(intro music) >> Narrator: "theCUBE's" live coverage is made possible by funding from Dell Technologies, creating technologies that drive human progress. (gentle music) >> Hey everyone, welcome back to "theCUBE," the leader in live and emerging tech coverage. As you well know, we are live at MWC23 in Barcelona, Spain. Lisa Martin with Dave Nicholson. Day three of our coverage, as you know, 'cause you've been watching the first two days. A lot of conversations about ecosystem, a lot about disruption in the telco industry. We're going to be talking about Open RAN. You've heard some of those great conversations, the complexities, the opportunities. Two guests join Dave and me. Abdullah Abuzaid, Technical Product Manager at Dell, and Gil Hellmann, VP Telecom Solutions Engineering and Architecture at Wind River. Welcome to the program guys. >> Thank you. >> Nice to be here. >> Let's talk a little bit about Dell and Wind River. We'll each ask you both the same question, and talk to us about how you're working together to really address the complexities that organizations are having when they're considering moving from a closed environment to an open environment. >> Definitely. Thank you for hosting us. By end of the day, the relationship between Dell and Wind River is not a new. We've been collaborating in the open ecosystem for long a time enough. And that's one of the, our partnership is a result of this collaboration where we've been trying to make more efficient operation in the ecosystem. The open environment ecosystem, it has the plus and a concern. The plus of simplicity, choice of multiple vendors, and then the concern of complexity managing these vendors. Especially if we look at examples for the Open RAN ecosystem, dealing with multiple vendors, trying to align them. It bring a lot of operational complexity and TCO challenges for our customers, from this outcome where we build our partnership with Wind River in order to help our customer to simplify, or run deployment, operation, and lifecycle management and sustain it. >> And who are the customers, by the way? >> Mainly the CSP customers who are targeting Open RAN and Virtual RAN deployments. That digital transformation moving towards unified cloud environment, or a seamless cloud experience from Core to RAN, these are the customers we are working with them. >> You'll give us your perspective, your thoughts on the partnership, and the capabilities that you're enabling, the CSPs with that. >> Sure. It's actually started last year here in Barcelona, when we set together, and started to look at the, you know, the industry, the adoption of Open RAN, and the challenges. And Open RAN brings a lot of possibilities and benefit, but it does bring a lot of challenges of reintegrating what you desegregate. In the past, you purchase everything from one vendor, they provide the whole solution. Now you open it, you have different layers. So if you're looking at Open RAN, you have, I like to look at it as three major layers, the management, application, and the infrastructure. And we're starting to look what are the challenges. And the challenges of integration, of complexity, knowledge that operator has with cloud infrastructure. And this is where we basically, Dell and Winder River set together and say, "How can we ease this? "How we can make it simpler?" And we decided to partner and bring a joint infrastructure solution to market, that's not only integrated at a lab at the factory level, but it basically comes with complete lifecycle management from the day zero deployment, through the day two operation, everything done through location, through Dell supported, working out of the box. So basically taking this whole infrastructure layer integration pain out, de-risking everything, and then continuing from there to work with the ecosystem vendor to reintegrate, validate the application, on top of this infrastructure. >> So what is the, what is the Wind River secret sauce in this, in this mix, for folks who aren't familiar with what Wind River does? >> Yes, absolutely. So Wind River, for many, many don't know, we're in business since 1981. So over 40 years. We specialize high performance, high reliability infrastructure. We touch every aspect of your day and your life. From the airplane that you fly, the cars, the medical equipment. And if we go into the telco, most of the telco equipment that it's not virtualized, not throughout the fight today, using our operating system. So from all the leading equipment manufacturers and even the smaller one. And as the world started to go into desegregation in cloud, Wind River started to look at this and say, "Okay, everything is evolving. Instead of a device that included the application, the hardware, everything fused together, it's now being decomposed. So instead of providing the operating environment to develop and deploy the application to the device manufacturer, now we're providing it basically to build the cloud. So to oversimplify, I call it a cloud OS, okay. It's a lot more than OS, it's an operating environment. But we took basically our experience, the same experience that, you know, we used in all those years with the telco equipment manufacturer, and brought it into the cloud. So we're basically providing solution to build an on-premises scalable cloud from the core all the way to the far edge, that doesn't compromise reliability, doesn't compromise performance, and address all the telco needs. >> So I, Abdullah, maybe you can a answer