have clusters, there's standing up infrastructure for analytical infrastructures that do analytics, obviously AI drives that, but now you have the Cloud being a power base. Changing that analytics infrastructure. How has it impacted you guys? BlueData, how are you guys impacted by the Cloud? Tailwind for you guys? Helpful? Good? >> You described it well, it is a tailwind. This space is about the data, not where the data lives necessarily, but the robustness of the data. So whether that's in the Cloud, whether that's on Premise, whether that's on Premise in your own private Cloud, I think anywhere where there's data that can be gathered, modeled, and new insights being pulled out of, this is wonderful, so as we ditched data, whether it's born in the Cloud or born on Premise, this is actually an accelerant to the solutions that we built together. >> As BlueData, we're all in on the Cloud, we support all the three major Cloud providers that was the big announcement that we made this week, we're generally available for AWS, GCP, and Azure, and, in particular, we start with customers who weren't born in the Cloud, so we're talking about some of the large financial services >> We had Barclays UK here who we nominated, they won the Cloud Era Data Impact Award, and what they're actually going through right now, is they started on Prem, they have these really packaged certified technology stacks, whether they are Cloud Era Hadoop, whether they are Anaconda for data science, and what they're trying to do right now is, they're obviously getting value from that on Premise with BlueData, and now they want to leverage the Cloud. They want to be able to extend into the Cloud. So, we as a company have made our product a hybrid Cloud-ready platform, so it can span on Prem as well as multiple Clouds, and you have the ability to move the workloads from one to the other, depending on data gravity, SLA considerations. >> Compliancy. >> I think it's one more thing, I want to test this with you guys, John, and that is, analytics is, I don't want to call it inert, or passive, but analytics has always been about getting the right data to human beings so they can make decisions, and now we're seeing, because of AI, the distinction that we draw between analytics and AI is, AI is about taking action on the data, it's about having a consequential action, as a result of the data, so in many respects, NCL, Kubernetes, a lot of these are not only do some interesting things for the infrastructure associated with big data, but they also facilitate the incorporation of new causes of applications, that act on behalf of the brand. >> Here's the other thing I'll add to it, there's a time element here. It used to be we were passive, and it was in the past, and you're trying to project forward, that's no longer the case. You can do it right now. Exactly. >> In many respects, the history of the computing industry can be drawn in this way, you focused on the past, and then with spreadsheets in the 80s and personal computing, you focused on getting everybody to agree on the future, and now, it's about getting action to happen right now. >> At the moment it happens. >> And that's why there's so much action. We're passed the set-up phase, and I think this is why we're hearing, seeing machine learning being so popular because it's like, people want to take action there's a demand, that's a signal that it's time to show where the ROI is and get action done. Clearly we see that. >> We're capitalists, right? We're all trying to figure out how to make money in these spaces. >> Certainly there's a lot of movement, and Cloud has proven that spinning up an instance concept has been a great thing, and certainly analytics. It's okay to have these workloads, but how do you tie it together? So, I want to ask you, because you guys have been involved in containers, Cloud has certainly been a tailwind, we agree with you 100 percent on that. What is the relevance of Kubernetes and Istio? You're starting to see these new trends. Kubernetes, Istio, Cupflow. Higher level microservices with all kinds of stateful and stateless dynamics. I call it API 2.0, it's a whole other generation of abstractions that are going on, that are creating some goodness for people. What is the impact, in your opinion, of Kubernetes and this new revolution? >> I think the impact of Kubernetes is, I just gave a talk here yesterday, called Hadoop-la About Kubernetes. We were thinking very deeply about this. We're thinking deeply about this. So I think Kubernetes, if you look at the genesis, it's all about stateless applications, and I think as new applications are being written folks are thinking about writing them in a manner that are decomposed, stateless, microservices, things like Cupflow. When you write it like that, Kubernetes fits in very well, and you get all the benefits of auto-scaling, and so control a pattern, and ultimately Kubernetes is this finite state machine-type model where you describe what the state should be, and it will work and crank towards making it towards that state. I think it's a little bit harder for stateful applications, and I think that's where we believe that the Kubernetes community has to do a lot more work, and folks like BlueData are going to contribute to that work which is, how do you bring stateful applications like Hadoop where there's a lot of interdependent services, they're not necessarily microservices, they're actually almost close to monolithic applications. So I think new applications, new AI ML tooling that's going to come out, they're going to be very conscious of how they're running in a Cloud world today that folks weren't aware of seven or eight years ago, so it's really going to make a huge difference. And I think things like Istio are going to make a huge difference because you can start in the cloud and maybe now expand on to Prem. So there's going to be some interesting dynamics. >> Without hopping management frameworks, absolutely. >> And this is really critical, you just nailed it. Stateful is where ML will shine, if you can then cross the chasma to the on Premise where the workloads can have state sharing. >> Right. >> Scales beautifully. It's a whole other level. >> Right. You're going to the data into the action, or the activity, you're going to have to move the processing to the data, and you want to have nonetheless, a common, seamless management development framework so that you have the choices about where you do those things. >> Absolutely. >> Great stuff. We can do a whole Cube segment just on that. We love talking about these new dynamics going on. We'll see you in CF CupCon coming up in Seattle. Great to have you guys on. Thanks, and congratulations on the relationship between BlueData and Dell EMC and Ready Solutions. This is Cube, with the Ready Solutions here. New York City, talking about big data and the impact, the future of AI, all things stateful, stateless, Cloud and all. It's theCUBE bringing you all the action. Stay with us for more after this short break.

