(upbeat instrumental music) >> TheCUBE's live coverage is made possible by funding from Dell Technologies, creating technologies that drive human progress. (uplifting instrumental music) >> Hey everyone. Welcome to Barcelona, Spain. It's theCUBE Live at MWC '23. I'm Lisa Martin, Dave Vellante, our co-founder, our co-CEO of theCUBE, you know him, you love him. He's here as my co-host. Dave, we have a great couple of guests here to break down day one keynote. Lots of meat. I can't wait to be part of this conversation. Chris Lewis joins us, the founder and MD of Lewis Insight. And Sarbjeet Johal, one of you know him as well. He's a Cube contributor, cloud architect. Guys, welcome to the program. Thank you so much for joining Dave and me today. >> Lovely to be here. >> Thank you. >> Chris, I want to start with you. You have covered all aspects of global telecoms industries over 30 years working as an analyst. Talk about the evolution of the telecom industry that you've witnessed, and what were some of the things you heard in the keynote that excite you about the direction it's going? >> Well, as ever, MWC, there's no lack of glitz and glamour, but it's the underlying issues of the industry that are really at stake here. There's not a lot of new revenue coming into the telecom providers, but there's a lot of adjustment, readjustment of the underlying operational environment. And also, really importantly, what came out of the keynotes is the willingness and the necessity to really engage with the API community, with the developer community, people who traditionally, telecoms would never have even touched. So they're sorting out their own house, they're cleaning their own stables, getting the cost base down, but they're also now realizing they've got to engage with all the other parties. There's a lot of cloud providers here, there's a lot of other people from outside so they're realizing they cannot do it all themselves. It's quite a tough lesson for a very conservative, inward looking industry, right? So should we be spending all this money and all this glitz and glamour of MWC and all be here, or should would be out there really building for the future and making sure the services are right for yours and my needs in a business and personal lives? So a lot of new changes, a lot of realization of what's going on outside, but underlying it, we've just got to get this right this time. >> And it feels like that monetization is front and center. You mentioned developers, we've got to work with developers, but I'm hearing the latest keynote from the Ericsson CEOs, we're going to monetize through those APIs, we're going to charge the developers. I mean, first of all, Chris, am I getting that right? And Sarbjeet, as somebody who's close to the developer community, is that the right way to build bridges? But Chris, are we getting that right? >> Well, let's take the first steps first. So, Ericsson, of course, acquired Vonage, which is a massive API business so they want to make money. They expect to make money by bringing that into the mainstream telecom community. Now, whether it's the developers who pay for it, or let's face it, we are moving into a situation as the telco moves into a techco model where the techco means they're going to be selling bits of the technology to developer guys and to other application developers. So when he says he needs to charge other people for it, it's the way in which people reach in and will take going through those open APIs like the open gateway announced today, but also the way they'll reach in and take things like network slicing. So we're opening up the telecom community, the treasure chest, if you like, where developers' applications and other third parties can come in and take those chunks of technology and build them into their services. This is a complete change from the old telecom industry where everybody used to come and you say, "all right, this is my product, you've got to buy it and you're going to pay me a lot of money for it." So we are looking at a more flexible environment where the other parties can take those chunks. And we know we want collectivity built into our financial applications, into our government applications, everything, into the future of the metaverse, whatever it may be. But it requires that change in attitude of the telcos. And they do need more money 'cause they've said, the baseline of revenue is pretty static, there's not a lot of growth in there so they're looking for new revenues. It's in a B2B2X time model. And it's probably the middle man's going to pay for it rather than the customer. >> But the techco model, Sarbjeet, it looks like the telcos are getting their money on their way in. The techco company model's to get them on their way out like the app store. Go build something of value, build some kind of app or data product, and then when it takes off, we'll take a piece of the action. What are your thoughts from a developer perspective about how the telcos are approaching it? >> Yeah, I think before we came here, like I said, I did some tweets on this, that we talk about all kind of developers, like there's game developers and front end, back end, and they're all talking about like what they're building on top of cloud, but nowhere you will hear the term "telco developer," there's no API from telcos given to the developers to build IoT solutions on top of it because telco as an IoT, I think is a good sort of hand in hand there. And edge computing as well. The glimmer of hope, if you will, for telcos is the edge computing, I believe. And even in edge, I predicted, I said that many times that cloud players will dominate that market with the private 5G. You know that story, right? >> We're going to talk about that. (laughs) >> The key is this, that if you see in general where the population lives, in metros, right? That's where the world population is like flocking to and we have cloud providers covering the local zones with local like heavy duty presence from the big cloud providers and then these telcos are getting sidetracked by that. Even the V2X in cars moving the autonomous cars and all that, even in that space, telcos are getting sidetracked in many ways. What telcos have to do is to join the forces, build some standards, if not standards, some consortium sort of. They're trying to do that with the open gateway here, they have only eight APIs. And it's 2023, eight APIs is nothing, right? (laughs) So they should have started this 10 years back, I think. So, yeah, I think to entice the developers, developers need the employability, we need to train them, we need to show them some light that hey, you can build a lot on top of it. If you tell developers they can develop two things or five things, nobody will come. >> So, Chris, the cloud will dominate the edge. So A, do you buy it? B, the telcos obviously are acting like that might happen. >> Do you know I love people when they've got their heads in the clouds. (all laugh) And you're right in so many ways, but if you flip it around and think about how the customers think about this, business customers and consumers, they don't care about all this background shenanigans going on, do they? >> Lisa: No. >> So I think one of the problems we have is that this is a new territory and whether you call it the edge or whatever you call it, what we need there is we need connectivity, we need security, we need storage, we need compute, we need analytics, and we need applications. And are any of those more important than the others? It's the collective that actually drives the real value there. So we need all those things together. And of course, the people who represented at this show, whether it's the cloud guys, the telcos, the Nokia, the Ericssons of this world, they all own little bits of that. So that's why they're all talking partnerships because they need the combination, they cannot do it on their own. The cloud guys can't do it on their own. >> Well, the cloud guys own all of those things that you just talked about though. (all laugh) >> Well, they don't own the last bit of connectivity, do they? They don't own the access. >> Right, exactly. That's the one thing they don't own. So, okay, we're back to pipes, right? We're back to charging for connectivity- >> Pipes are very valuable things, right? >> Yeah, for sure. >> Never underestimate pipes. I don't know about where you live, plumbers make a lot of money where I live- >> I don't underestimate them but I'm saying can the telcos charge for more than that or are the cloud guys going to mop up the storage, the analytics, the compute, and the apps? >> They may mop it up, but I think what the telcos are doing and we've seen a lot of it here already, is they are working with all those major cloud guys already. So is it an unequal relationship? The cloud guys are global, massive global scale, the telcos are fundamentally national operators. >> Yep. >> Some have a little bit of regional, nobody has global scale. So who stitches it all together? >> Dave: Keep your friends close and your enemies closer. >> Absolutely. >> I know that saying never gets old. It's true. Well, Sarbjeet, one of the things that you tweeted about, I didn't get to see the keynote but I was looking at your tweets. 46% of telcos think they won't make it to the next decade. That's a big number. Did that surprise you? >> No, actually it didn't surprise me because the competition is like closing in on them and the telcos are competing with telcos as well and the telcos are competing with cloud providers on the other side, right? So the smaller ones are getting squeezed. It's the bigger players, they can hook up the newer platforms, I think they will survive. It's like that part is like any other industry, if you will. But the key is here, I think why the pain points were sort of described on the main stage is that they're crying out loud to tell the big tech cloud providers that "hey, you pay your fair share," like we talked, right? You are not paying, you're generating so much content which reverses our networks and you are not paying for it. So they are not able to recoup the cost of laying down their networks. By the way, one thing actually I want to mention is that they said the cloud needs earth. The cloud and earth, it's like there's no physical need to cloud, you know that, right? So like, I think it's the other way around. I think the earth needs the cloud because I'm a cloud guy. (Sarbjeet and Lisa laugh) >> I think you need each other, right? >> I think so too. >> They need each other. When they said cloud needs earth, right? I think they're still in denial that the cloud is a big force. They have to partner. When you can't compete with somebody, what do you do? Partner with them. >> Chris, this is your world. Are they in denial? >> No, I think they're waking up to the pragmatism of the situation. >> Yeah. >> They're building... As we said, most of the telcos, you find have relationships with the cloud guys, I think you're right about the industry. I mean, do you think what's happened since US was '96, the big telecom act when we started breaking up all the big telcos and we had lots of competition came in, we're seeing the signs that we might start to aggregate them back up together again. So it's been an interesting experiment for like 30 years, hasn't it too? >> It made the US less competitive, I would argue, but carry on. >> Yes, I think it's true. And Europe is maybe too competitive and therefore, it's not driven the investment needed. And by the way, it's not just mobile, it's fixed as well. You saw the Orange CEO was talking about the her investment and the massive fiber investments way ahead of many other countries, way ahead of the UK or Germany. We need that fiber in the ground to carry all your cloud traffic to do this. So there is a scale issue, there is a competition issue, but the telcos are very much aware of it. They need the cloud, by the way, to improve their operational environments as well, to change that whole old IT environment to deliver you and I better service. So no, it absolutely is changing. And they're getting scale, but they're fundamentally offering the basic product, you call it pipes, I'll just say they're offering broadband to you and I and the business community. But they're stepping on dangerous ground, I think, when saying they want to charge the over the top guys for all the traffic they use. Those over the top guys now build a lot of the global networks, the backbone submarine network. They're putting a lot of money into it, and by giving us endless data for our individual usage, that cat is out the bag, I think to a large extent. >> Yeah. And Orange CEO basically said that, that they're not paying their fair share. I'm for net neutrality but the governments are going to have to fund this unless you let us charge the OTT. >> Well, I mean, we could of course renationalize. Where would that take us? (Dave laughs) That would make MWC very interesting next year, wouldn't it? To renationalize it. So, no, I think you've got to be careful what we wish for here. Creating the absolute clear product that is required to underpin all of these activities, whether it's IoT or whether it's cloud delivery or whether it's just our own communication stuff, delivering that absolutely ubiquitously high quality for business and for consumer is what we have to do. And telcos have been too conservative in the past. >> I think they need to get together and create standards around... I think they have a big opportunity. We know that the clouds are being built in silos, right? So there's Azure stack, there's AWS and there's Google. And those are three main ones and a few others, right? So that we are fighting... On the cloud side, what we are fighting is the multicloud. How do we consume that multicloud without having standards? So if these people get together and create some standards around IoT and edge computing sort of area, people will flock to them to say, "we will use you guys, your API, we don't care behind the scenes if you use AWS or Google Cloud or Azure, we will come to you." So market, actually is looking for that solution. I think it's an opportunity for these guys, for telcos. But the problem with telcos is they're nationalized, as you said Chris versus the cloud guys are still kind of national in a way, but they're global corporations. And some of the telcos are global corporations as well, BT covers so many countries and TD covers so many... DT is in US as well, so they're all over the place. >> But you know what's interesting is that the TM forum, which is one of the industry associations, they've had an open digital architecture framework for quite some years now. Google had joined that some years ago, Azure in there, AWS just joined it a couple of weeks ago. So when people said this morning, why isn't AWS on the keynote? They don't like sharing the limelight, do they? But they're getting very much in bed with the telco. So I think you'll see the marriage. And in fact, there's a really interesting statement, if you look at the IoT you mentioned, Bosch and Nokia have been working together 'cause they said, the problem we've got, you've got a connectivity network on one hand, you've got the sensor network on the other hand, you're trying to merge them together, it's a nightmare. So we are finally seeing those sort of groups talking to each other. So I think the standards are coming, the cooperation is coming, partnerships are coming, but it means that the telco can't dominate the sector like it used to. It's got to play ball with everybody else. >> I think they have to work with the regulators as well to loosen the regulation. Or you said before we started this segment, you used Chris, the analogy of sports, right? In sports, when you're playing fiercely, you commit the fouls and then ask for ref to blow the whistle. You're now looking at the ref all the time. The telcos are looking at the ref all the time. >> Dave: Yeah, can I do this? Can I do that? Is this a fair move? >> They should be looking for the space in front of the opposition. >> Yeah, they should be just on attack mode and commit these fouls, if you will, and then ask for forgiveness then- >> What do you make of that AWS not you there- >> Well, Chris just made a great point that they don't like to share the limelight 'cause I thought it was very obvious that we had Google Cloud, we had Microsoft there on day one of this 80,000 person event. A lot of people back from COVID and they weren't there. But Chris, you brought up a great point that kind of made me think, maybe you're right. Maybe they're in the afternoon keynote, they want their own time- >> You think GSMA invited them? >> I imagine so. You'd have to ask GSMA. >> I would think so. >> Get Max on here and ask that. >> I'm going to ask them, I will. >> But no, and they don't like it because I think the misconception, by the way, is that everyone says, "oh, it's AWS, it's Google Cloud and it's Azure." They're not all the same business by any stretch of the imagination. AWS has been doing loads of great work, they've been launching private network stuff over the last couple of weeks. Really interesting. Google's been playing catch up. We know that they came in readily late to the market. And Azure, they've all got slightly different angles on it. So perhaps it just wasn't right for AWS and the way they wanted to pitch things so they don't have to be there, do they? >> That's a good point. >> But the industry needs them there, that's the number one cloud. >> Dave, they're there working with the industry. >> Yeah, of course. >> They don't have to be on the keynote stage. And in fact, you think about this show and you mentioned the 80,000 people, the activity going on around in all these massive areas they're in, it's fantastic. That's where the business is done. The business isn't done up on the keynote stage. >> That's why there's the glitz and the glamour, Chris. (all laugh) >> Yeah. It's not glitz, it's espresso. It's not glamour anymore, it's just espresso. >> We need the espresso. >> Yeah. >> I think another thing is that it's interesting how an average European sees the tech market and an average North American, especially you from US, you have to see the market. Here, people are more like process oriented and they want the rules of the road already established before they can take a step- >> Chris: That's because it's your pension in the North American- >> Exactly. So unions are there and the more employee rights and everything, you can't fire people easily here or in Germany or most of the Europe is like that with the exception of UK. >> Well, but it's like I said, that Silicone Valley gets their money on the way out, you know? And that's how they do it, that's how they think it. And they don't... They ask for forgiveness. I think the east coast is more close to Europe, but in the EU, highly regulated, really focused on lifetime employment, things like that. >> But Dave, the issue is the telecom industry is brilliant, right? We keep paying every month whatever we do with it. >> It's a great business, to your point- >> It's a brilliant business model. >> Dave: It's fantastic. >> So it's about then getting the structure right behind it. And you know, we've seen a lot of stratification where people are selling off towers, Orange haven't sold their towers off, they made a big point about that. Others are selling their towers off. Some people are selling off their underlying network, Telecom Italia talking about KKR buying the whole underlying network. It's like what do you want to be in control of? It's a great business. >> But that's why they complain so much is that they're having to sell their assets because of the onerous CapEx requirements, right? >> Yeah, they've had it good, right? And dare I say, perhaps they've not planned well enough for the future. >> They're trying to protect their past from the future. I mean, that's... >> Actually, look at the... Every "n" number of years, there's a new faster network. They have to dig the ground, they have to put the fiber, they have to put this. Now, there are so many booths showing 6G now, we are not even done with 5G yet, now the next 6G you know, like then- >> 10G's coming- >> 10G, that's a different market. (Dave laughs) >> Actually, they're bogged down by the innovation, I think. >> And the generational thing is really important because we're planning for 6G in all sorts of good ways but actually what we use in our daily lives, we've gone through the barrier, we've got enough to do that. So 4G gives us enough, the fiber in the ground or even old copper gives us enough. So the question is, what are we willing to pay for more than that basic connectivity? And the answer to your point, Dave, is not a lot, right? So therefore, that's why the emphasis is on the business market on that B2B and B2B2X. >> But we'll pay for Netflix all day long. >> All day long. (all laugh) >> The one thing Chris, I don't know, I want to know your viewpoints and we have talked in the past as well, there's absence of think tanks in tech, right? So we have think tanks on the foreign policy and economic policy in every country, and we have global think tanks, but tech is becoming a huge part of the economy, global economy as well as national economies, right? But we don't have think tanks on like policy around tech. For example, this 4G is good for a lot of use cases. Then 5G is good for smaller number of use cases. And then 6G will be like, fewer people need 6G for example. Why can't we have sort of those kind of entities dictating those kind of like, okay, is this a wiser way to go about it? >> Lina Khan wants to. She wants to break up big tech- >> You're too young to remember but the IT used to have a show every four years in Geneva, there were standards around there. So I think there are bodies. I think the balance of power obviously has gone from the telecom to the west coast to the IT markets. And it's changing the balance about, it moves more quickly, right? Telecoms has never moved quickly enough. I think there is hope by the way, that telecoms now that we are moving to more softwarized environment, and God forbid, we're moving into CICD in the telecom world, right? Which is a massive change, but I think there's hopes for it to change. The mentality is changing, the culture is changing, but to change those old structured organizations from the British telecom or the France telecom into the modern world, it's a hell of a long journey. It's not an overnight journey at all. >> Well, of course the theme of the event is velocity. >> Yeah, I know that. >> And it's been interesting sitting here with the three of you talking about from a historic perspective, how slow and molasseslike telecom has been. They don't have a choice anymore. As consumers, we have this expectation we're going to get anything we want on our mobile device, 24 by seven. We don't care about how the sausage is made, we just want the end result. So do you really think, and we're only on day one guys... And Chris we'll start with you. Is the theme really velocity? Is it disruption? Are they able to move faster? >> Actually, I think invisibility is the real answer. (Lisa laughs) We want communication to be invisible, right? >> Absolutely. >> We want it to work. When we switch our phones on, we want it to work and we want to... Well, they're not even phones anymore, are they really? I mean that's the... So no, velocity, we've got... There is momentum in the industry, there's no doubt about that. The cloud guys coming in, making telecoms think about the way they run their own business, where they meet, that collision point on the edges you talked about Sarbjeet. We do have velocity, we've got momentum. There's so many interested parties. The way I think of this is that the telecom industry used to be inward looking, just design its own technology and then expect everyone else to dance to our tune. We're now flipping that 180 degrees and we are now having to work with all the different outside forces shaping us. Whether it's devices, whether it's smart cities, governments, the hosting guys, the Equinoxis, all these things. So everyone wants a piece of this telecom world so we've got to make ourselves more open. That's why you get in a more open environment. >> But you did... I just want to bring back a point you made during COVID, which was when everybody switched to work from home, started using their landlines again, telcos had to respond and nothing broke. I mean, it was pretty amazing. >> Chris: It did a good job. >> It was kind of invisible. So, props to the telcos for making that happen. >> They did a great job. >> So it really did. Now, okay, what have you done for me lately? So now they've got to deal with the future and they're talking monetization. But to me, monetization is all about data and not necessarily just the network data. Yeah, they can sell that 'cause they own that but what kind of incremental value are they going to create for the consumers that... >> Yeah, actually that's a problem. I think the problem is that they have been strangled by the regulation for a long time and they cannot look at their data. It's a lot more similar to the FinTech world, right? I used to work at Visa. And then Visa, we did trillion dollars in transactions in '96. Like we moved so much money around, but we couldn't look at these things, right? So yeah, I think regulation is a problem that holds you back, it's the antithesis of velocity, it slows you down. >> But data means everything, doesn't it? I mean, it means everything and nothing. So I think the challenge here is what data do the telcos have that is useful, valuable to me, right? So in the home environment, the fact that my broadband provider says, oh, by the way, you've got 20 gadgets on that network and 20 on that one... That's great, tell me what's on there. I probably don't know what's taking all my valuable bandwidth up. So I think there's security wrapped around that, telling me the way I'm using it if I'm getting the best out of my service. >> You pay for that? >> No, I'm saying they don't do it yet. I think- >> But would you pay for that? >> I think I would, yeah. >> Would you pay a lot for that? I would expect it to be there as part of my dashboard for my monthly fee. They're already charging me enough. >> Well, that's fine, but you pay a lot more in North America than I do in Europe, right? >> Yeah, no, that's true. >> You're really overpaying over there, right? >> Way overpaying. >> So, actually everybody's looking at these devices, right? So this is a radio operated device basically, right? And then why couldn't they benefit from this? This is like we need to like double click on this like 10 times to find out why telcos failed to leverage this device, right? But I think the problem is their reliance on regulations and their being close to the national sort of governments and local bodies and authorities, right? And in some countries, these telcos are totally controlled in very authoritarian ways, right? It's not like open, like in the west, most of the west. Like the world is bigger than five, six countries and we know that, right? But we end up talking about the major economies most of the time. >> Dave: Always. >> Chris: We have a topic we want to hit on. >> We do have a topic. Our last topic, Chris, it's for you. You guys have done an amazing job for the last 25 minutes talking about the industry, where it's going, the evolution. But Chris, you're registered blind throughout your career. You're a leading user of assertive technologies. Talk about diversity, equity, inclusion, accessibility, some of the things you're doing there. >> Well, we should have had 25 minutes on that and five minutes on- (all laugh) >> Lisa: You'll have to come back. >> Really interesting. So I've been looking at it. You're quite right, I've been using accessible technology on my iPhone and on my laptop for 10, 20 years now. It's amazing. And what I'm trying to get across to the industry is to think about inclusive design from day one. When you're designing an app or you're designing a service, make sure you... And telecom's a great example. In fact, there's quite a lot of sign language around here this week. If you look at all the events written, good to see that coming in. Obviously, no use to me whatsoever, but good for the hearing impaired, which by the way is the biggest category of disability in the world. Biggest chunk is hearing impaired, then vision impaired, and then cognitive and then physical. And therefore, whenever you're designing any service, my call to arms to people is think about how that's going to be used and how a blind person might use it or how a deaf person or someone with physical issues or any cognitive issues might use it. And a great example, the GSMA and I have been talking about the app they use for getting into the venue here. I downloaded it. I got the app downloaded and I'm calling my guys going, where's my badge? And he said, "it's top left." And because I work with a screen reader, they hadn't tagged it properly so I couldn't actually open my badge on my own. Now, they changed it overnight so it worked this morning, which is fantastic work by Trevor and the team. But it's those things that if you don't build it in from scratch, you really frustrate a whole group of users. And if you think about it, people with disabilities are excluded from so many services if they can't see the screen or they can't hear it. But it's also the elderly community who don't find it easy to get access to things. Smart speakers have been a real blessing in that respect 'cause you can now talk to that thing and it starts talking back to you. And then there's the people who can't afford it so we need to come down market. This event is about launching these thousand dollars plus devices. Come on, we need below a hundred dollars devices to get to the real mass market and get the next billion people in and then to educate people how to use it. And I think to go back to your previous point, I think governments are starting to realize how important this is about building the community within the countries. You've got some massive projects like NEOM in Saudi Arabia. If you have a look at that, if you get a chance, a fantastic development in the desert where they're building a new city from scratch and they're building it so anyone and everyone can get access to it. So in the past, it was all done very much by individual disability. So I used to use some very expensive, clunky blind tech stuff. I'm now using mostly mainstream. But my call to answer to say is, make sure when you develop an app, it's accessible, anyone can use it, you can talk to it, you can get whatever access you need and it will make all of our lives better. So as we age and hearing starts to go and sight starts to go and dexterity starts to go, then those things become very useful for everybody. >> That's a great point and what a great champion they have in you. Chris, Sarbjeet, Dave, thank you so much for kicking things off, analyzing day one keynote, the ecosystem day, talking about what velocity actually means, where we really are. We're going to have to have you guys back 'cause as you know, we can keep going, but we are out of time. But thank you. >> Pleasure. >> We had a very spirited, lively conversation. >> Thanks, Dave. >> Thank you very much. >> For our guests and for Dave Vellante, I'm Lisa Martin, you're watching theCUBE live in Barcelona, Spain at MWC '23. We'll be back after a short break. See you soon. (uplifting instrumental music)

(upbeat music) >> Hello everyone, welcome back to theCUBE's coverage of Cloud Native SecurityCon North America 2023. Obviously, CUBE's coverage with our CUBE Center Report. We're not there on the ground, but we have folks and our CUBE Alumni there. We have entrepreneurs there. Of course, we want to be there in person, but we're remote. We've got Ben Hirschberg, CTO and Co-Founder of Armo, a cloud native security startup, well positioned in this industry. He's there in Seattle. Ben, thank you for coming on and sharing what's going on with theCUBE. >> Yeah, it's great to be here, John. >> So we had written on you guys up on SiliconANGLE. Congratulations on your momentum and traction. But let's first get into what's going on there on the ground? What are some of the key trends? What's the most important story being told there? What is the vibe? What's the most important story right now? >> So I think, I would like to start here with the I think the most important thing was that I think the event is very successful. Usually, the Cloud Native Security Day usually was part of KubeCon in the previous years and now it became its own conference of its own and really kudos to all the organizers who brought this up in, actually in a short time. And it wasn't really clear how many people will turn up, but at the end, we see a really nice turn up and really great talks and keynotes around here. I think that one of the biggest trends, which haven't started like in this conference, but already we're talking for a while is supply chain. Supply chain is security. I think it's, right now, the biggest trend in the talks, in the keynotes. And I think that we start to see companies, big companies, who are adopting themselves into this direction. There is a clear industry need. There is a clear problem and I think that the cloud native security teams are coming up with tooling around it. I think for right now we see more tools than adoption, but the adoption is always following the tooling. And I think it already proves itself. So we have just a very interesting talk this morning about the OpenSSL vulnerability, which was I think around Halloween, which came out and everyone thought that it's going to be a critical issue for the whole cloud native and internet infrastructure and at the end it turned out to be a lesser problem, but the reason why I think it was understood that to be a lesser problem real soon was that because people started to use (indistinct) store software composition information in the environment so security teams could look into, look up in their systems okay, what, where they're using OpenSSL, which version they are using. It became really soon real clear that this version is not adopted by a wide array of software out there so the tech surface is relatively small and I think it already proved itself that the direction if everyone is talking about. >> Yeah, we agree, we're very bullish on this move from the Cloud Native Foundation CNCF that do the security conference. Amazon Web Services has re:Invent. That's their big show, but they also have re:Inforce, the security show, so clearly they work together. I like the decoupling, very cohesive. But you guys have Kubescape of Kubernetes security. Talk about the conversations that are there and that you're hearing around why there's different event what's different around KubeCon and CloudNativeCon than this Cloud Native SecurityCon. It's not called KubeSucSecCon, it's called Cloud Native SecurityCon. What's the difference? Are people confused? Is it clear? What's the difference between the two shows? What are you hearing? >> So I think that, you know, there is a good question. Okay, where is Cloud Native Computing Foundation came from? Obviously everyone knows that it was somewhat coupled with the adoption of Kubernetes. It was a clear understanding in the industry that there are different efforts where the industry needs to come together without looking be very vendor-specific and try to sort out a lot of issues in order to enable adoption and bring great value and I think that the main difference here between KubeCon and the Cloud Native Security Conference is really the focus, and not just on Kubernetes, but the whole ecosystem behind that. The way we are delivering software, the way we are monitoring software, and all where Kubernetes is only just, you know, maybe the biggest clog in the system, but, you know, just one of the others and it gives great overview of what you have in the whole ecosystem. >> Yeah, I think it's a good call. I would add that what I'm hearing too is that security is so critical to the business model of every company. It's so mainstream. The hackers have a great business model. They make money, their costs are lower than the revenue. So the business of hacking in breaches, ransomware all over the place is so successful that they're playing offense, everyone's playing defense, so it's about time we can get focus to really be faster and more nimble and agile on solving some of these security challenges in open source. So I think that to me is a great focus and so I give total props to the CNC. I call it the event operating system. You got the security group over here decoupled from the main kernel, but they work together. Good call and so this brings back up to some of the things that are going on so I have to ask you, as your startup as a CTO, you guys have the Kubescape platform, how do you guys fit into the landscape and what's different from your tools for Kubernetes environments versus what's out there? >> So I think that our journey is really interesting in the solution space because I think that our mode really tries to understand where security can meet the actual adoption because as you just said, somehow we have to sort out together how security is going to be automated and integrated in its best way. So Kubescape project started as a Kubernetes security posture tool. Just, you know, when people are really early in their adoption of Kubernetes systems, they want to understand whether the installation is is secure, whether the basic configurations are look okay, and giving them instant feedback on that, both in live systems and in the CICD, this is where Kubescape came from. We started as an open source project because we are big believers of open source, of the power of open source security, and I can, you know I think maybe this is my first interview when I can say that Kubescape was accepted to be a CNCF Sandbox project so Armo was actually donating the project to the CNCF, I think, which is a huge milestone and a great way to further the adoption of Kubernetes security and from now on we want to see where the users in Armo and Kubescape project want to see where the users are going, their Kubernetes security journey and help them to automatize, help them to to implement security more fast in the way the developers are using it working. >> Okay, if you don't mind, I want to just get clarification. What's the difference between the Armo platform and Kubescape because you have Kubescape Sandbox project and Armo platform. Could you talk about the differences and interaction? >> Sure, Kubescape is an open source project and Armo platform is actually a managed platform which runs Kubescape in the cloud for you because Kubescape is part, it has several parts. One part is, which is running inside the Kubernetes cluster in the CICD processes of the user, and there is another part which we call the backend where the results are stored and can be analyzed further. So Armo platform gives you managed way to run the backend, but I can tell you that backend is also, will be available within a month or two also for everyone to install on their premises as well, because again, we are an open source company and we are, we want to enable users, so the difference is that Armo platform is a managed platform behind Kubescape. >> How does Kubescape differ from closed proprietary sourced solutions? >> So I can tell you that there are closed proprietary solutions which are very good security solutions, but I think that the main difference, if I had to pick beyond the very specific technicalities is the worldview. The way we see that our user is not the CISO. Our user is not necessarily the security team. From our perspective, the user is the DevOps and the developers who are working on the Kubernetes cluster day to day and we want to enable them to improve their security. So actually our approach is more developer-friendly, if I would need to define it very shortly. >> What does this risk calculation score you guys have in Kubscape? That's come up and we cover that in our story. Can you explain to the folks how that fits in? Is it Kubescape is the platform and what's the benefit, what's the purpose? >> So the risk calculation is actually a score we are giving to clusters in order for the users to understand where they are standing in the general population, how they are faring against a perfect hardened cluster. It is based on the number of different tests we are making. And I don't want to go into, you know, the very specifics of the mathematical functions, but in general it takes into account how many functions are failing, security tests are failing inside your cluster. How many nodes you are having, how many workloads are having, and creating this number which enables you to understand where you are standing in the global, in the world. >> What's the customer value that you guys pitching? What's the pitch for the Armo platform? When you go and talk to a customer, are they like, "We need you." Do they come to you? Is it word of mouth? You guys have a strategy? What's the pitch? What's so appealing to the customers? Why are they enthusiastic about you guys? >> So John, I can tell you, maybe it's not so easy to to say the words, but I nearly 20 years in the industry and though I've been always around cyber and the defense industry and I can tell you that I never had this journey where before where I could say that the the customers are coming to us and not we are pitching to customers. Simply because people want to, this is very easy tool, very very easy to use, very understandable and it very helps the engineers to improve security posture. And they're coming to us and they're saying, "Well, awesome, okay, how we can like use it. Do you have a graphical interface?" And we are pointing them to the Armor platform and they are falling in love and coming to us even more and we can tell you that we have a big number of active users behind the platform itself. >> You know, one of the things that comes up every time at KubeCon and Cloud NativeCon when we're there, and we'll be in Amsterdam, so folks watching, you know, we'll see onsite, developer productivity is like the number one thing everyone talks about and security is so important. It's become by default a blocker or anchor or a drag on productivity. This is big, the things that you're mentioning, easy to use, engineering supporting it, developer adoption, you know we've always said on theCUBE, developers will be the de facto standards bodies by their choices 'cause developers make all the decisions. So if I can go faster and I can have security kind of programmed in, I'm not shifting left, it's just I'm just having security kind of in there. That's the dream state. Is that what you guys are trying to do here? Because that's the nirvana, everyone wants to do that. >> Yeah, I think your definition is like perfect because really we had like this, for a very long time we had this world where we decoupled security teams from developers and even for sometimes from engineering at all and I think for multiple reasons, we are more seeing a big convergence. Security teams are becoming part of the engineering and the engineering becoming part of the security and as you're saying, okay, the day-to-day world of developers are becoming very tangled up in the good way with security, so the think about it that today, one of my developers at Armo is creating a pull request. He's already, code is already scanned by security scanners for to test for different security problems. It's already, you know, before he already gets feedback on his first time where he's sharing his code and if there is an issue, he already can solve it and this is just solving issues much faster, much cheaper, and also you asked me about, you know, the wipe in the conference and we know no one can deny the current economic wipe we have and this also relates to security teams and security teams has to be much more efficient. And one of the things that everyone is talking, okay, we need more automation, we need more, better tooling and I think we are really fitting into this. >> Yeah, and I talked to venture capitalists yesterday and today, an angel investor. Best time for startup is right now and again, open source is driving a lot of value. Ben, it's been great to have you on and sharing with us what's going on on the ground there as well as talking about some of the traction you have. Just final question, how old's the company? How much funding do you have? Where you guys located? Put a plug in for the company. You guys looking to hire? Tell us about the company. Were you guys located? How much capital do you have? >> So, okay, the company's here for three years. We've passed a round last March with Tiger and Hyperwise capitals. We are located, most of the company's located today in Israel in Tel Aviv, but we have like great team also in Ukraine and also great guys are in Europe and right now also Craig Box joined us as an open source VP and he's like right now located in New Zealand, so we are a really global team, which I think it's really helps us to strengthen ourselves. >> Yeah, and I think this is the entrepreneurial equation for the future. It's really great to see that global. We heard that in Priyanka Sharma's keynote. It's a global culture, global community. >> Right. >> And so really, really props you guys. Congratulations on Armo and thanks for coming on theCUBE and sharing insights and expertise and also what's happening on the ground. Appreciate it, Ben, thanks for coming on. >> Thank you, John. >> Okay, cheers. Okay, this is CUB coverage here of the Cloud Native SecurityCon in North America 2023. I'm John Furrier for Lisa Martin, Dave Vellante. We're back with more of wrap up of the event after this short break. (gentle upbeat music)