this. >> Yeah. >> What is the, what does the go-to-market motion look like, considering that you have two separate companies that can address customers directly, separately. What does that, what does that look like if you're approaching a possible customer who is, who's knocking on the door? >> How does that work? >> Exactly. And this effort is a Dell turnkey sales service offering, or solution offering to our customers. Where Dell, in collaboration with Wind River, we proactively validate, integrate, and productize the solution as engineered system, knock door on our customer who are trying to transform to Open RAN or open ecosystem. We can help you to go through that seamless experience, by pre-validating with whatever workload you want to introduce, enable zero touch provisioning, and during the day one deployment, and ensure we have sustainable lifecycle management throughout the lifecycle of the product in, in operate, in operational network, as well as having a unified single call of support from Dell side. >> Okay. So I was just going to ask you about support. So I'm a CSP, I have the solution, I go to Dell for support. >> Exactly. >> Okay. So start with Dell, and level one, level two. And if there are complex issues related to the cloud core itself, then Wind River will be on our back supporting us. >> Talk a little bit about a cust, a CSP example that is, is using the technology, and some of the outcomes that they're able to achieve. I'd love to get both of your perspectives on that. >> Vodafone is a great example. We're here in Barcelona. Vodafone is the first ora network in Europe, and it's using our joint solution. >> What are some of the, the outcomes that it's helping them to achieve? >> Faster time to market. As you see, they already started to deploy the ORAN in commercial network, and very successful in the trials that they did last year. We're also not stopping there. We're evolving, working with them together to improve like stuff around energy efficiency. So continue to optimize. So the outcome, it's just simplifying it, and you know, ready to go. Using experience that we have, Wind River is powering the first basically virtualized RAN 5G network in the world. This is with Verizon. We're at the very large scale. We started this deployment in late '20 and '19, the first site. And then through 2020 to 2022, we basically rolled in large scale. We have a lot of experience learning from it, which what we brought into the table when we partnered with Dell. A lot of experience from how you deploy at scale. Many sites from a central location, updates, upgrade. So the whole day two operation, and this is coming to bearing the solution that basically Vodafone is deploying now, and which allowed them... If I, if I look at my engagement with Verizon, started years before we started. And it took quite some time until we got stuff running. And if you look at the Vodafone time schedule, was significantly compressed compared to the Verizon first deployment. And I can tell you that there are other service providers that were announced here by KDI, for example. It's another one moving even faster. So it's accelerating the whole movement to Ora. >> We've heard a lot of acceleration talk this week. I'd love to get your perspective, Abdullah, talking about, you know, you, you just mentioned two huge names in Telco, Vodafone and Verizon. >> Yep. >> Talk a little bit about Dell's commitment to helping telecommunications companies really advance, accelerate innovation so that all of us on the other end have this thing that just works wherever we are 24 by 7. >> Not exactly. And this, we go back to the challenges in Open ecosystem. Managing multiple vendors at the same time, is a challenge for our customers. And that's why we are trying to simplify their life cycle by have, by being a trusted partner, working with our customer through all the journey. We started with Dish in their 5G deployment. Also with Vodafone. We're finding the right partners working with them proactively before getting into, in front of the customer to, we've done our homework, we are ready to simplify the process for you to go for it. If you look at the RAN in particular, we are talking with the 5g. We have ran the simplification, but they still have on the other side, limited resources and skillset can support it. So, bringing a pro, ahead of time engineer system, with a zero touch of provisioning enablement, and sustainable life cycle management, it lead to the faster time to market deployment, TCO savings, improved margins for our customers, and faster business revenue for their end users. >> Solid outcomes. >> And, and what you just just described, justifies the pain associated with disaggregating and reintegrating, which is the way that Gill referenced it, which I think is great because you're not, you're not, you're not re-aggregating, (laughs) you're reintegrating, and you're creating something that's better. >> Exactly. >> Moving forward. Otherwise, why would you do it? >> Exactly. And if you look at it, the player in the ecosystem, you have the vendors, you have the service integrators, you have the automation enablers, but kind of they are talking in silos. Everyone, this is my raci, this is what I'm responsible for. I, I'm not able, I don't want to get into something else while we are going the extra mile by working proactively in that ecosystem to... Let's bring brains together, find out what's one plus one can bring three for our customers, so we make