- [Announcer] Live, from New York, it's theCUBE. Covering theCUBE New York City 2018. Brought to you by SiliconANGLE Media, and its ecosystem partners. - Hello everyone, welcome back to CUBE NYC. This is a CUBE special presentation of something that we've done now for the past couple of years. IBM has sponsored an influencer panel on some of the hottest topics in the industry, and of course, there's no hotter topic right now than AI. So, we've got nine of the top influencers in the AI space, and we're in Hell's Kitchen, and it's going to get hot in here. (laughing) And these guys, we're going to cover the gamut. So, first of all, folks, thanks so much for joining us today, really, as John said earlier, we love the collaboration with you all, and we'll definitely see you on social after the fact. I'm Dave Vellante, with my cohost for this session, Peter Burris, and again, thank you to IBM for sponsoring this and organizing this. IBM has a big event down here, in conjunction with Strata, called Change the Game, Winning with AI. We run theCUBE NYC, we've been here all week. So, here's the format. I'm going to kick it off, and then we'll see where it goes. So, I'm going to introduce each of the panelists, and then ask you guys to answer a question, I'm sorry, first, tell us a little bit about yourself, briefly, and then answer one of the following questions. Two big themes that have come up this week. One has been, because this is our ninth year covering what used to be Hadoop World, which kind of morphed into big data. Question is, AI, big data, same wine, new bottle? Or is it really substantive, and driving business value? So, that's one question to ponder. The other one is, you've heard the term, the phrase, data is the new oil. Is data really the new oil? Wonder what you think about that? Okay, so, Chris Penn, let's start with you. Chris is cofounder of Trust Insight, long time CUBE alum, and friend. Thanks for coming on. Tell us a little bit about yourself, and then pick one of those questions. - Sure, we're a data science consulting firm. We're an IBM business partner. When it comes to "data is the new oil," I love that expression because it's completely accurate. Crude oil is useless, you have to extract it out of the ground, refine it, and then bring it to distribution. Data is the same way, where you have to have developers and data architects get the data out. You need data scientists and tools, like Watson Studio, to refine it, and then you need to put it into production, and that's where marketing technologists, technologists, business analytics folks, and tools like Watson Machine Learning help bring the data and make it useful. - Okay, great, thank you. Tony Flath is a tech and media consultant, focus on cloud and cyber security, welcome. - Thank you. - Tell us a little bit about yourself and your thoughts on one of those questions. - Sure thing, well, thanks so much for having us on this show, really appreciate it. My background is in cloud, cyber security, and certainly in emerging tech with artificial intelligence. Certainly touched it from a cyber security play, how you can use machine learning, machine control, for better controlling security across the gamut. But I'll touch on your question about wine, is it a new bottle, new wine? Where does this come from, from artificial intelligence? And I really see it as a whole new wine that is coming along. When you look at emerging technology, and you look at all the deep learning that's happening, it's going just beyond being able to machine learn and know what's happening, it's making some meaning to that data. And things are being done with that data, from robotics, from automation, from all kinds of different things, where we're at a point in society where data, our technology is getting beyond us. Prior to this, it's always been command and control. You control data from a keyboard. Well, this is passing us. So, my passion and perspective on this is, the humanization of it, of IT. How do you ensure that people are in that process, right? - Excellent, and we're going to come back and talk about that. - Thanks so much. - Carla Gentry, @DataNerd? Great to see you live, as opposed to just in the ether on Twitter. Data scientist, and owner of Analytical Solution. Welcome, your thoughts? - Thank you for having us. Mine is, is data the new oil? And I'd like to rephrase that is, data equals human lives. So, with all the other artificial intelligence and everything that's going on, and all the algorithms and models that's being created, we have to think about things being biased, being fair, and understand that this data has impacts on people's lives. - Great. Steve Ardire, my paisan. - Paisan. - AI startup adviser, welcome, thanks for coming to theCUBE. - Thanks Dave. So, uh, my first career was geology, and I view AI as the new oil, but data is the new oil, but AI is the refinery. I've used that many times before. In fact, really, I've moved from just AI to augmented intelligence. So, augmented intelligence is really the way forward. This was a presentation I gave at IBM Think last spring, has almost 100,000 impressions right now, and the fundamental reason why is machines can attend to vastly more information than humans, but you still need humans in the loop, and we can talk about what they're bringing in terms of common sense reasoning, because big data does the who, what, when, and where, but not the why, and why is really the Holy Grail for causal analysis and reasoning. - Excellent, Bob Hayes, Business Over Broadway, welcome, great to see you again. - Thanks for having me. So, my background is in psychology, industrial psychology, and I'm interested in things like customer experience, data science, machine learning, so forth. And I'll answer the question around big data versus AI. And I think there's other terms we could talk about, big data, data science, machine learning, AI. And to me, it's kind of all the same. It's always been about analytics, and getting value from your data, big, small, what have you. And there's subtle differences among those terms. Machine learning is just about making a prediction, and knowing if things are classified correctly. Data science is more about understanding why things work, and understanding maybe the ethics behind it, what variables are predicting that outcome. But still, it's all the same thing, it's all about using data in a way that we can get value from that, as a society, in residences. - Excellent, thank you. Theo Lau, founder of Unconventional Ventures. What's your story? - Yeah, so, my background is driving technology innovation. So, together with my partner, what our work does is we work with organizations to try to help them leverage technology to drive systematic financial wellness. We connect founders, startup founders, with funders, we help them get money in the ecosystem. We also work with them to look at, how do we leverage emerging technology to do something good for the society. So, very much on point to what Bob was saying about. So when I look at AI, it is not new, right, it's been around for quite a while. But what's different is the amount of technological power that we have allow us to do so much more than what we were able to do before. And so, what my mantra is, great ideas can come from anywhere in the society, but it's our job to be able to leverage technology to shine a spotlight on people who can use this to do something different, to help seniors in our country to do better in their financial planning. - Okay, so, in your mind, it's not just a same wine, new bottle, it's more substantive than that. - [Theo] It's more substantive, it's a much better bottle. - Karen Lopez, senior project manager for Architect InfoAdvisors, welcome. - Thank you. So, I'm