(upbeat music) >> You got it, it's theCUBE. We are in Vegas. This is the Cube's live coverage day one of the full event coverage of AWS reInvent '22 from the Venetian Expo Center. Lisa Martin here with Dave Vellante. We love being in Vegas, Dave. >> Well, you know, this is where Super Cloud sort of was born. >> It is. >> Last year, just about a year ago. Steve Mullaney, CEO of of Aviatrix, you know, kind of helped us think it through. And we got some fun stories around. It's happening, but... >> It is happening. We're going to be talking about Super Cloud guys. >> I guess I just did the intro, Steve Mullaney >> You did my intro, don't do it again. >> Sorry I stole that from you, yeah. >> Steve Mullaney, joined just once again, one of our alumni. Steve, great to have you back on the program. >> Thanks for having me back. >> Dave: It's happening. >> It is happening. >> Dave: We talked about a year ago. Net Studio was right there. >> That was two years. Was that year ago, that was a year ago. >> Dave: It was last year. >> Yeah, I leaned over >> What's happening? >> so it's happening. It's happening. You know what, the thing I noticed what's happening now is the maturity of the cloud, right? So, if you think about this whole journey to cloud that has been, what, AWS 12 years. But really over the last few years is when enterprises have really kind of joined that journey. And three or four years ago, and this is why I came out of retirement and went to Aviatrix, was they all said, okay, now we're going to do cloud. You fast forward now three, four years from now, all of a sudden those five-year plans of evacuating the data center, they got one year left, two year left, and they're going, oh crap, we don't have five years anymore. We're, now the maturity's starting to say, we're starting to put more apps into the cloud. We're starting to put business critical apps like SAP into the cloud. This is not just like the low-hanging fruit anymore. So what's happening now is the business criticality, the scale, the maturity. And they're all now starting to hit a lot of limits that have been put into the CSPs that you never used to hit when you didn't have business critical and you didn't have that scale. They were always there. The rocks were always there. Just it was, you never hit 'em. People are starting to hit 'em now. So what's happening now is people are realizing, and I'm going to jump the gun, you asked me for my bumper sticker. The bumper sticker for Aviatrix is, "Good enough is no longer good enough." Now it's funny, it came in a keynote today, but what we see from our customers is it's time to upgrade the native constructs of networking and network security to be enterprise-grade now. It's no longer good enough to just use the native constructs because of a lack of visibility, the lack of controls, the lack of troubleshooting capabilities, all these things. "I now need enterprise grade networking." >> Let me ask you a question 'cause you got a good historical perspective on the industry. When you think about when Maritz was running VMWare. He was like any app, he said basically we're building a software mainframe. And they kind of did that, right? But then they, you know, hit the issue with scale, right? And they can't replicate the cloud. Are there things that we can draw from that experience and apply that to the cloud? What's the same, what's different? >> Oh yeah. So, 1992, do you remember what happened in 1992? I do this, weird German software company called SAP >> Yeah, R3. announced a release as R/3. Which was their first three-tier client-server application of SAP. Before that it ran on mainframes, TCP/IP. Remember that Protocol War? Guess what happened post-1992, everybody goes up like this. Infrastructure completely changes. Cisco, EMC, you name it, builds out these PCE client-server architectures. The WAN changes, MPLS, the campus, everything's home running back to that data center running SAP. That was the last 30 years ago. Great transformation of SAP. They've did it again. It's called S/4Hana. And now it's running and people are switching to S/4Hana and they're moving to the cloud. It's just starting. And that is going to alter how you build infrastructure. And so when you have that, being able to troubleshoot in hours versus minutes is a big deal. This is business critical, millions of dollars. This is not fun and games. So again, back to my, what was good enough for the last three or four years for enterprises no longer good enough, now I'm running business critical apps like SAP, and it's going to completely change infrastructure. That's happening in the cloud right now. And that's obviously a significant seismic shift, but what are some of the barriers that customers have been able to eliminate in order to get there? Or is it just good enough isn't good enough anymore? >> Barriers in terms of, well, I mean >> Lisa: The adoption. Yeah well, I mean, I think it's all the things that they go to cloud is, you know, the complexity, really, it's the agility, right? So the barrier that they have to get over is how do I keep the developer happy because the developer went to the cloud in the first place, why? Swipe the credit card because IT wasn't doing their job, 'cause every time I asked them for something, they said no. So I went around 'em. We need that. That's what they have to overcome in the move to the cloud. That is the obstacle is how do I deliver that visibility, that control, the enterprise, great functionality, but yet give the developer what they want. Because the minute I stop giving them that swipe the card operational model, what do you think they're going to do? They're going to go around me again and I can't, and the enterprise can't have that. >> That's a cultural shift. >> That's the main barrier they've got to overcome. >> Let me ask you another question. Is what we think of as mission critical, the definition changing? I mean, you mentioned SAP, obviously that's mission critical for operations, but you're also seeing new applications being developed in the cloud. >> I would say anything that's, I call business critical, same thing, but it's, business critical is internal to me, like SAP, but also anything customer-facing. That's business critical to me. If that app goes down or it has a problem, I'm not collecting revenue. So, you know, back 30 years ago, we didn't have a lot of customer-facing apps, right? It really was just SAP. I mean there wasn't a heck of a lot of cust- There were customer-facing things. But you didn't have all the digitalization that we have now, like the digital economy, where that's where the real explosion has come, is you think about all the customer-facing applications. And now every enterprise is what? A technology, digital company with a customer-facing and you're trying to get closer and closer to who? The consumer. >> Yeah, self-service. >> Self-service, B2C, everybody wants to do that. Get out of the middle man. And those are business critical applications for people. >> So what's needed under the covers to make all this happen? Give us a little double click on where you guys fit. >> You need consistent architecture. Obviously not just for one cloud, but for any cloud. But even within one cloud, forget multicloud, it gets worst with multicloud. You need a consistent architecture, right? That is automated, that is as code. I can't have the human involved. These are all, this is the API generation, you've got to be able to use automation, Terraform. And all the way from the application development platform you know, through Jenkins and all other software, through CICD pipeline and Terraform, when you, when that developer says, I want infrastructure, it has to go build that infrastructure in real time. And then when it says, I don't need it anymore it's got to take it away. And you cannot have a human involved in that process. That's what's completely changed. And that's what's giving the agility. And that's kind of a cloud model, right? Use software. >> Well, okay, so isn't that what serverless does, right? >> That's part of it. Absolutely. >> But I might still want control sometimes over the runtime if I'm running those mission critical applications. Everything in enterprise is a heterogeneous thing. It's like people, people say, well there's going to, the people going to repatriate back to on-prem, they are not repatriating back to on-prem. >> We were just talking about that, I'm like- >> Steve: It's not going to happen, right? >> It's a myth, it's a myth. >> And there's things that maybe shouldn't have ever gone into the cloud, I get that. Look, do people still have mainframes? Of course. There's certain things that you just, doesn't make sense to move to the new generation. There were things, certain applications that are very static, they weren't dynamic. You know what, keeping it on-prem it's, probably makes sense. So some of those things maybe will go back, but they never should have gone. But we are not repatriating ever, you know, that's not going to happen. >> No I agree. I mean, you know, there was an interesting paper by Andreessen, >> Yeah. >> But, I mean- >> Steve: Yeah it was a little self-serving for some company that need more funding, yeah. You look at the numbers. >> Steve: Yeah. >> It tells the story. It's just not happening. >> No. And the reason is, it's that agility, right? And so that's what people, I would say that what you need to do is, and in order to get that agility, you have to have that consistency. You have to have automation, you have to get these people out of the way. You have to use software, right? So it's that you have that swipe the card operational model for the developers. They don't want to hear the word no. >> Lisa: Right. >> What do you think is going to happen with AWS? Because we heard, I don't know if you heard Selipsky's keynote this morning, but you've probably heard the hallway talk. >> Steve: I did, yeah. >> Okay. You did. So, you know, connecting the dots, you know doubling down on all the primitives, that we expected. We kind of expected more of the higher level stuff, which really didn't see much of that, a little bit. >> Steve: Yeah. So, you know, there's a whole thing about, okay, does the cloud get commoditized? Does it not? I think the secret weapon's the ecosystem, right? Because they're able to sell through with guys like you. Make great margins on that. >> Steve: Yeah, well, yeah. >> What are your thoughts though on the future of AWS? >> IAS is going to get commoditized. So this is the fallacy that a lot of the CSPs have, is they thought that they were going to commoditize enterprise. It never happens that way. What's going to happen is infrastructure as a service, the lower level, which is why you see all the CSPs talking about what? Oracle Cloud, industry cloud. >> Well, sure, absolutely, yeah. >> We got to get to the apps, we got to get to SAP, we got to get to all that, because that's not going to get commoditized, right. But all the infrastructural service where AWS is king that is going to get commoditized, absolutely. >> Okay, so, but historically, you know Cisco's still got 60% plus gross margins. EMC always had good margin. How pure is the lone survivor in Flash? They got 70% gross margins. So infrastructure actually has always been a pretty good business. >> Yeah that's true. But it's a hell of a lot easier, particularly with people like Aviatrix and others that are building these common architectural things that create simplicity and abstract the way the complexities of underneath such that we allow your network to run an AWS, Azure, Google, Oracle, whatever, exactly the same. So it makes it a hell of a lot easier >> Dave: Super cloud. >> to go move. >> But I want to tap your brain because you have a good perspective of this because servers used to be a great margin business too on-prem and now it's not. It's a low margin business 'cause all the margin went to Intel. >> Yeah. But the cloud guys, you know, AWS in particular, makes a ton of dough on servers, so, or compute. So it's going to be interesting to see over time if that gets com- that's why they're going so hard after silicon. >> I think if they can, I think if you can capture the workload. So AWS and everyone else, as another example, this SAP, they call that a gravity workload. You know what gravity workload is? It's a black hole. It drags everything else with it. If you get SAP or Oracle or a mainframe app, it ain't going anywhere. And then what's going to happen is all your other apps are going to follow it. So that's what they're all going to fight for, is type of app. >> You said something earlier about, forget multicloud, for a moment, but, that idea of the super cloud, this abstraction layer, I mean, is that a real business value for customers other than, oh I got all these clouds, I need 'em to work together. You know, from your perspective from Aviatrix perspective, is it an opportunity for you to build on top of that? Or are you just looking at, look, I'm going to do really good work in AWS, in Azure? Now we're making the same experience. >> I hear this every single day from our customers is they look and they say, good enough isn't good enough. I've now hit the point, I'm hitting route limitations. I'm hitting, I'm doing things manually, and that's fine when I don't have that many applications or I don't have mission critical. The dogs are eating the dog food, we're going into the cloud and they're looking and then saying this is not an operational model for me. I've hit the point where I can't keep doing this, I can't throw bodies at this, I need software. And that's the opportunity for us, is they look and they say, I'm doing it in one cloud, but, and there's zero chance I'm going to be able to figure that out in the two or three other clouds. Every enterprise I talk to says multicloud is inevitable. Whether they're in it now, they all know they're going to go, because it's the business units that demand it. It's not the IT teams that demand it, it's the line of business that says, I like GCP for this reason. >> The driver's functionality that they're getting. >> It's the app teams that say, I have this service and GCP's better at it than AWS. >> Yeah, so it's not so much a cost game or the end all coffee mug, right? >> No, no. >> Google does this better than Microsoft, or better than- >> If you asked an IT person, they would rather not have multicloud. They actually tried to fight it. No, why would you want to support four clouds when you could support one right? That's insane. >> Dave and Lisa: Right. If they didn't have a choice and, and so it, the decision was made without them, and actually they weren't even notified until day before. They said, oh, good news, we're going to GCP tomorrow. Well, why wasn't I notified? Well, we're notifying you now. >> Yeah, you would've said, no. >> Steve: This is cloud bottle, let's go. >> Super cloud again. Did you see the Berkeley paper, sky computing I think they call it? Down at Berkeley, yep Dave Linthicum from Deloitte. He's talking about, I think he calls it meta cloud. It's happening. >> Yeah, yeah, yeah. >> It's happening. >> No, and because customers, customers want that. They... >> And talk about some customer example or two that you think really articulates the value of why it's happening and the outcomes that it's generating. >> I mean, I was just talking to Lamb Weston last night. So we had a reception, Lamb Weston, huge, frozen potatoes. They serve like, I dunno, some ungodly percentage of all the french fries to all the fast food. It's unbelievable what they do. Do you know, they have special chemicals they put on the french fries. So when you get your DoorDash, they stay crispy longer. They've invented that patented it. But anyway, it's all these businesses you've never heard of and they do all the, and again, they're moving to SAP or they're actually SAP in the cloud, they're one of the first ones. They did it through Accenture. They're pulling it back off from Accenture. They're not happy with the service they're getting. They're going to use us for their networking and network security because they're going to get that visibility and control back. And they're going to repatriate it back from a managed service and bring it back and run it in-house. And the SAP basis engineers want it to happen because they see the visibility and control that the infrastructure guy's going to get because of us, which leads to, all they care about is uptime and performance. That's it. And they're going to say the infrastructure team's going to lead to better uptime and better performance if it's running on Aviatrix. >> And business performance and uptime, business critical >> That is the business. That is the business. >> It is. So what are some of the things next coming down the pike from Aviatrix? Any secret sauce you can share? >> Lot of secrets. So, two secrets. One, the next thing people really want to do, embedded network security into the network. We've kind of talked about this. You're going to be seeing some things from us. Where does network security belong? In the network. Embedded in the fabric of the network, not as this dumb device called the next-gen firewall that you steer traffic to. It has to be into the fabric of what we do, what we call airspace. You're going to see us talk about that. And then the next thing, back to the maturity of the cloud, as they build out the core, guess what they're doing? It's this thing called edge, Dave, right? And guess what they're going to do? It's not about connecting the cloud to the edge to the cloud with dumb things like SD-WAN, right? Or SaaS. It's actually the other way around. Go into the cloud, turn around, look out at the edge and say, how do I extend the cloud out to the edge, and make it look like a VPC. That's what people are doing. Why, 'cause I want the operational model. I want all the things that I can do in the cloud out at the edge. And everyone knows it's been in networking. I've been in networking for 37 years. He who wins the core does what? Wins the edge, 'cause that's what happens. You do it first in the core and then you want one architecture, one common architecture, one consistent way of doing everything. And that's going to go out to the edge and it's going to look like a VPC from an operational model. >> And Amazon's going to support that, no doubt. >> Yeah, I mean every, you know, every, and then it's just how do you want to go do that? And us as the networking and network security provider, we're getting dragged to the edge by our customer. Because you're my networking provider. And that means, end to end. And they're trying to drag us into on-prem too, yeah. >> Lot's going on, you're going to have to come back- >> Because they want one networking vendor. >> But wait, and you say what? >> We will never do like switches and any of the keep Arista, the Cisco, and all that kind of stuff. But we will start sucking in net flow. We will start doing, from an operational perspective, we will integrate a lot of the things that are happening in on-prem into our- >> No halfway house. >> Copilot. >> No halfway house, no two architectures. But you'll take the data in. >> You want one architecture. >> Yeah. >> Yeah, totally. >> Right play. >> Amazing stuff. >> And he who wins the core, guess what's more strategic to them? What's more strategic on-prem or cloud? Cloud. >> It flipped three years ago. >> Dave: Yeah. >> So he who wins in the clouds going to win everywhere. >> Got it, We'll keep our eyes on that. >> Steve: Cause and effect. >> Thank you so much for joining us. We've got your bumper sticker already. It's been a great pleasure having you on the program. You got to come back, there's so, we've- >> You posting the bumper sticker somewhere? >> Lisa: It's going to be our Instagram. >> Oh really, okay. >> And an Instagram sto- This is new for you guys. Always coming up with new ideas. >> Raising the bar. >> It is, it is. >> Me advance, I mean, come on. >> I love it. >> All right, for our guest Steve Mullaney and Dave Vellante, I'm Lisa Martin. You're watching theCUBE, the leader in live enterprise and emerging tech coverage.

(upbeat music) >> Good afternoon, fellow cloud nerds, and welcome back to theCUBE's live coverage of AWS re:Invent, here in a fabulous Sin City, Las Vegas, Nevada. My name is Savannah Peterson, joined by my fabulous co-host, John Furrier. John, how you feeling? >> Great, feeling good Just getting going. Day one of four more, three more days after today. >> Woo! Yeah. >> So much conversation. Talking about business transformation as cloud goes next level- >> Hot topic here for sure. >> Next generation. Data's classic is still around, but the next gen cloud's here, it's changing the game. Lot more AI, machine learning, a lot more business value. I think it's going to be exciting. Next segment's going to be awesome. >> It feels like one of those years where there's just a ton of momentum. I don't think it's just because we're back in person at scale, you can see the literally thousands of people behind us while we're here on set conducting these interviews. Our bold and brave guests, just like the two we have here, combating the noise, the libations, and everything else going on on the show floor. Please help me welcome Mike from Science Logic and Ajay from Zebrium. Gentlemen, welcome to the show floor. >> Thank you. >> Thank you Savannah. It's great to be here. >> How you feeling? Are you feeling the buzz, Mike? Feeling the energy? >> It's tough to not feel and hear the buzz, Savannah >> Savannah: Yeah. (all laughing) >> John: Can you hear me? >> Savannah: Yeah, yeah, yeah. Can you hear me now? What about you, Ajay? How's it feel to be here? >> Yeah, this is high energy. I'm really happy it's bounced back from COVID. I was a little concerned about attendance. This is hopping. >> Yeah, I feel it. It just, you can definitely feel the energy, the sense of community. We're all here for the right reasons. So I know that, I want to set the stage for everyone watching, Zebrium was recently acquired by Science Logic. Mike, can you tell us a little bit about that and what it means for the company? >> Mike: Sure, sure. Well, first of all, science logic, as you may know, has been in the monitoring space for a long time now, and what- >> Savannah: 20 years I believe. >> Yeah. >> Savannah: Just about. >> And what we've seen is a shift from kind of monitoring infrastructure, to monitoring these increasingly complex modern cloud native applications, right? And so this is part of a journey that we've been on at Science Logic to really modernize how enterprises of all sizes manage their IT estate. Okay? So, managing, now workloads that are increasingly in the public cloud, outside the four walls of the enterprise, workloads that are increasingly complex. They're microservices based, they're container based. >> Mhmm. >> Mike: And the rate of change, just because of things like CICD, and agile development has also increased the complexity in the typical IT environment. So all these things have conspired to make the traditional tools and processes of managing IT and IT applications much more difficult. They just don't scale. One of the things that we've seen recently, Savannah is this shift in sort of moving to cloud native applications, right? >> Huge shift. >> Mike: Today it only incorporates about roughly 25% of the typical IT portfolio, but most of the projections we've seen indicate that that's going to invert in about three years. 75% of applications will be what I call cloud native. And so this really requires different technologies to understand what's going on with those applications. And so Zebrium interested us when we were looking at partners at the beginning of this year as they have a super innovative approach to understanding really what's going on with any cloud native application. And they really distill, they separate the complexity out of the equation and they used machine learning to tremendous effect to rapidly understand the root cause of an application failure. And so I was introduced to Ajay, beginning of this year, actually. It feels like it's been a long time now. But we've been on this journey together throughout 2022, and we're thrilled to have Zebrium now, part of the Science Logic family. >> Ajay, Zebrium saves people a lot of time. Obviously, I've worked with developers and seen that struggle when things break, shortening that time to recovery and understanding is so critical. Can you tell us a little bit about what's under the hood and how the ML works to make that happen? >> Ajay: Yeah. So the goal is to figure out not just that something went wrong, but what went wrong. >> Savannah: Right. >> And we took, you know, based on a couple of decades of experience from my co-founders- >> Savannah: Casual couple of decades, came into went into this product just to call that out. Yeah, great. >> Exactly. It took some general learnings about the nature of software and when software breaks, what tends to happen, you tend to see unusual things happen, and they lead to bad things happening. It's very simple. >> Yes. >> It turns out- >> Savannah: Mutations lead to bad things happening, generally speaking. >> So what Zebrium's really good at is identifying those rare things accurately and then figuring out how they connect, or correlate to the bad things, the errors, the warnings, the alerts. So the machine learning has many stages to it, but at its heart it's classifying the event, catalog of any application stack, figuring out what's rare, and when things start to break it's telling you this cluster of events is both unusual, and unlikely to be random, and it's very likely the root cause report for the problem you're trying to solve. We then added some nice enhancements, such as correlation with knowledge spaces in, on the public internet. If someone's ever solved that problem before, we're able to find a match, and pull that back into our platform. But the at the heart, it was a technology that can find rare events and find the connections with other events. >> John: Yeah, and this is the theme of re:Invent this year, data, the role of data, solving end-to-end complexities. One, you mentioned that. Two, I think the Mike, your point about developers and the CICD pipeline is where DevOps is. That is what IT now is. So, if you take digital transformation to its conclusion, or its path and continue it, IT is DevOps. So the developers are actually doing the IT in their coding, hence the shift to autonomous IT. >> Mike: Right, right. Now, those other functions at IT used to be a department, not anymore, or they still are, so, but they'll go away, is security and data teams. You're starting to see the formation of- >> Mike: Yep. >> New replacements to IT as a function to support the developers who are building the applications that will be the company. >> That's right. Yeah. >> John: I mean that's, and do you agree with that statement? >> Yeah, I really do. And you know, collectively independent of whether it's like traditional IT, or it's DevOps, or whatever it is, the enterprise as a whole needs to understand how the infrastructure is deployed, the health of that infrastructure, and more importantly the applications that are hosted in the infrastructure. How are they doing? What's the health? And what we are seeing, and what we're trying to facilitate at Science Logic is really changed the lens of IT, from being low level compute, storage, and networking, to looking at everything through a services lens, looking at the services being delivered by IT, back to the business, and understanding things through a services lens. And Zebrium really compliments that mission that we've been on, by providing, cause a lot of cases, service equal equal application, and they can provide that kind of very real time view of service health in, you know, kind of the IT- >> And automation is beautiful there too, because, as you get into some of the scale- >> Yeah >> Ajay's. understanding how to do this fast is a key component. >> Yeah. So scale, you, you've pinpointed one of the dimensions that makes AI really important when it comes to troubleshooting. The humans just can't scale as fast as data, nor can they keep up with complexity of modern applications. And the third element that we feel is really important is the velocity with which people are now rolling out changes. People develop new features within hours, push them out to production. And in a world like that, the human has just no ability or time to understand what's normal, what's bad, to update their alert rules. And you need a machine, or an AI technology, to go help you with that. And that's basically what we're about. >> So this is where AI Ops comes in, right? Perfectly. Yeah. >> Yeah. You know, and John started to allude to it earlier, but having the insight on what's going on, we believe is only half of the equation, right? Once you understand what's going on, you naturally want to take action to remediate it or optimize it. And we believe automation should not be an exercise that's left to the reader. >> Yeah. >> As a lot of traditional platforms have done. Instead, we have a very robust, no-code, low-code automation built into our platform that allows you to take action in context with what you're seeing right then and there with the service. >> John: Yeah. Essentially monitoring, a term you use observability, some used as a fancy word today, is critical in all operating environments. So if we, if we kind of holistically, hey we're a distributed computing system, aka cloud, you got to track stuff at scale and you got to understand what it, what the impact is from a systems perspective. There's consequences to understanding what goes wrong. So as you look at that, what's the challenge for customers to do that? Because that seems to be the hard part as they lift and shift to the cloud, run their apps on the cloud, now they got to go take it to the next level, which is more developer velocity, faster productivity, and secure. >> Yeah. >> I mean, that seems to be the table stakes now. >> Yeah. >> How are companies forming around that? Are they there yet? Are they halfway there? Are they, where are they in the progression of, one, are they changing? And if so- >> Yeah that's a great question. I mean, I think whether it's an IT use case or a security use case, you can't manage what you don't know about. So visibility, discoverability, understanding what's going on, in a lot of ways that's the really hard problem to solve. And traditionally, we've approached that by like, harvesting data off of all these machines and devices in the infrastructure. But as we've seen with Zebrium and with related machine learning technologies, there's multiple ways of gaining insight as to what's going on. Once you have the insight be it an IT issue, like a service outage, or a security vulnerability, then you can take action. And the idea is you want to make that action as seamless as possible. But I think to answer your question, John, enterprises are still kind of getting their heads around how can we break down all the silos that have built up over the last decade or two, internally, and get visibility across the estate that really matters. And I think that's the real challenge. >> And I mean, and, at the velocity that applications are growing, just looking at our notes here, number of applications scaling from 64 million in 2017 to 147 million in 2021. That goes to what you were talking about, even with those other metrics earlier, 582 million by 2026 is what Morgan Stanley predicts. So, not only do we need to get out of silos we need to be able to see everything all the time, all at once, from the past legacy, as well as as we extend at scale. How are you thinking about that, Ajay? You're now with a big partner as an umbrella. What's next for you all? How, how are you going to help people solve problems faster? >> Yeah, so one of the attractions to the Zebrium team about Science Logic, aside from the team, and the culture, was the product portfolio was so complimentary. As Mike mentioned, you need visibility, you need mapping from low level building blocks to business services. And the end, at the end of the spectrum, once you know something's wrong you need to be able to take action automatically. And again, Science Logic has a very strong product, set of product capabilities and automated actions. What we bring to the table is the middle layer, which is from visibility, understanding what went wrong, figuring out the root cause. So to us, it was really exciting to be a very nice tuck in into this broader platform where we helped complete the story. >> Savannah: Yeah, that's, that's exciting. >> John: Should we do the Insta challenge? >> I was just getting ready to do that. You go for it John. You go ahead and kick it off. >> So we have this little tradition now, Instagram real, short and sweet. If you were going to see yourself on Instagram, what would be the Instagram reel of why this year's re:Invent is so important, and why people should pay attention to what's going on right now in the industry, or your company? >> Well, I think partly what Ajay was saying it's good to be back, right? So seeing just the energy and being back in 3D, you know en mass, is awesome again. It really is. >> Yeah. >> Mike: But, you know, I think this is where it's happening. We are at an inflection point of our industry and we're seeing a sea change in the way that applications and software delivered to businesses, to enterprises. And it's happening right here. This is the nexus of it. And so we're thrilled to be here as a part of all this, and excited about the future. >> All right, Ajay- >> Well done. He passes >> Your Instagram reel. >> Knowing what's happening in the broader economy, in the business context, it's, it feels even more important that companies like us are working on technologies that empower the same number of people to do more. Because it may not be realistic to just add on more headcount given what's going on in the world. But your deliverables and your roadmaps aren't slowing down. So, still the same amount of complexity, the same growth rates, but you're going to have to deal with all of that with fewer resources and be smarter about it. So, the approaches we're taking feel very much off the moment, you know, given what's going on in the real world. >> I love it. I love it. I've got, I've got kind of a finger to the wind, potentially hardball question for you here to close it out. But, given that you both have your finger really on the pulse right here, what percentage of current IT operations do you think will eventually be automated by AI and ML? Or AI ops? >> Well, I think a large percentage of traditional IT operations, and I'm talking about, you know, network operating center type of, you know, checking heartbeat monitors of compute storage and networking health. I think a lot of those things are going to be automated, right? Machine learning, just because of the scale. You can't scale, you can't hire enough NOC engineers to scale that kind of complexity. But I think IT talents, and what they're going to be focusing on is going shift, and they're going to be focusing on different parts. And I believe a lot of IT is going to be a much more of an enabler for the business, versus just managing things when they go wrong. So that's- >> All right. >> That's what I believe is part of the change. >> That's your, all right Ajay what about your hot take? >> Knowing how error-prone predictions are, (all laughing) I'll caveat my with- >> Savannah: We're allowing for human error here. >> I could be wildly wrong, but if I had to guess, you know, in 10 years you know, as much as 50% of the tasks will be automated. >> Mike: Oh, you- >> I love it. >> Mike: You threw a number out there. >> I love it. I love that he put his finger out- >> You got to see, you got to say the matrix. We're all going to be part of the matrix. >> Well, you know- >> And Star Trek- >> Skynet >> We can only turn back to this footage in a few years and quote you exactly when you have the, you know Mackenzie Research or the Morgan Stanley research that we've been mentioning here tonight and say that you've called it accurately. So I appreciate that. Ajay, it was wonderful to have you here. Congratulations on the acquisition. Thank you. Mike, thank you so much for being here on the Science Logic side, and congratulations to the team on 20 years. That's very exciting. John. Thank you. >> I try, I tried. Thank you. >> You try, you succeed. And thank you to all of our fabulous viewers out there at home. Be sure and tweet us at theCUBE. Say hello, Furrier, Sav is savvy. Let us know what you're thinking of AWS re:Invent where we are live from Las Vegas all week. You're watching theCUBE, the leader in high tech coverage. My name's Savannah Peterson, and we'll see you soon. (upbeat music)