it end-to-end seamless experience, not only on the technical part, but also on the business aspect side of it. >> So, so the partnership, it's about reducing the pen. I will say eliminating it. So this is the, the core of it. And you mentioned getting better coverage for your phone. I do want to point out that the phones are great, but if you look at the premises of a 5G network, it's to enable a lot more things that will touch your life that are beyond the consumer and the phone. Stuff like connected vehicles. So for example, something as simple as collision avoidance, the ability for the car that goes in front of you to be able to see what's happening and broadcast this information to the car behind that have no ability to see it. And basically affect our life in a way that makes our driving safer. And for this, you need a ultra low, reliable low latency communication. You need a 5G network. >> I'm glad you brought that up, because you know, we think about, "Well we just have to be connected all the time." But those are some of the emerging technologies that are going to be potentially lifesaving, and, and really life transforming that you guys are helping to enable. So, really great stuff there, but so much promise coming down the road. What's next for Dell and Wind River? And, and when you're in conversations with prospective CSP's, what is the superpower that you deliver together? I'd love to get both of your perspectives. >> So, if you look at it, number one, customers look at it, last savings and their day-to-day operation. In 5G nature, we are talking the introduction of ORAN. This is still picking up. But there is a mutualization and densification of ORAN. And this is where we're talking on monetizing my deployment. Then the third phase, we're talking sustainability and advanced service introduction. Where I want to move not only ORAN, I want to bring the edge at the same side, I want to define the advanced use cases of edge, where it enables me with this pre-work being done to deliver more services and better SLA services. By end of the day, 5G as a girl mentioned earlier, is not about a good better phone coverage, or a better speed robot, but what customized SLA's I can deliver. So it enables me to deliver different business streams to my end users. >> Yeah. >> So yeah. I will say there are two pens. One, it's the technology side. So for an example, energy efficiency. It's a very big pin point. And sustainability. So we work a lot around this, and basically to advance this. So if you look at the integrated solution today, it's very highly optimized for resource consumption. But to be able to more dynamically be able to change your power profile without compromising the SLA. So this is one side. The other side, it's about all those applications that will come to the 5G network to make our life better. It's about integrating, validating, certifying those applications. So, it's not just easy to deploy an ORAN network, but it's easy to deploy those applications. >> I'd be curious to get your perspective on the question of ROI in this, in this space. Specifically with the sort of the macro headwinds (clears throat) the economies of the world are facing right now, if you accept that. What does the ROI timeline look like when you're talking about moving towards ORAN, adopting VRAN, an amazing, you know, a plethora of new services that can be delivered, but will these operators have the appetite to take that, make that investment and take on that risk based upon the ROI time horizon? Any thoughts on that? >> Yeah. So if you look at the early days or ORAN introduction in particular, most of the entrepreneurs of ORAN and Virtual RAN ran into the challenges of not only the complexity of open ecosystem, but the integration, is like the redos of the work. And that's where we are trying to address it via pre-engineered system or building an engineer system proactively before getting it to the customers. Per our result or outcomes we get, we are talking about 30 to 50% savings on the optics. We are talking 110 ROI for our customers, simply because we are reducing the redos, the time spent to discover and explore. Because we've done that rework ahead of time, we found the optimization issues. Just for example, any customer can buy the same components from any multiple vendors, but how I can bring them together and give, deliver for me the best performance that I can fully utilize, that's, that's where it brings the value for our customer, and accelerate the deployment and the operation of the network. >> Do you have anything to add before we close in the next 30 seconds? >> Yeah. Yeah. (laughs) >> Absolutely. I would say, we start to see the data coming from two years of operation at scale. And the data supports performance. It's the same or better than traditional system. And the cost of operation, it's as good or better than traditional. Unfortunately, I can't provide more specific data. But the point is, when something is unknown in the beginning, of course you're more afraid, you take more conservative approach. Now the data starts to flow. And from here, the intention needs to go even better. So more efficiency, so cost less than traditional system, both to operate as well as to build up. But it's definitely the data that we have today says, the, ORAN system is at part, at the minimum. >> So, definite ROI there. Guys, thank you so much for joining Dave and me talking about how you're helping organizations not just address the complexities of moving from close to open, but to your point, eliminating them. We appreciate your time and, and your insights. >> Thank you. >> All right. For our guests and for Dave Nicholson, I'm Lisa Martin. You're watching "theCUBE," the leader in live and emerging tech coverage. Live from MWC23. We'll be back after a short break. (outro music)