DataChick on twitter, and so that kind of tells my focus is that I'm here, I also call myself a data evangelist, and that means I'm there at organizations helping stand up for the data, because to me, that's the proxy for standing up for the people, and the places and the events that that data describes. That means I have a focus on security, data privacy and protection as well. And I'm going to kind of combine your two questions about whether data is the new wine bottle, I think is the combination. Oh, see, now I'm talking about alcohol. (laughing) But anyway, you know, all analogies are imperfect, so whether we say it's the new wine, or, you know, same wine, or whether it's oil, is that the analogy's good for both of them, but unlike oil, the amount of data's just growing like crazy, and the oil, we know at some point, I kind of doubt that we're going to hit peak data where we have not enough data, like we're going to do with oil. But that says to me that, how did we get here with big data, with machine learning and AI? And from my point of view, as someone who's been focused on data for 35 years, we have hit this perfect storm of open source technologies, cloud architectures and cloud services, data innovation, that if we didn't have those, we wouldn't be talking about large machine learning and deep learning-type things. So, because we have all these things coming together at the same time, we're now at explosions of data, which means we also have to protect them, and protect the people from doing harm with data, we need to do data for good things, and all of that. - Great, definite differences, we're not running out of data, data's like the terrible tribbles. (laughing) - Yes, but it's very cuddly, data is. - Yeah, cuddly data. Mark Lynd, founder of Relevant Track? - That's right. - I like the name. What's your story? - Well, thank you, and it actually plays into what my interest is. It's mainly around AI in enterprise operations and cyber security. You know, these teams that are in enterprise operations both, it can be sales, marketing, all the way through the organization, as well as cyber security, they're often under-sourced. And they need, what Steve pointed out, they need augmented intelligence, they need to take AI, the big data, all the information they have, and make use of that in a way where they're able to, even though they're under-sourced, make some use and some value for the organization, you know, make better use of the resources they have to grow and support the strategic goals of the organization. And oftentimes, when you get to budgeting, it doesn't really align, you know, you're short people, you're short time, but the data continues to grow, as Karen pointed out. So, when you take those together, using AI to augment, provided augmented intelligence, to help them get through that data, make real tangible decisions based on information versus just raw data, especially around cyber security, which is a big hit right now, is really a great place to be, and there's a lot of stuff going on, and a lot of exciting stuff in that area. - Great, thank you. Kevin L. Jackson, author and founder of GovCloud. GovCloud, that's big. - Yeah, GovCloud Network. Thank you very much for having me on the show. Up and working on cloud computing, initially in the federal government, with the intelligence community, as they adopted cloud computing for a lot of the nation's major missions. And what has happened is now I'm working a lot with commercial organizations and with the security of that data. And I'm going to sort of, on your questions, piggyback on Karen. There was a time when you would get a couple of bottles of wine, and they would come in, and you would savor that wine, and sip it, and it would take a few days to get through it, and you would enjoy it. The problem now is that you don't get a couple of bottles of wine into your house, you get two or three tankers of data. So, it's not that it's a new wine, you're just getting a lot of it. And the infrastructures that you need, before you could have a couple of computers, and a couple of people, now you need cloud, you need automated infrastructures, you need huge capabilities, and artificial intelligence and AI, it's what we can use as the tool on top of these huge infrastructures to drink that, you know. - Fire hose of wine. - Fire hose of wine. (laughs) - Everybody's having a good time. - Everybody's having a great time. (laughs) - Yeah, things are booming right now. Excellent, well, thank you all for those intros. Peter, I want to ask you a question. So, I heard there's some similarities and some definite differences with regard to data being the new oil. You have a perspective on this, and I wonder if you could inject it into the conversation. - Sure, so, the perspective that we take in a lot of conversations, a lot of folks here in theCUBE, what we've learned, and I'll kind of answer both questions a little bit. First off, on the question of data as the new oil, we definitely think that data is the new asset that business is going to be built on, in fact, our perspective is that there really is a difference between business and digital business, and that difference is data as an asset. And if you want to understand data transformation, you understand the degree to which businesses reinstitutionalizing work, reorganizing its people, reestablishing its mission around what you can do with data as an asset. The difference between data and oil is that oil still follows the economics of scarcity. Data is one of those things, you can copy it, you can share it, you can easily corrupt it, you can mess it up, you can do all kinds of awful things with it if you're not careful. And it's that core fundamental proposition that as an asset, when we think about cyber security, we think, in many respects, that is the approach to how we can go about privatizing data so that we can predict who's actually going to be able to appropriate returns on it. So, it's a good analogy, but as you said, it's not entirely perfect, but it's not perfect in a really fundamental way. It's not following the laws of scarcity, and that has an enormous effect. - In other words, I could put oil in my car, or I could put oil in my house, but I can't put the same oil in both. - Can't put it in both places. And now, the issue of the wine, I think it's, we think that it is, in fact, it is a new wine, and very simple abstraction, or generalization we come up with is the issue of agency. That analytics has historically not taken on agency, it hasn't acted on behalf of the brand. AI is going to act on behalf of the brand. Now, you're going to need both of them, you can't separate them. - A lot of implications there in terms of bias. - Absolutely. - In terms of privacy. You have a thought, here, Chris? - Well, the scarcity is our compute power, and our ability for us to process it. I mean, it's the same as oil, there's a ton of oil under the ground, right, we can't get to it as efficiently, or without severe environmental consequences to use it. Yeah, when you use it, it's transformed, but our scarcity is compute power, and our ability to use it intelligently. - Or even when you find it. I have data, I can apply it to six different applications, I have oil, I can apply it to one, and that's going to matter in how we think about work. - But one thing I'd like to add, sort of, you're talking about data as