(upbeat music) >> The stampede to cloud and massive VC investments has led to the emergence of a new generation of object store based data lakes. And with them two important trends, actually three important trends. First, a new category that combines data lakes and data warehouses aka the lakehouse is emerged as a leading contender to be the data platform of the future. And this novelty touts the ability to address data engineering, data science, and data warehouse workloads on a single shared data platform. The other major trend we've seen is query engines and broader data fabric virtualization platforms have embraced NextGen data lakes as platforms for SQL centric business intelligence workloads, reducing, or somebody even claim eliminating the need for separate data warehouses. Pretty bold. However, cloud data warehouses have added complimentary technologies to bridge the gaps with lakehouses. And the third is many, if not most customers that are embracing the so-called data fabric or data mesh architectures. They're looking at data lakes as a fundamental component of their strategies, and they're trying to evolve them to be more capable, hence the interest in lakehouse, but at the same time, they don't want to, or can't abandon their data warehouse estate. As such we see a battle royale is brewing between cloud data warehouses and cloud lakehouses. Is it possible to do it all with one cloud center analytical data platform? Well, we're going to find out. My name is Dave Vellante and welcome to the data platform's power panel on theCUBE. Our next episode in a series where we gather some of the industry's top analysts to talk about one of our favorite topics, data. In today's session, we'll discuss trends, emerging options, and the trade offs of various approaches and we'll name names. Joining us today are Sanjeev Mohan, who's the principal at SanjMo, Tony Baers, principal at dbInsight. And Doug Henschen is the vice president and principal analyst at Constellation Research. Guys, welcome back to theCUBE. Great to see you again. >> Thank guys. Thank you. >> Thank you. >> So it's early June and we're gearing up with two major conferences, there's several database conferences, but two in particular that were very interested in, Snowflake Summit and Databricks Data and AI Summit. Doug let's start off with you and then Tony and Sanjeev, if you could kindly weigh in. Where did this all start, Doug? The notion of lakehouse. And let's talk about what exactly we mean by lakehouse. Go ahead. >> Yeah, well you nailed it in your intro. One platform to address BI data science, data engineering, fewer platforms, less cost, less complexity, very compelling. You can credit Databricks for coining the term lakehouse back in 2020, but it's really a much older idea. You can go back to Cloudera introducing their Impala database in 2012. That was a database on top of Hadoop. And indeed in that last decade, by the middle of that last decade, there were several SQL on Hadoop products, open standards like Apache Drill. And at the same time, the database vendors were trying to respond to this interest in machine learning and the data science. So they were adding SQL extensions, the likes Hudi and Vertical we're adding SQL extensions to support the data science. But then later in that decade with the shift to cloud and object storage, you saw the vendor shift to this whole cloud, and object storage idea. So you have in the database camp Snowflake introduce Snowpark to try to address the data science needs. They introduced that in 2020 and last year they announced support for Python. You also had Oracle, SAP jumped on this lakehouse idea last year, supporting both the lake and warehouse single vendor, not necessarily quite single platform. Google very recently also jumped on the bandwagon. And then you also mentioned, the SQL engine camp, the Dremios, the Ahanas, the Starbursts, really doing two things, a fabric for distributed access to many data sources, but also very firmly planning that idea that you can just have the lake and we'll help you do the BI workloads on that. And then of course, the data lake camp with the Databricks and Clouderas providing a warehouse style deployments on top of their lake platforms. >> Okay, thanks, Doug. I'd be remiss those of you who me know that I typically write my own intros. This time my colleagues fed me a lot of that material. So thank you. You guys make it easy. But Tony, give us your thoughts on this intro. >> Right. Well, I very much agree with both of you, which may not make for the most exciting television in terms of that it has been an evolution just like Doug said. I mean, for instance, just to give an example when Teradata bought AfterData was initially seen as a hardware platform play. In the end, it was basically, it was all those after functions that made a lot of sort of big data analytics accessible to SQL. (clears throat) And so what I really see just in a more simpler definition or functional definition, the data lakehouse is really an attempt by the data lake folks to make the data lake friendlier territory to the SQL folks, and also to get into friendly territory, to all the data stewards, who are basically concerned about the sprawl and the lack of control in governance in the data lake. So it's really kind of a continuing of an ongoing trend that being said, there's no action without counter action. And of course, at the other end of the spectrum, we also see a lot of the data warehouses starting to edit things like in database machine learning. So they're certainly not surrendering without a fight. Again, as Doug was mentioning, this has been part of a continual blending of platforms that we've seen over the years that we first saw in the Hadoop years with SQL on Hadoop and data warehouses starting to reach out to cloud storage or should say the HDFS and then with the cloud then going cloud native and therefore trying to break the silos down even further. >> Now, thank you. And Sanjeev, data lakes, when we first heard about them, there were such a compelling name, and then we realized all the problems associated with them. So pick it up from there. What would you add to Doug and Tony? >> I would say, these are excellent points that Doug and Tony have brought to light. The concept of lakehouse was going on to your point, Dave, a long time ago, long before the tone was invented. For example, in Uber, Uber was trying to do a mix of Hadoop and Vertical because what they really needed were transactional capabilities that Hadoop did not have. So they weren't calling it the lakehouse, they were using multiple technologies, but now they're able to collapse it into a single data store that we call lakehouse. Data lakes, excellent at batch processing large volumes of data, but they don't have the real time capabilities such as change data capture, doing inserts and updates. So this is why lakehouse has become so important because they give us these transactional capabilities. >> Great. So I'm interested, the name is great, lakehouse. The concept is powerful, but I get concerned that it's a lot of marketing hype behind it. So I want to examine that a bit deeper. How mature is the concept of lakehouse? Are there practical examples that really exist in the real world that are driving business results for practitioners? Tony, maybe you could kick that off. >> Well, put it this way. I think what's interesting is that both data lakes and data warehouse that each had to extend themselves. To believe the Databricks hype it's that this was just a natural extension of the data lake. In point of fact, Databricks had to go outside its core technology of Spark to make the lakehouse possible. And it's a very similar type of thing on the part with data warehouse folks, in terms of that they've had to go beyond SQL, In the case of Databricks. There have been a number of incremental improvements to Delta lake, to basically make the table format more performative, for instance. But the other thing, I think the most dramatic change in all that is in their SQL engine and they had to essentially pretty much abandon Spark SQL because it really, in off itself Spark SQL is essentially stop gap solution. And if they wanted to really address that crowd, they had to totally reinvent SQL or at least their SQL engine. And so Databricks SQL is not Spark SQL, it is not Spark, it's basically SQL that it's adapted to run in a Spark environment, but the underlying engine is C++, it's not scale or anything like that. So Databricks had to take a major detour outside of its core platform to do this. So to answer your question, this is not mature because these are all basically kind of, even though the idea of blending platforms has been going on for well over a decade, I would say that the current iteration is still fairly immature. And in the cloud, I could see a further evolution of this because if you think through cloud native architecture where you're essentially abstracting compute from data, there is no reason why, if let's say you are dealing with say, the same basically data targets say cloud storage, cloud object storage that you might not apportion the task to different compute engines. And so therefore you could have, for instance, let's say you're Google, you could have BigQuery, perform basically the types of the analytics, the SQL analytics that would be associated with the data warehouse and you could have BigQuery ML that does some in database machine learning, but at the same time for another part of the query, which might involve, let's say some deep learning, just for example, you might go out to let's say the serverless spark service or the data proc. And there's no reason why Google could not blend all those into a coherent offering that's basically all triggered through microservices. And I just gave Google as an example, if you could generalize that with all the other cloud or all the other third party vendors. So I think we're still very early in the game in terms of maturity of data lakehouses. >> Thanks, Tony. So Sanjeev, is this all hype? What are your thoughts? >> It's not hype, but completely agree. It's not mature yet. Lakehouses have still a lot of work to do, so what I'm now starting to see is that the world is dividing into two camps. On one hand, there are people who don't want to deal with the operational aspects of vast amounts of data. They are the ones who are going for BigQuery, Redshift, Snowflake, Synapse, and so on because they want the platform to handle all the data modeling, access control, performance enhancements, but these are trade off. If you go with these platforms, then you are giving up on vendor neutrality. On the other side are those who have engineering skills. They want the independence. In other words, they don't want vendor lock in. They want to transform their data into any number of use cases, especially data science, machine learning use case. What they want is agility via open file formats using any compute engine. So why do I say lakehouses are not mature? Well, cloud data warehouses they provide you an excellent user experience. That is the main reason why Snowflake took off. If you have thousands of cables, it takes minutes to get them started, uploaded into your warehouse and start experimentation. Table formats are far more resonating with the community than file formats. But once the cost goes up of cloud data warehouse, then the organization start exploring lakehouses. But the problem is lakehouses still need to do a lot of work on metadata. Apache Hive was a fantastic first attempt at it. Even today Apache Hive is still very strong, but it's all technical metadata and it has so many different restrictions. That's why we see Databricks is investing into something called Unity Catalog. Hopefully we'll hear more about Unity Catalog at the end of the month. But there's a second problem. I just want to mention, and that is lack of standards. All these open source vendors, they're running, what I call ego projects. You see on LinkedIn, they're constantly battling with each other, but end user doesn't care. End user wants a problem to be solved. They want to use Trino, Dremio, Spark from EMR, Databricks, Ahana, DaaS, Frink, Athena. But the problem is that we don't have common standards. >> Right. Thanks. So Doug, I worry sometimes. I mean, I look at the space, we've debated for years, best of breed versus the full suite. You see AWS with whatever, 12 different plus data stores and different APIs and primitives. You got Oracle putting everything into its database. It's actually done some interesting things with MySQL HeatWave, so maybe there's proof points there, but Snowflake really good at data warehouse, simplifying data warehouse. Databricks, really good at making lakehouses actually more functional. Can one platform do it all? >> Well in a word, I can't be best at breed at all things. I think the upshot of and cogen analysis from Sanjeev there, the database, the vendors coming out of the database tradition, they excel at the SQL. They're extending it into data science, but when it comes to unstructured data, data science, ML AI often a compromise, the data lake crowd, the Databricks and such. They've struggled to completely displace the data warehouse when it really gets to the tough SLAs, they acknowledge that there's still a role for the warehouse. Maybe you can size down the warehouse and offload some of the BI workloads and maybe and some of these SQL engines, good for ad hoc, minimize data movement. But really when you get to the deep service level, a requirement, the high concurrency, the high query workloads, you end up creating something that's warehouse like. >> Where do you guys think this market is headed? What's going to take hold? Which projects are going to fade away? You got some things in Apache projects like Hudi and Iceberg, where do they fit Sanjeev? Do you have any thoughts on that? >> So thank you, Dave. So I feel that table formats are starting to mature. There is a lot of work that's being done. We will not have a single product or single platform. We'll have a mixture. So I see a lot of Apache Iceberg in the news. Apache Iceberg is really innovating. Their focus is on a table format, but then Delta and Apache Hudi are doing a lot of deep engineering work. For example, how do you handle high concurrency when there are multiple rights going on? Do you version your Parquet files or how do you do your upcerts basically? So different focus, at the end of the day, the end user will decide what is the right platform, but we are going to have multiple formats living with us for a long time. >> Doug is Iceberg in your view, something that's going to address some of those gaps in standards that Sanjeev was talking about earlier? >> Yeah, Delta lake, Hudi, Iceberg, they all address this need for consistency and scalability, Delta lake open technically, but open for access. I don't hear about Delta lakes in any worlds, but Databricks, hearing a lot of buzz about Apache Iceberg. End users want an open performance standard. And most recently Google embraced Iceberg for its recent a big lake, their stab at having supporting both lakes and warehouses on one conjoined platform. >> And Tony, of course, you remember the early days of the sort of big data movement you had MapR was the most closed. You had Horton works the most open. You had Cloudera in between. There was always this kind of contest as to who's the most open. Does that matter? Are we going to see a repeat of that here? >> I think it's spheres of influence, I think, and Doug very much was kind of referring to this. I would call it kind of like the MongoDB syndrome, which is that you have... and I'm talking about MongoDB before they changed their license, open source project, but very much associated with MongoDB, which basically, pretty much controlled most of the contributions made decisions. And I think Databricks has the same iron cloud hold on Delta lake, but still the market is pretty much associated Delta lake as the Databricks, open source project. I mean, Iceberg is probably further advanced than Hudi in terms of mind share. And so what I see that's breaking down to is essentially, basically the Databricks open source versus the everything else open source, the community open source. So I see it's a very similar type of breakdown that I see repeating itself here. >> So by the way, Mongo has a conference next week, another data platform is kind of not really relevant to this discussion totally. But in the sense it is because there's a lot of discussion on earnings calls these last couple of weeks about consumption and who's exposed, obviously people are concerned about Snowflake's consumption model. Mongo is maybe less exposed because Atlas is prominent in the portfolio, blah, blah, blah. But I wanted to bring up the little bit of controversy that we saw come out of the Snowflake earnings call, where the ever core analyst asked Frank Klutman about discretionary spend. And Frank basically said, look, we're not discretionary. We are deeply operationalized. Whereas he kind of poo-pooed the lakehouse or the data lake, et cetera, saying, oh yeah, data scientists will pull files out and play with them. That's really not our business. Do any of you have comments on that? Help us swing through that controversy. Who wants to take that one? >> Let's put it this way. The SQL folks are from Venus and the data scientists are from Mars. So it means it really comes down to it, sort that type of perception. The fact is, is that, traditionally with analytics, it was very SQL oriented and that basically the quants were kind of off in their corner, where they're using SaaS or where they're using Teradata. It's really a great leveler today, which is that, I mean basic Python it's become arguably one of the most popular programming languages, depending on what month you're looking at, at the title index. And of course, obviously SQL is, as I tell the MongoDB folks, SQL is not going away. You have a large skills base out there. And so basically I see this breaking down to essentially, you're going to have each group that's going to have its own natural preferences for its home turf. And the fact that basically, let's say the Python and scale of folks are using Databricks does not make them any less operational or machine critical than the SQL folks. >> Anybody else want to chime in on that one? >> Yeah, I totally agree with that. Python support in Snowflake is very nascent with all of Snowpark, all of the things outside of SQL, they're very much relying on partners too and make things possible and make data science possible. And it's very early days. I think the bottom line, what we're going to see is each of these camps is going to keep working on doing better at the thing that they don't do today, or they're new to, but they're not going to nail it. They're not going to be best of breed on both sides. So the SQL centric companies and shops are going to do more data science on their database centric platform. That data science driven companies might be doing more BI on their leagues with those vendors and the companies that have highly distributed data, they're going to add fabrics, and maybe offload more of their BI onto those engines, like Dremio and Starburst. >> So I've asked you this before, but I'll ask you Sanjeev. 'Cause Snowflake and Databricks are such great examples 'cause you have the data engineering crowd trying to go into data warehousing and you have the data warehousing guys trying to go into the lake territory. Snowflake has $5 billion in the balance sheet and I've asked you before, I ask you again, doesn't there has to be a semantic layer between these two worlds? Does Snowflake go out and do M&A and maybe buy ad scale or a data mirror? Or is that just sort of a bandaid? What are your thoughts on that Sanjeev? >> I think semantic layer is the metadata. The business metadata is extremely important. At the end of the day, the business folks, they'd rather go to the business metadata than have to figure out, for example, like let's say, I want to update somebody's email address and we have a lot of overhead with data residency laws and all that. I want my platform to give me the business metadata so I can write my business logic without having to worry about which database, which location. So having that semantic layer is extremely important. In fact, now we are taking it to the next level. Now we are saying that it's not just a semantic layer, it's all my KPIs, all my calculations. So how can I make those calculations independent of the compute engine, independent of the BI tool and make them fungible. So more disaggregation of the stack, but it gives us more best of breed products that the customers have to worry about. >> So I want to ask you about the stack, the modern data stack, if you will. And we always talk about injecting machine intelligence, AI into applications, making them more data driven. But when you look at the application development stack, it's separate, the database is tends to be separate from the data and analytics stack. Do those two worlds have to come together in the modern data world? And what does that look like organizationally? >> So organizationally even technically I think it is starting to happen. Microservices architecture was a first attempt to bring the application and the data world together, but they are fundamentally different things. For example, if an application crashes, that's horrible, but Kubernetes will self heal and it'll bring the application back up. But if a database crashes and corrupts your data, we have a huge problem. So that's why they have traditionally been two different stacks. They are starting to come together, especially with data ops, for instance, versioning of the way we write business logic. It used to be, a business logic was highly embedded into our database of choice, but now we are disaggregating that using GitHub, CICD the whole DevOps tool chain. So data is catching up to the way applications are. >> We also have databases, that trans analytical databases that's a little bit of what the story is with MongoDB next week with adding more analytical capabilities. But I think companies that talk about that are always careful to couch it as operational analytics, not the warehouse level workloads. So we're making progress, but I think there's always going to be, or there will long be a separate analytical data platform. >> Until data mesh takes over. (all laughing) Not opening a can of worms. >> Well, but wait, I know it's out of scope here, but wouldn't data mesh say, hey, do take your best of breed to Doug's earlier point. You can't be best of breed at everything, wouldn't data mesh advocate, data lakes do your data lake thing, data warehouse, do your data lake, then you're just a node on the mesh. (Tony laughs) Now you need separate data stores and you need separate teams. >> To my point. >> I think, I mean, put it this way. (laughs) Data mesh itself is a logical view of the world. The data mesh is not necessarily on the lake or on the warehouse. I think for me, the fear there is more in terms of, the silos of governance that could happen and the silo views of the world, how we redefine. And that's why and I want to go back to something what Sanjeev said, which is that it's going to be raising the importance of the semantic layer. Now does Snowflake that opens a couple of Pandora's boxes here, which is one, does Snowflake dare go into that space or do they risk basically alienating basically their partner ecosystem, which is a key part of their whole appeal, which is best of breed. They're kind of the same situation that Informatica was where in the early 2000s, when Informatica briefly flirted with analytic applications and realized that was not a good idea, need to redouble down on their core, which was data integration. The other thing though, that raises the importance of and this is where the best of breed comes in, is the data fabric. My contention is that and whether you use employee data mesh practice or not, if you do employee data mesh, you need data fabric. If you deploy data fabric, you don't necessarily need to practice data mesh. But data fabric at its core and admittedly it's a category that's still very poorly defined and evolving, but at its core, we're talking about a common meta data back plane, something that we used to talk about with master data management, this would be something that would be more what I would say basically, mutable, that would be more evolving, basically using, let's say, machine learning to kind of, so that we don't have to predefine rules or predefine what the world looks like. But so I think in the long run, what this really means is that whichever way we implement on whichever physical platform we implement, we need to all be speaking the same metadata language. And I think at the end of the day, regardless of whether it's a lake, warehouse or a lakehouse, we need common metadata. >> Doug, can I come back to something you pointed out? That those talking about bringing analytic and transaction databases together, you had talked about operationalizing those and the caution there. Educate me on MySQL HeatWave. I was surprised when Oracle put so much effort in that, and you may or may not be familiar with it, but a lot of folks have talked about that. Now it's got nowhere in the market, that no market share, but a lot of we've seen these benchmarks from Oracle. How real is that bringing together those two worlds and eliminating ETL? >> Yeah, I have to defer on that one. That's my colleague, Holger Mueller. He wrote the report on that. He's way deep on it and I'm not going to mock him. >> I wonder if that is something, how real that is or if it's just Oracle marketing, anybody have any thoughts on that? >> I'm pretty familiar with HeatWave. It's essentially Oracle doing what, I mean, there's kind of a parallel with what Google's doing with AlloyDB. It's an operational database that will have some embedded analytics. And it's also something which I expect to start seeing with MongoDB. And I think basically, Doug and Sanjeev were kind of referring to this before about basically kind of like the operational analytics, that are basically embedded within an operational database. The idea here is that the last thing you want to do with an operational database is slow it down. So you're not going to be doing very complex deep learning or anything like that, but you might be doing things like classification, you might be doing some predictives. In other words, we've just concluded a transaction with this customer, but was it less than what we were expecting? What does that mean in terms of, is this customer likely to turn? I think we're going to be seeing a lot of that. And I think that's what a lot of what MySQL HeatWave is all about. Whether Oracle has any presence in the market now it's still a pretty new announcement, but the other thing that kind of goes against Oracle, (laughs) that they had to battle against is that even though they own MySQL and run the open source project, everybody else, in terms of the actual commercial implementation it's associated with everybody else. And the popular perception has been that MySQL has been basically kind of like a sidelight for Oracle. And so it's on Oracles shoulders to prove that they're damn serious about it. >> There's no coincidence that MariaDB was launched the day that Oracle acquired Sun. Sanjeev, I wonder if we could come back to a topic that we discussed earlier, which is this notion of consumption, obviously Wall Street's very concerned about it. Snowflake dropped prices last week. I've always felt like, hey, the consumption model is the right model. I can dial it down in when I need to, of course, the street freaks out. What are your thoughts on just pricing, the consumption model? What's the right model for companies, for customers? >> Consumption model is here to stay. What I would like to see, and I think is an ideal situation and actually plays into the lakehouse concept is that, I have my data in some open format, maybe it's Parquet or CSV or JSON, Avro, and I can bring whatever engine is the best engine for my workloads, bring it on, pay for consumption, and then shut it down. And by the way, that could be Cloudera. We don't talk about Cloudera very much, but it could be one business unit wants to use Athena. Another business unit wants to use some other Trino let's say or Dremio. So every business unit is working on the same data set, see that's critical, but that data set is maybe in their VPC and they bring any compute engine, you pay for the use, shut it down. That then you're getting value and you're only paying for consumption. It's not like, I left a cluster running by mistake, so there have to be guardrails. The reason FinOps is so big is because it's very easy for me to run a Cartesian joint in the cloud and get a $10,000 bill. >> This looks like it's been a sort of a victim of its own success in some ways, they made it so easy to spin up single note instances, multi note instances. And back in the day when compute was scarce and costly, those database engines optimized every last bit so they could get as much workload as possible out of every instance. Today, it's really easy to spin up a new node, a new multi node cluster. So that freedom has meant many more nodes that aren't necessarily getting that utilization. So Snowflake has been doing a lot to add reporting, monitoring, dashboards around the utilization of all the nodes and multi node instances that have spun up. And meanwhile, we're seeing some of the traditional on-prem databases that are moving into the cloud, trying to offer that freedom. And I think they're going to have that same discovery that the cost surprises are going to follow as they make it easy to spin up new instances. >> Yeah, a lot of money went into this market over the last decade, separating compute from storage, moving to the cloud. I'm glad you mentioned Cloudera Sanjeev, 'cause they got it all started, the kind of big data movement. We don't talk about them that much. Sometimes I wonder if it's because when they merged Hortonworks and Cloudera, they dead ended both platforms, but then they did invest in a more modern platform. But what's the future of Cloudera? What are you seeing out there? >> Cloudera has a good product. I have to say the problem in our space is that there're way too many companies, there's way too much noise. We are expecting the end users to parse it out or we expecting analyst firms to boil it down. So I think marketing becomes a big problem. As far as technology is concerned, I think Cloudera did turn their selves around and Tony, I know you, you talked to them quite frequently. I think they have quite a comprehensive offering for a long time actually. They've created Kudu, so they got operational, they have Hadoop, they have an operational data warehouse, they're migrated to the cloud. They are in hybrid multi-cloud environment. Lot of cloud data warehouses are not hybrid. They're only in the cloud. >> Right. I think what Cloudera has done the most successful has been in the transition to the cloud and the fact that they're giving their customers more OnRamps to it, more hybrid OnRamps. So I give them a lot of credit there. They're also have been trying to position themselves as being the most price friendly in terms of that we will put more guardrails and governors on it. I mean, part of that could be spin. But on the other hand, they don't have the same vested interest in compute cycles as say, AWS would have with EMR. That being said, yes, Cloudera does it, I think its most powerful appeal so of that, it almost sounds in a way, I don't want to cast them as a legacy system. But the fact is they do have a huge landed legacy on-prem and still significant potential to land and expand that to the cloud. That being said, even though Cloudera is multifunction, I think it certainly has its strengths and weaknesses. And the fact this is that yes, Cloudera has an operational database or an operational data store with a kind of like the outgrowth of age base, but Cloudera is still based, primarily known for the deep analytics, the operational database nobody's going to buy Cloudera or Cloudera data platform strictly for the operational database. They may use it as an add-on, just in the same way that a lot of customers have used let's say Teradata basically to do some machine learning or let's say, Snowflake to parse through JSON. Again, it's not an indictment or anything like that, but the fact is obviously they do have their strengths and their weaknesses. I think their greatest opportunity is with their existing base because that base has a lot invested and vested. And the fact is they do have a hybrid path that a lot of the others lack. >> And of course being on the quarterly shock clock was not a good place to be under the microscope for Cloudera and now they at least can refactor the business accordingly. I'm glad you mentioned hybrid too. We saw Snowflake last month, did a deal with Dell whereby non-native Snowflake data could access on-prem object store from Dell. They announced a similar thing with pure storage. What do you guys make of that? Is that just... How significant will that be? Will customers actually do that? I think they're using either materialized views or extended tables. >> There are data rated and residency requirements. There are desires to have these platforms in your own data center. And finally they capitulated, I mean, Frank Klutman is famous for saying to be very focused and earlier, not many months ago, they called the going on-prem as a distraction, but clearly there's enough demand and certainly government contracts any company that has data residency requirements, it's a real need. So they finally addressed it. >> Yeah, I'll bet dollars to donuts, there was an EBC session and some big customer said, if you don't do this, we ain't doing business with you. And that was like, okay, we'll do it. >> So Dave, I have to say, earlier on you had brought this point, how Frank Klutman was poo-pooing data science workloads. On your show, about a year or so ago, he said, we are never going to on-prem. He burnt that bridge. (Tony laughs) That was on your show. >> I remember exactly the statement because it was interesting. He said, we're never going to do the halfway house. And I think what he meant is we're not going to bring the Snowflake architecture to run on-prem because it defeats the elasticity of the cloud. So this was kind of a capitulation in a way. But I think it still preserves his original intent sort of, I don't know. >> The point here is that every vendor will poo-poo whatever they don't have until they do have it. >> Yes. >> And then it'd be like, oh, we are all in, we've always been doing this. We have always supported this and now we are doing it better than others. >> Look, it was the same type of shock wave that we felt basically when AWS at the last moment at one of their reinvents, oh, by the way, we're going to introduce outposts. And the analyst group is typically pre briefed about a week or two ahead under NDA and that was not part of it. And when they dropped, they just casually dropped that in the analyst session. It's like, you could have heard the sound of lots of analysts changing their diapers at that point. >> (laughs) I remember that. And a props to Andy Jassy who once, many times actually told us, never say never when it comes to AWS. So guys, I know we got to run. We got some hard stops. Maybe you could each give us your final thoughts, Doug start us off and then-- >> Sure. Well, we've got the Snowflake Summit coming up. I'll be looking for customers that are really doing data science, that are really employing Python through Snowflake, through Snowpark. And then a couple weeks later, we've got Databricks with their Data and AI Summit in San Francisco. I'll be looking for customers that are really doing considerable BI workloads. Last year I did a market overview of this analytical data platform space, 14 vendors, eight of them claim to support lakehouse, both sides of the camp, Databricks customer had 32, their top customer that they could site was unnamed. It had 32 concurrent users doing 15,000 queries per hour. That's good but it's not up to the most demanding BI SQL workloads. And they acknowledged that and said, they need to keep working that. Snowflake asked for their biggest data science customer, they cited Kabura, 400 terabytes, 8,500 users, 400,000 data engineering jobs per day. I took the data engineering job to be probably SQL centric, ETL style transformation work. So I want to see the real use of the Python, how much Snowpark has grown as a way to support data science. >> Great. Tony. >> Actually of all things. And certainly, I'll also be looking for similar things in what Doug is saying, but I think sort of like, kind of out of left field, I'm interested to see what MongoDB is going to start to say about operational analytics, 'cause I mean, they're into this conquer the world strategy. We can be all things to all people. Okay, if that's the case, what's going to be a case with basically, putting in some inline analytics, what are you going to be doing with your query engine? So that's actually kind of an interesting thing we're looking for next week. >> Great. Sanjeev. >> So I'll be at MongoDB world, Snowflake and Databricks and very interested in seeing, but since Tony brought up MongoDB, I see that even the databases are shifting tremendously. They are addressing both the hashtag use case online, transactional and analytical. I'm also seeing that these databases started in, let's say in case of MySQL HeatWave, as relational or in MongoDB as document, but now they've added graph, they've added time series, they've added geospatial and they just keep adding more and more data structures and really making these databases multifunctional. So very interesting. >> It gets back to our discussion of best of breed, versus all in one. And it's likely Mongo's path or part of their strategy of course, is through developers. They're very developer focused. So we'll be looking for that. And guys, I'll be there as well. I'm hoping that we maybe have some extra time on theCUBE, so please stop by and we can maybe chat a little bit. Guys as always, fantastic. Thank you so much, Doug, Tony, Sanjeev, and let's do this again. >> It's been a pleasure. >> All right and thank you for watching. This is Dave Vellante for theCUBE and the excellent analyst. We'll see you next time. (upbeat music)