(gentle music) >> Announcer: "theCube's" live coverage is made possible by funding from Dell Technologies, creating technologies that drive human progress. (upbeat music) (logo whooshing) >> Hey, everyone, welcome back to "theCube's" coverage of day one, MWC23 live from Barcelona, Lisa Martin here with Dave Vellante. Dave, we've got some great conversations so far This is the biggest, most packed show I've been to in years. About 80,000 people here so far. >> Yeah, down from its peak of 108, but still pretty good. You know, a lot of folks from China come to this show, but with the COVID situation in China, that's impacted the attendance, but still quite amazing. >> Amazing for sure. We're going to be talking about trends and mobility, and all sorts of great things. We have a couple of guests joining us for the first time on "theCUBE." Please welcome Dr. Srinivas Mukkamala or Sri, chief product officer at Ivanti. And Dave Shepherd, VP Ivanti. Guys, welcome to "theCUBE." Great to have you here. >> Thank you. >> So, day one of the conference, Sri, we'll go to you first. Talk about some of the trends that you're seeing in mobility. Obviously, the conference renamed from Mobile World Congress to MWC mobility being part of it, but what are some of the big trends? >> It's interesting, right? I mean, I was catching up with Dave. The first thing is from the keynotes, it took 45 minutes to talk about security. I mean, it's quite interesting when you look at the shore floor. We're talking about Edge, we're talking about 5G, the whole evolution. And there's also the concept of are we going into the Cloud? Are we coming back from the Cloud, back to the Edge? They're really two different things. Edge is all decentralized while you recompute. And one thing I observed here is they're talking about near real-time reality. When you look at automobiles, when you look at medical, when you look at robotics, you can't have things processed in the Cloud. It'll be too late. Because you got to make millisecond-based stations. That's a big trend for me. When I look at staff... Okay, the compute it takes to process in the Cloud versus what needs to happen on-prem, on device, is going to revolutionize the way we think about mobility. >> Revolutionize. David, what are some of the things that you're saying? Do you concur? >> Yeah, 100%. I mean, look, just reading some of the press recently, they're predicting 22 billion IoT devices by 2024. Everything Sri just talked about there. It's growing exponentially. You know, problems we have today are a snapshot. We're probably in the slowest place we are today. Everything's just going to get faster and faster and faster. So it's a, yeah, 100% concur with that. >> You know, Sri, on your point, so Jose Maria Alvarez, the CEO of Telefonica, said there are three pillars of the future of telco, low latency, programmable networks, and Cloud and Edge. So, as to your point, Cloud and low latency haven't gone hand in hand. But the Cloud guys are saying, "All right, we're going to bring the Cloud to the Edge." That's sort of an interesting dynamic. We're going to bypass them. We heard somebody, another speaker say, "You know, Cloud can't do it alone." You know? (chuckles) And so, it's like these worlds need each other in a way, don't they? >> Definitely right. So that's a fantastic way to look at it. The Cloud guys can say, "We're going to come closer to where the computer is." And if you really take a look at it with data localization, where are we going to put the Cloud in, right? I mean, so the data sovereignty becomes a very interesting thing. The localization becomes a very interesting thing. And when it comes to security, it gets completely different. I mean, we talked about moving everything to a centralized compute, really have massive processing, and give you the addition back wherever you are. Whereas when you're localized, I have to process everything within the local environment. So there's already a conflict right there. How are we going to address that? >> Yeah. So another statement, I think, it was the CEO of Ericsson, he was kind of talking about how the OTT guys have heard, "We can't let that happen again. And we're going to find new ways to charge for the network." Basically, he's talking about monetizing the API access. But I'm interested in what you're hearing from customers, right? 