an asset. The issue we're having right now is we're trying to learn how to manage that asset. Artificial intelligence is a way of managing that asset, and that's important if you're going to use and leverage big data. - Yeah, but see, everybody's talking about the quantity, the quantity, it's not always the quantity. You know, we can have just oodles and oodles of data, but if it's not clean data, if it's not alphanumeric data, which is what's needed for machine learning. So, having lots of data is great, but you have to think about the signal versus the noise. So, sometimes you get so much data, you're looking at over-fitting, sometimes you get so much data, you're looking at biases within the data. So, it's not the amount of data, it's the, now that we have all of this data, making sure that we look at relevant data, to make sure we look at clean data. - One more thought, and we have a lot to cover, I want to get inside your big brain. - I was just thinking about it from a cyber security perspective, one of my customers, they were looking at the data that just comes from the perimeter, your firewalls, routers, all of that, and then not even looking internally, just the perimeter alone, and the amount of data being pulled off of those. And then trying to correlate that data so it makes some type of business sense, or they can determine if there's incidents that may happen, and take a predictive action, or threats that might be there because they haven't taken a certain action prior, it's overwhelming to them. So, having AI now, to be able to go through the logs to look at, and there's so many different types of data that come to those logs, but being able to pull that information, as well as looking at end points, and all that, and people's houses, which are an extension of the network oftentimes, it's an amazing amount of data, and they're only looking at a small portion today because they know, there's not enough resources, there's not enough trained people to do all that work. So, AI is doing a wonderful way of doing that. And some of the tools now are starting to mature and be sophisticated enough where they provide that augmented intelligence that Steve talked about earlier. - So, it's complicated. There's infrastructure, there's security, there's a lot of software, there's skills, and on and on. At IBM Think this year, Ginni Rometty talked about, there were a couple of themes, one was augmented intelligence, that was something that was clear. She also talked a lot about privacy, and you own your data, etc. One of the things that struck me was her discussion about incumbent disruptors. So, if you look at the top five companies, roughly, Facebook with fake news has dropped down a little bit, but top five companies in terms of market cap in the US. They're data companies, all right. Apple just hit a trillion, Amazon, Google, etc. How do those incumbents close the gap? Is that concept of incumbent disruptors actually something that is being put into practice? I mean, you guys work with a lot of practitioners. How are they going to close that gap with the data haves, meaning data at their core of their business, versus the data have-nots, it's not that they don't have a lot of data, but it's in silos, it's hard to get to? - Yeah, I got one more thing, so, you know, these companies, and whoever's going to be big next is, you have a digital persona, whether you want it or not. So, if you live in a farm out in the middle of Oklahoma, you still have a digital persona, people are collecting data on you, they're putting profiles of you, and the big companies know about you, and people that first interact with you, they're going to know that you have this digital persona. Personal AI, when AI from these companies could be used simply and easily, from a personal deal, to fill in those gaps, and to have a digital persona that supports your family, your growth, both personal and professional growth, and those type of things, there's a lot of applications for AI on a personal, enterprise, even small business, that have not been done yet, but the data is being collected now. So, you talk about the oil, the oil is being built right now, lots, and lots, and lots of it. It's the applications to use that, and turn that into something personally, professionally, educationally, powerful, that's what's missing. But it's coming. - Thank you, so, I'll add to that, and in answer to your question you raised. So, one example we always used in banking is, if you look at the big banks, right, and then you look at from a consumer perspective, and there's a lot of talk about Amazon being a bank. But the thing is, Amazon doesn't need to be a bank, they provide banking services, from a consumer perspective they don't really care if you're a bank or you're not a bank, but what's different between Amazon and some of the banks is that Amazon, like you say, has a lot of data, and they know how to make use of the data to offer something as relevant that consumers want. Whereas banks, they have a lot of data, but they're all silos, right. So, it's not just a matter of whether or not you have the data, it's also, can you actually access it and make something useful out of it so that you can create something that consumers want? Because otherwise, you're just a pipe. - Totally agree, like, when you look at it from a perspective of, there's a lot of terms out there, digital transformation is thrown out so much, right, and go to cloud, and you migrate to cloud, and you're going to take everything over, but really, when you look at it, and you both touched on it, it's the economics. You have to look at the data from an economics perspective, and how do you make some kind of way to take this data meaningful to your customers, that's going to work effectively for them, that they're going to drive? So, when you look at the big, big cloud providers, I think the push in things that's going to happen in the next few years is there's just going to be a bigger migration to public cloud. So then, between those, they have to differentiate themselves. Obvious is artificial intelligence, in a way that makes it easy to aggregate data from across platforms, to aggregate data from multi-cloud, effectively. To use that data in a meaningful way that's going to drive, not only better decisions for your business, and better outcomes, but drives our opportunities for customers, drives opportunities for employees and how they work. We're at a really interesting point in technology where we get to tell technology what to do. It's going beyond us, it's no longer what we're telling it to do, it's going to go beyond us. So, how we effectively manage that is going to be where we see that data flow, and those big five or big four, really take that to the next level. - Now, one of the things that Ginni Rometty said was, I forget the exact step, but it was like, 80% of the data, is not searchable. Kind of implying that it's sitting somewhere behind a firewall, presumably on somebody's premises. So, it was kind of interesting. You're talking about, certainly, a lot of momentum for public cloud, but at the same time, a lot of data is going to stay where it is. - Yeah, we're assuming that a lot of this data is just sitting there, available and ready, and we look at the desperate, or disparate kind of database situation, where you have 29 databases, and two of them have unique quantifiers