(cheerful music) >> Hello everyone, welcome to today's session of theCUBE's presentation of AWS Startup Showcase, New Breakthroughs in DevOps, Data Analytics, Cloud Management Tools, featuring Okera from the cloud management migration track. I'm John Furrier, your host. We've got two great special guests today, Nong Li, founder and CTO of Okera, and Sanjeev Mohan, principal @SanjMo, and former research vice president of big data and advanced analytics at Gartner. He's a legend, been around the industry for a long time, seen the big data trends from the past, present, and knows the future. Got a great lineup here. Gentlemen, thank you for this, so, life in the trenches, lessons learned across compliance, cloud migration, analytics, and use cases for Fortune 1000s. Thanks for joining us. >> Thanks for having us. >> So Sanjeev, great to see you, I know you've seen this movie, I was saying that in the open, you've at Gartner seen all the visionaries, the leaders, you know everything about this space. It's changing extremely fast, and one of the big topics right out of the gate is not just innovation, we'll get to that, that's the fun part, but it's the regulatory compliance and audit piece of it. It's keeping people up at night, and frankly if not done right, slows things down. This is a big part of the showcase here, is to solve these problems. Share us your thoughts, what's your take on this wide-ranging issue? >> So, thank you, John, for bringing this up, and I'm so happy you mentioned the fact that, there's this notion that it can slow things down. Well I have to say that the old way of doing governance slowed things down, because it was very much about control and command. But the new approach to data governance is actually in my opinion, it's liberating data. If you want to democratize or monetize, whatever you want to call it, you cannot do it 'til you know you can trust said data and it's governed in some ways, so data governance has actually become very interesting, and today if you want to talk about three different areas within compliance regulatory, for example, we all know about the EU GDPR, we know California has CCPA, and in fact California is now getting even a more stringent version called CPRA in a couple of years, which is more aligned to GDPR. That is a first area we know we need to comply to that, we don't have any way out. But then, there are other areas, there is insider trading, there is how you secure the data that comes from third parties, you know, vendors, partners, suppliers, so Nong, I'd love to hand it over to you, and see if you can maybe throw some light into how our customers are handling these use cases. >> Yeah, absolutely, and I love what you said about balancing agility and liberating, in the face of what may be seen as things that slow you down. So we work with customers across verticals with old and new regulations, so you know, you brought up GDPR. One of our clients is using this to great effect to power their ecosystem. They are a very large retail company that has operations and customers across the world, obviously the importance of GDPR, and the regulations that imposes on them are very top of mind, and at the same time, being able to do effective targeting analytics on customer information is equally critical, right? So they're exactly at that spot where they need this customer insight for powering their business, and then the regulatory concerns are extremely prevalent for them. So in the context of GDPR, you'll hear about things like consent management and right to be forgotten, right? I, as a customer of that retailer should say "I don't want my information used for this purpose," right? "Use it for this, but not this." And you can imagine at a very, very large scale, when you have a billion customers, managing that, all the data you've collected over time through all of your devices, all of your telemetry, really, really challenging. And they're leveraging Okera embedded into their analytics platform so they can do both, right? Their data scientists and analysts who need to do everything they're doing to power the business, not have to think about these kind of very granular customer filtering requirements that need to happen, and then they leverage us to do that. So that's kind of new, right, GDPR, relatively new stuff at this point, but we obviously also work with customers that have regulations from a long long time ago, right? So I think you also mentioned insider trading and that supply chain, so we'll talk to customers, and they want really data-driven decisions on their supply chain, everything about their production pipeline, right? They want to understand all of that, and of course that makes sense, whether you're the CFO, if you're going to make business decisions, you need that information readily available, and supply chains as we know get more and more and more complex, we have more and more integrated into manufacturing and other verticals. So that's your, you're a little bit stuck, right? You want to be data-driven on those supply chain analytics, but at the same time, knowing the details of all the supply chain across all of your dependencies exposes your internal team to very high blackout periods or insider trading concerns, right? For example, if you knew Apple was buying a bunch of something, that's maybe information that only a select few people can have, and the way that manifests into data policies, 'cause you need the ability to have very, very scalable, per employee kind of scalable data restriction policies, so they can do their job easier, right? If we talk about speeding things up, instead of a very complex process for them to get approved, and approved on SEC regulations, all that kind of stuff, you can now go give them access to the part of the supply chain that they need, and no more, and limit their exposure and the company's exposure and all of that kind of stuff. So one of our customers able to do this, getting two orders of magnitude, a 100x reduction in the policies to manage the system like that. >> When I hear you talking like that, I think the old days of "Oh yeah, regulatory, it kind of slows down innovation, got to go faster," pretty basic variables, not a lot of combination of things to check. Now with cloud, there seems to be combinations, Sanjeev, because how complicated has the regulatory compliance and audit environment gotten in the past few years, because I hear security in a supply chain, I hear insider threats, I mean these are security channels, not just compliance department G&A kind of functions. You're talking about large-scale, potentially combinations of access, distribution, I mean it seems complicated. How much more complicated is it now, just than it was a few years ago? >> So, you know the way I look at it is, I'm just mentioning these companies just as an example, when PayPal or Ebay, all these companies started, they started in California. Anybody who ever did business on Ebay or PayPal, guess where that data was? In the US in some data center. Today you cannot do it. Today, data residency laws are really tough, and so now these organizations have to really understand what data needs to remain where. On top of that, we now have so many regulations. You know, earlier on if you were healthcare, you needed to be HIPAA compliant, or banking PCI DSS, but today, in the cloud, you really need to know, what data I have, what sensitive data I have, how do I discover it? So that data discovery becomes really important. What roles I have, so for example, let's say I work for a bank in the US, and I decide to move to Germany. Now, the old school is that a new rule will be created for me, because of German... >> John: New email address, all these new things happen, right? >> Right, exactly. So you end up with this really, a mass of rules and... And these are all static. >> Rules and tools, oh my god. >> Yeah. So Okera actually makes a lot of this dynamic, which reduces your cloud migration overhead, and Nong used some great examples, in fact, sorry if I take just a second, without mentioning any names, there's one of the largest banks in the world is going global in the digital space for the first time, and they're taking Okera with them. So... >> But what's the point? This is my next topic in cloud migration, I want to bring this up because, complexity, when you're in that old school kind of data center, waterfall, these old rules and tools, you have to roll this out, and it's a pain in the butt for everybody, it's a hassle, huge hassle. Cloud gives the agility, we know that, and cloud's becoming more secure, and I think now people see the on-premise, certainly things that'd be on-premises for secure things, I get that, but when you start getting into agility, and you now have cloud regions, you can start being more programmatic, so I want to get you guys' thoughts on the cloud migration, how companies who are now lifting and shifting, replatforming, what's the refactoring beyond that, because you can replatform in the cloud, and still some are kind of holding back on that. Then when you're in the cloud, the ones that are winning, the companies that are winning are the ones that are refactoring in the cloud. Doing things different with new services. Sanjeev, you start. >> Yeah, so you know, in fact lot of people tell me, "You know, we are just going to lift and shift into the cloud." But you're literally using cloud as a data center. You still have all the, if I may say, junk you had on-prem, you just moved it into the cloud, and now you're paying for it. In cloud, nothing is free. Every storage, every processing, you're going to pay for it. The most successful companies are the ones that are replatforming, they are taking advantage of the platform as a service or software as a service, so that includes things like, you pay as you go, you pay for exactly the amount you use, so you scale up and scale down or scale out and scale in, pretty quickly, you know? So you're handling that demand, so without replatforming, you are not really utilizing your- >> John: It's just hosting. >> Yeah, you're just hosting. >> It's basically hosting if you're not doing anything right there. >> Right. The reason why people sometimes resist to replatform, is because there's a hidden cost that we don't really talk about, PaaS adds 3x to IaaS cost. So, some organizations that are very mature, and they have a few thousand people in the IT department, for them, they're like "No, we just want to run it in the cloud, we have the expertise, and it's cheaper for us." But in the long run, to get the most benefit, people should think of using cloud as a service. >> Nong what's your take, because you see examples of companies, I'll just call one out, Snowflake for instance, they're essentially a data warehouse in the cloud, they refactored and they replatformed, they have a competitive advantage with the scale, so they have things that others don't have, that just hosting. Or even on-premise. The new model developing where there's real advantages, and how should companies think about this when they have to manage these data lakes, and they have to manage all these new access methods, but they want to maintain that operational stability and control and growth? >> Yeah, so. No? Yeah. >> There's a few topics that are all (indistinct) this topic. (indistinct) enterprises moving to the cloud, they do this maybe for some cost savings, but a ton of it is agility, right? The motor that the business can run at is just so much faster. So we'll work with companies in the context of cloud migration for data, where they might have a data warehouse they've been using for 20 years, and building policies over that time, right? And it's taking a long time to go proof of access and those kind of things, made more sense, right? If it took you months to procure a physical infrastructure, get machines shipped to your data center, then this data access taking so long feels okay, right? That's kind of the same rate that everything is moving. In the cloud, you can spin up new infrastructure instantly, so you don't want approvals for getting policies, creating rules, all that stuff that Sanjeev was talking about, that being slow is a huge, huge problem. So this is a very common environment that we see where they're trying to do that kind of thing. And then, for replatforming, again, they've been building these roles and processes and policies for 20 years. What they don't want to do is take 20 years to go migrate all that stuff into the cloud, right? That's probably an experience nobody wants to repeat, and frankly for many of them, people who did it originally may or may not be involved in this kind of effort. So we work with a lot of companies like that, they have their, they want stability, they got to have the business running as normal, they got to get moving into the new infrastructure, doing it in a new way that, you know, with all the kind of lessons learned, so, as Sanjeev said, one of these big banks that we work with, that classical story of on-premise data warehousing, maybe a little bit of Hadoop, moved onto AWS, S3, Snowflake, that kind of setup, extremely intricate policies, but let's go reimagine how we can do this faster, right? What we like to talk about is, you're an organization, you need a design that, if you onboarded 1000 more data users, that's got to be way, way easier than the first 10 you onboarded, right? You got to get it to be easier over time, in a really, really significant way. >> Talk about the data authorization safety factor, because I can almost imagine all the intricacies of these different tools creates specialism amongst people who operate them. And each one might have their own little authorization nuance. Trend is not to have that siloed mentality. What's your take on clients that want to just "Hey, you know what? I want to have the maximum agility, but I don't want to get caught in the weeds on some of these tripwires around access and authorization." >> Yeah, absolutely, I think it's real important to get the balance of it, right? Because if you are an enterprise, or if you have diversive teams, you want them to have the ability to use tools as best of breed for their purpose, right? But you don't want to have it be so that every tool has its own access and provisioning and whatever, that's definitely going to be a security, or at least, a lot of friction for you to get things going. So we think about that really hard, I think we've seen great success with things like SSO and Okta, right? Unifying authentication. We think there's a very, very similar thing about to happen with authorization. You want that single control plane that can integrate with all the tools, and still get the best of what you need, but it's much, much easier (indistinct). >> Okta's a great example, if people don't want to build their own thing and just go with that, same with what you guys are doing. That seems to be the dots that are connecting you, Sanjeev. The ease of use, but yet the stability factor. >> Right. Yeah, because John, today I may want to bring up a SQL editor to go into Snowflake, just as an example. Tomorrow, I may want to use the Azure Bot, you know? I may not even want to go to Snowflake, I may want to go to an underlying piece of data, or I may use Power BI, you know, for some reason, and come from Azure side, so the point is that, unless we are able to control, in some sort of a centralized manner, we will not get that consistency. And security you know is all or nothing. You cannot say "Well, I secured my Snowflake, but if you come through HTFS, Hadoop, or some, you know, that is outside of my realm, or my scope," what's the point? So that is why it is really important to have a watertight way, in fact I'm using just a few examples, maybe tomorrow I decide to use a data catalog, or I use Denodo as my data virtualization and I run a query. I'm the same identity, but I'm using different tools. I may use it from home, over VPN, or I may use it from the office, so you want this kind of flexibility, all encompassed in a policy, rather than a separate rule if you do this and this, if you do that, because then you end up with literally thousands of rules. >> And it's never going to stop, either, it's like fashion, the next tool's going to come out, it's going to be cool, and people are going to want to use it, again, you don't want to have to then move the train from the compliance side this way or that way, it's a lot of hassle, right? So we have that one capability, you can bring on new things pretty quickly. Nong, am I getting it right, this is kind of like the trend, that you're going to see more and more tools and/or things that are relevant or, certain use cases that might justify it, but yet, AppSec review, compliance review, I mean, good luck with that, right? >> Yeah, absolutely, I mean we certainly expect tools to continue to get more and more diverse, and better, right? Most innovation in the data space, and I think we... This is a great time for that, a lot of things that need to happen, and so on and so forth. So I think one of the early goals of the company, when we were just brainstorming, is we don't want data teams to not be able to use the tools because it doesn't have the right security (indistinct), right? Often those tools may not be focused on that particular area. They're great at what they do, but we want to make sure they're enabled, they do some enterprise investments, they see broader adoption much easier. A lot of those things. >> And I can hear the sirens in the background, that's someone who's not using your platform, they need some help there. But that's the case, I mean if you don't get this right, there are some consequences, and I think one of the things I would like to bring up on next track is, to talk through with you guys is, the persona pigeonhole role, "Oh yeah, a data person, the developer, the DevOps, the SRE," you start to see now, developers and with cloud developers, and data folks, people, however they get pigeonholed, kind of blending in, okay? You got data services, you got analytics, you got data scientists, you got more democratization, all these things are being kicked around, but the notion of a developer now is a data developer, because cloud is about DevOps, data is now a big part of it, it's not just some department, it's actually blending in. Just a cultural shift, can you guys share your thoughts on this trend of data people versus developers now becoming kind of one, do you guys see this happening, and if so, how? >> So when, John, I started my career, I was a DBA, and then a data architect. Today, I think you cannot have a DBA who's not a developer. That's just my opinion. Because there is so much of CICD, DevOps, that happens today, and you know, you write your code in Python, you put it in version control, you deploy using Jenkins, you roll back if there's a problem. And then, you are interacting, you're building your data to be consumed as a service. People in the past, you would have a thick client that would connect to the database over TCP/IP. Today, people don't want to connect over TCP/IP necessarily, they want to go by HTTP. And they want an API gateway in the middle. So, if you're a data architect or DBA, now you have to worry about, "I have a REST API call that's coming in, how am I going to secure that, and make sure that people are allowed to see that?" And that was just yesterday. >> Exactly. Got to build an abstraction layer. You got to build an abstraction layer. The old days, you have to worry about schema, and do all that, it was hard work back then, but now, it's much different. You got serverless, functions are going to show way... It's happening. >> Correct, GraphQL, and semantic layer, that just blows me away because, it used to be, it was all in database, then we took it out of database and we put it in a BI tool. So we said, like BusinessObjects started this whole trend. So we're like "Let's put the semantic layer there," well okay, great, but that was when everything was surrounding BusinessObjects and Oracle Database, or some other database, but today what if somebody brings Power BI or Tableau or Qlik, you know? Now you don't have a semantic layer access. So you cannot have it in the BI layer, so you move it down to its own layer. So now you've got a semantic layer, then where do you store your metrics? Same story repeats, you have a metrics layer, then the data centers want to do feature engineering, where do you store your features? You have a feature store. And before you know, this stack has disaggregated over and over and over, and then you've got layers and layers of specialization that are happening, there's query accelerators like Dremio or Trino, so you've got your data here, which Nong is trying really hard to protect, and then you've got layers and layers and layers of abstraction, and networks are fast, so the end user gets great service, but it's a nightmare for architects to bring all these things together. >> How do you tame the complexity? What's the bottom line? >> Nong? >> Yeah, so, I think... So there's a few things you need to do, right? So, we need to re-think how we express security permanence, right? I think you guys have just maybe in passing (indistinct) talked about creating all these rules and all that kind of stuff, that's been the way we've done things forever. We get to think about policies and mechanisms that are much more dynamic, right? You need to really think about not having to do any additional work, for the new things you add to the system. That's really, really core to solving the complexity problem, right? 'Cause that gets you those orders of magnitude reduction, system's got to be more expressive and map to those policies. That's one. And then second, it's got to be implemented at the right layer, right, to Sanjeev's point, close to the data, and it can service all of those applications and use cases at the same time, and have that uniformity and breadth of support. So those two things have to happen. >> Love this universal data authorization vision that you guys have. Super impressive, we had a CUBE Conversation earlier with Nick Halsey, who's a veteran in the industry, and he likes it. That's a good sign, 'cause he's seen a lot of stuff, too, Sanjeev, like yourself. This is a new thing, you're seeing compliance being addressed, and with programmatic, I'm imagining there's going to be bots someday, very quickly with AI that's going to scale that up, so they kind of don't get in the innovation way, they can still get what they need, and enable innovation. You've got cloud migration, which is only going faster and faster. Nong, you mentioned speed, that's what CloudOps is all about, developers want speed, not things in days or hours, they want it in minutes and seconds. And then finally, ultimately, how's it scale up, how does it scale up for the people operating and/or programming? These are three major pieces. What happens next? Where do we go from here, what's, the customer's sitting there saying "I need help, I need trust, I need scale, I need security." >> So, I just wrote a blog, if I may diverge a bit, on data observability. And you know, so there are a lot of these little topics that are critical, DataOps is one of them, so to me data observability is really having a transparent view of, what is the state of your data in the pipeline, anywhere in the pipeline? So you know, when we talk to these large banks, these banks have like 1000, over 1000 data pipelines working every night, because they've got that hundred, 200 data sources from which they're bringing data in. Then they're doing all kinds of data integration, they have, you know, we talked about Python or Informatica, or whatever data integration, data transformation product you're using, so you're combining this data, writing it into an analytical data store, something's going to break. So, to me, data observability becomes a very critical thing, because it shows me something broke, walk me down the pipeline, so I know where it broke. Maybe the data drifted. And I know Okera does a lot of work in data drift, you know? So this is... Nong, jump in any time, because I know we have use cases for that. >> Nong, before you get in there, I just want to highlight a quick point. I think you're onto something there, Sanjeev, because we've been reporting, and we believe, that data workflows is intellectual property. And has to be protected. Nong, go ahead, your thoughts, go ahead. >> Yeah, I mean, the observability thing is critically important. I would say when you want to think about what's next, I think it's really effectively bridging tools and processes and systems and teams that are focused on data production, with the data analysts, data scientists, that are focused on data consumption, right? I think bridging those two, which cover a lot of the topics we talked about, that's kind of where security almost meets, that's kind of where you got to draw it. I think for observability and pipelines and data movement, understanding that is essential. And I think broadly, on all of these topics, where all of us can be better, is if we're able to close the loop, get the feedback loop of success. So data drift is an example of the loop rarely being closed. It drifts upstream, and downstream users can take forever to figure out what's going on. And we'll have similar examples related to buy-ins, or data quality, all those kind of things, so I think that's really a problem that a lot of us should think about. How do we make sure that loop is closed as quickly as possible? >> Great insight. Quick aside, as the founder CTO, how's life going for you, you feel good? I mean, you started a company, doing great, it's not drifting, it's right in the stream, mainstream, right in the wheelhouse of where the trends are, you guys have a really crosshairs on the real issues, how you feeling, tell us a little bit about how you see the vision. >> Yeah, I obviously feel really good, I mean we started the company a little over five years ago, there are kind of a few things that we bet would happen, and I think those things were out of our control, I don't think we would've predicted GDPR security and those kind of things being as prominent as they are. Those things have really matured, probably as best as we could've hoped, so that feels awesome. Yeah, (indistinct) really expanded in these years, and it feels good. Feels like we're in the right spot. >> Yeah, it's great, data's competitive advantage, and certainly has a lot of issues. It could be a blocker if not done properly, and you're doing great work. Congratulations on your company. Sanjeev, thanks for kind of being my cohost in this segment, great to have you on, been following your work, and you continue to unpack it at your new place that you started. SanjMo, good to see your Twitter handle taking on the name of your new firm, congratulations. Thanks for coming on. >> Thank you so much, such a pleasure. >> Appreciate it. Okay, I'm John Furrier with theCUBE, you're watching today's session presentation of AWS Startup Showcase, featuring Okera, a hot startup, check 'em out, great solution, with a really great concept. Thanks for watching. (calm music)

(gentle music) >> Hello and welcome today's session theCUBE presentation of AWS Startup Showcase powered by theCUBE, I'm John Furrier, for your host of theCUBE. This is a session on breaking through with DevOps data analytics tools, cloud management tools with MontyCloud and cloud management migration, I'm your host. Thanks for joining me, I've got two great guests. Venkat Krishnamachari who's the co-founder and CEO of MontyCloud and Shruthi Sreenivasa Murthy, solution architect research computing group St. Louis University. Thanks for coming on to talk about transforming IT, day one day two operations. Venkat, great to see you. >> Great to see you again, John. So in this session, I really want to get into this cloud powerhouse theme you guys were talking about before on our previous Cube Conversations and what it means for customers, because there is a real market shift happening here. And I want to get your thoughts on what solution to the problem is basically, that you guys are targeting. >> Yeah, John, cloud migration is happening rapidly. Not an option. It is the current and the immediate future of many IT departments and any type of computing workloads. And applications and services these days are better served by cloud adoption. This rapid acceleration is where we are seeing a lot of challenges and we've been helping customers with our platform so they can go focus on their business. So happy to talk more about this. >> Yeah and Shruthi if you can just explain your relationship with these guys, because you're a cloud architect, you can try to put this together. MontyCloud is your customer, talk about your solution. >> Yeah I work at the St. Louis University as the solutions architect for the office of Vice President of Research. We can address St. Louis University as SLU, just to keep it easy. SLU is a 200-year-old university with more focus on research. And our goal at the Research Computing Group is to help researchers by providing the right infrastructure and computing capabilities that help them to advance their research. So here in SLU research portfolio, it's quite diverse, right? So we do research on vaccines, economics, geospatial intelligence, and many other really interesting areas, and you know, it involves really large data sets. So one of the research computing groups' ambitious plan is to move as many high-end computation applications from on-prem to the AWS. And I lead all the cloud initiatives for the St. Louis university. >> Yeah Venkat and I, we've been talking, many times on theCUBE, previous interviews about, you know, the rapid agility that's happening with serverless and functions, and, you know, microservices start to see massive acceleration of how fast cloud apps are being built. It's put a lot of pressure on companies to hang on and manage all this. And whether you're a security group was trying to lock down something, or it's just, it's so fast, the cloud development scene is really fun and you're implementing it at a large scale. What's it like these days from a development standpoint? You've got all this greatness in the cloud. What's the DevOps mindset right now? >> SLU is slowly evolving itself as the AWS Center of Excellence here in St. Louis. And most of the workflows that we are trying to implement on AWS and DevOps and, you know, CICD Pipelines. And basically we want it ready and updated for the researchers where they can use it and not have to wait on any of the resources. So it has a lot of importance. >> Research as code, it's like the internet, infrastructure as code is DevOps' ethos. Venkat, let's get into where this all leads to because you're seeing a culture shift in companies as they start to realize if they don't move fast, and the blockers that get in the way of the innovation, you really can't get your arms around this growth as an opportunity to operationalize all the new technology, could you talk about the transformation goals that are going on with your customer base. What's going on in the market? Can you explain and unpack the high level market around what you guys are doing? >> Sure thing, John. Let's bring up the slide one. So they have some content that Act-On tabs. John, every legal application, commercial application, even internal IT departments, they're all transforming fast. Speed has never been more important in the era we are today. For example, COVID research, you know, analyzing massive data sets to come up with some recommendations. They don't demand a lot from the IT departments so that researchers and developers can move fast. And I need departments that are not only moving current workloads to the cloud they're also ensuring the cloud is being consumed the right way. So researchers can focus on what they do best, what we win, learning and working closely with customers and gathering is that there are three steps or three major, you know, milestone that we like to achieve. I would start the outcome, right? That the important milestone IT departments are trying to get to is transforming such that they're directly tied to the key business objectives. Everything they do has to be connected to the business objective, which means the time and you know, budget and everything's aligned towards what they want to deliver. IT departments we talk with have one common goal. They want to be experts in cloud operations. They want to deliver cloud operations excellence so that researchers and developers can move fast. But they're almost always under the, you know, they're time poor, right? And there is budget gaps and that is talent and tooling gap. A lot of that is what's causing the, you know, challenges on their path to journey. And we have taken a methodical and deliberate position in helping them get there. >> Shruthi hows your reaction to that? Because, I mean, you want it faster, cheaper, better than before. You don't want to have all the operational management hassles. You mentioned that you guys want to do this turnkey. Is that the use case that you're going after? Just research kind of being researchers having the access at their fingertips, all these resources? What's the mindset there, what's your expectation? >> Well, one of the main expectations is to be able to deliver it to the researchers as demand and need and, you know, moving from a traditional on-prem HBC to cloud would definitely help because, you know, we are able to give the right resources to the researchers and able to deliver projects in a timely manner, and, you know, with some additional help from MontyCloud data platform, we are able to do it even better. >> Yeah I like the onboarding thing and to get an easy and you get value quickly, that's the cloud business model. Let's unpack the platform, let's go into the hood. Venkat let's, if you can take us through the, some of the moving parts under the platform, then as you guys have it's up at the high level, the market's obvious for everyone out there watching Cloud ops, speed, stablism. But let's go look at the platform. Let's unpack that, do you mind pick up on slide two and let's go look at the what's going on in the platform. >> Sure. Let's talk about what comes out of the platform, right? They are directly tied to what the customers would like to have, right? Customers would like to fast track their day one activities. Solution architects, such as Shruthi, their role is to try and help get out of the way of the researchers, but we ubiquitous around delegating cloud solutions, right? Our platform acts like a seasoned cloud architect. It's as if you've instantly turned on a cloud solution architect that should, they can bring online and say, Hey, I want help here to go faster. Our lab then has capabilities that help customers provision a set of governance contracts, drive consumption in the right way. One of the key things about driving consumption the right way is to ensure that we prevent a security cost or compliance issues from happening in the first place, which means you're shifting a lot of the operational burden to left and make sure that when provisioning happens, you have a guard rails in place, we help with that, the platform solves a problem without writing code. And an important takeaway here, John is that a was built for architects and administrators who want to move fast without having to write a ton of code. And it is also a platform that they can bring online, autonomous bots that can solve problems. For example, when it comes to post provisioning, everybody is in the business of ensuring security because it's a shared model. Everybody has to keep an eye on compliance, that is also a shared responsibility, so is cost optimization. So we thought wouldn't it be awesome to have architects such as Shruthi turn on a compliance bot on the platform that gives them the peace of mind that somebody else and an autonomous bot is watching our 24 by 7 and make sure that these day two operations don't throw curve balls at them, right? That's important for agility. So platform solves that problem with an automation approach. Going forward on an ongoing basis, right, the operation burden is what gets IT departments. We've seen that happen repeatedly. Like IT department, you know, you know this, John, maybe you have some thoughts on this. You know, you know, if you have some comments on how IT can face this, then maybe that's better to hear from you. >> No, well first I want to unpack that platform because I think one of the advantages I see here and that people are talking about in the industry is not only is the technology's collision colliding between the security postures and rapid cloud development, because DevOps and cloud, folks, are moving super fast. They want things done at the point of coding and CICB pipeline, as well as any kind of changes, they want it fast, not weeks. They don't want to have someone blocking it like a security team, so automation with the compliance is beautiful because now the security teams can provide policies. Those policies can then go right into your platform. And then everyone's got the rules of the road and then anything that comes up gets managed through the policy. So I think this is a big trend that nobody's talking about because this allows the cloud to go faster. What's your reaction to that? Do you agree? >> No, precisely right. I'll let Shurthi jump on that, yeah. >> Yeah, you know, I just wanted to bring up one of the case studies that we read on cloud and use their compliance bot. So REDCap, the Research Electronic Data Capture also known as REDCap is a web application. It's a HIPAA web application. And while the flagship projects for the research group at SLU. REDCap was running on traditional on-prem infrastructure, so maintaining the servers and updating the application to its latest version was definitely a challenge. And also granting access to the researchers had long lead times because of the rules and security protocols in place. So we wanted to be able to build a secure and reliable enrollment on the cloud where we could just provision on demand and in turn ease the job of updating the application to its latest version without disturbing the production environment. Because this is a really important application, most of the doctors and researchers at St. Louis University and the School of Medicine and St. Louis University Hospital users. So given this challenge, we wanted to bring in MontyCloud's cloud ops and, you know, security expertise to simplify the provisioning. And that's when we implemented this compliance bot. Once it is implemented, it's pretty easy to understand, you know, what is compliant, what is noncompliant with the HIPAA standards and where it needs an remediation efforts and what we need to do. And again, that can also be automated. It's nice and simple, and you don't need a lot of cloud expertise to go through the compliance bot and come up with your remediation plan. >> What's the change in the outcome in terms of the speed turnaround time, the before and after? So before you're dealing with obviously provisioning stuff and lead time, but just a compliance closed loop, just to ask a question, do we have, you know, just, I mean, there's a lot of manual and also some, maybe some workflows in there, but not as not as cool as an instant bot that solve yes or no decision. And after MontyCloud, what are some of the times, can you share any data there just doing an order of magnitude. >> Yeah, definitely. So the provisioning was never simpler, I mean, we are able to provision with just one or two clicks, and then we have a better governance guardrail, like Venkat says, and I think, you know, to give you a specific data, it, the compliance bot does about more than 160 checks and it's all automated, so when it comes to security, definitely we have been able to save a lot of effort on that. And I can tell you that our researchers are able to be 40% more productive with the infrastructure. And our research computing group is able to kind of save the time and, you know, the security measures and the remediation efforts, because we get customized alerts and notifications and you just need to go in and, you know. >> So people are happier, right? People are getting along at the office or virtually, you know, no one is yelling at each other on Slack, hey, where's? Cause that's really the harmony here then, okay. This is like a, I'm joking aside. This is a real cultural issue between speed of innovation and the, what could be viewed as a block, or just the time that say security teams or other teams might want to get back to you, make sure things are compliant. So that could slow things down, that tension is real and there's some disconnects within companies. >> Yeah John, that's spot on, and that means we have to do a better job, not only solving the traditional problems and make them simple, but for the modern work culture of integrations. You know, it's not uncommon like you cut out for researchers and architects to talk in a Slack channel often. You say, Hey, I need this resource, or I want to reconfigure this. How do we make that collaboration better? How do you make the platform intelligent so that the platform can take off some of the burden off of people so that the platform can monitor, react, notify in a Slack channel, or if you should, the administrator say, Hey, next time, this happens automatically go create a ticket for me. If it happens next time in this environment automatically go run a playbook, that remediates it. That gives a lot of time back that puts a peace of mind and the process that an operating model that you have inherited and you're trying to deliver excellence and has more help, particularly because it is very dynamic footprint. >> Yeah, I think this whole guard rail thing is a really big deal, I think it's like a feature, but it's a super important outcome because if you can have policies that map into these bots that can check rules really fast, then developers will have the freedom to drive as fast as they want, and literally go hard and then shift left and do the coding and do all their stuff on the hygiene side from the day, one on security is really a big deal. Can we go back to this slide again for the other project? There's another project on that slide. You talked about RED, was it REDCap, was that one? >> Yeah. Yeah, so REDCap, what's the other project. >> So SCAER, the Sinfield Center for Applied Economic Research at SLU is also known as SCAER. They're pretty data intensive, and they're into some really sophisticated research. The Center gets daily dumps of sensitive geo data sensitive de-identified geo data from various sources, and it's a terabyte so every day, becomes petabytes. So you know, we don't get the data in workable formats for the researchers to analyze. So the first process is to convert this data into a workable format and keep an analysis ready and doing this at a large scale has many challenges. So we had to make this data available to a group of users too, and some external collaborators with ads, you know, more challenges again, because we also have to do this without compromising on the security. So to handle these large size data, we had to deploy compute heavy instances, such as, you know, R5, 12xLarge, multiple 12xLarge instances, and optimizing the cost and the resources deployed on the cloud again was a huge challenge. So that's when we had to take MontyCloud help in automating the whole process of ingesting the data into the infrastructure and then converting them into a workable format. And this was all automated. And after automating most of the efforts, we were able to bring down the data processing time from two weeks or more to three days, which really helped the researchers. So MontyCloud's data platform also helped us with automating the risk, you know, the resource optimization process and that in turn helped bring the costs down, so it's been pretty helpful then. >> That's impressive weeks to days, I mean, this is the theme Venkat speed, speed, speed, hybrid, hybrid. A lot of stuff happening. I mean, this is the new normal, this is going to make companies more productive if they can get the apps built faster. What do you see as the CEO and founder of the company you're out there, you know, you're forging new ground with this great product. What do you see as the blockers from customers? Is it cultural, is it lack of awareness? Why aren't people jumping all over this? >> Only people aren't, right. They go at it in so many different ways that, you know, ultimately be the one person IT team or massively well-funded IT team. Everybody wants to Excel at what they're delivering in cloud operations, the path to that as what, the challenging part, right? What are you seeing as customers are trying to build their own operating model and they're writing custom code, then that's a lot of need for provisioning, governance, security, compliance, and monitoring. So they start integrating point tools, then suddenly IT department is now having a, what they call a tax, right? They have to maintain the technical debt while cloud service moving fast. It's not uncommon for one of the developers or one of the projects to suddenly consume a brand new resource. And as you know, AWS throws up a lot more services every month, right? So suddenly you're not keeping up with that service. So what we've been able to look at this from a point of view of how do we get customers to focus on what they want to do and automate things that we can help them with? >> Let me, let me rephrase the question if you don't mind. Cause I I didn't want to give the impression that you guys aren't, you guys have a great solution, but I think when I see enterprises, you know, they're transforming, right? So it's not so much the cloud innovators, like you guys, it's really that it's like the mainstream enterprise, so I have to ask you from a customer standpoint, what's some of the cultural things are technical reasons why they're not going faster? Cause everyone's, maybe it's the pandemic's forcing projects to be double down on, or some are going to be cut, this common theme of making things available faster, cheaper, stronger, more secure is what cloud does. What are some of the enterprise challenges that they have? >> Yeah, you know, it might be money for right, there's some cultural challenges like Andy Jassy or sometimes it's leadership, right? You want top down leadership that takes a deterministic step towards transformation, then adequately funding the team with the right skills and the tools, a lot of that plays into it. And there's inertia typically in an existing process. And when you go to cloud, you can do 10X better, people see that it doesn't always percolate down to how you get there. So those challenges are compounded and digital transformation leaders have to, you know, make that deliberate back there, be more KPI-driven. One of the things we are seeing in companies that do well is that the leadership decides that here are our top business objectives and KPIs. Now if we want the software and the services and the cloud division to support those objectives when they take that approach, transformation happens. But that is a lot more easier said than done. >> Well you're making it really easy with your solution. And we've done multiple interviews. I've got to say you're really onto something really with this provisioning and the compliance bots. That's really strong, that the only goes stronger from there, with the trends with security being built in. Shruthi, got to ask you since you're the customer, what's it like working with MontyCloud? It sounds so awesome, you're customer, you're using it. What's your review, what's your- What's your, what's your take on them? >> Yeah they are doing a pretty good job in helping us automate most of our workflows. And when it comes to keeping a tab on the resources, the utilization of the resources, so we can keep a tab on the cost in turn, you know, their compliance bots, their cost optimization tab. It's pretty helpful. >> Yeah well you're knocking projects down from three weeks to days, looking good, I mean, looking real strong. Venkat this is the track record you want to see with successful projects. Take a minute to explain what else is going on with MontyCloud. Other use cases that you see that are really primed for MontyCloud's platform. >> Yeah, John, quick minute there. Autonomous cloud operations is the goal. It's never done, right? It there's always some work that you hands-on do. But if you set a goal such that customers need to have a solution that automates most of the routine operations, then they can focus on the business. So we are going to relentlessly focused on the fact that autonomous operations will have the digital transformation happen faster, and we can create a lot more value for customers if they deliver to their KPIs and objectives. So our investments in the platform are going more towards that. Today we already have a fully automated compliance bot, a security bot, a cost optimization recommendation engine, a provisioning and governance engine, where we're going is we are enhancing all of this and providing customers lot more fluidity in how they can use our platform Click to perform your routine operations, Click to set up rules based automatic escalation or remediation. Cut down the number of hops a particular process will take and foster collaboration. All of this is what our platform is going and enhancing more and more. We intend to learn more from our customers and deliver better for them as we move forward. >> That's a good business model, make things easier, reduce the steps it takes to do something, and save money. And you're doing all those things with the cloud and awesome stuff. It's really great to hear your success stories and the work you're doing over there. Great to see resources getting and doing their job faster. And it's good and tons of data. You've got petabytes of that's coming in. It's it's pretty impressive, thanks for sharing your story. >> Sounds good, and you know, one quick call out is customers can go to MontyCloud.com today. Within 10 minutes, they can get an account. They get a very actionable and valuable recommendations on where they can save costs, what is the security compliance issues they can fix. There's a ton of out-of-the-box reports. One click to find out whether you are having some data that is not encrypted, or if any of your servers are open to the world. A lot of value that customers can get in under 10 minutes. And we believe in that model, give the value to customers. They know what to do with that, right? So customers can go sign up for a free trial at MontyCloud.com today and get the value. >> Congratulations on your success and great innovation. A startup showcase here with theCUBE coverage of AWS Startup Showcase breakthrough in DevOps, Data Analytics and Cloud Management with MontyCloud. I'm John Furrier, thanks for watching. (gentle music)