'Cause our mindset is, what value you're going to give to customers that they're going to pay for, versus, "I got this data I'm going to charge developers for." But what are you hearing from customers? >> It's amazing, Dave, the way you're looking at it, right? So if we take a look at what we were used to perpetual, and we said we're going to move to a subscription, right? I mean, everybody talks about subscription economy. Telcos on the other hand, had subscription economy for a long time, right? They were always based on usage, right? It's a usage economy. But today, we are basically realizing on compute. We haven't even started charging for compute. If you go to AWS, go to Azure, go to GCP, they still don't quite charge you for actual compute, right? It's kind of, they're still leaning on it. So think about API-based, we're going to break the bank. What people don't realize is, we do millions of API calls for any high transaction environment. A consumer can't afford that. What people don't realize is... I don't know how you're going to monetize. Even if you charge a cent a call, that is still going to be hundreds and thousands of dollars a day. And that's where, if you look at what you call low-code no-code motion? You see a plethora of companies being built on that. They're saying, "Hey, you don't have to write code. I'll give you authentication as a service. What that means is, Every single time you call my API to authenticate a user, I'm going to charge you." So just imagine how many times we authenticate on a single day. You're talking a few dozen times. And if I have to pay every single time I authenticate... >> Real friction in the marketplace, David. >> Yeah, and I tell you what. It's a big topic, right? And it's a topic that we haven't had to deal with at the Edge before, and we hear it probably daily really, complexity. The complexity's growing all the time. That means that we need to start to get insight, visibility. You know? I think a part of... Something that came out of the EU actually this week, stated, you know, there's a cyber attack every 11 seconds. That's fast, right? 2016, that was 40 seconds. So actually that speed I talked about earlier, everything Sri says that's coming down to the Edge, we want to embrace the Edge and that is the way we're going to move. But customers are mindful of the complexity that's involved in that. And that, you know, lens thought to how are we going to deal with those complexities. >> I was just going to ask you, how are you planning to deal with those complexities? You mentioned one ransomware attack every 11 seconds. That's down considerably from just a few years ago. Ransomware is a household word. It's no longer, "Are we going to get attacked?" It's when, it's to what extent, it's how much. So how is Ivanti helping customers deal with some of the complexities, and the changes in the security landscape? >> Yeah. Shall I start on that one first? Yeah, look, we want to give all our customers and perspective customers full visibility of their environment. You know, devices that are attached to the environment. Where are they? What are they doing? How often are we going to look for those devices? Not only when we find those devices. What applications are they running? Are those applications secure? How are we going to manage those applications moving forward? And overall, wrapping it round, what kind of service are we going to do? What processes are we going to put in place? To Sri's point, the low-code no-code angle. How do we build processes that protect our organization? But probably a point where I'll pass to Sri in a moment is how do we add a level of automation to that? How do we add a level of intelligence that doesn't always require a human to be fixing or remediating a problem? >> To Sri, you mentioned... You're right, the keynote, it took 45 minutes before it even mentioned security. And I suppose it's because they've historically, had this hardened stack. Everything's controlled and it's a safe environment. And now that's changing. So what would you add? >> You know, great point, right? If you look at telcos, they're used to a perimeter-based network. >> Yep. >> I mean, that's what we are. Boxed, we knew our perimeter. Today, our perimeter is extended to our home, everywhere work, right? >> Yeah- >> We don't have a definition of a perimeter. Your browser is the new perimeter. And a good example, segueing to that, what we have seen is horizontal-based security. What we haven't seen is verticalization, especially in mobile. We haven't seen vertical mobile security solutions, right? Yes, you hear a little bit about automobile, you hear a little bit about healthcare, but what we haven't seen is, what about food sector? What about the frontline in food? What about supply chain? What security are we really doing? And I'll give you a simple example. You brought up ransomware. Last night, Dole was attacked with ransomware. We have seen the beef producer colonial pipeline. Now, if we have seen agritech being hit, what does it mean? We are starting to hit humanity. If you can't really put food on the table, you're starting to really disrupt the supply chain, right? In a massive way. So you got to start thinking about that. Why is Dole related to mobility? Think about that. They don't carry service and computers. What they carry is mobile devices. that's where the supply chain works. And then that's where you have to start thinking about it. And the evolution of ransomware, rather than a single-trick pony, you see them using multiple vulnerabilities. And Pegasus was the best example. Spyware across all politicians, right? And CEOs. It is six or seven vulnerabilities put together that actually was constructed to do an attack. >> Yeah. How does AI kind of change this? Where does it fit in? The attackers are going to have AI, but we could use AI to defend. But attackers are always ahead, right? (chuckles) So what's your... Do you have a point of view on that? 