that tie together, and the rest of them don't. So, there's nothing that you can do with that data. So, artificial intelligence is just that, it's artificial intelligence, so, they know, that's machine learning, that's natural language, that's classification, there's a lot of different parts of that that are moving, but we also have to have IT, good data infrastructure, master data management, compliance, there's so many moving parts to this, that it's not just about the data anymore. - I want to ask Steve to chime in here, go ahead. - Yeah, so, we also have to change the mentality that it's not just enterprise data. There's data on the web, the biggest thing is Internet of Things, the amount of sensor data will make the current data look like chump change. So, data is moving faster, okay. And this is where the sophistication of machine learning needs to kick in, going from just mostly supervised-learning today, to unsupervised learning. And in order to really get into, as I said, big data, and credible AI does the who, what, where, when, and how, but not the why. And this is really the Holy Grail to crack, and it's actually under a new moniker, it's called explainable AI, because it moves beyond just correlation into root cause analysis. Once we have that, then you have the means to be able to tap into augmented intelligence, where humans are working with the machines. - Karen, please. - Yeah, so, one of the things, like what Carla was saying, and what a lot of us had said, I like to think of the advent of ML technologies and AI are going to help me as a data architect to love my data better, right? So, that includes protecting it, but also, when you say that 80% of the data is unsearchable, it's not just an access problem, it's that no one knows what it was, what the sovereignty was, what the metadata was, what the quality was, or why there's huge anomalies in it. So, my favorite story about this is, in the 1980s, about, I forget the exact number, but like, 8 million children disappeared out of the US in April, at April 15th. And that was when the IRS enacted a rule that, in order to have a dependent, a deduction for a dependent on your tax returns, they had to have a valid social security number, and people who had accidentally miscounted their children and over-claimed them, (laughter) over the years them, stopped doing that. Well, some days it does feel like you have eight children running around. (laughter) - Agreed. - When, when that rule came about, literally, and they're not all children, because they're dependents, but literally millions of children disappeared off the face of the earth in April, but if you were doing analytics, or AI and ML, and you don't know that this anomaly happened, I can imagine in a hundred years, someone is saying some catastrophic event happened in April, 1983. (laughter) And what caused that, was it healthcare? Was it a meteor? Was it the clown attacking them? - That's where I was going. - Right. So, those are really important things that I want to use AI and ML to help me, not only document and capture that stuff, but to provide that information to the people, the data scientists and the analysts that are using the data. - Great story, thank you. Bob, you got a thought? You got the mic, go, jump in here. - Well, yeah, I do have a thought, actually. I was talking about, what Karen was talking about. I think it's really important that, not only that we understand AI, and machine learning, and data science, but that the regular folks and companies understand that, at the basic level. Because those are the people who will ask the questions, or who know what questions to ask of the data. And if they don't have the tools, and the knowledge of how to get access to that data, or even how to pose a question, then that data is going to be less valuable, I think, to companies. And the more that everybody knows about data, even people in congress. Remember when Zuckerberg talked about? (laughter) - That was scary. - How do you make money? It's like, we all know this. But, we need to educate the masses on just basic data analytics. - We could have an hour-long panel on that. - Yeah, absolutely. - Peter, you and I were talking about, we had a couple of questions, sort of, how far can we take artificial intelligence? How far should we? You know, so that brings in to the conversation of ethics, and bias, why don't you pick it up? - Yeah, so, one of the crucial things that we all are implying is that, at some point in time, AI is going to become a feature of the operations of our homes, our businesses. And as these technologies get more powerful, and they diffuse, and know about how to use them, diffuses more broadly, and you put more options into the hands of more people, the question slowly starts to turn from can we do it, to should we do it? And, one of the issues that I introduce is that I think the difference between big data and AI, specifically, is this notion of agency. The AI will act on behalf of, perhaps you, or it will act on behalf of your business. And that conversation is not being had, today. It's being had in arguments between Elon Musk and Mark Zuckerberg, which pretty quickly get pretty boring. (laughing) At the end of the day, the real question is, should this machine, whether in concert with others, or not, be acting on behalf of me, on behalf of my business, or, and when I say on behalf of me, I'm also talking about privacy. Because Facebook is acting on behalf of me, it's not just what's going on in my home. So, the question of, can it be done? A lot of things can be done, and an increasing number of things will be able to be done. We got to start having a conversation about should it be done? - So, humans exhibit tribal behavior, they exhibit bias. Their machine's going to pick that up, go ahead, please. - Yeah, one thing that sort of tag onto agency of artificial intelligence. Every industry, every business is now about identifying information and data sources, and their appropriate sinks, and learning how to draw value out of connecting the sources with the sinks. Artificial intelligence enables you to identify those sources and sinks, and when it gets agency, it will be able to make decisions on your behalf about what data is good, what data means, and who it should be. - What actions are good. - Well, what actions are good. - And what data was used to make those actions. - Absolutely. - And was that the right data, and is there bias of data? And all the way down, all the turtles down. - So, all this, the data pedigree will be driven by the agency of artificial intelligence, and this is a big issue. - It's really fundamental to understand and educate people on, there are four fundamental types of bias, so there's, in machine learning, there's intentional bias, "Hey, we're going to make "the algorithm generate a certain outcome "regardless of what the data says." There's the source of the data itself, historical data that's trained on the models built on flawed data, the model will behave in a flawed way. There's target source, which is, for example, we know that if you pull data from a certain social network, that network itself has an inherent bias. No matter how representative you try to make the data, it's still going to have flaws in it. Or, if you pull healthcare data about, for example, African-Americans from the US healthcare system, because of societal