>> From around the globe, it's theCUBE, covering Data Citizens '21. Brought to you by Collibra. >> For the past decade organizations have been effecting very deliberate data strategies and investing quite heavily in people, processes and technology, specifically designed to gain insights from data, better serve customers, drive new revenue streams we've heard this before. The results quite frankly have been mixed. As much of the effort is focused on analytics and technology designed to create a single version of the truth, which in many cases continues to be elusive. Moreover, the world of data is changing. Data is increasingly distributed making collaboration and governance more challenging, especially where operational use cases are a priority. Hello, everyone. My name is Dave Vellante and you're watching theCUBE coverage of Data Citizens '21. And we're pleased to welcome Michele Goetz who's the vice president and principal analyst at Forrester Research. Hello, Michele. Welcome to theCUBE. >> Hi, Dave. Thanks for having me today. >> It's our pleasure. So I want to start, you serve have a wide range of roles including enterprise architects, CDOs, chief data officers that is, analyst, the analyst, et cetera, and many data-related functions. And my first question is what are they thinking about today? What's on their minds, these data experts? >> So there's actually two things happening. One is what is the demand that's placed on data for our new intelligent digital systems. So we're seeing a lot of investment and interest in things like edge computing. And then how does that intersect with artificial intelligence to really run your business intelligently and drive new value propositions to be both adaptive to the market as well as resilient to changes that are unforeseen. The second thing is then you create this massive complexity to managing the data, governing the data, orchestrating the data because it's not just a centralized data warehouse environment anymore. You have a highly diverse and distributed landscape that you both control internally, as well as taking advantage of third party information. So really what the struggle then becomes is how do you trust the data? How do you govern it, and secure, and protect that data? And then how do you ensure that it's hyper contextualized to the types of value propositions that our intelligence systems are going to serve? >> Well, I think you're hitting on the key issues here. I mean, you're right. The data and I sort of refer to this as well is sort of out there, it's distributed at the edge. But generally our data organizations are actually quite centralized and as well you talk about the need to trust the data obviously that's crucial. But are you seeing the organization change? I know you're talking about this to clients, your discussion about collaboration. How are you seeing that change? >> Yeah, so as you have to bring data into context of the insights that you're trying to get or the intelligence that's automating and scaling out the value streams and outcomes within your business, we're actually seeing a federated model emerge in organizations. So while there's still a centralized data management and data services organization led typical enterprise architects for data, a data engineering team that's managing warehouses as in data lakes. They're creating this great platform to access and orchestrate information, but we're also seeing data, and analytics, and governance teams come together under chief data officers or chief data and analytics officers. And this is really where the insights are being generated from either BI and analytics or from data science itself and having dedicated data engineers and stewards that are helping to access and prepare data for analytic efforts. And then lastly, this is the really interesting part is when you push data into the edge the goal is that you're actually driving an experience and an application. And so in that case we are seeing data engineering teams starting to be incorporated into the solutions teams that are aligned to lines of business or divisions themselves. And so really what's happening is if there is a solution consultant who is also overseeing value-based portfolio management when you need to instrument the data to these new use cases and keep up with the pace of the business it's this engineering team that is part of the DevOps work bench to execute on that. So really the balances we need the core, we need to get to the insights and build our models for AI. And then the next piece is how do you activate all that? And there's a team over there to help. So it's really spreading the wealth and expertise where it needs to go. >> Yeah, I love that. You took a couple of things that really resonated with me. You talked about context a couple of times and this notion of a federated model, because historically the sort of big data architecture, the team, they didn't have the context, the business context, and my inference is that's changing and I think that's critical. Your talk at Data Citizens is called how obsessive collaboration fuels scalable DataOps. You talk about the data, the DevOps team. What's the premise you put forth to the audience? >> So the point about obsessive collaboration is sort of taking the hubris out of your expertise on the data. Certainly there's a recognition by data professionals that the business understands and owns their data. They know the semantics, they know the context of it and just receiving the requirements on that was assumed to be okay. And then you could provide a data foundation, whether it's just a lake or whether you have a warehouse environment where you're pulling for your analytics. The reality is that as we move into more of AI machine learning type of model, one, more context is necessary. And you're kind of balancing between what are the things that you can ascribe to the data globally which is what data engineers can support. And then there's what is unique about the data and the context of the data that is related to the business value and outcome as well as the feature engineering that is being done on the machine learning models. So there has to be a really tight link and collaboration between the data engineers, the data scientists, and analysts, and the business stakeholders themselves. You see a lot of pods starting up that way to build the intelligence within the system. And then lastly, what do you do with that model? What do you do with that data? What do you do with that insight? You now have to shift your collaboration over to the work bench that is going to pull all these components together to create the experiences and the automation that you're looking for. And that requires a different collaboration model around software development. And still incorporating the business expertise from those stakeholders, so that you're satisfying, not only the quality of the code to run the solution, but the quality towards the outcome that meets the expectation and the time to value that your stakeholders have. So data teams aren't just sitting in the basement or in another part of the organization and digitally disconnected anymore. You're finding that they're having to work much more closely and side by side with their colleagues and stakeholders. >> I think it's clear that you understand this space really well. Hubris out context in, I mean, that's kind of what's been lacking. And I'm glad you said you used the word anymore because I think it's a recognition that that's kind of what it was. They were down in the basement or out in some kind of silo. And I think, and I want to ask you this. I come back to organization because I think a lot of organizations look the most cost effective way for us to serve the business is to have a single data team with hyper specialized roles. That'll be the cheapest way, the most efficient way that we can serve them. And meanwhile, the business, which as you pointed out has the context is frustrated. They can't get to data. So there's this notion of a federated governance model is actually quite interesting. Are you seeing actual common use cases where this is being operationalized? >> Absolutely, I think the first place that you were seeing it was within the operational technology use cases. There the use cases where a lot of the manufacturing industrial device. Any sort of IOT based use case really recognized that without applying data and intelligence to whatever process was going to be executed. It was really going to be challenging to know that you're creating the right foundation, meeting the SLA requirements, and then ultimately bringing the right quality and integrity to the data, let alone any sort of data protection and regulatory compliance that has to be necessary. So you already started seeing the solution teams coming together with the data engineers, the solution developers, the analysts, and data scientists, and the business stakeholders to drive that. But that is starting to come back down into more of the IT mindset as well. And so DataOps starts to emerge from that paradigm into more of the corporate types of use cases and sort of parrot that because there are customer experience use cases that have an IOT or edge component to though. We live on our smart phones, we live on our smart watches, we've got our laptops. All of us have been put into virtual collaboration. And so we really need to take into account not just the insight of analytics but how do you feed that forward. And so this is really where you're seeing sort of the evolution of DataOps as a competency not only to engineer the data and collaborate but ensure that there sort of an activation and alignment where the value is going to come out, and still being trusted and governed. >> I got kind of a weird question, but I'm going. I was talking to somebody in Israel the other day and they told me masks are off, the economy's booming. And he noted that Israel said, hey, we're going to pay up for the price of a vaccine. The cost per dose out, 28 bucks or whatever it was. And he pointed out that the EU haggled big time and they don't want to pay $19. And as a result they're not as far along. Israel understood that the real value was opening up the economy. And so there's an analogy here which I want to come back to my organization and it relates to the DataOps. Is if the real metric is, hey, I have an idea for a data product. How long does it take to go from idea to monetization? That seems to me to be a better KPI than how much storage I have, or how much geometry petabytes I'm managing. So my question is, and it relates to DataOps. Can that DataOps, should that DataOps individual maybe live, and then maybe even the data engineer live inside of the business and is that even feasible technically with this notion of federated governance? Are you seeing that and maybe talk a little bit more about this DataOps role. Is it. >> Yeah. >> Fungible. >> Yeah, it's definitely fungible. And in fact, when I talked about sort of those three units of there's your core enterprise data services, there's your BI and data, and then there's your line of business. All of those, the engineering and the ops is the DataOps which is living in all of those environments and being as close as possible to where the value proposition is being defined and designed. So absolutely being able to federate that. And I think the other piece on DataOps that is really important is recognizing how the practices around continuous integration and continuous deployment using agile methodologies is really reshaping. A lot of the waterfall approaches that were done before where data was lagging 12 to 18 months behind any sort of insights, but a lot of the platforms today assume that you're moving into a standard mature software development life cycle. And you can start seeing returns on investment within a quarter, really, so that you can iterate and then speed that up so that you're delivering new value every two weeks. But it does change the mindset this DataOps team aligned to solution development, aligned to a broader portfolio management of business capabilities and outcomes needs to understand how to appropriately scope the data products that they're delivering to incremental value-based milestones. So the business feels that they're getting improvements over time and not just waiting. So there's an MVP, you move forward on that and optimize, optimize, extend scale. So again, that CICD mindset is helping to not bottleneck and wait for the complete field of dreams to come from your data and your insights. >> Thank you for that, Michelle. I want to come back to this idea of collaboration because over the last decade we've seen attempts, I've seen software come out to try to help the various roles collaborate and some of it's been okay, but you have these hyper specialized roles. You've got data scientists, data engineers, quality engineers, analysts, et cetera. And they tend to be in their own little worlds. But at the end of the day we rely on them all to get answers. So how can these data scientists, all these stewards, how can they collaborate better? What are you seeing there? >> You need to get them onto the same process. That's really what it comes down to. If you're working from different points of view, that's one thing. But if you're working from different processes collaborating is really challenging. And I think the one thing that's really come out of this move to machine learning and AI is recognizing that you need processes that reinforce collaboration. So that's number one. So you see agile development in CICD not just for DataOps, not just for DevOps, but also encouraging and propelling these projects and iterations for the data science teams as well or even if there's machine learning engineers incorporated. And then certainly the business stakeholders are inserted within there as appropriate to accept what it is that is going to be developed. So processes is number one. And number two is what is the platform that's going to reinforce those processes and collaboration. And it's really about what's being shared. How do you share? So certainly what we're seeing within the platforms themselves is everybody contributing into some sort of a library where their components and products are being ascribed to and then that's able to help different teams grab those components and build out what those solutions are going to be. And in fact, what gets really cool about that is you don't always need hardcore data scientists anymore as you have this social platform for data product and analytic product development. This is where a lot of the auto ML begins because those who are less data science-oriented but can build an insight pipeline, can grab all the different components from the pipelines to the transformations, to capture mechanisms, to bolting into the model itself and allowing that to be delivered to the application. So really kind of balancing out between process and platforms that enable and encourage, and almost force you to collaborate and manage through sharing. >> Thank you for that. I want to ask you about the role data governance. You've mentioned trust and that's data quality, and you've got teams that are focused on and specialists focused on data quality. There's the data catalog. Here's my question. You mentioned edge a couple of times and I can see a lot of that. I mean, today, most AI is are a lot of value, I would say most is modeling. And in the future, you mentioned edge it's going to be a lot of influencing in real time. And people maybe not going to have the time or be involved in that decision. So what are you seeing in terms of data governance, federate. We talked about federated governance, this notion of a data catalog and maybe automating data quality without necessarily having it be so labor intensive. What are you seeing the trends there? >> Yeah, so I think our new environment, our new normal is that you have to be composable, interoperable, and portable. Portability is really the key here. So from a cataloging perspective and governance we would bring everything together into our catalogs and business glossaries. And it would be a reference point, it was like a massive Wiki. Well, that's wonderful, but why just how's it in a museum. You really want to activate that. And I think what's interesting about the technologies today for governance is that you can turn those rules, and business logic, and policies into services that are composable components and bring those into the solutions that you're defining. And in that way what happens is that creates portability. You can drive them wherever they need to go. But from the composability and the interoperability portion of that you can put those services in the right place at the right time for what you need for an outcome so that you start to become behaviorally driven on executing on governance rather than trying to write all of the governance down into transformations and controls to where the data lives. You can have quality and observability of that quality and performance right at the edge and context of behavior and use of that solution. You can run those services and in governance on gateways that are managing and routing information at those edge solutions and we synchronization between the edge and the cloud comes up. And if it's appropriate during synchronization of the data back into the data lake you can run those services there. So there's a lot more flexibility and elasticity for today's modern approaches to cataloging, and glossaries, and governance of data than we had before. And that goes back into what we talked about earlier of like, this is the new wave of DataOps. This is how you bring data products to fruition now. Everything is about activation. >> So how do you see the future of DataOps? I mean, I kind of been pushing you to a more decentralized model where the business has more control 'cause the business has the context. I mean, I feel as though, hey, we've done a great job of contextualizing our operational systems. The sales team they know when the data is crap within my CRM, but our data systems are context agnostic generally. And you obviously understand that problem well. But so how do you see the future of DataOps? >> So I think what's kind of interesting about that is we're going to go to governance on greed versus governance on right more so. What do I mean by that? That means that from a business perspective there's two sides of it. There's ensuring that where governance is run is as we talked about before executing at the appropriate place at the appropriate time. It's semantically domain-centric driven not logical and systems centric. So that's number one. Number two is also recognizing that business owners or business operations actually plays a role in this, because as you're working within your CRM systems, like a Salesforce, for example you're using an iPaaS MuleSoft to connect to other applications, connect to other data sources, connect to other analytics sources. And what's happening there is that the data is being modeled and personalized to whatever view insight our task has to happen within those processes. So even CRM environments where we think of as sort of traditional technologies that we're used to are getting a lift, both in terms of intelligence from the data but also your flexibility and how you execute governance and quality services within that environment. And that actually opens up the data foundations a lot more and avoids you from having to do a lot of moving, copying centralizing data and creating an over-weighted business application and an over, both in terms of the data foundation but also in terms of the types of business services, and status updates, and processes that happen in the application itself. You're drawing those tasks back down to where they should be and where performance can be managed rather than trying to over customize your application environment. And that gives you a lot more flexibility later too for any sort of upgrades or migrations that you want to make because all of the logic is contained back down in a service layer instead. >> Great perspectives, Michelle, you obviously know your stuff and it's been a pleasure having you on. My last question is when you look out there anything that really excites you or any specific research that you're working on that you want to share, that you're super pumped about? >> I think there's two things. One is it's truly incredible the amount of insight and growth that is coming through data profiling and observation. Really understanding and contextualizing data anomalies so that you understand is data helping or hurting the business value and tying it very specifically to processes and metrics, which is fantastic as well as models themselves like really understanding how data inputs and outputs are making a difference whether the model performs or not. And then I think the second thing is really the emergence of more active data, active insights. And as what we talked about before your ability to package up services for governance and quality in particular that allow you to scale your data out towards the edge or where it's needed. And doing so not just so that you can run analytics but that you're also driving overall processes and value. So the research around the operationalization and activation of data is really exciting. And looking at the networks and service mesh to bring those things together is kind of where I'm focusing right now because what's the point of having data in a database if it's not providing any value. >> Michele Goetz, Forrester Research, thanks so much for coming on theCUBE. Really awesome perspectives. You're in an exciting space, so appreciate your time. >> Absolutely, thank you. >> And thank you for watching Data Citizens '21 on theCUBE. My name is Dave Vellante. (upbeat music)

>> From around the globe, it's theCube covering Data Citizens '21, brought to you by Collibra. >> For the past decade, organizations have been effecting very deliberate data strategies investing quite heavily in people, processes, and technology specifically designed to gain insights from data, better serve customers, drive new revenue streams, we've heard this before. The results quite frankly have been mixed. As much of the effort is focused on analytics and technology designed to create a single version of the truth, which in many cases continues to be elusive. Moreover, the world of data is changing, data is increasingly distributed making collaboration in governance more challenging especially where operational use cases are a priority. Hello, everyone, my name is Dave Vellante and you're watching theCube's coverage of Data Citizens '21. And we're pleased to welcome Michele Goetz, who's the Vice President and Principal Analyst at Forrester Research. Hello, Michele, welcome to theCube. >> Hi, Dave thanks for having me today. >> It's our pleasure. So I want to start, you serve have a wide range of roles including enterprise architects, CDOs, chief data officers that is, the analyst et cetera, and many data related functions. And my first question is what are they thinking about today? What's on their minds? These data experts. >> So there's actually two things happening. One is what is the demand that's placed on data for our new intelligent digital systems. So we're seeing a lot of investment and interest in things like edge computing. And then how does that intersect with artificial intelligence to really run your business intelligently and drive new value propositions, to be both adaptive to the market as well as resilient to changes that are unforeseen. The second thing is then you create this massive complexity to managing the data, governing the data, orchestrating the data, because it's not just a centralized data warehouse environment anymore. You have a highly diverse and distributed landscape that you both control internally, as well as taking advantage of third party information. So really what the struggle then becomes is how do you trust the data? How do you govern it and secure or protect that data? And then how do you ensure that it's hyper-contextualized to the types of value propositions that our intelligence systems are going to serve? >> Well, I think you're hitting on the key issues here. I mean, you're right, the data and I sort of refer to this as well as sort of out there it's distributed as at the edge, but generally our data organizations are actually quite centralized. And as well, you talk about the need to trust the data, obviously that's crucial. But are you seeing the organization change? I know you're talking about this to clients, your discussion about collaboration. How are you seeing that change? >> Yeah, so as you have to bring data into context of the insights that you're trying to get or the intelligence that's automating and scaling out the value streams and outcomes within your business. We're actually seeing a federated model emerge in organizations. So while there's still a centralized data management and data services organization led typically by enterprise architects for data, a data engineering team that's managing warehouses and data lakes. They're creating this great platform to access and orchestrate information, but we're also seeing data and analytics and governance teams come together under chief data officers or chief data and analytics officers. And this is really where the insights are being generated from either BI and analytics or from data science itself and having dedicated data engineers and stewards that are helping to access and prepare data for analytic efforts. And then lastly, this is the really interesting part is when you push data into the edge, the goal is that you're actually driving an experience and an application. And so in that case, we are seeing data engineering teams starting to be incorporated into the solutions teams that are aligned to lines of business or divisions themselves. And so really what's happening is if there is a solution consultant who is also overseeing value-based portfolio management when you need to instrument the data to these new use cases and keep up with the pace of the business, it's this engineering team that is part of the DevOps work bench to execute on that. So really the balances we need the core, we need to get to the insights and build our models for AI. And then the next piece is how do you activate all that and there's a team over there to help? So it's really spreading the wealth and expertise where it needs to go. >> Yeah, I love that you to, a couple of things that really resonated with me. You talked about context a couple of times and this notion of a federated model, because historically the sort of big data architecture, the team, they didn't have the context, the business context, and you're the, my inference is that's changing. And I think that's critical. Your talk at Data Citizens is called how obsessive collaboration fuels scalable DataOps. You talk about the data, the DevOps team. What's the premise you put forth to the audience? >> So the point about obsessive collaboration is sort of taking the hubris out of your expertise on the data. Certainly, there's a recognition by data professionals that the business understands and owns their data. They know the semantics, they know the context of it and just receiving the requirements on that was assumed to be okay. And then you could provide a data foundation whether it's just a lake or whether you have a warehouse environment where you're pulling for your analytics. The reality is that as we move into more of AI machine learning type of model, one, more context is necessary and you're kind of balancing between what are the things that you can ascribe to the data globally which is what data engineers can support. And then there's what is unique about the data and the context of about the data that is related to the business value and outcome as well as the feature engineering that is being done on the machine learning models. So there has to be a really tight link and collaboration between the data engineers, the data scientists, and analysts, and the business stakeholders themselves. You see a lot of pods starting up that way to build the intelligence within the system. And then lastly, what do you do with that model? What do you do with that data? What do you do with that insight? You now have to shift your collaboration over to the work bench that is going to pull all these components together to create the experiences and the automation that you're looking for. And that requires a different collaboration model around software development and still incorporating the business expertise from those stakeholders so that you're satisfying, not only the quality of the code to run the solution, but the quality towards the outcome that meets the expectation and the time to value that your stakeholders have. So data teams aren't just sitting in the basement or in another part of the organization and digitally, disconnected anymore. You're finding that they're having to work much more closely and side by side with their colleagues and stakeholders. >> I think it's clear that you understand this space really well, hubris out, context in, I mean, that's kind of what's been lacking. And I'm glad you said, you used the word anymore because I think it's a recognition that that's kind of what it was. They were down in the basement or out in some kind of silo. And I think, and I want to ask you this, I'll come back to organization because I think a lot of organizations, look the most cost effective way for us to serve the businesses to have a single data team with hyper-specialized roles, that'll be the cheapest way, the most efficient way that we can serve them. And meanwhile, the business which as you pointed out has the context is frustrated. They can't get to data. So this notion of a federated governance model is actually quite interesting. Are you seeing actual common use cases where this is being operationalized? >> Absolutely, I think the first place that you were seeing it was within the operational technology use cases. The use cases where a lot of the manufacturing, industrial device, any sort of IoT-based use case really recognized that without applying data and intelligence to whatever process was going to be executed, it was really going to be challenging to know that you're creating the right foundation, meeting the SLA requirements, and then ultimately bringing the right quality and integrity to the data, let alone any sort of data protection and regulatory compliance that has to be necessary. So you already started seeing the solution teams coming together with the data engineers, the solution developers, the analysts, and data scientists, and the business stakeholders to drive that. But that is starting to come back down into more of the IT mindset as well. And so DataOps starts to emerge from that paradigm into more of the corporate types of use cases and sort of parrot that because there are customer experience use cases that have an IoT or edge component to them. We live on our smart phones, we live on our smart watches, we've got our laptops, all of us have been put into virtual collaboration. And so we really need to take into account not just the insight of analytics, but how do you feed that, you know, feed that forward. And so this is really where you're seeing sort of the evolution of DataOps as a competency not only to engineer the data and collaborate, but ensure that there sort of an activation and alignment where the value is going to come out and still being trusted and governed. >> I've got kind of a weird question, but I'm going to (indistinct). I was talking to somebody in Israel the other day and they told me masks are off, the economy's booming. And he noted that Israel said, "Hey, we're going to pay up for the price of a vaccine, the cost per dose around 28 bucks," or whatever it was. And he pointed out that the EU haggled big time and they go, "We're going to pay $19." And as a result, they're not, you know, as far along Israel understood that the real value was opening up the economy. And so there's an analogy here, which I want to come back to my organization and it relates to the DataOps. If the real metric is, "Hey, I have an idea for a data product." How long does it take to go from idea to monetization? That seems to me to be a better KPI than, you know, how much storage I have or how much petabytes I'm managing. So my question is, and it relates to DataOps, can that DataOps, should that DataOps individual maybe live and then maybe even the data engineer live inside of the business and is that even feasible technically with this notion of federated governance? Are you seeing that? And maybe talk a little bit more about this DataOps role. Is it-- >> Yeah. >> Fungible? >> Yeah, it's definitely fungible. And in fact, when I talked about sort of those three units of there's your core enterprise data services, there's your BI and data and then there's your line of business. All of those, the engineering and the ops is the DataOps which is living in all of those environments and being as close as possible to where the value proposition is being defined and designed. So absolutely being able to federate that. And I think the other piece on DataOps that is really important is recognizing how the practices around continuous integration and continuous deployment using agile methodologies is really reshaping a lot of the waterfall approaches that were done before where data was lagging 12 to 18 months behind any sort of insights, but a lot of the platforms today assume that you're moving into a standard mature software development life cycle. And you can start seeing returns on investment within a quarter really, so that you can iterate and then speed that up so that you're delivering new value every two weeks. But it does change the mindset, this DataOps team align to solution development, align to a broader portfolio management of business capabilities and outcomes needs to understand how to appropriately stop the data products that they're delivering to incremental value based milestones. So the business feels that they're getting improvements over time and not just waiting. So there's an MVP, you move forward on that and optimize, optimize, extend scale. So again, that CICD mindset is helping to not bottleneck and wait for the complete field of dreams to come from your data and your insights. >> Thank you for that, Michele. I want to come back to this idea of collaboration 'cause over the last decade, we've seen attempts. I've seen software come out to try to help the various roles, collaborate and some of it's been okay, but you have these hyper-specialized roles. You've got data scientists, data engineers, quality engineers, analysts, et cetera. And they tend to be in their own little worlds. But at the end of the day, we rely on them all to get answers. So how can these data scientists, all these stewards, how can they collaborate better? What are you seeing there? >> You need to get them onto the same process, that's really what it comes down to. If you're working from different points of view, that's one thing. But if you're working from different processes, collaborating is really challenging. And I think the one thing that's really come out of this move to machine learning and AI is recognizing that you need processes that reinforce collaboration. So that's number one. So you see agile development in CICD not just for DataOps, not just for DevOps, but also encouraging and propelling these projects and iterations before the data science teams as well or even if there's machine learning engineers incorporated. And then, certainly the business stakeholders are inserted within there as appropriate to accept what it is that is going to be developed. So process is number one. Number two is what is the platform that's going to reinforce those processes and collaboration. And it's really about what's being shared. How do you share? So certainly what we're seeing within the platforms themselves is everybody contributing into some sort of a library where their components and products are being ascribed to and then that's able to help different teams grab those components and build out what those solutions are going to be. And in fact, what gets really cool about that is you don't always need hardcore data scientists anymore as you have this social platform for data product and analytic product development. This is where a lot of the auto ML begins because those who are less data science oriented but can build an insight pipeline, can grab all the different components from the pipelines to the transformations, to capture mechanisms, to bolting into the model itself and allowing that to be delivered to the application. So really kind of balancing out between process and platforms that enable and encourage and almost force you to collaborate and manage through sharing. >> Thank you for that I want to ask you about the role of data governance. You've mentioned trust and that's data quality and you've got teams that are focused on and specialists focused on data quality. There's the data catalog and here's my question. You mentioned edge a couple of times and I can see a lot of that. I mean, today, most AI is a lot of the AI, I would say most is modeling. And in the future, you mentioned edge. It's going to be a lot of inferencing in real-time. And you know people maybe not going to have the time or be involved in that decision. So what are you seeing in terms of data governance, federate, we talked about federated governance, this notion of a data catalog and maybe automating data quality without necessarily having it be so labor-intensive. What are you seeing trends there? >> Yeah, so I think our new environment, our new normal is that you have to be composable, interoperable, and portable. Portability is really the key here. So from a cataloging perspective, in governance we would bring everything together into our catalogs and business glossaries. And it would be a reference point. It was like a massive Wiki. Well, that's wonderful, but why just how's it in a museum you really want to activate that. And I think what's interesting about the technologies today for governance is that you can turn those rules and business logic and policies into services that are composable components and bring those into the solutions that you're defining. And in that way, what happens is that creates portability. You can drive them wherever they need to go. But from the composability and the interoperability portion of that, you can put those services in the right place at the right time for what you need for an outcome so that you start to become behaviorally-driven on executing on governance, rather than trying to write all of the governance down into transformations and controls to where the data lives. You can have quality and observability of that quality and performance right at the edge in context of behavior and use of that solution. You can run those services and in governance on gateways that are managing and routing information at those edge solutions and where synchronization between the edge and the cloud comes up. And if it's appropriate during synchronization of the data back into the data lake, you can run those services there. So there's a lot more flexibility and elasticity for today's modern approaches to cataloging and glossaries and governance of data than we had before. And that goes back into what we talked about earlier of like this is the new wave of DataOps. This is how you bring data products to fruition now everything is about activation. >> So how do you see the future of DataOps? I mean, I kind of been pushing you to a more decentralized model where the business has more control 'cause the business has the context. I mean, I feel as though, hey, we've done a great job of contextualizing our operational systems. The sales team, they know when the data is crap within my CRM, but our data systems are context agnostic, which you know, generally and you obviously understand that problem well but so how do you see the future of DataOps? >> So I think what's kind of interesting about that is we're going to go to governance on greed versus governance on right, more so. What do I mean by that? That means that from a business perspective there's two sides of it. There's ensuring that where governance is run as we talked about before executing at the appropriate place at the appropriate time. It's semantically domain centric driven not logical and systems centric. So that's number one. Number two is also recognizing that business owners or business operations actually plays a role in this because as you're working within your CRM systems like a Salesforce, for example, you're using an I-PASS environment MuleSoft to connect to other applications, connect to other data sources, connect to other analytics sources, and what's happening there is that the data is being modeled and personalized to whatever view, insight, or task has to happen within those processes. So even CRM environments where we think of as sort of traditional technologies that we're used to are getting a lift to both in terms of intelligence from the data but also your flexibility and how you execute governance and quality services within that environment. And that actually opens up the data foundations a lot more and avoids you from having to do a lot of moving, copying, centralizing data, and creating an over-weighted business application and an over, you know, both in terms of the data foundation but also in terms of the types of business services and status updates and processes that happen in the application itself. You're drawing those tasks back down to where they should be and where performance can be managed rather than trying to over customize your application environment. And that gives you a lot more flexibility later too for any sort of upgrades or migrations that you want to make because all of the logic is contained back down in a service layer instead. >> Great perspectives, Michele, you obviously know your stuff and it's been a pleasure having you on. My last question is when you look out there anything that really excites you or any specific research that you're working on that you want to share that you're super-pumped about. >> I think there's two things. One is it's truly incredible the amount of insight and growth that is coming through data profiling and observation, really understanding and contextualizing data anomalies so that you understand is data helping or hurting the business value. And, you know tying it very specifically to processes and metrics which is fantastic as well as models themselves like really understanding how data inputs and outputs are making a difference whether the model performs or not. And then I think the second thing is really the emergence of more active data, active insights, as what we talked about before your ability to package up services for governance and quality in particular that allow you to scale your data out towards the edge or where it's needed and doing so, you know not just so that you can run analytics but that you're also driving overall processes and value. So the research around the operationalization and activation of data is really exciting. And looking at the networks and service mesh to bring those things is kind of where I'm focusing right now because what's the point of having data in a database if it's not providing any value. >> Michele Goetz, Forrester Research, thanks so much for coming on theCube really awesome perspectives. You're in an exciting space. So appreciate your time. >> Absolutely, thank you. >> And thank you for watching Data Citizens '21 on theCube. My name is Dave Vellante. (upbeat music)