'Cause everybody's crazy about ChatGPT, right? The banks have all banned it. Certain universities in the United States have banned it. Another one's forcing his students to learn how to use ChatGPT to prompt it. It's all over the place. You have a point of view on this? >> So definitely, Dave, it's a great point. First, we all have to have our own generative AI. I mean, I look at it as your digital assistant, right? So when you had calculators, you can't function without a calculator today. It's not harmful. It's not going to take you away from doing multiplication, right? So we'll still teach arithmetic in school. You'll still use your calculator. So to me, AI will become an integral part. That's one beautiful thing I've seen on the short floor. Every little thing there is a AI-based solution I've seen, right? So ChatGPT is well played from multiple perspective. I would rather up level it and say, generated AI is the way to go. So there are three things. There is human intense triaging, where humans keep doing easy work, minimal work. You can use ML and AI to do that. There is human designing that you need to do. That's when you need to use AI. >> But, I would say this, in the Enterprise, that the quality of the AI has to be better than what we've seen so far out of ChatGPT, even though I love ChatGPT, it's amazing. But what we've seen from being... It's got to be... Is it true that... Don't you think it has to be cleaner, more accurate? It can't make up stuff. If I'm going to be automating my network with AI. >> I'll answer that question. It comes down to three fundamentals. The reason ChatGPT is giving addresses, it's not trained on the latest data. So for any AI and ML method, you got to look at three things. It's your data, it's your domain expertise, who is training it, and your data model. In ChatGPT, it's older data, it's biased to the people that trained it, right? >> Mm-hmm. >> And then, the data model is it's going to spit out what it's trained on. That's a precursor of any GPT, right? It's pre-trained transformation. >> So if we narrow that, right? Train it better for the specific use case, that AI has huge potential. >> You flip that to what the Enterprise customers talk about to us is, insight is invaluable. >> Right. >> But then too much insight too quickly all the time means we go remediation crazy. So we haven't got enough humans to be fixing all the problems. Sri's point with the ChatGPT data, some of that data we are looking at there could be old. So we're trying to triage something that may still be an issue, but it might have been superseded by something else as well. So that's my overriding when I'm talking to customers and we talk ChatGPT, it's in the news all the time. It's very topical. >> It's fun. >> It is. I even said to my 13-year-old son yesterday, your homework's out a date. 'Cause I knew he was doing some summary stuff on ChatGPT. So a little wind up that's out of date just to make that emphasis around the model. And that's where we, with our Neurons platform Ivanti, that's what we want to give the customers all the time, which is the real-time snapshot. So they can make a priority or a decision based on what that information is telling them. >> And we've kind of learned, I think, over the last couple of years, that access to real-time data, real-time AI, is no longer nice to have. It's a massive competitive advantage for organizations, but it's going to enable the on-demand, everything that we expect in our consumer lives, in our business lives. This is going to be table stakes for organizations, I think, in every industry going forward. >> Yeah. >> But assumes 5G, right? Is going to actually happen and somebody's going to- >> Going to absolutely. >> Somebody's going to make some money off it at some point. When are they going to make money off of 5G, do you think? (all laughing) >> No. And then you asked a very good question, Dave. I want to answer that question. Will bad guys use AI? >> Yeah. Yeah. >> Offensive AI is a very big thing. We have to pay attention to it. It's got to create an asymmetric war. If you look at the president of the United States, he said, "If somebody's going to attack us on cyber, we are going to retaliate." For the first time, US is willing to launch a cyber war. What that really means is, we're going to use AI for offensive reasons as well. And we as citizens have to pay attention to that. And that's where I'm worried about, right? AI bias, whether it's data, or domain expertise, or algorithmic bias, is going to be a big thing. And offensive AI is something everybody have to pay attention to. >> To your point, Sri, earlier about critical infrastructure getting hacked, I had this conversation with Dr. Robert Gates several years ago, and I said, "Yeah, but don't we have the best offensive, you know, technology in cyber?" And he said, "Yeah, but we got the most to lose too." >> Yeah, 100%. >> We're the wealthiest nation of the United States. The wealthiest is. So you got to be careful. But to your point, the president of the United States saying, "We'll retaliate," right? Not necessarily start the war, but who started it? >> But that's the thing, right? Attribution is the hardest part. And then you talked about a very interesting thing, rich nations, right? There's emerging nations. There are nations left behind. One thing I've seen on the show floor today is, digital inequality. Digital poverty is a big thing. While we have this amazing technology, 90% of the world doesn't have access to this. >> Right. >> What we have done is we have created an inequality across, and especially in mobility and cyber, if this technology doesn't reach to the last mile, which is emerging nations, I think we are creating a crater back again and putting societies a few miles back. >> And at much