biases, that data will always be flawed. And then there's tool bias, there's limitations to what the tools can do, and so we will intentionally exclude some kinds of data, or not use it because we don't know how to, our tools are not able to, and if we don't teach people what those biases are, they won't know to look for them, and I know. - Yeah, it's like, one of the things that we were talking about before, I mean, artificial intelligence is not going to just create itself, it's lines of code, it's input, and it spits out output. So, if it learns from these learning sets, we don't want AI to become another buzzword. We don't want everybody to be an "AR guru" that has no idea what AI is. It takes months, and months, and months for these machines to learn. These learning sets are so very important, because that input is how this machine, think of it as your child, and that's basically the way artificial intelligence is learning, like your child. You're feeding it these learning sets, and then eventually it will make its own decisions. So, we know from some of us having children that you teach them the best that you can, but then later on, when they're doing their own thing, they're really, it's like a little myna bird, they've heard everything that you've said. (laughing) Not only the things that you said to them directly, but the things that you said indirectly. - Well, there are some very good AI researchers that might disagree with that metaphor, exactly. (laughing) But, having said that, what I think is very interesting about this conversation is that this notion of bias, one of the things that fascinates me about where AI goes, are we going to find a situation where tribalism more deeply infects business? Because we know that human beings do not seek out the best information, they seek out information that reinforces their beliefs. And that happens in business today. My line of business versus your line of business, engineering versus sales, that happens today, but it happens at a planning level, and when we start talking about AI, we have to put the appropriate dampers, understand the biases, so that we don't end up with deep tribalism inside of business. Because AI could have the deleterious effect that it actually starts ripping apart organizations. - Well, input is data, and then the output is, could be a lot of things. - Could be a lot of things. - And that's where I said data equals human lives. So that we look at the case in New York where the penal system was using this artificial intelligence to make choices on people that were released from prison, and they saw that that was a miserable failure, because that people that release actually re-offended, some committed murder and other things. So, I mean, it's, it's more than what anybody really thinks. It's not just, oh, well, we'll just train the machines, and a couple of weeks later they're good, we never have to touch them again. These things have to be continuously tweaked. So, just because you built an algorithm or a model doesn't mean you're done. You got to go back later, and continue to tweak these models. - Mark, you got the mic. - Yeah, no, I think one thing we've talked a lot about the data that's collected, but what about the data that's not collected? Incomplete profiles, incomplete datasets, that's a form of bias, and sometimes that's the worst. Because they'll fill that in, right, and then you can get some bias, but there's also a real issue for that around cyber security. Logs are not always complete, things are not always done, and when things are doing that, people make assumptions based on what they've collected, not what they didn't collect. So, when they're looking at this, and they're using the AI on it, that's only on the data collected, not on that that wasn't collected. So, if something is down for a little while, and no data's collected off that, the assumption is, well, it was down, or it was impacted, or there was a breach, or whatever, it could be any of those. So, you got to, there's still this human need, there's still the need for humans to look at the data and realize that there is the bias in there, there is, we're just looking at what data was collected, and you're going to have to make your own thoughts around that, and assumptions on how to actually use that data before you go make those decisions that can impact lots of people, at a human level, enterprise's profitability, things like that. And too often, people think of AI, when it comes out of there, that's the word. Well, it's not the word. - Can I ask a question about this? - Please. - Does that mean that we shouldn't act? - It does not. - Okay. - So, where's the fine line? - Yeah, I think. - Going back to this notion of can we do it, or should we do it? Should we act? - Yeah, I think you should do it, but you should use it for what it is. It's augmenting, it's helping you, assisting you to make a valued or good decision. And hopefully it's a better decision than you would've made without it. - I think it's great, I think also, your answer's right too, that you have to iterate faster, and faster, and faster, and discover sources of information, or sources of data that you're not currently using, and, that's why this thing starts getting really important. - I think you touch on a really good point about, should you or shouldn't you? You look at Google, and you look at the data that they've been using, and some of that out there, from a digital twin perspective, is not being approved, or not authorized, and even once they've made changes, it's still floating around out there. Where do you know where it is? So, there's this dilemma of, how do you have a digital twin that you want to have, and is going to work for you, and is going to do things for you to make your life easier, to do these things, mundane tasks, whatever? But how do you also control it to do things you don't want it to do? - Ad-based business models are inherently evil. (laughing) - Well, there's incentives to appropriate our data, and so, are things like blockchain potentially going to give users the ability to control their data? We'll see. - No, I, I'm sorry, but that's actually a really important point. The idea of consensus algorithms, whether it's blockchain or not, blockchain includes games, and something along those lines, whether it's Byzantine fault tolerance, or whether it's Paxos, consensus-based algorithms are going to be really, really important. Parts of this conversation, because the data's going to be more distributed, and you're going to have more elements participating in it. And so, something that allows, especially in the machine-to-machine world, which is a lot of what we're talking about right here, you may not have blockchain, because there's no need for a sense of incentive, which is what blockchain can help provide. - And there's no middleman. - And, well, all right, but there's really, the thing that makes blockchain so powerful is it liberates new classes of applications. But for a lot of the stuff that we're talking about, you can use a very powerful consensus algorithm without having a game side, and do some really amazing things at scale. - So, looking at blockchain, that's a great thing to bring up, right. I think what's inherently wrong with the way we do things today, and the whole overall design of technology, whether it be on-prem, or off-prem, is both the lock and key is