>> Announcer: From around the globe, it's theCUBE, with digital coverage of AWS re:Invent 2020, sponsored by Intel, AWS and our community partners. >> Hey is Keith Townsend a CTO Advisor on the Twitter and we have yet another CUBE alum for this, AWS re:Invent 2020 virtual coverage. AWS re:Invent 2020 unlike any other, I think it's safe to say unlike any other virtual event, AWS, nearly 60, 70,000 people in person, every conference, there's hundreds of thousands of people tuning in to watch the coverage, and we're talking to builders. No exception to that is our friends at Densify, co founder and CTO of Densify Andrew Hillier, welcome back to the show. >> Thanks, Keith, it's great to be with you again. >> So we're recording this right before it gets cold in Toronto. I hope you're enjoying some of this, breaking the cold weather? >> Yeah, no, we're getting the same whether you are right now it's fantastic. We're ready for the worst, I think in the shorter days, but we'll get through it. >> So for those of you that haven't watched any of the past episodes of theCUBE in which Andrew has appeared. Andrew can you recap, Densify, what do you guys do? >> Well, we're analytics where you can think of us as very advanced cost analytics for cloud and containers. And when I say advanced, what I mean is, there's a number of different aspects of cost, there's understanding your bill, there's how to purchase. And we do those, but we also focus heavily on the resources that you're buying, and try to change that behavior. So it's basically, boils down to a business value of saving a ton of money, but by actually changing what you're using in the cloud, as well as providing visibility. So it's, again, a form of cost optimization, but combined with resource optimization. >> So cost of resource optimization, we understand this stuff on-premises, we understand network, compute, storage, heating, cooling, etc. All of that is abstracted from us in the public cloud, what are the drivers for cost in the public cloud? >> Well, I think you directly or indirectly pay for all of those things. The funny thing about it is that it happens in a very different way. And I think everybody's aware, of course, on-demand, and be able to get resources when you need them. But the flip side of on-demand, the not so good size, is it causes what we call micro-purchasing. So when you're buying stuff, if you go and turn on a, like an Amazon Cloud instance, you're paying for that instance, you're paying Rogers and storage as well. And, implicitly for some networking, a few dollars at a time. And that really kind of creates a new situation and scale because all of a sudden now what was a control purchase on-prem, becomes a bunch of possibly junior people buying things in a very granular way, that adds up to a huge amount of money. So the very thing that makes cloud powerful, the on-demand aspects, the elasticity, also causes a very different form of purchasing behavior, which I think is one of the causes of the cost problem. >> So we're about 10, 12 years into this cloud movement, where public cloud has really become mainstream inside of traditional enterprises. What are some of the common themes you've seen when it comes to good cloud management, the cost management, hygiene across organizations? >> Yeah, and hygiene is a great word for that. I think it's evolved, you're right it's been around this is nothing new. I mean, we've probably been going to cloud expos for over a decade now. But it's kind of coming waves as far as the business problem, I think the initial problem was more around, I don't understand this bill. 'Cause to your point, all those things that you purchase on-prem, you're still purchasing in some way, and a bunch of other services. And it all shows up in this really complicated bill. And so you're trying to figure out, well, who in my organization owes what. And so that was a very early driver years ago, we saw a lot of focus on slicing and dicing the bill, as we like to call it. And then that led to well, now I know where my costs are going, can I purchase a little more intelligently. And so that was the next step. And that was an interesting step because what the problem is, the people that care about cost can't always change what's being used, but they can buy discounts and coupons, and RIs and Savings Plans. So we saw that there was a, then start to be focused on, I'm going to come up with ways of buying it, where I can get a bit of a discount. And it's like having a phone bill where I can't stop people making long distance calls, but I can get on a better phone plan. And that, kind of the second wave, and what we're seeing is the next big wave now is that, okay, I've done that, now I actually should just change what I'm actually using because, there's a lot of inefficiency in there. I've got a handle on those other problems, I need to actually, hopefully make people not buy giant instances all the time, for example. >> So let's talk about that feedback loop, understand what's driving the cost, the people that's consuming that, those services and need to understand those costs. How does Densify breach that gap? >> Well, again, we have aspects of our product that lineup with basically all three of those business problems I mentioned. So there's a there's a cloud cost intelligence module that basically lets you look at the bill any different ways by different tags. Look for anomalies, we find that very important, you say, well, this something unusual happened in my bill. So there's aspect that just focuses on kind of accountability of what's happening in the cost world. And then now, one of the strengths of our product is that when we do our analytics, we look at a whole lot of things at once. So we look at, the instances and their utilization, and what the catalog is, and the RIs and Savings Plans, and everything all together. So if you want to purchase more intelligently, that can be very complicated. So we see a lot of customers that say, well, I do want to buy savings plans, but man, it's difficult to figure out exactly what to do. So we like to think of ourselves as kind of a, it's almost like a, an analytics engine that's got an equation with a lot of terms in. It's got a lot of detail of what we're taking into account when we tell you what you should be doing. And that helps you by more intelligently, it also helps you consume more intelligently, 'cause they're all interrelated. I don't want to change an instance I'm using if there's no RI on it, that would take you backwards. I don't want to buy RIs for instances that I shouldn't be using, that takes you backwards. So it's all interconnected. And we feel that looking at everything at once is the path to getting the right answer. And having the right answer is the path to having people actually make a change. >> So when I interviewed you a few years ago, we talked about very high level containers, and how containers is changing the way that we can consume Cloud Services, containers introduced this concept of oversubscription, and the public cloud. We couldn't really oversubscribe and for large instance, back then. But we can now with containers, how are containers in general complicating cloud costing? >> So it's interesting because they do allow overcommit but not in the same way that a virtual environment does. So in a virtual environment, if I say I need two CPUs for job X, I need two CPUs for job Y, I can put them both on a machine that has two CPUs, and there will be over committed. So over committed in a virtual environment, it is a very well established operation. It lets you get past people asking for too much effectively. Containers don't quite do that in the same way, when they refer to overcommit, they refer to the fact that you can ask for one CPU, but you can use up to four, and that difference is if you overcommit. But the fact that I'm asking for one CPU is actually a pretty big problem. So let me give an example. If I look into my laptop here, and I've got Outlook and Word and all these things on it, and I had to tell you how many millicores I had to give each one, or with Zoom, let's see I'm running Zoom. Now, well, I want Zoom to work well, I want to give it $4,000 millicores, I want to give it four CPUs, because it uses that when it needs it. But my PowerPoint, I also want to give 4000 or $2,000 millicores. So I add all these things up of what I need based on the actual more granular requirements. And it might add up to four laptops. But containers don't overcommit the same way, if I asked for those requests by using containers, I actually will use for laptops. So it's those request values that are the trick, if I say I need a CPU, I get a CPU, it's not the same as a virtual CPU would be in a virtual environment. So we see that as the cause of a lot of the problem and that people quite rationally say I need these resources for these containers. But because containers are much more granular, I'm asking for a lot of individual resource, that when you add them up, it's a ton of resources. So almost every container running, we see that they're very low utilization, because everybody, rightfully so asked for individual resources for each container, but they are the wrong resources, or in aggregate, it's not creating the behavior you wanted. So we find containers are a bit, people think they're going to magically cause problems to go away. But in fact, what happens is, when you start running a lot of them, you end up just with a ton of cost. And people are just starting to get to that point now. >> Yeah, I can see how that could easily be the case inside of a virtual environment. I can easily save my VM needs four CPUs, four VCPUs. And I can do that across 100 applications. And that really doesn't cost me a lot in the private data center, tools like VMware, DRS, and all of that kind of fix that for me on the back-end is magical. In the public cloud, if I ask for four CPUs, I get four CPUs, and I'm going to pay for four CPUs, even if I don't utilize it, there's no auto-balancing. So how does Densify help actually solve that problem? >> Well, so they, there's multiple aspects for that problem, ones of the thing was that people don't necessarily ask for the right thing in the first place, that's one of the biggest ones. So, I give the example of, I need to give Zoom 4,000 millicores, that's probably not true at all, if I analyze what it's doing, maybe for a second it uses that, but for the most of the time, it's not using nearly those resources. So the first step is to analyze the container behavior patterns, and say, well, those numbers should be different. And so for example, the one thing we do with that is, we say, well if a developer is using terraform templates to stand up containers, we can say, instead of putting the number 1000, in that, a thousand millercores, or 400 millicores in your template, just put a variable and that references our analytics, just let the analytics figure what that number should be. And so it's a very elegant solution to say, the machine learning will actually figure out what resources that container needs, 'cause humans are not very good at it, especially when there's 10s of thousands of containers. So that's kind of the, one of the big things is to optimize the container of requests. And then once you've done that the nodes that you're running on can be optimized, because now they start to look different. Maybe you don't have, you don't need as much memory or as much CPU. So it's all again, it's all interrelated, but it's a methodical step that's based on analytics. And, people, they're too busy to figure this out, that they can't figure it out for thousands of things. Again, if I asked you don't get your laptop, on your laptop, how many miillicores do you need to get PowerPoint? You don't know. But in containers, you have to know. So we're saying let the machine figure out. >> Yes kind of like when you're asked how many miillicores do you need to give Zoom answer's yes. >> Yeah exactly. >> (laughs) So at the end of the day, you need some way to quantify that. So you guys are doing the two things. One, you're quantifying, you're measuring how much this application typically take. And then when I go to provision it, we're using a tool like terraform. Though then instead of me answering the question, the answer is go ask Densify, and Densify will tell you, and then I'll optimize my environment. So I get both ends of that equation, if I'm kind of summarizing it correctly. >> Absolutely. And that last part is extremely important because, in a legacy environment, like in a virtual environment, I can call an API and change the size of VM, and it will stay that way. And so that's a viable automation strategy for those types of environments. In the cloud, or when you're using terraform, or in containers, they will go right back to what's in the terraform template, that's one of the powerful things about terraform is that it always matches what's in the code. So I can't go and change the cloud, it'll just go back to whatever is in the terraform template next time, it's provision. So we have to go upstream, you have to actually do it at the source, when you're provisioning applications, the actual resource specifications should be coming through at that point, you can't, you don't want to change them after the fact, you can update the terraform and redeploy with a new value, that that's the way to do automation in a container environment, it doesn't, you can't do it, like you did in a VMware environment, because it won't stick, it just gets undone the next time the DevOps pipeline triggers. So it's both a, it's a big opportunity for a kind of a whole new generation of automation, doing it, we call it CICDCO. It's, Continuous Integration, Continuous Delivery, Continuous Optimization. It's just part of the, of the fabric of the way you deploy Ops, and it's a much more elegant way to do it. >> So you hit two trigger words, or a few trigger terms, one, DevOps, two, I'm saying DevOps, CICD, and Continuous Operations. What is the typical profile of a Densify customer? >> Well, usually, they're a mix of a bunch of different technologies. So I don't want to make it sound like you have to be a DevOps shop to benefit from this, most of our customers have some DevOps teams, they also have a lot of legacy workloads, they have virtual environments, they have cloud environments. So don't necessarily have 100%, of all of these things. But usually, it's a mix of things where, there might be some newer born in the cloud as being deployed, and this whole CICDCO concept really makes sense for them, they might just have another few thousand cloud instances that they stood up, not as a part of a DevOps pipeline, but just to run apps or maybe even migrated from on-prem. So it's a pretty big mix, we see almost every company has a mix, unless you just started a company yesterday, you're going to have a mix of some EC2 services that are kind of standalone and static, maybe some skill groups running, or containers running skill groups. And there's a generally a mix of these things. So the things I'm describing do not require DevOps, the notion of optimizing the cloud instances, by changing the marching orders when they're provisioned not after the fact, that that applies to any anybody using the cloud. And our customers tend to be a mix, some again very new, new school processes and born in the cloud. And some more legacy applications that are running that look a little more like on-prem environment would, where they're not turning on and off dynamically, they're just running transactional workloads. >> So let's talk about the kind of industries, because you you hit on a key point, we kind of associate a certain type of company with born in the cloud, et cetera. What type of organizations or industries are we seeing Densify deployed in. >> So we don't really have a specific market vertical that we focus on, we have a wide variety. So we find we have a lot of customers in financial services, banks, insurance companies. And I think that's because those are very large, complicated environments, where analytics really pay dividends, if you have a lot of business services, that are doing different things, and different criticality levels. The things I'm describing are very important. But we also have logistics companies, software companies. So again, complexity plays a part, I think elasticity plays a part in the organization that wants to be able to make use of the cloud in a smart way where they're more elastic, and obviously drive costs down. So again, we have customers across all different types of industries, manufacturing, pharmaceutical. So it's a broad range, we have partners as well that use our like IBM, that use our product, and their customers. So there's no one type of company that we focus on, certainly. But we do see, again, environments that are complicated or mission critical, or that they really want to run in a more of elastic way, those tend to be very good customers for us. >> Well, CUBE alum Andrew Hillier, thank you for joining us on theCUBE coverage of AWS re:Invent 2020 virtual. Say goodbye to a couple hundred thousand of your closest friends. >> Okay, and thanks for having me. >> That concludes our interview with Densify. We really appreciate the folks that Densify, having us again to have this conversation around workload analytics and management. To find out more of, well or find out just more great CUBE coverage, visit us on the web SiliconANGLE TV. Talk to you next episode of theCUBE. (upbeat music)

>> Narrator: From around the globe, It's theCube, with digital coverage, of Actifio data-driven 2020, brought to you by Actifio. >> Welcome back, I'm Stu Miniman, and this is theCube coverage of Actifio Data-driven 2020. Really excited to, dig into a fun topic. I have a Cube alumni with us he is a DevOps author, and researcher Gene Kim. Unicorn Project is the most recent, Gene, great to see you, thanks so much for joining us. >> Stu, great to see you again, here at the Actifio conference, this is all fantastic. >> Yeah, so your new book, it was much awaited out there, you know, Unicorn's always discussed out there, but you know, the Phoenix Project, as I said, is really this seminal, book when people say, What is that DevOps thing and how do I do it? So, why don't you give us a little bit as to The Unicorn Project, why is it important? Why we're excited to dig into this and, we'll, we'll tie it into the discussion we're having here for the next normal, at Actifio. >> For sure, yeah, in fact, yeah. As you might have heard in the keynote address, you know, the what, what vexed me, after the Phoenix project came out in 2013 is that there is still looming problems that still remain, seven years after the Phoenix project was written. And, you know, these problems I think are very important, around you and what does it really take to enable developers to truly be productive, instead of being locked in a tundra of technical debt. Two is, you know, how do we unlock truly the power of data so that we can help everyone make better decisions, whether it's a developer, or anyone, within the business units and the organizations that we serve. And then three is like, what are really the behaviors that we need from leadership to make these amazing transformations possible? And so The Unicorn Project really is, the fifth project retold, but instead of through the eyes of Ops leadership, is told through the eyes, of a phenomenal developer. And so it was amazing to revisit the, the Phoenix project universe, I in the same timeline, but told from a different point of view. And it was such a fun project to work on, just because, you know, to relive the story, and just expose all these other problems, not happening, not on the side, but from, the development and data side. >> Yeah. They've always these characters in there that, I know I personally, and many people I talked to can, you know, really associate with, there was a return of certain characters, quite prominent, like Brent, you know, don't be the bottleneck in your system. It's great, if you're a fighter firefighter, and can solve everything, but if everything has to come through you, you know, Pedro is always going off, he's getting no sleep and, you know, you'd just get stressed out. You talked a bit more, about the organization and there are the five ideals in the book. So maybe if you can, you know, strongly recommend, of course, anybody at ending active you, got a copy of the books they'll be able to read the whole thing, but, you know, give us the bumper sticker on some of those key learnings. >> Yeah, for sure, yeah. So the five ideals represents five ideas, I think are just very important, for everyone, the organization, serves, especially leadership. The first ideal is locality and simplicity. In other words, when you need to get something done, we should be able to get it done within our team, without having to do a lot of communication coordination, with people outside of our team. The worst, the most horrible feeling is that in order, to do a small little thing, you actually have, to have a, coordinated action that spans 15 teams, right. And that's why you can't get anything done, right? And so that's so much the hallmark of large complex organizations. The second ideal is that what I think the outcomes are, which is focused flow and joy, you know, I've not just now started to for the first time in 20 years, self identify, not as an ops person, but as a developer. And, I really now understand, why we got into technology in the first place. This so that we can solve the business problem at hand unencumbered by minute share. And that allows for a sense of focus flow and even joy. And I love how Dr. Mihaly Csikszentmihalyi, describe it. He said, flow is a state that we feel when we love our work, so much that we lose track of time, and maybe even sense of self. And so I think we all in technology understand, you know, that that is how it is on the best of days and how terrible it is, you know, when we don't have that sense of flow. Third ideal is improvement of daily work, being even more important than daily work itself. The notion is greatness is never free, we must create it and must prioritize it, for the psychological safety. And the fifth is customer focus. So those are all the things I think are so important, for modern leaders, because it really defines the future of work. >> Yeah, we love that flow and it happens otherwise we're stuck, in that waiting place as you quoted Dr. Csi. So one of my favorite books there, there also. So Gene, for this audience here, there was, you know, yes, CICD is wonderful and I need to be able to move and ship fast, but the real transformational power, for that organization was unlocking the value of data, which is, I think something that everybody here can. So maybe to talk a little bit about that you know, we, there there's, we've almost talked too much, you know, data is the new oil and things like that, but it's that, you know, that allowing everybody to tap in and leverage, you know, real time what's happening there were just at the early parts of the industry being able to unlock that future. >> Oh yeah, I love that phrase. Data is new oil, especially since oil, you know, the last 50 years, the standard Port 500 was dominated by, you know, resource extraction oil company and so forth. And now that is no longer true, it's dominated by the tech giants. And, Columbia there was a Columbia journalism review article that said, data's not only the new oil, is really the new soil. And for me, you know, my area of passion for the last seven years has been studying the DevOps enterprise community where, we're taking all the learnings that were really pioneered by the tech giants, Facebook, Amazon, Netflix, Google, Microsoft, and seeing how they're being adopted by the largest, most complex organizations on the planet, the best known brands across every industry vertical. And it's so true that, you know, where the real learning gets exploited right, is through data. I realized, this is how we get to know our customers better. This is how we understand their wants and needs. This is how we test, and make offers to them to see if they like it or not to see if they value it or not. And, and so for me, one of the best examples, of this was, the target transformation and Adidas how it was just an amazing example of, to what links they went to, to liberate developers from, being shackled by ancient systems of records, data warehouses, and truly enabled developers to get access to the data they need modify it, even delete information, all without having to be dependent on, you know, integration teams that were essentially holding them hostage for six to nine months. And, these programs really enable some of the most strategic programs at their organizations, you know, enabling hundreds of projects over the years. So, I think that is really, just showing to what extent, the value that is created by unlocking data for individuals. And sorry Stu, one more thing that I'm just always dazzled by my friend, Chris Berg. He told me that, somewhere between a third and a half of all company employees use data in their daily work. They either use data or manipulate data as part of the daily work, which, you know, that, population is actually larger than the number of developers in an organization. So it just shows you how big this problem is, and how much value we can create by addressing this problem. >> Well, it's interesting if it's only a third, we still have work to do. What we've been saying for years is, you know, when you talk about digital transformation, the thing that separates those that have transformed and those that haven't is data needs to be at the core. I just can't be doing things the way I was or doing things off intuition, you know, being data-driven, I'm sure you know, the same Gene, if you're not, if you don't have data, you know, you're just some other person with an opinion. >> Yeah, yeah. That's it this is a great point. And in Risto Siilasmaa's amazing book, Transforming Nokia, I mean, he was, he said exactly that. And he said something that was even more astonishing. He said, there's not only at the core, but data also has to be at the edges. You know, he was describing at Amazon, anyone can do an experiment @booking.com. Anyone can do an experiment to see, if they can create value for the customer. They don't need approvals from, committees or their manager. This is something that is really truly part of everyone's daily work. And so, to me, that was a huge aha moment that says, you know, to what degree, you know. Our cultures need to change so that we can not only, use data, but also create learnings and create new data, you know, that the rest of the organization can learn from as well. >> Yeah. One of the other things I definitely, you know, felt in your book, you synthesize so much of the learnings that you've had over the years from like the DevOps enterprise summit. The question I have for you is, you know, you hear some of these, you know, great stories, but the question is, our companies, are they moving fast enough? Have they transformed the entire business or have they taken, you know, we've got one slice of the business that is kind of modernized and we're going to get to the other 30 pieces along the way, but you know, there's wholesale change, you know, 2020 has had such a big impact. What's your thoughts on, you know, how we are doing in the enterprise on pace of change these days? >> That's a great question. I mean, I think some people, when they ask me, you know, how far are we into kind of total adoption of DevOps? It's a newer better way of working. And I would say probably somewhere between 5 and 7%, right, and the math I would take them through is, you know, there are about 20 million developers on the planet of which at best, I think, a million of them are working in a DevOps type way. But yet now that's only growing. I think it was an amazing presentation at DevOps surprise summit in London that was virtual from nationwide building society, the largest organization of its kind. It's a large financially mutually owned organization for housing in the UK. And, they touched about how, you know, post COVID post lockdown suddenly they found themselves able to do them reckless things that would have normally taken four years, in four weeks. And I think that's what almost every organization is learning these days is, when survival is at stake, you know, we can throw the rules out of the window, right. And do things in a way that are safe and responsible, but, you know, create satisfy the business urgent needs, like, you know, provisioning tens of thousand people to work from home safely. You know, I think the shows, I think it's such a powerful proof point of what technology can do when it is unleashed from, you know, perhaps unnecessarily burdensome rules and process. And I think the other point I would make Stu is that, what has been so rewarding is the population of these technology leaders presenting at DevOps enterprise, they're all being promoted, they're all being, being given new responsibilities because they, are demonstrating that they have the best longterm interest of the organization at heart. And, they're being given even more responsibilities because, to make a bigger impact through the organization. So I'm incredibly optimistic about the direction we're heading and even the pace we're going at. >> Well, Gene definitely 2020 has put a real highlight on how fast things have changed, not just work from home, but, but the homeschooling, you know, telehealth, there are so many things out there where there was no choice, but to move forward. So the, the second presentation you participated in was talking about that next normal. So give us a little bit of, you know, what does that mean? You know, what, what we should be looking at going forward? >> Yeah, it was great to catch up with my friend Paul Forte, who I've known for many, many years, and now, now a VP of sales at the Actifio and yeah, I think it is amazing that academic Dr. Colada Perez, she said, you know, in every turning point, you know, where, there's such a the stage for decades of economic prosperity usually comes, by something exactly like what we're going through now, a huge economic recession or depression, following a period of intense re regulations there's new, technology that's unlocking, you know, new ways of working. And she pointed exactly to what's happening in the Covid pandemic in terms of, how much, the way we're working is being revolutionized, not by choice, but out of necessity. And, you know, as she said, you know, we're now learning to what degree we can actually do our daily work without getting on airplanes or, you know, meeting people in person. So, I'm a hue, I have so many friends in the travel industry, right. I think we all want normalcy to return, but I think we are learning, you know, potentially, you know, there are more efficient ways to do things, that don't require a day of travel for a couple hour meeting and day to return, right. So, yeah, I think this is being demonstrated. I think this will unlock a whole bunch of ways of interacting that will create efficiency. So I don't think we're going, as you suggested, right. There will be a new normal, but the new normal is not going to be the same as your old normal. And I think it will be, in general for the better. >> So, Gene, you, you've gone to gotten to see some of the transformation happening in the organizations when it comes to developers, you know, the, the DevOps enterprise summit, the, the state of DevOps, you know. I think five years ago, we knew how important developers were, but there was such a gap between, well, the developers are kind of in the corner, they don't pay for anything. They're not tied to the enterprise. And today it feels like we have a more cohesive story that there, there is that if you put in The Unicorn Project, it's, you know, business and IT, you know. IT, and the developers can actually drive that change and the survival of the business. So, you know, are we there yet success or net developers now have a seat at the table? Or, you know, what do you see on that, that we still need to do? >> Yeah, I think we're still, I mean, I think we're getting there, we're closer than ever. And as my friend, Chris O'Malley the CEO of the famously resurgent mainframe vendor Compuware said, you know, it is, everyone is aware that you can't do any major initiatives these days without some investment in technology, right? In fact, you can't invest in anything without technology. So I think that is now better understood than ever. And, yeah, just the digital, it's a whole digital disruption, I think is really, no one needs to be convinced that if we organize large complex organizations, don't change, they're at a risk of, you know, being decimated by the organizations that can change using an exploiting technology, you know, to their benefit and to the other person's detriment. So, and that primarily comes through software and who creates software developers. So I, by the way, I love the Stripe it was a CFO for Stripe who said, the largest, constraint for them is, and their peers is not access to capital, it is access development talent. I think when you have CFOs talking like that, right. It does says it's suggested something really has changed in the economic environment that we all compete in. >> So, I mentioned that on the research side, one of the things I've loved reading over the years is that, fundamental discussion that, going faster does not mean, that I am sacrificing security, or, you know, the product itself, you know, in the last couple of years, it's, you know, what separates those really high performing companies, and, you know, just kind of the middle of the ground. So, what, what, what advice would you give out there, to make sure that I'm moving my company more along to those high performing methods. >> Yeah, but just to resonate with that, I was interviewing a friend of mine, Mike Nygaard, long time friend of mine, and we were talking on and we were recalling the first time we both heard the famous 2009 presentation doing 10 deploys a day, every day at flicker, by John Allspaw and Paul Hammond. And we were both incredulous, right there? We thought it was irresponsible reckless, and maybe even immoral what they were doing, because, you know, I think most organizations were doing three a year, and that was very problematic. How could one do 10 deploys a day. And I think, what we now know, with the size of evidence, especially through the state of DevOps research, is that, you know, for six years, 35,000 plus respondents, the only way that you can be reliable, and secure, is to do smaller deployments more frequently, right? It makes you, be able to respond quicker in the marketplace, allows you to have better stability and reliability in the operational environment, allows you to be more secure. It allows you to be able to, you know, increase market share, increase productivity, and, you know, have happier employees. So, you know, at this point, I think the research is so decisive, that, you know, we can, as a whole book accelerate, that really makes the case for that, that this is something that I now have moral certainty or even absolute certainty oh, right. It's, you know, self evident to me, and it, I think we should have confidence that that really is true. >> Wonderful work, Gene, thanks so much for giving us the update. I really appreciate it, some really good sessions here in Actifio, as well as the book. Thanks so much, great to talk to you. >> Stu is always a pleasure to see you again, and thank you so much. >> Alright, that's our coverage from Actifio Data-driven, be sure to check out thecube.net for all of the, on demand content, as well as, as I said, if you were part of the show, definitely recommend reading Gene's book, The Unicorn Project. I'm Stu Miniman. And thank you for watching the cube. (soft upbeat music)