behind the same wall. Whether that wall is in a cloud, or behind a firewall. So, really, when there is an audit, or when there is a forensics, it always comes down to a sysadmin, or something else, and the system administrator will have the finger pointed at them, because it all resides, you can edit it, you can augment it, or you can do things with it that you can't really determine. Now, take, as an example, blockchain, where you've got really the source of truth. Now you can take and have the lock in one place, and the key in another place. So that's certainly going to be interesting to see how that unfolds. - So, one of the things, it's good that, we've hit a lot of buzzwords, right now, right? (laughing) AI, and ML, block. - Bingo. - We got the blockchain bingo, yeah, yeah. So, one of the things is, you also brought up, I mean, ethics and everything, and one of the things that I've noticed over the last year or so is that, as I attend briefings or demos, everyone is now claiming that their product is AI or ML-enabled, or blockchain-enabled. And when you try to get answers to the questions, what you really find out is that some things are being pushed as, because they have if-then statements somewhere in their code, and therefore that's artificial intelligence or machine learning. - [Peter] At least it's not "go-to." (laughing) - Yeah, you're that experienced as well. (laughing) So, I mean, this is part of the thing you try to do as a practitioner, as an analyst, as an influencer, is trying to, you know, the hype of it all. And recently, I attended one where they said they use blockchain, and I couldn't figure it out, and it turns out they use GUIDs to identify things, and that's not blockchain, it's an identifier. (laughing) So, one of the ethics things that I think we, as an enterprise community, have to deal with, is the over-promising of AI, and ML, and deep learning, and recognition. It's not, I don't really consider it visual recognition services if they just look for red pixels. I mean, that's not quite the same thing. Yet, this is also making things much harder for your average CIO, or worse, CFO, to understand whether they're getting any value from these technologies. - Old bottle. - Old bottle, right. - And I wonder if the data companies, like that you talked about, or the top five, I'm more concerned about their nearly, or actual $1 trillion valuations having an impact on their ability of other companies to disrupt or enter into the field more so than their data technologies. Again, we're coming to another perfect storm of the companies that have data as their asset, even though it's still not on their financial statements, which is another indicator whether it's really an asset, is that, do we need to think about the terms of AI, about whose hands it's in, and who's, like, once one large trillion-dollar company decides that you are not a profitable company, how many other companies are going to buy that data and make that decision about you? - Well, and for the first time in business history, I think, this is true, we're seeing, because of digital, because it's data, you're seeing tech companies traverse industries, get into, whether it's content, or music, or publishing, or groceries, and that's powerful, and that's awful scary. - If you're a manger, one of the things your ownership is asking you to do is to reduce asset specificities, so that their capital could be applied to more productive uses. Data reduces asset specificities. It brings into question the whole notion of vertical industry. You're absolutely right. But you know, one quick question I got for you, playing off of this is, again, it goes back to this notion of can we do it, and should we do it? I find it interesting, if you look at those top five, all data companies, but all of them are very different business models, or they can classify the two different business models. Apple is transactional, Microsoft is transactional, Google is ad-based, Facebook is ad-based, before the fake news stuff. Amazon's kind of playing it both sides. - Yeah, they're kind of all on a collision course though, aren't they? - But, well, that's what's going to be interesting. I think, at some point in time, the "can we do it, should we do it" question is, brands are going to be identified by whether or not they have gone through that process of thinking about, should we do it, and say no. Apple is clearly, for example, incorporating that into their brand. - Well, Silicon Valley, broadly defined, if I include Seattle, and maybe Armlock, not so much IBM. But they've got a dual disruption agenda, they've always disrupted horizontal tech. Now they're disrupting vertical industries. - I was actually just going to pick up on what she was talking about, we were talking about buzzword, right. So, one we haven't heard yet is voice. Voice is another big buzzword right now, when you couple that with IoT and AI, here you go, bingo, do I got three points? (laughing) Voice recognition, voice technology, so all of the smart speakers, if you think about that in the world, there are 7,000 languages being spoken, but yet if you look at Google Home, you look at Siri, you look at any of the devices, I would challenge you, it would have a lot of problem understanding my accent, and even when my British accent creeps out, or it would have trouble understanding seniors, because the way they talk, it's very different than a typical 25-year-old person living in Silicon Valley, right. So, how do we solve that, especially going forward? We're seeing voice technology is going to be so more prominent in our homes, we're going to have it in the cars, we have it in the kitchen, it does everything, it listens to everything that we are talking about, not talking about, and records it. And to your point, is it going to start making decisions on our behalf, but then my question is, how much does it actually understand us? - So, I just want one short story. Siri can't translate a word that I ask it to translate into French, because my phone's set to Canadian English, and that's not supported. So I live in a bilingual French English country, and it can't translate. - But what this is really bringing up is if you look at society, and culture, what's legal, what's ethical, changes across the years. What was right 200 years ago is not right now, and what was right 50 years ago is not right now. - It changes across countries. - It changes across countries, it changes across regions. So, what does this mean when our AI has agency? How do we make ethical AI if we don't even know how to manage the change of what's right and what's wrong in human society? - One of the most important questions we have to worry about, right? - Absolutely. - But it also says one more thing, just before we go on. It also says that the issue of economies of scale, in the cloud. - Yes. - Are going to be strongly impacted, not just by how big you can build your data centers, but some of those regulatory issues that are going to influence strongly what constitutes good experience, good law, good acting on my behalf, agency. - And one thing that's underappreciated in the marketplace right now is the impact of data sovereignty, if you get back to data, countries are now recognizing the importance of managing that data, and they're implementing data sovereignty rules. Everyone talks about California issuing a new law that's aligned with GDPR, and you know what that meant. There are 30 other states in the United States alone that are modifying their laws to address this issue. - Steve. - So, um, so, we got a number of years, no matter what Ray Kurzweil says, until we get to artificial general intelligence. - The singularity's not so near? (laughing) - You know that he's changed the date over the last 10 years. - I did know it. - Quite a bit. And I don't even prognosticate where it's going to be. But really, where we're at right now, I keep coming back to, is that's why augmented intelligence is really going to be the new rage, humans working with machines. One of the hot topics, and the reason I chose to speak about it is, is the future of work. I don't care if you're a millennial, mid-career, or a baby boomer, people are paranoid. As machines get smarter, if your job is routine cognitive, yes, you have a higher propensity to be automated. So, this really shifts a number of things. A, you have to be a lifelong learner, you've got to learn new skillsets. And the dynamics are changing fast. Now, this is also a great equalizer for emerging startups, and even in SMBs. As the AI improves, they can become more nimble. So back to your point regarding colossal trillion dollar, wait a second, there's going to be quite a sea change going on right now, and regarding demographics, in 2020, millennials take over as the majority of the workforce, by 2025 it's 75%. - Great news. (laughing) - As a baby boomer, I try my damnedest to stay relevant. - Yeah, surround yourself with millennials is the takeaway there. - Or retire. (laughs) - Not yet. - One thing I think, this goes back to what Karen was saying, if you want a basic standard to put around the stuff, look at the old ISO 38500 framework. Business strategy, technology strategy. You have risk, compliance, change management, operations, and most importantly, the balance sheet in the financials. AI and what Tony was saying, digital transformation, if it's of meaning, it belongs on a balance sheet, and should factor into how you value your company. All the cyber security, and all of the compliance, and all of the regulation, is all stuff, this framework exists, so look it up, and every time you start some kind of new machine learning project, or data sense project, say, have we checked the box on each of these standards that's within this machine? And if you haven't, maybe slow down and do your homework. - To see a day when data is going to be valued on the balance sheet. - It is. - It's already valued as part of the current, but it's good will. - Certainly market value, as we were just talking about. - Well, we're talking about all of the companies that have opted in, right. There's tens of thousands of small businesses just in this region alone that are opt-out. They're small family businesses, or businesses that really aren't even technology-aware. But data's being collected about them, it's being on Yelp, they're being rated, they're being reviewed, the success to their business is out of their hands. And I think what's really going to be interesting is, you look at the big data, you look at AI, you look at things like that, blockchain may even be a potential for some of that, because of mutability, but it's when all of those businesses, when the technology becomes a cost, it's cost-prohibitive now, for a lot of them, or they just don't want to do it, and they're proudly opt-out. In fact, we talked about that last night at dinner. But when they opt-in, the company that can do that, and can reach out to them in a way that is economically feasible, and bring them back in, where they control their data, where they control their information, and they do it in such a way where it helps them build their business, and it may be a generational business that's been passed on. Those kind of things are going to make a big impact, not only on the cloud, but the data being stored in the cloud, the AI, the applications that you talked about earlier, we talked about that. And that's where this bias, and some of these other things are going to have a tremendous impact if they're not dealt with now, at least ethically. - Well, I feel like we just got started, we're out of time. Time for a couple more comments, and then officially we have to wrap up. - Yeah, I had one thing to say, I mean, really, Henry Ford, and the creation of the automobile, back in the early 1900s, changed everything, because now we're no longer stuck in the country, we can get away from our parents, we can date without grandma and grandpa setting on the porch with us. (laughing) We can take long trips, so now we're looked at, we've sprawled out, we're not all living in the country anymore, and it changed America. So, AI has that same capabilities, it will automate mundane routine tasks that nobody wanted to do anyway. So, a lot of that will change things, but it's not going to be any different than the way things changed in the early 1900s. - It's like you were saying, constant reinvention. - I think that's a great point, let me make one observation on that. Every period of significant industrial change was preceded by the formation, a period of formation of new assets that nobody knew what to do with. Whether it was, what do we do, you know, industrial manufacturing, it was row houses with long shafts tied to an engine that was coal-fired, and drove a bunch of looms. Same thing, railroads, large factories for Henry Ford, before he figured out how to do an information-based notion of mass production. This is the period of asset formation for the next generation of social structures. - Those ship-makers are going to be all over these cars, I mean, you're going to have augmented reality right there, on your windshield. - Karen, bring it home. Give us the drop-the-mic moment. (laughing) - No pressure. - Your AV guys are not happy with that. So, I think the, it all comes down to, it's a people problem, a challenge, let's say that. The whole AI ML thing, people, it's a legal compliance thing. Enterprises are going to struggle with trying to meet five billion different types of compliance rules around data and its uses, about enforcement, because ROI is going to make risk of incarceration as well as return on investment, and we'll have to manage both of those. I think businesses are struggling with a lot of this complexity, and you just opened a whole bunch of questions that we didn't really have solid, "Oh, you can fix it by doing this." So, it's important that we think of this new world of data focus, data-driven, everything like that, is that the entire IT and business community needs to realize that focusing on data means we have to change how we do things and how we think about it, but we also have some of the same old challenges there. - Well, I have a feeling we're going to be talking about this for quite some time. What a great way to wrap up CUBE NYC here, our third day of activities down here at 37 Pillars, or Mercantile 37. Thank you all so much for joining us today. - Thank you. - Really, wonderful insights, really appreciate it, now, all this content is going to be available on theCUBE.net. We are exposing our video cloud, and our video search engine, so you'll be able to search our entire corpus of data. I can't wait to start searching and clipping up this session. Again, thank you so much, and thank you for watching. We'll see you next time.