>> From the Cube studios in Palo, Alto and Boston, connecting with thought leaders all around the world, this is a Cube conversation. >> Welcome to this Cube conversation. I'm Paul Gillin, Enterprise Editor at SiliconAngle. We've been talking a lot about cloud native on SiliconAngle lately, and my guest is someone who had a seminal role in defining the the principal architecture, some of the foundational technologies for cloud native applications. Ben Golub is the CEO of Storj, a company that has a really interesting new approach to storage management that we'll talk about in just a bit. Ben is probably best known to many people as the former CEO of Docker, which pioneered software containers and was one of the fastest growing companies in Silicon Valley, he's great. Ben, thanks for joining us, feel appreciated you being here today. >> Thank you, it's great to be here. >> So, let's get into the question of cloud native is a theme that we're focusing on right now. How important is it for organizations you believe that are moving to the cloud to choose to re architect around cloud native principles? >> Well, I think it's, I mean, two points. First of all, I think that the cloud native is sort of a spectrum. And for many people, there is a point along the spectrum that makes sense. At the far end of the spectrum is applications that are deployed on a massive scale. They're components to thousands of microservices, heavily orchestrated with things like Kubernetes, scaling up scaling down, and for many organizations, they don't need to go all the way there to get real benefits. And maybe the related thing is that I think, for organizations, there's absolutely, for most organizations, there's value in moving along that spectrum, but they should be thoughtful about where it is that they're going, and why they're going there. >> Either or thing and they applications can live along the spectrum. you submitted some comments recently for an article we did on this topic and among them you said that some applications may make sense being containerized or Dockerized but not being orchestrator with Kubernetes. Can you give an example of something that meets that criteria? >> Sure, well, I can I think that almost all applications can benefit from running on more cloud like infrastructure. There's certainly value in having infrastructure that that scales up, or that scales out there, where people are able to sort of dynamically use resources and not have it have to rely on big iron. But in terms of the applications themselves traditional applications can run well in cloud environments provided that some steps are taken. And often for many organizations, the first step with a traditional application is simply to containerize your Docker engine. That gives a lot of benefits including, ease of migration, greater ease of adopting things like CICD, and you don't necessarily have to take all of your traditional applications, break them into lots of micro services, start orchestrating them with Kubernetes on day one, for some reasons, case may never make sense because they're not going to be run at massive scale. >> Many people assume that containers and cloud native architecture are inextricably linked. Is that your opinion? >> Well, so I think that cloudy infrastructure tends to benefit from from containerization. But really, it's more of an application question. If you are breaking your application or you're writing applications that are composed of lots of different services, almost inevitably, you want to have those services in containers so that they have clean interfaces between them. And so, that you can do the things people want to do with cloud native, which is, make changes to your microservice A with a small team and do so rapidly without unintentionally screwing up microservice, B, C, D, et cetera. And Dockers in a containerization, among other things, provides that nice clean interface. >> All right, how have you seen I mean, since you left Docker three years ago, how have you seen the container technology evolve? What do you think are some of the most important evolutions we've seen in container technology since then? >> Yeah, so I think, what has been really important for us to see that the community continues to grow. So, once the Docker community continues to grow, there are now lots of other communities around it the cloud native computing Foundation, Kubernetes. And I think what you're seeing is really the maturing of this technology. So that applications can be written in a cloud native way much more more easily. The barriers to making an application cloud native really come down, but also the potential for running applications and really massive scale have have have increased and there are certainly a number of interesting things that have happened in the storage space in terms of persistent volumes. things that have happened in terms of service technology service measures, like STO these are all really great examples of how the community is filling in around containers. >> We've heard a lot about the benefits of portability that come from using containers. But being portable can involve some trade offs, because you have to give up some of the native functionality of branded cloud platforms. Do you think the goal of multi cloud is overblown? >> I think there is real value in being multi cloud. And I think that while you know, the larger stock traders have provided great services, it is in their names, of course, we're trying to get have all of the workloads run within their four walls. And I think for most organizations, locking is a bad idea, regardless, right? We're in a distributed world, most people want to be able to run their applications at scale in a distributed way and they want to be able to take advantage of spare cycles and the most efficient way and concise way of doing so. And so, having locking, I think is a bad idea. And for most organizations, the investment to become portable, while not trivial pays off in the long run. >> How about of the cultural issues is something that you also mentioned in the comments you contributed to us earlier, we hear often that the biggest impediments are not technical or even skills but actually changing the culture to adapt to a cloud native way of building applications. How should organizations prepare for that shift? >> Well, I mean, I think they should recognize what those differences are going to be. And if you're writing, the traditional method was you write a large monolithic application, because it's so big and complicated. Generally speaking, people follow sort of a waterfall procedure. They have large teams working on it, and you update the application once or twice a year. The cloud native approach is let's write applications that are composed of lots of smaller services produced by smaller teams that move very rapidly. And a lot of the testing and the deployment happens in a very automated way. And the cultural barriers are pretty large. I think most people are happy at the end of the journey. But there's a period in between where things are difficult that you're, you're breaking glass as it were. So, I think for a lot of large organizations, the approach that often works best is to have a few sort of isolated, Greenfield application approaches, where you have a small team that is sort of proving out and becoming good ambassadors for doing things in cloud native way. But there's also a an evolutionary way to bring the older applications along there for many organizations is really helpful. That doesn't have any running head on it. Other cultural issues with the traditional application. >> So, break them up into teams and have different teams at different stages of evolution. >> Right, and so, I think you can have a small advanced team that is working on new applications at Greenfield, cloud native way. But then the transition path for the teams that are working on the older application, traditional applications that were not initially architected in a cloud native way is to break them down in an evolutionary way, first dockerize, containerize, the traditional applications, then maybe break them down from a monolith into, say, three tiers, each of those tiers being containerized. And then potentially pull out one of those services if it's a common service across all the applications and start using that and the process. I think we find organizations get the sort of muscle memory around doing things in a more continuous way in a more agile way. And they get experienced with tools like CICD, Docker, Kubernetes te cetera in a more organic way. >> Do you find that people who come from the traditional waterfall development background eventually can't make that shift? >> I think some can, there are pluses and minuses. But I think that most organizations find that as they get more agile things that used to be very difficult become a lot easier, right? So, rather than having big masses of code that needs to be rewritten and changed, you change something in one area and it breaks things and unexpected way in another area. Right, then you're trying to get large teams of people to sort of agree on things which we know is not the way the world works. When you get to smaller teams working on more atomic pieces of the code with clean interfaces between them, and can iterate more rapidly without having unintended consequences. For most organizations that not only makes them faster, but it gives higher quote, quality, safer et cetera. >> Another topic we hear a lot about today is application modernization, what does that mean to you? >> So, I think for me application modernization means that you're re architect, you're making the application itself more cloud like, which doesn't mean that you made it full scale cloud native on day one, right? But that you, for example, taking a traditional application and Docker rising, or containerizing it, just containers in the monolith actually gives some real advantages. And that then sets people off to say, let's not only take the advantages that we now have in terms of portability, but let's start exploring the advantages that we can get from having more frequent deployments or more automated testing. And so, really it's modernizing the application but also modernizing the environment around it and in the culture for how you build and deploy applications. >> Let's turn to your current venture Storj. You've been CEO there for about two and a half years now. Very interesting decentralized approach to storj using blockchain. Just tell us quickly how you're re imagining Storj? >> Sure, sure, well I mean for, for of course, most of computing history storage was done, like people buying their own disk drives and then storing data on it. And if they failed or got lost as a problem, or if they had to buy too much, I was expensive. Then we move to centralized clouds where you were storing data on drives that one organization was running, we started taking it a step further, where we built a storage service. But we don't run your own any disk drives. We're sort of like ABNB, for restaurants, right? But we've gotten 10s of thousand people around the globe. Generally, data centers who have spare capacity enabled them to rent out that spare capacity. And we're offering our customers a way to do storage that is much safer, much more private, faster and far less expensive, than with the traditional cloud. >> Certainly intuitively, it would be less expensive. How is it faster? Well, it's faster for a lot of the same reason the the internet, if you will is faster than the traditional approach was landlines, right? We were able to take advantage of parallelism, right? So, we break every file up into a large number of pieces, which are then distributed across the network. And so, first of all, we don't get slowed down. If some of the drives are slow, or they happen to be in an area where there's network congestion. It doesn't slow down. We also end up having, generally speaking, have our data much closer to the edge. So, if you're in Kenya, and you're viewing a video that sort of serve from our network, chances are the data is getting served from graduate cluster view rather than driving or in Kansas. >> It sounds like there are some sort of cloud native aspects to what you're doing. In fact, are you adopting some cloud native principle on-- >> Well, so kind of we put our service in the cloud native way. But it really takes the cloud native notion of distribution and takes it even a step further, which is that things are highly decentralized. And so, we built our service in a very particular way because we are not directly controlling the disk drives, so, we basically use algorithms and math to make sure that we're resilient against any failure. and things are done in a highly automated and scalable way so that there's really no single points of failure. And there's infinite scalability, which is which is really the goal of cloud native, but we take it a step further. >> And blockchain is what knits us all together, right? Well, it tracks the location of all the all the data. >> I don't know, cause actually none of us 'cause we use blockchain for certain purposes, namely, compensating the people who are running the drives. So, they do cryptographic proofs to prove that the data they have, they shouldn't have to get compensated for running it. But then we've tried to use a large range of different kinds of peer to peer technologies. And even frankly, some very cool very old technology like racial coding which is on the on the Voyager spacecraft to make sure that it all fits together in a way that's safe, secure, private and super fast. >> All right, there other applications of this technology have developed be on Storj? >> Well, so we are working on decentralized storage. Other people are out there working on decentralized computing, where the application can be written and run on. Sorry, can be run on using CPUs that are all around the globe, we happen to think storage is probably the most important problem to solve first. Because, death, taxes and data are things that never go away. And the world's creating more and more every year, it would actually, the data created this year would have filled a stack of CD ROMs to orbit of Mars and back. It's going to grow from there. >> I love those analogies. >> Yeah, some of that's cat videos, but a lot of it is really super critical data on finding, therapeutics for COVID are the cure for cancer or new forms of energy. And so, find a way to use to give people the ability to store their data in a highly secure, highly efficient and very cost effective way we think is really important. >> And what should we be looking for from Storj for the next year, >> Let's say Storj is in production. We are adding end users. We're starting to see some larger users, which is a very 10 for us, today, we're used primarily for sort of second tier storage, but we expect to be moving into sort of primary storage and even CDN down the road. It turns out that what we built is a really great way to distribute large files, including video and photos and x-rays and satellite images and things like that. >> Well, Ben, thanks for joining us today. I know you're a Cube alum you've been many times on the Cube. I think this is the first time we've done it virtually though. >> I know, I do miss being in the same room as you and you're colleagues but this is a very nice thing too. >> So, do we believe me? Ben Golub, CEO of Storj. Thanks for taking time for being with us today. This has been a Cube conversation. I'm Paul Gillin. Thank you for joining us, be well. (bright upbeat music)

>> Announcer: From around the globe, it's theCUBE. With digital coverage of Dockercon live 2020. Brought to you by, Docker, and its ecosystem partners. >> Hey, welcome back everyone to the DockerCon 2020, #DockerCon20. This is The Cube virtual coverage with Docker on their event here. And we're in the studio in Palo Alto, I'm John Furrier your host of theCUBE, we're here with a great guest to talk about Docker Desktop, the Microsoft relationship, and the key news that's coming out. Ben De St Paer-Gotch is the product manager for Docker Desktop. Ben, great for coming on, thanks for spending the time with me. >> Thanks for having me, I really appreciate it. >> So obviously, this is a virtual conference, we wish we could be in person, but given the state of affairs we're going to do remotely, but the momentum Docker has is phenomenal, it's always been great with containers. It's the number one downloaded app around for developers. Microsoft just had their Build conference, which was again virtual as well, or digital, as they say, it's interchangeable. But clear momentum now with Docker as containers actually is the standard, you guys are doing great. What's the key news out of the Microsoft world for people who missed it last week with MS Build? >> Yeah, so last year at Build, Microsoft announced WSO2 to the Windows subsystem with Linux two. (mumbles) The mapping between the windows (mumbles) Which, went really well but it just didn't provide the same centered needed Linux experience. Last year, they announced Windows subsystem Linux two, (Provides an actual Linux one on windows machine, and we've been working hard with Microsoft over the last year to integrate proper desktop as a main desktop application for working with containers with WSO2. A build this year, Microsoft has gone on and announced that WSO2 is going to have a few new features, and it's going to have new features. (mumbles) Mention Linux graphical, Linux applications, you can access the file system, the installation is going to become a slicker which I guess I'm the most excited about that pitch. But the most exciting announcement is, they will be bringing GPU support to WSO2 which means that we will be able to provide and give you support through Docker desktop or container workloads that peoples are working on. And now we're launching Gray and Agua through containers and docks and desktops and Windows which is really cool because we haven't been able to do that before. >> So is this the first GPU support on Microsoft Windows for Docker, with Docker? >> It's, yeah, it's the first GPU Support for Docker Desktop or Mac or Windows. So, previously the hypervisor hasn't passed through the GPU, pretty much, which meant that we couldn't access it from Docker desktop. So Docker desktop isn't about a lightweight VM we sorts of plumb all that in for you. But we're limited about what we could get access to from the hypervisor, Microsoft putting this through and giving us access for the first time, we can actually, we can go. >> Not to go on a side tangent here, but you know, all these virtual events, and I was watching some of the build stuff as well, as well as us immediate streamers and doing stuff, you can see people's home rigs. And you talk to any Developer, video streamer, or anyone who is working remotely, if you don't have the best GPU's in there, I mean, this has just become, I mean, quite frankly, you need the GPU's. So this is important, it's not only from a vanity standpoint performance. Having that support, I'm going to want the best GPU's, I'm always going to be upgrading my machine for that extra power. What's the impact? What does it mean for me as a Developer? Does it increase stuff? What's the bottom line? >> As a Developer, it means you actually have access to it. So, especially when you're doing workloads on the CPU, you've got minimum amounts of power utilization you can do. When you're running workloads for an L Development, you have a lot of power up process you've got to log, to do your mobile training. So, in an element cycle, you're likely to have your application which you're going to use to produce a modeling, you're going to have training data. Taking that training data and producing a model requires lots of panel processing which is an enormous calculations in producing with finer waitings. Doing that on a CPU has to be done on a serial fashion rather than parallel, which is huge and intensive and takes a really long time. Whereas on a GPU, you can do all of that in parallel which massively reduces the amount of time it will take to run those training functions. Either just straight up in Linux or running them in a container, which as the more of people are looking at running container with workloads, it's how I first, the first team that I was on actually used Docker. I was working in Amazon Alexa, and my team picked up the opportunity to run our workload in container. And that was my first experience, so even though my team backed down, so I could see the system. >> Yeah, ML workloads automations could be critical of that performance. Okay, let's get into some of the momentum with Microsoft, you guys have obviously, builds over, we're here now at DockerCon, there's news. Could you share some of the tidbits for what's being talked about now with Docker and DockerCon. >> Yeah, absolutely, so, along with everything else we've been doing, we've been partnering with Microsoft trying to make the best experience generally with Docker desktop, and with WSO2 and with the VSCO. I've been working closely with Microsoft guys to actually try and improve our experience in Windows as it is today, and to improve some of those integrations with VSCO, and also working with the VSCO team on the Docker plugin for VSCO to give our feedback, and to hear feedback from those guys on the errors and issues they're seeing with Docker desktop and to really try to produce the best experience we can on Windows. End to end, from very front end running all the way through that first push, that first run on the cloud using Docker. >> So what is some of the new product management processes and customer support things that you guys are doing? This comes up a lot, obviously, we had a great conversation around shift left with security. That's great news there. You start to see a lot of this added value for Developers, wanted their support right? So how do I get things I need, and from a customer standpoint? It's kind of a moving train this world and it's only getting better and better from a Developer standpoint. But there's more complexity, it's got to be abstract the way you've got, you know, this new abstraction layers developing. You've got a lot of automation. How does the customer get the support they need in the same agile way that Developers are cranking out code? >> It's a really good question, it's something I think we're still working on as well. So, we're trying to working out and one of the big things I'm trying to work out is, how to make it easier for people to get started with Docker, and how do we also make sure with the things we build, we don't leave a cliff edge instead of a lining path. You don't get to a certain point in an easy process, and then the next step, takes you straight off a cliff, so that's not useful for anyone. So, producing those parts and those ways for people to learn and actually progress is something we're really trying to work out. How to make it natural from the first experience all the way through. From an actual support perspective, the other thing we're looking at, is we're trying to do more things in the open. We're really trying at Docker to bring as many of the new features and pieces we're developing which we have to do that in the open with community visibility, so that if people really want it fixed, they can open the PR and they can help us out. And then the last thing that my team really stood out was our Docker of having actions. As creators, someone already finished, could you do this? Someone else had a PR and emerged it. So, to a certain extent, you've got your one side which had you on board and this ever growth spiral and you keep learning. The other side is how'd you fix the board when you find an issue? In that one, we're really trying to work with the community, a lot more than we have in the last couple of years. >> Awesome, some folks watching, hit him up on Twitter, he's the Product Manager for Docker Desktop among other things. You guys are very transparent, you've got your Twitter handle on the lower third. People can chime in or just jump on the chat, we'll follow up and get you the info. Final question for you Ben, as you look at this reality we're in, there's kind of a holistic kind of moment now where people kind of realizing the new realities here. You're looking at the.. you get the keys to the kingdom with Docker Desktop, okay. You got some momentum with Microsoft, the developer role is moving fast and fast as the head room increases for capabilities with automation. And I know you mentioned a few of those things. GPU is now available. What's the future look like for these Developers? The next short, medium and long term? What's your view as you look out over the landscape because you've got to look at the product roadmap, your engagement with the community. Can you share some insight into how you're thinking about Docker Desktop going forward? >> Yeah, absolutely. So, I think what really interesting point as you say, which is that, if you look at sort of a lot of the Developer side of things that have sort of come out in the last like six months, six to eighteen months. The things I see, I see daily like you mention, things like orchestrating for containers gaining momentum. If you think about crossing the Kaizen model, we're just passed the early Dockers now. We're kind of into the early majority, but we're going to start to move over the next few years into the late majority. What that really means is that people here have been using one of two of these technologies. Maybe you've been using cloud, maybe you've been using Edge, maybe you've been using containers, maybe you've been using CICD, maybe you are using Expiration, maybe you're not. Maybe you've got a Microservice application, maybe it's a little bit of a mole rat. What we're really going to see is, you're going to start to see, all of these changes intersecting and overlapping. And people who have started to pick up model two of these will start to pick up all of them. And that's probably going to happen as we move into the majority of users. So from a what's coming instead of a lot of those thing that you see in best practice in the ideal Developer setup, so a beautiful CICD, a more of an orchestrated environment, Microservice architecture, we're going to see a lot more of that becoming the norm. But I think along with that, we'll also see a level of recognition coming along that a single Microservice alone doesn't provide value. And that's it's going to be some of those groups of services that will provide the user outcome. And that's where my focus is at the end which is you know, an authentication service is great but it doesn't provide value unless you give access to something as authentic. >> It's been issued that the new Docker is all about Developer experience. This is really the core mission. I mean, since the sale of the piece of morantis, Docker has retrenched and reinvented, but stayed core to its principles. Just share with the Developers who've been watching that are coming back into the ecosystem, what is this new Docker vibe? Share your thoughts. >> The new Docker vibe is about working in the open, and it's about solving problems for Developments. The original goal of Docker was to make it easy to pack and ship. It was to reduce Developer friction. As we move more into, sort of, the enterprise space, we worry more about Ops and DevOps. We're not trying to re-focus on Developer and if you sort of think there's two parts to the Developer life cycle, where you've got your work, where you're doing your creative work, where you're writing code. And then you've sort of got your part of the inner loop. And then you've got your part where you're trying to get that code out to production, you're trying to get your value to someone else. Instead of your outer loop, we're really trying to focus on the inner loop And sort of our mantra is that any bit for a Developer should spend as much as their time as possible creating new and exciting things and we're onto those holes that reduce those boring, Monday, repetitive tasks, that we're really trying to work out how we take those boring repetitive pieces and how do we make them just vanish like magic from new users or how do we reduce the friction for the experience from users? From both desktop and hub, we're really trying to bring those two together to achieve that. >> You know what's great about folks who have been in the class since day one. All of us have scar tissue experiences, you know the one thing that's constant is constant change. And one of the things that you guys have done at Docker, and hats off to the whole, you know, original team, is that brand of Docker has symbolized quality openness, and set the standard, I mean, if you look back and containers were really coming around, it's not a new concept. But Docker really set the industry on this path and it's been great to follow every DockerCon at TheCube coverage, but more importantly, as the demand for Developers to build these next wave of Cambrian explosion of applications. It's going to be more important than ever to have more of these abstractions, more of these tools in this real time, more Developers experience because there's more building going on. And it's not just one cloud, it's all clouds, it's all things. >> Yeah, I think it was like when IDC analyzed the future report a couple years ago, I think it was maybe the 2018 one. They said that maybe 2017. They said to date, we've built 500 millions applications worldwide and by 2023, we'll build another 500 million. The rate of creation is just insane, it's exponential growth of us producing more and more applications and connecting more and more devices to do them. The sheer volume of creation and the rate of new technology supporting, even with the rate of companies adopting, I guess more of a warm cloud. I think it's like 60 percent of companies are now more than one cloud provider. Maybe even more, maybe it's like 80 percent. It's ridiculous. >> I was just having this debate on Twitter about this multi-cloud. Someone tried to call us out saying, "Oh you guys were pooing on multi-cloud in 2016 and 18." I go "Look at, no one was Pooping on multi-cloud, it didn't exist." I had multiple clouds but there was no real use case. Now you're starting to see the use cases, where yeah, I had multiple clouds and I got Azure here, I got this over here. But no one wakes up and spreads their workloads wrong. This is going back a few years. Certainly the hybrid was developing, but I think now you're starting to see with networking and some of these inter-operable dynamics, you start to see innovation pockets in wide spaces in large market opportunities for start-ups and companies to thread the clouds together at the right place. So I think multi-cloud is becoming apparent from a use case stand point. Still a ton of work to do, I mean direct connects, got SLA's, I mean all kinds of stuff at the networking level but it is real. It's going to be one of those realities that everyone has, at least one or two, if not three. It could be optimization, this is what Developers do right? Solve problems. >> Yeah, absolutely, I mean if nothing else, I've encounter a couple of companies even just where redundancy is handled by multi-cloud strategy. If you want to achieve more nines and you're just balancing workloads between two clouds. >> I mean, the Zoom news was really a testament to that because everyone got into a twist over that. Oh Zoom moves off Amazon, no they didn't move off Amazon, they went to Oracle, they got Adge, they're everywhere. Why wouldn't they be? They need to pass it, they fail over, they need fall tolerance, I mean, these are basic distributing computing concepts that is one on one. You've got to have these co-locations. And optimization for those clouds and the apps on Microsoft as well, so why wouldn't you do it? >> Exactly. And that's that hybrid, that multi-cloud, compounding that some of which you said earlier, that over changes when you're looking at how you go to CICD, how you're bundling these applications, creating more applications than ever. Coming back, sort of, with more AI workloads, much like GPU and you combine that with, sort of, last in the growth of age devices as well. It sort of makes for a really interesting future. And Docker is sort of, that summation SOV, what we're using to frame how we're thinking about our product and what we should be building. >> Great, for the audience out there, hit him up on Twitter, Ben's available, they're out in the open, if you're interested in how Docker makes life easier on the Windows platform, with the GPU support, they've got security now built in, shifting left. Give these guys a call and of course, we love the mission, out in the open. It's theCUBE's mission as well and great to chat with you. Ben, thanks for spending the time with me today. >> Been an absolute pleasure, thank you for having me. >> Okay, just TheCube's coverage, the virtual Cube with DockerCon co-creating together out in the open. DockerCon20, #Docker20, I'm John Fer with TheCube, stay tuned for our next segment, and thanks for watching. (ambient music)

>> Announcer: From around the globe, it's theCUBE with digital coverage of DockerCon Live 2020. Brought to you by Docker and its ecosystem partners. >> Okay Jenny, great to see you again. >> Good to see you. >> James Governor, nail on the Keynote there. Chat was phenomenal. That was pre-recorded but James is also in the chat stream. A lot of good conversations. That hit home for me that keynote. One, because memory lane was going down right into the 80s when it was a revolution. And we got him in the green room here. James Governor, welcome. >> James is here, hi James. >> Here we go. >> Fresh off the keynote. >> It's always a revolution. (John laughs) >> Well, in the 80s, I used to love your talk. A couple of key points I want to share and get your thoughts on was just to some highlights for the crowd is one, you walk through. Some of the key inflection points that I think were instrumental and probably some other ones depending on your perspective of where you were in the industry at that time. Whether you were a systems programmer or a networking guy, there was a proprietary world and it was a revolution back then. And UNIX was owned by AT&T if no one remembers. You couldn't even use the word. You had to trade market. So we actually had to call it XINU which is UNIX spelled backwards in all the text and whatnot. And even open source software freeware was kind of illegal. MIT did some work, Northeastern and Berkeley and other schools. It was radical back then so-- >> Yeah, we've come a long way for sure. I think that for me that was one of the things that I wanted to really point to in the keynote was that yes we have definitely come a long way and development culture is about open culture. >> I think the thing that I like to point out especially hate to sound like I'm old but I am. But I lived through that and the younger generation coming and have all these new tools. And I got to say not that I walked through to school in the snow with no shoes on but it's a pretty cool developer environment now. But remember things were proprietary back then. If you start to see the tea leaves now, I look at the world, you see these silos. You see silos that's kind of, they're not nestle proprietary but they might necessarily be open. So you kind of have a glimpse of open source on these projects and these companies. Whether they're tech companies, it feels open but it might not be. It could be walled garden. It could be data being hoarded. So as data opens up, this is interesting to me because I want to get your thoughts on this because in a way it feels proprietary but technically it's not proprietary. What's your thoughts on this? Because this is going to be the next 20 years of evolution. What's your thoughts? >> I think the productivity wins. Whoever packages technology in a way that makes it most productive for people. That's what wins. And open source, what's productive. It is very accessible. It enabled new waves. Get installed and you've got a package from... You got access to just a world of open-source. A world of software that was a big revolution. And I guess the cloud sort of came next and I think that's been one of the big shifts. You talk about proprietary. What matters is how easy you make things to people to do their work. And in that regard, obviously Amazon is in fact a bigger distribution network. Makes technology super consumable by so many people. I guess I would say that open is good and important but it's not the only thing. As you say, data is a lock-in and it's right and people are choosing services that make them productive. Nobody worries about whether Amazon Lambda is proprietary. They just know that they can build companies or businesses or business processes on it. >> You know it's interesting back in the day just to kind of segue with the next topic. We were fighting proprietary operating systems, UNIX and others. We're also fighting for proprietary Network protocol stacks. SNA was owned by IBM. DECnet was digital, the number one network. And then TCP/IP and OpenSan's interconnect came out. That's the OSI model for us old ones. That set the table. That changed the face of everything. It really enabled a lot. So when I see containers, what Docker did early on the pioneering phases of Docker containers, it unleashed a new reality of coolness and scale and capabilities. And then in comes Kubernetes and in comes micro services. So this path is showing some real strength for new kinds of capabilities. So how does a developer navigate all this because data lock-in does it a data plane seems to be a control point. What are we fighting now in your opinion? shouldn't say we're fighting but what are we trying to avoid if operating systems was for closing opportunities and network protocol stacks before closing in the past? What do you see as barriers that need to be broken down in the open source world around going down this great path of micro services, decomposed applications, highly cohesive architectures? >> Honestly there's enough work to be getting on with without like fighting someone in that regard. I mean we're fighting against technical debt. I just don't think that people are serrated about fighting against proprietary anymore. I think that's less than a concern. Open-source technology is great. It's how most work gets done in our industry today. So you mentioned Kubernetes and certainly Docker. Though we did a phenomenal job of packaging up and experience that map to see CICD. That map to the developer workplace people like do. Phenomenal job and I think that for me at least when I look at where we are as an industry, it's all about productivity. So there are plenty of interesting new platforms. I think in my keynote, that's my question. I'm less interested in microservices than I am in distributed work. I'm interested in one of the tools that are going to enable us to become more productive, solve more problems, build more applications and get better at building software. So I think that's my sort of focus. There will always be lock-in. And I think you will also have technologies mitigate against that. I mean clear messages today from Docker about supporting multiple clouds. For a while at least multiclouds seem like something only the kind waivers were interested in but increasingly we're seeing organizations where that is definitely part of how they're using the cloud. And again I think very often it's within specific areas. And so we see organizations that are using particular clouds for different things. And we'll see more of that. >> And the productivity. I love the passion, love that in the keynote. That was loud and clear. Two key points I want to get your reaction on that. You mentioned one was inclusion. Including more people, not seeing news. It's kind of imperative. And also virtual work environments, virtual events. You kind of made a highlight there. So again people are distributed remote first. It's an opportunity to be productive. Can you share your thoughts on those two points? One is, as we're distributed, that's going to open the aperture of more engagement. More people coming in. So code of conduct not as a file you must read or some rule. Culturally embracing a code of conduct. And then also, virtual events, virtual groups convening like we're doing here. >> Yeah I mean for me at least Allison McMillan from github and she just gave such a great demo at the recent sunlight event where she finished and she was like, it was all about, I want to be able to put the kids to bed for a nap and then go code. And I think that's sort of thinking people band around the phrase ruling this together but I mean certainly parenting is a team sport. But I think it's interesting we're not welcome. It was interesting that was looking at the chat, going through, I was being accused of being woke. I was being accused of being a social justice warrior. But look at the math. The graph is pretty clear. Women are not welcomed in tech. And that means we're wasting 50% of available resource to us. And we're treating people like shit. So I thought I underplayed that in the talk actually. Something like, "Oh, why is he complaining about Linus?" Well, the fact is that Linus himself admitted he needed to change his persona in order to just be more modern and welcoming in terms of building software and building communities. So look we've got people from around the world. Different cultural norms. All of the women I know who work in tech suffer so much from effectively daily harassment. Their bonafides are challenged. These are things that we need to change because women are brilliant. I'm not letting you signaling or maybe I am. The fact is that women are amazing at software and we do a terrible job of supporting them. So women of other nationalities, we're not going to be traveling as much. I think you can also grow. No we can't keep flying around as much. Make an industry where single parents can participate more effectively. Where we could take advantage of that. There're 200 million people in Nigeria. That hunger to engage. We won't even give them a visa and then we may not be treating them right. I just think we need an industry reset. I think from a we need to travel less. We need to do better work. And we need to be more welcoming in order that that could be the case. >> Yeah, there's no doubt a reset is here and you look at the COVID crisis is forcing that function there because one, people are resetting and reinventing and trying to figure out a growth strategy. Whether it's a business or teams. And what's interesting is new roles and new responsibilities is going to emerge and I think you're right about the women in tech. I completely agree and have evidence myself and reported on it ad nauseam. But the thing is data trumps opinion. And the data is clear on this issue. So if anyone will call you a social justice warrior I just say pound sand and tell them that go on their way. And just look at the data and clear. And also the field is getting wider. When I was in computer science major back in the day, it was male-dominated yes but it was very narrow. Wasn't as broad as it is now. You can do things so much more and in fact in Kelsey Hightower's talk, he talks to persona developers. The ones that love to learn and ones that don't want to learn anything. Just want to code and do their thing. And ones that care about just app development and ones that just want to get in and sling k-8 around like it's nobody's business or work with APIs, work with infrastructure. Some just want to write code. So there's more and more surface area in computer science and coding. Or not even computer science, it's just coding, developing. >> Well, I mean it's a bigger industry. We've got clearly all sorts of challenges that need to be solved. And the services that we've got available are incredible. I mean if you look at the work of companies like Netlify in terms of developer experience. You look at the emergence of JamStack and the productivity that we're seeing there, it's a really exciting time in the industry. >> No doubt about that. >> And as I say I mean it's an exciting time. It's a scary time. But I think that we're moving to a world of more distributed work. And that's my point about open source and working on code bases from different places and what the CapCloud can enable. We can work in a different way and we don't all need to be in San Francisco, London, or Berlin as I said in the Keynote. >> I love the vision there and the passion. I totally agree with it. I think that's a whole another distributed paradigm that's going to move up the stack if you will and software. I think it's going to be codified in cloud native and cloud scale creates new services. I mean it's the virtual world. You mentioned virtual events. Groups convening like the 67,000 people coming together virtually here at DockerCon. Large, small one-on-ones group dynamics are a piece of it. So share your thoughts on virtual events and certainly it's people are now just kicking the tires, learning. You do a zoom, you do a livestream. You do some chat. It's going to evolve and I think it's going to look more like a CICD pipeline and anything else. As you start to bring media together, we get 43 sessions here. Why not make it a hundred sessions? So I think this is going to be one of those learning environments where it's not linear, it's different. What's your vision of all this if you had to give advice for the folks out there? Not event plans, with people who want to gather groups and be productive. What's your thinking on this? >> Well, it sort of has to happen. I mean there are a lot of people doing good work in this regard. Patrick Dubois, founder of DevOps days. He's doing some brilliant work delineating. Just what are all the different platforms? What does the streaming platform look like that you can use? Obviously you've got one here with theCUBE. Yeah, I mean I think the numbers are pretty clear. I mean Microsoft Build had 245,000 registered attendees and I think something that might have been to begin. The patterns are slightly different. It's not like they're going to be there the whole time but the opportunity to meet people where they are, I think is something that we shouldn't ignore. Particularly in a world not everyone again has the privilege of being able to travel. You're in a different country or as I say perhaps your life circumstances mean you can't travel. From an accessibility perspective, clearly virtual events offer an opportunity that we haven't fully nailed. I think Microsoft performance in this regard has been super interesting. They were already moving that way and Kobe just slammed it up to another level. What they did with Build recently was actually, I mean they're a media company, right? But certainly developed a focused media company. So I think you'll be okay. You're about the business of software John. Don't worry Microsoft don't give you some space there. (John and James laughing) We're under the radar at theCUBE 365 for the folks who are watching this. This is our site that we built with our software. So we're open and Docker was instrumental and I think the Docker captains were also very instrumental and trying to help us figure out the best way to preserve the content value. I personally think we're in this early stage of, content and community are clearly go hand in hand and I think as you look at the chat, some of the names that are on there. Some of the comments, really there's a new flywheel of production and this to me is the ultimate collaboration when you have these distinct groups coming together. And I think it's going to just be a data dream where people aren't the product, they're actually a contributor. And I think this open source framework that you're talking about is going to be certainly just going to evolve rapidly. I think it's just not even scratching the surface. I just think this is going to be pretty massive. And services whatever you want to define that. It could be an API to anything. It's going to be essentially the scale point. I mean why have a monolith piece of software running something. Something Microsoft teams will work well here. Zoom will work well there but ultimately what's in it for me the person? This is the key question. Developers just want to develop. You're going to hear that throughout the day. Kelsey Hightower brings up some great points in his session and Amanda silver at Microsoft, she had a quote on one of her videos. She said, "App developers are the first responders "in this crisis." And that's the first time I've heard someone say that out loud and that hits home for me because it's true. And right now app developers are one of the front lines. They're providing the app support. They're providing to the practitioners in the field. This is something that's not really written about in the press. What's your reaction to app developers are the first responders in this crisis. >> Well I mean first I think it's important to pay tribute to people that actually are first responders. Writing code can make us responsive but let's not forget there are people that are lacking PPE and they are on the frontline. So not precise manner but I might frame it slightly differently. But certainly what the current situation has shown us is productivity is super important. Target has made huge investments in building out its own software development capabilities. So they used to be like 70% external 30% internal and they turn that round to like 80% internal 20 external. And they've been turning on a dime and well there's so much going on at the moment. I'm like talking about target then I'm remembering what's happening in Minneapolis today. But anyway we'll talk about that. But yeah organizations are responding quickly. Look at the numbers that Shopify is happening because all sorts of business is something like we need to be an online business. What's the quickest way to do that. And Shopify was able to package something up in a way that they they could respond to challenges. Huge social challenges. I'm a big believer the future's unwritten at this point and I think there's a lot of problems out there you point out and the first responders are there I agree. I'm just thinking that there's got to be a better path for all of us. And this brings up the whole new roles and responsibilities around this new environment and I know you're doing a lot of research. Can you share some thoughts on what you're kind of working on now James? That's important, I'll see what's trending here at DockerCon is. Compose the relationship with Microsoft, we've got security, Dockers now, multicloud approach, making it easier, that's their bread and butter. That's what they're known for. They kind of going back to that roots of why they pioneered in the first place. So as that continues ease-of-use, what's your focus area right now that you're researching that you could share with the audience? >> Well, I mean I'd say this year for me I've got probably three key areas. One is what's called GitOps. So it's the notion that you're using Git as a system of record. So that started off randomly making changes, you have an audit trail. You begin to have some sort of sense of compliance in software changes. I think the idea of everything has to be by a sort of a pull request. That automation model is super thing to me. So I've been looking at that. A lot of development teams are using those approaches. Observability is a huge trend. We're moving to the idea of testing and production. The kind of stuff that's been evangelized so successfully by charity majors honeycomb. It's super exciting to me and it's true because in effect, you're always testing in production, your dev environment. I mean we used to have this idea that you'd have a Dev and a Dev stage. You're have a staging environment. The only environment that really matters is where the rubber meets the road. And that is deployment. So I think that having having better tools for that is one of the areas I'm looking at. So how are tools innovating that area? And it won't be the thing that this is my own personal thing. I've been talking about progressive delivery which is asking a question about reducing risk by really understanding the blast radius of the service to be able to roll it out to specific use of populations first. Understanding who they are and enrolling it up so it's the idea that like maybe you brought something out to your employees first. Maybe you are in California and you roll something out in Tokyo knowing that not many people are using that service. It is a live environment but people are not going to be adversely affected if it happens. So Canary's Blue-Green deployments and also experimentation. This is sort of one of the areas I'm being sort of pulled towards. It's sort of product management and how that's really converging with software development. I feel like that's one of the things I haven't fully, I mean I think it's when they have research focused but you have to respond to new information. Anyhow, I'm spending a lot of time thinking about the world of product management. It's those companies to be most respect in terms of companies that are crushing it in the digital economy. They have such a strong product management focused. Everything is driven by product managers that understand technology and that's an exciting shift. The one that I'm paying greater attention. >> You do some great work and I love the focus on productivity software development. Getting those app developers out there and it's interesting. I just think that it's such an exciting time. It's almost intoxicating. Some people drinking on Twitter online and having beers because they're in different time zone. But if you look up and down the action that's going on, you got at the application developers side, all the things you were mentioning services. But when you look at the cloud side, you got almost this operating system reset. It's a systems architecture. So you have the hall and that's up and down. The middle of the stack to the bottom, you have this operating systems thinking and evolution. And then you got at the top, the pure software developers. And this is again to me the big aha moment. For the industry there's a true opportunity to scale that in unbelievable ways. And you don't have to pick a side. You can do a top of the stack bottom stack. So I think kubernetes and micro services really bring this whole enablement piece to the table. And that fascinates me and I think that's going to change what the apps will look like. It'll give more productivity and then making the internet programmable unit, that's new systems. So that seems to be the trend. You're a systems guy, your girl or you're a developer. How do you see that evolving? Do you get to that level? >> Developer experience is not necessarily the key value of Kubernetes. It's supremely flexible sort of system. It does offer you that portability. But I think what I'm seeing now is how people are taking Kubernetes and kind of thinking, so you've got VMware, acquires Heptio, brings Pivotal into the fold, starting about what that platform looks like. I think Pivotal with cloud foundry did a great job of thinking through operator experience. Operator experience is not the same as developer experience. I think we're going to see a bit more specialization of roles. Meanwhile at that point, you've got the cloud players all doing pretty awesome job supporting Kubernetes. But it gives that portability promise. So I think for me, one of the things is not expecting everyone to do everything. It's like Kelsey said, some people just want to come into work and do their job and they're super important. And so VMware I think a history of certification of application environments. So of them it's sort of quite--and certification of humans. It's quite natural that they would be somebody that would think about how do we make Kurbenetes more consumable and packaged in a way that more people take advantage of it. Docker was such a phenomenon and now seeing how that sort of evolving into that promise of portability is beginning to be realized. So I think the specialization, the pendulum is going to swing back just a little bit. >> I think it's just great timing and congratulations on all the work and thanks for taking the time for participating in DockerCon with the Keynote. Taking time out of your day and coming in and doing this live interview. The chat looks good. Hit some great, get some fans in there. It's a great opportunity and I think Docker as the pioneers, pivoting in a new direction, it's all about developer productivity and James you've been on it. @monkchips is his Twitter handle, follow him, hit him up. I'm John Furrier here in the studio for DockerCon 2020. Ginebra CEO and you got Brett Fisher on the captain's channel. If you go to the site, you'll see the calendar. Jump into any session you want. They'll be live on the time or on-demand instantly. TheCUBE track has a series of enemies. You've got Amazon, we got Microsoft, get some great guests, great practitioners that are literally having an impact on society. So thanks for watching. James, thanks for spending the time. >> Thank you very much John. >> Okay James Governor, founder of Monkchips, great firm, great person-- >> RedMonk, RedMonk is the company. Monkchips is the Twitter. >> Redmonk, Monkchips. RedMonk, RedMonk. >> RedMonk is the company. >> RedMonk, RedMonk. >> @monkchips is his Twitter handle and RedMonk is the firm, thank you for the correction. Okay more coverage DockerCon after this short break. Stay with us. The next segment is coming up. Stay with us here at theCUBE DockerCon. (gentle music)

>> Announcer: From San Francisco, it's theCUBE, covering GitLab Commit 2020. Brought to you by GitLab. >> Hi and welcome to CUBE's coverage of GitLab Commit 2020. We're here in San Francisco, actually, the first CUBE event of the year, and I'm Stu Miniman here with John Furrier, the founder of SiliconANGLE, one of our main CUBE hosts. John, always great to kick off the year with you, and of course, we're digging in on the developer world, cloud native. Nothing better than, you know, the opening keynote talks about, you know, there's a line we've been talking for years, software's eating the world and what are the ripples that are happening on. So, Tom, great to see you, and how come it's so cold here in San Francisco? I mean, I could be back in Boston. >> Coldest winter. I've spent summers here years ago, but it's not summer anymore. But Stu, it's football playoffs. Patriots aren't in, so sorry to hear that our Pats didn't make it. But great to see you. I think one of the things this year in 2020, a new decade, 10 years of theCUBE, looking back, we have been on all the major developer waves since 2010. We jumped on the Hadoop wave with Cloudera. We saw the beginning of that wave of OpenStack to cloud, Kubernetes, containers, the whole nine yards. We've been in the developer community. But this year, cloud native not only is going to continue that expansion of developer CUBE action, but the cross-connect with mainstream, and this is to me the biggest trend of the next 20 years is going to be the open systems model of cloud, just like the open systems interconnect in the '80s created a whole new computer industry, changed the landscape, changed the value proposition, this year, I think we're going to start to see real visibility of value creation where the developers are not just the cliche of the value proposition. That's the cliche. Oh yeah, developers (mumbles). No, no, this is a whole nother game change. With CloudScale, with data, with AI, you're seeing again the importance of this. I think cloud native represents to me that next generation, because with multicloud, there are new criterias out there for success, new requirements. Same game, writing software. Whole new dynamic. Networking, Stu. >> Yeah. >> Compute. >> Yeah, John, and I love actually, I think this was a great show to help us kick it off because you talked about those mega waves out there. We've been watching the growth of some of the huge platforms. AWS was on the keynote stage this morning, Google is doing the closing keynote, and of course one of the major acquisitions, you know, in the relatively recent past was Microsoft buying GitHub. And so we know that developers are so important, but the message we heard from GitLab is it's not about silos anymore. They said not only the dev, the sec, and the ops, but finance and marketing. Everyone needs to get on the same page. GitLab's vision, of course, is that everyone should be using the same tools. That was something that I heard, that we both heard last year at AnsibleFest, that if you're in the same tools, sharing the same information, in the same communication channels, you're going to be able to move fast, and that is what companies need to do. They need to be able to react fast. The business should be able to move. Those software cycles need to be shortened. And that's the mission and the big goal that GitLab has, and I think it's representative of the wave we've been seeing. >> Let's get into the keynote analysis, but before we get to that, I want to, you brought up a point about GitHub. I think there's a real dynamic of GitHub being acquired by Microsoft for many reasons. One is Microsoft's got this cloud called Azure, and not the only cloud in town. Amazon has AWS. And so multicloud is going to be a theme we're going to see more and more of. And so this idea of open and transparent community in open source is interesting in a world where everyone's siloing. I mean, let's face it, GitHub is owned by Microsoft. LinkedIn was acquired by Microsoft. You're starting to see the walled garden world come back again where data is really valuable. And so what's interesting to see is you're seeing a company with GitLab, really one of the first ones to say, "Hey, you know what? "We're going to be anti-walled garden. "We're going to be open. "We're going to be transparent." And again, integrated platform. The cloud is demanding companies have integration requirements that are well above what we saw years ago, and this is now a new table stake. This to me is the real walkaway. What's your thoughts on the GitLab keynote and those industry dynamics? >> Yeah, some great points there, John. Right, first of all, open, fully open. You know, the CEO and the CMO, some of the things they were talking about is sometimes the team doesn't know who's doing the contribution because they're getting regular contribution. They said, "Hey, I didn't see them in the group." Oh wait, that's a customer, that's a partner, someone from the outside doing it. Fully open and transparent and remote. They now have over 1100 employees. Four years ago there were nine of them. And it is fully remote. Actually, do a little compare and contrast. Talk about Amazon. John, how many people do we know that have joined Amazon, and the first thing you do is you move to Seattle, because that's just where they have. Now, of course they've got multiple locations. They've got thousands of employees down in DC, in Massachusetts, in New York City, all over the place, but the core decision-making, even though they are very distributed, Seattle is where everything happens. That's where most of the people live. So GitLab, not only is the company remote, but that's the tooling that they've built really is to enable people to work wherever they are. From GitLab's standpoint, they said hey, we have, one of our software people, she lives in New Zealand, and she has her own power. She's completely off the grid except for her internet. As long as she has internet, she can contribute to the team and participate in the building of GitLab. So it's fascinating. You know, we've talked for years ago the future of work and how that happens. So the tooling as enablement not only to allow everybody to work together, but work together wherever they are and that remote capability, and it is very challenging. You know, we watched Zoom IPO last year, and they're trying to help with that whole wave, but we know that there's a challenging dynamic of being able to work wherever you are. >> So they brought up some stats, interesting. Scale and integration are a big theme. Looks like GitLab's getting it. They made some good calls. Have integration, very friendly integration, very open. And they're essentially consolidating a lot of the different tool chains out there. You look at Jenkins and other things out there, from continuous integration and variety through now mainstream. They got 1100 employees, okay. They got a valuation of $2 billion. They just raised $436 million. They have cash on hand of 350 million and they're going to do revenue. So you have essentially scale in GitLab with an integration story which the cloud guys are being forced. That's my opinion. Do you agree with that and do you think that GitLab can continue the pace of growth given where they're at? >> Well, John, they have something that everybody wants. It's that recurring revenue. So in February 2020, they will have passed the 100 million of ARR, and they've announced that they're going to IPO later this year. We're going to have the CEO on later. I'm a little surprised how fast they are looking to IPO, John. We've seen so many companies that not only do they do big raises, but it's not $100 million, it's two or $300 million. You know, when do you have profitability? When do you go public? So I'm a little curious why there's almost a race for GitLab to go IPO. But absolutely they are catching a lot of these waves. When GitHub was taken off the table, boy did I see Google moving fast to work closer with them. It's no coincidence that Amazon is here, because there's been a little bit of concern from GitHub as to, oh, if I'm doing GitHub, does that mean that I'm kind of being pushed closer to Microsoft Azure, as you said, that cloud. I've read recently GitHub's trying to make sure that they stay independent. We know the GitHub team. And the other big thing we saw is GitLab, about three years ago, they really differentiated themself. They are not just a GitHub alternative. You talked about Jenkins. The CICD is a huge piece of what they're doing. The source code management and CICD, putting those together are the core of what they're doing, but they're trying to be a single tool chain. Boy, when I look at the, you know, the mesh of tooling that GitLab kind of is poking at a little bit, we know a lot of these companies. Some of them are public. Some of them are unicorns. You know, to say that, oh, well, we're going to all of your security chaining. We know how deep and gnarly the security world is. But GitLab, being open, they're going to partner with all of these environments. It's not that you can only use the GitLab pieces. But the audacious goal to say that they are going to be kind of the one tool chain to rule them all is a good goal. I'm hugely supportive my entire career of trying to get rid of silos. But we know that you're still going to have corner cases and use cases that I'm going to need to go deeper. I'm still going to use those best of breeds. And that's one of the things that we're going to look at this year, John, that platform, just like I could go all in on AWS, but I'm still going to use lots of tools on Amazon and I'm going to use other clouds. >> What's your take on, great analysis, by the way. What's your take on as cloud native becomes multicloud where you got edge developing, we got outposts. You're seeing Azure with their stuff. Outposts is Amazon. You now have more pressure on speed and agility than ever before. How does GitLab's story play well into that, and as enterprises have to be faster. Not just enterprises, service providers. There's other new companies doing more cloud and on-premises and edge, AKA multicloud, too. >> Yeah, so I actually, I loved the problem statement that they nailed with talking about the tool chain that's out there is they said more than 50% of devops time is wasted on logistics and repetitive tasks. And John, if you talk about multicloud, it's not just simple to say, "Oh, hey, I threw in a Kubernetes layer "and therefore I can move from my Auzre "to my GCP to my AWS." That's not how it works. I have all the underlying things. I have the interface. That tool and user interface knowledge is challenging to overcome. There are some tools like GitLab, of course, that help me span across those environments. HashiCorp is here at the show, a partner of GitLab. I was just meeting with them recently. And of course, they're going to spread across the multiple cloud environments. But that is really where the meat on the bone is, John, if you talk about multicloud and cloud native. Where are these pieces that can help customers make sure that I'm not too deeply locked into one environment and still being able to leverage the various services that I might want to use across multiple clouds. >> Yeah, I mean, to me, the big takeaway, Stu, on the keynote I made in my notes here is that what I was impressed with is, obviously the transparency that they have is, I love the openness. You know, I mean, this whole silo thing's definitely real. You're seeing more and more. So open and transparent's key. But when you look at what they really have here is the integration story, and cloud is forcing that, in my opinion. But they announced what they call a complete devops platform delivered as a single application, from manage, plan, create, verify, package, secure, release, configure, monitor, and defend. The spectrum of a devops platform. So that to me, I think, is the step that needs to be taken. The question I have is how real is it, in your opinion? Is that what a lot of other people are saying that they have? What's your analysis of that story, reality, legit, and what's their prospects? >> Yeah, well, definitely GitLab has great adoption. The two pieces is the SCM and the CI are the core of what they're doing, and they know that's where people usually kind of walk in the door. Then they kind of land and they look to expand from that. GitLab's made a number of acquisitions, and from 2020, they are going to really double down on making sure that they dig deeper into some of those environments, especially security, planning, and ops were the three priorities that they had there. So, you know, John, we know when you talk about you're trying to be all things to all people, there are going to be things that you will do well and things that you can do great, but, so it is an audacious goal, and with a broad community supporting it. >> Well, we know, you've reported on this and we've told stories about it is that if there's too many tools in an enterprise, you have this tool shed effect where there's no real platform around it, and I call it a tool shed, but if you have too many tools laying around, they're not cohesively integrated, that's a problem that becomes tool sprawl. So this has become an issue. We saw it in the big data world. We saw unification as a strategy for that. Databricks, for example, is a great example of one company that's taken advantage of that trend. Is there a tool problem in the dev space that GitLab's taken advantage of? >> Absolutely, John. And I think something we're going to dig in deep today, we've got a couple of practitioners on, we've got the partners, we've got the executive team from GitLab. John, thank you so much for helping me kick off GitLab Commit 2020 and a massive schedule of theCUBE coverage throughout the entire cloud native multicloud ecosystem. All right, be sure to check out thecube.net for all of the shows that we will be out in 2020 as well as a tremendous back catalog that you can search. For John Furrier, I'm Stu Miniman. Thank you for watching theCUBE. (electronic music)

>> Announcer: Live from San Diego, California, it's theCUBE covering KubeCon and CloudNativeCon. Brought to you by RedHat, the CloudNative Computing Foundation, and its ecosystem partners. >> Stu: Welcome back to theCUBE, I'm Stu Miniman. My cohost for three days of coverage is John Troyer. We're here at KubeCon CloudNativeCon in San Diego, over 12,000 in attendance and happy to welcome back a CUBE alumni and veteran of generations of the stacks that we've seen come together and change over the time, Sheng Liang, who is the co-founder and CEO of Rancher Labs. Thanks so much, great to see you. >> Shang: Thank you Stuart, is very glad to be here. >> All right, so you know Kubernetes, flash to the pan nobody's all that excited about it. I mean, we've seen all these things come and go over the years, Sheng. No but seriously, the excitement is palpable. Every year, you know, so many more people, so many more projects, so much more going on. Help set the stage for you, as to what you see and the importance today of kind of CloudNative in general and you know, this ecosystem specifically. >> Yeah you're so right though, Stuart. Community as a whole and Kubernetes has really come a long way. In the early days, Kubernetes was a uh, you know, somewhat of a technical community, lot of Linux people. But not a whole lot of end users. Not a whole lot of Enterprise customers. I walk in today and just the kind of people I've met, I've probably talked to fifty people already who are just really at the beginning of the show and uh there's a very very large number Enterprise customers. And this does feel like Kubernetes has crossed the chasm and headed in to the mainstream Enterprise market. >> Yeah it's interesting you know I've talked to you know plenty of the people here probably if you brought up things like OpenStack and CloudStack they wouldn't even know what we were talking about. The wave of containerization really seemed to spread far and wide. At Rancher you've done some surveys, give us some of the insight. What are you seeing? You've talked to plenty of customers. Give us where we are with the maturity. >> Definitely, definitely. Enterprise Kubernetes adoption is ready for prime time. You know the So what we're really seeing is some of the early challenges a few years ago a lot of people were having problems with just installing Kubernetes. They were literally just making sure to get people educated about container as a concept. Those have been overcome. Now, uh, we're really facing next generation of growth. And people solve these days solve problems like how do I get my new applications onboarding to Kubernetes. How do I really integrate Kubernetes into my multicloud and hybrid-Cloud strategy? And as Enterprise's need to perform computing in places beyond just the data centers and the cloud, we're also seeing tremendous amount of interest in running Kubernetes on the Edge. So those are some of the major findings of our survey. >> John: That's great. So Sheng I'd love for you to kind of elaborate or elaborate for us where Rancher fits into this. Right. Rancher is, you've been around, you've a mature stack of technology and also some new announcements today so I'd kind of love for you to kind of tell us how you fit in to that landscape you just described. >> Absolutely. This is very exciting and very very fast changing industry. So one of the things that Rancher is able to play very well is we're really able to take work with the community, take the latest and greatest open source technology and actually develop open source products on top this and make that technology useful and consumable for Enterprise at large. So the way we see it, to make Kubernetes work we really need to solve problems at three levels. At the lowest level, the industry need at lot of compliant and compatible certified Kubernetes distros and services. So that's table stakes now. Rancher is a leader in providing CNCF certified Kubernetes distro. We actually provide two of them. One of them is called RKE - Rancher Kubernetes Engine. Something we've been doing it for years. It's really one of the easiest to use and most widely deployed Kubernetes distributions. But we don't force our customers to only use our Kubernetes distribution. Rancher customers can use whatever CNCF certified Kubernetes distribution or Kubernetes services they want. So a lot of our customers use RKE(Rancher Kubernetes Engine) but they also use, when they go to the cloud, they use cloud hosted Kubernetes Services like GKE and EKS. There are really a lot of advantages in using those because cloud providers will help you run these Kubernetes clusters for free. And in many cases they even throw in the infrastructure it takes to run the Kubernetes masters and etcd databases for free. If you're in the cloud, there's really no reason not to be using these Kubernetes services. Now there's one area that Rancher ended up innovating at the Kubernetes distros, despite having these data center focus and cloud focus Kubernetes distros and services. And that is one of our, one of the two big announcements today. And that's called K3S. K3S is a great open source project. It's probably one of the most exciting open source projects in the Kubernetes ecosystem today. And what we did with K3S is we took Kubernetes that's been proven in data center and cloud and we brought it everywhere. So with K3S you can run Kubernetes on a Raspberry Pi. You can run Kubernetes in a surveillance camera. You can run Kubernetes in an ATM machine. You know, we have customers trying to run now Kubernetes in a uh, factory floor. So it really helps us realize our vision of Kubernetes as a new Linux and you run it everywhere. >> Well that's great 'cause you talk about that simplicity that we need and if you start talking about Edge deployment, I don't have the people, I don't have the skillset, and a lot times I don't have the gear, uh, to run that. So you know, help connect the dots as to you know, what led Rancher to do the K3S piece of it and you know, what did we take out? Or what's the differences between K8S and the K3S? >> That's a great question, you know. Even the name "K3S" is actually somewhat a wordplay on K8S You know we kind of cut half of 8 away and you're left with 3. It really happened with some of our early traction we sawing some customers. I remember, in retrospect it wasn't really that long ago. It was like middle of last year, we saw a blog coming out of Chick-fil-A and a group of technical enthusiasts were experimenting with actually running uh, Kubernetes in very, in like Intel Nook servers. You know, they were talking about potentially running three of those servers in every one of their stores and at the time they were using RKE and Rancher Kubernetes Engine to do that. And they run into a lot of issues. I mean to be honest if you think about running Kubernetes in the cloud in the database center, uh these servers have a lot of resources and you also have a dedicated operations teams. You have an SRE to manage them, right? But when you really bring it out into branch offices and Edge computing locations, now all of the sudden, number one, these uh, the software now has to take a lot less resource but also you don't really have SREs monitoring them every day anymore. And you, since these, Kubernetes distro really has to be zero touch and it has to run just like a, you know like a embedded window or Linux server. And that's what K3S was able to accomplish, we were able to really take away lot of the baggage that came with having all the drivers that were necessary to run Kubernetes in the cloud and we were also able to dramatically simplify what it takes to actually start Kubernetes and operate it. >> So unsolicited, I was doing an event right before this one and I asked some people what they looking forward to here at KubeCon. And independently, two different people said, "The thing I'm most excited about is K3S." And I think it's because it's the right slice through Kubernetes. I can run it in my lab. I can run it on my laptop. I can on a stack of Raspberry Pis or Nooks, but I could also run it in production if I, you know I can scale it up >> Stu: Yeah. >> John: And in fact they both got a twinkle in their eye and said well what if this is the future of Kubernetes, like you could take this and you could run it, you know? They were very excited about it. >> Absolutely! I mean, you know, I really think, you know, as a company we survive by, and thrive by delivering the kind of innovation that pushes the market forward right? I mean, we, otherwise people are not going to look at Rancher and say you guys are the originators of Kubernetes technology. So we're very happy to be able to come up with technologies like K3S that effectively greatly broadened the addressable market for everyone. Imagine you were a security vendor and before like all you really got to do is solving security problems. Or if you were a monitoring vendor you were able to solve monitoring problems for a data center and in the cloud. Now with K3S you end up getting to solve the same problems on the Edge and in branch offices. So that's why so many people are so excited about it. >> All right so Sheng you said K3S is one of the announcements this week, what's the rest of the news? >> Yeah so K3S, RKE, and all the GKE, AKS, EKS, they're really the fundamental layer of Kubernetes everywhere. Then on top of that one of the biggest piece of innovation that Rancher labs created is the idea of multi-cluster management. A few years ago it was pretty much of a revolutionary concept. Now it's widely understood. Of course an organization is not going to have just one cluster, they're going to have many clusters. So Rancher is the industry leader for doing multi-cluster management. And these clusters could span clouds, could span data centers, now all the way out to branch offices and the Edge. So we're exhibiting Rancher on the show floor. Everyone, most people I've met here, they know Rancher because of that flash of product. Now our second announcement though is yet another level above Rancher, so what we've seen is in order to really Kubernetes to achieve the next level of adoption in the Enterprise we're seeing you know some of the development teams and especially the less skilled dev ops teams, they're kind of struggling with the learning curve of Kubernetes and also some of the associated technologies around service mesh around Knative, around, you know, CICD, so we created a project called Rio, as in Rio de Janeiro the city. And the nice thing about Rio is it packaged together all these Cloud Native technologies and then we created very easy to use, very simple to understand user experience for developers and dev ops teams. So they no longer have to start with the training course on Kubernetes, on Istio, on Knative, on Tekton, just to get productive. They can pretty much get productive on day one. So that Rio project has hit a very important milestone today, we shipped the beta release for it and we're exhibiting it at the booth as well. >> Well that's great. You know, the beta release of Rio, pulling together a lot of these projects. Can you talk about some folks that, early adopters that have been using them or some folks that have been working with the project? >> Sheng: Yeah absolutely. So I talk about some of the early adoption we're seeing for both K3S and Rio. Uh, what we see the, first of all just the market reception of K3S, as you said, has been tremendous. Couple of even mentioned to you guys today in your earlier interviews. And it is primarily coming from customers who want to run Kubernetes in places you probably haven't quite anticipated before, so I kind of give you two examples. One is actually appliance manufacture. So if you think they used to ship appliances, then you can imagine these appliances come with Linux and they would image their appliance with an OS image with their applications. But what's happening is these applications are becoming so sophisticated they're now talking about running the entire data analytics stack and AI software. So it actually takes Kubernetes not necessarily, because it's one server in a situation of appliance. Kubernetes is not really managing a cluster, but it's managing all the application components and microservices. So they ended up bundling up K3S into their appliance. This is one example. Another example is actually an ISV, that's a very interesting use case as well. So uh, they ship a micro service based application software stack and again their software involves a lot of different complicated components. And they decided to replatform their software on Kubernetes. We've all heard a lot of that! But in their case they have to also ship, they don't just run the software themselves, they have to ship the software to the end users. And most of their end users are not familiar with Kubernetes yet, right? And they don't really want to say, to install our software you go provision the Kubernetes cluster and then you operate it from now on. So what they did is they took K3S and bundled into their application as if it were an application server, almost like a modern day WebLogic and WebSphere, then they shipped the whole thing to their customers. So I thought both of these use cases are really interesting. It really elevates the reach of Kubernetes from just being almost like a cloud platform in the old days to now being an application server. And then I'll also quickly talk about Rio. A lot of interest inside Rio is around really dev ops teams who've had, I mean, we did a survey early on and we found out that a lot of our customers they deploy Kubernetes in services. But they end up building a custom experience on top of their Kubernetes deployment, just so that most of their internal users wouldn't have to take a course on Kubernetes to start using it. So they can just tell that this thing that, this is where my source code is and then every thing from that point on will be automated. So now with Rio they wouldn't have to do that anymore. Effectively Rio is the direct source to URL type of, one step process. And they are able to adopt Rio for that purpose. >> So Sheng, I want to go back to when we started this conversation. You said, you know, the ecosystem growing. That not only, you know, so many vendors here, 129 end users, members of the CNCF. The theme we've been talking about is to really, you know, it's ready for production and people are all embracing it. But to get the vast majority of people, simplicity really needs to come front and center, I think. K3S really punctuates that. What else do we need to do as an ecosystem, you know, Rancher is looking to take a leadership position and help drive this, but what else do you want to see from your peers, the community, overall to help drive this to the promise that it could deliver. >> We really see the adoption of Kubernetes is probably going to wing at three, I mean. We see most organizations go through this three step journey. The first step is you got to install and operate Kubernetes. You know, day one, day two. And I think we've got it down. With K3S it becomes so easy. With GKE it becomes one API call or one simple UI interaction. And CNCS has really stepped up and created a great, you know, compliance certification program, right? So we're not seeing the kind of fragmentation that we saw with some of the other technologies. This is fantastic. Then the second step we see is, which a lot of our customers are going through now, is now you have all the Kubernetes clusters coming from different clouds, different infrastructure, potentially on the Edge. You have a management problem. Now you all of the sudden because we made Kubernetes clusters so easy to obtain you can potentially have a sprawl. If you are not careful you might leave them misconfigured. That could expose a security issue. So really it takes Rancher, it takes our ecosystem partners, like Twistlock, like Aqua. CICD partners, like CloudBees, GitLab. Just everyone really needs to come together, make that, solve that management problem. So not only, uh, you build this Kubernetes infrastructure but then you actually going to get a lot of users and they can use the cluster securely and reliably. Then I think the third step, which I think a lot of work still remain is we really want to focus on growing the footprint of workload, of enterprise workload, in the enterprise. So there the work is honestly just getting started. Anywhere from uh, if you walk into any enterprise you know what percentage of their total workload is running on Kubernetes today? I mean outside of Google and Uber, that percentage is probably very small, right? They're probably in the minority, maybe even in single digit percentage. So, we really need to do a lot of work. You know, we need to uh, Rancher created this project called LongHorn and we also work with a lot of our ecosystem partners in persistence storage area like Portworx, StorageOS, OpenEBS. Lot of us really need to come together and solve this problem of running persistent workload. I mean there was also a lot of talk about it at the keynote this morning, I was very encouraged to hear that. That could easily double, triple the amount of workload that could bring, that could be onboarded into Kubernetes and even experiences like Rio, you know? Make it further simpler, more accessible. That is really in the DNA of Rancher. Rancher wouldn't be surviving and thriving without our insight into how to make our technology consumable and widely adopted. So a lot of work we're doing is really to drive the adoption of Kubernetes in the enterprise beyond, you know, the current state and into something I really don't see in the future, Kubernetes wouldn't be as actually widely used as say AWS or vSphere. That would be my bar for success. Hopefully in a few years we can be talking about that. >> All right, that is a high bar Sheng. We look forward to more conversations with you going forward. Congratulations on the announcement. Great buzz on K3S, and yeah, thanks so much for joining us. >> Thank you very much. >> For John Troyer, I'm Stu Miniman, back with lots more coverage here from KubeCon CloudNativeCon 2019 in San Diego, you're watching theCUBE. [Upbeat music]