greater risk. >> 100%, right? >> Yeah. >> Because those are the guys. In cyber, all you need is a laptop and a brain to attack. >> Yeah. Yeah. >> If I don't have it, that's where the civil war is going to start again. >> Yeah. What are some of the things in our last minute or so, guys, David, we'll start with you and then Sri go to you, that you're looking forward to at this MWC? The theme is velocity. We're talking about so much transformation and evolution in the telecom industry. What are you excited to hear and learn in the next couple of days? >> Just getting a complete picture. One is actually being out after the last couple of years, so you learn a lot. But just walking around and seeing, from my perspective, some vendor names that I haven't seen before, but seeing what they're doing and bringing to the market. But I think goes back to the point made earlier around APIs and integration. Everybody's talking about how can we kind of do this together in a way. So integrations, those smart things is what I'm kind of looking for as well, and how we plug into that as well. >> Excellent, and Sri? >> So for us, there is a lot to offer, right? So while I'm enjoying what I'm seeing here, I'm seeing at an opportunity. We have an amazing portfolio of what we can do. We are into mobile device management. We are the last (indistinct) company. When people find problems, somebody has to go remediators. We are the world's largest patch management company. And what I'm finding is, yes, all these people are embedding software, pumping it like nobody's business. As you find one ability, somebody has to go fix them, and we want to be the (indistinct) company. We had the last smile. And I find an amazing opportunity, not only we can do device management, but do mobile threat defense and give them a risk prioritization on what needs to be remediated, and manage all that in our ITSM. So I look at this as an amazing, amazing opportunity. >> Right. >> Which is exponential than what I've seen before. >> So last question then. Speaking of opportunities, Sri, for you, what are some of the things that customers can go to? Obviously, you guys talk to customers all the time. In terms of learning what Ivanti is going to enable them to do, to take advantage of these opportunities. Any webinars, any events coming up that we want people to know about? >> Absolutely, ivanti.com is the best place to go because we keep everything there. Of course, "theCUBE" interview. >> Of course. >> You should definitely watch that. (all laughing) No. So we have quite a few industry events we do. And especially there's a lot of learning. And we just raised the ransomware report that actually talks about ransomware from a global index perspective. So one thing what we have done is, rather than just looking at vulnerabilities, we showed them the weaknesses that led to the vulnerabilities, and how attackers are using them. And we even talked about DHS, how behind they are in disseminating the information and how it's actually being used by nation states. >> Wow. >> And we did cover mobility as a part of that as well. So there's a quite a bit we did in our report and it actually came out very well. >> I have to check that out. Ransomware is such a fascinating topic. Guys, thank you so much for joining Dave and me on the program today, sharing what's going on at Ivanti, the changes that you're seeing in mobile, and the opportunities that are there for your customers. We appreciate your time. >> Thank you >> Thank you. >> Yes. Thanks, guys. >> Thanks, guys. >> For our guests and for Dave Vellante, I'm Lisa Martin. You're watching "theCUBE" live from MWC23 in Barcelona. As you know, "theCUBE" is the leader in live tech coverage. Dave and I will be right back with our next guest. (gentle upbeat music)

(light music) >> Narrator: theCUBE's live coverage is made possible by funding from Dell Technologies. Creating technologies that drive human progress. (upbeat music) >> Welcome back to Spain everyone. Lisa Martin here with theCUBE Dave Vellante, my co-host for the next four days. We're live in Barcelona, covering MWC23. This is only day one, but I'll tell you the theme of this conference this year is velocity. And I don't know about you Dave, but this day is flying by already. This is ecosystem day. We're going to have a great discussion on the ecosystem next. >> Well we're seeing the disaggregation of the hardened telco stack, and that necessitates an ecosystem open- we're going to talk about Open RAN, we've been talking about even leading up to the show. It's a critical technology enabler and it's compulsory to have an ecosystem to support that. >> Absolutely compulsory. We've got two guests here joining us, Gautam Bhagra, Vice President partnerships at Dell, and Scott Walker, Vice President of global Telco ecosystem at Wind River. Guys, welcome to the program. >> Nice to be here. >> Thanks For having us. >> Thanks for having us. >> So you've got some news, this is day one of the conference, there's some news, Gautam, and let's start with you, unpack it. >> Yeah, well there's a lot of news, as you know, on Dell World. One of the things we are very excited to announce today is the launch of the Open Telecom Ecosystems Community. I think Dave, as you mentioned, getting into an Open RAN world is a challenge. And we know some of the challenges that our customers face. To help solve for those challenges, Dell wants to work with like-minded partners and customers to build innovative solutions, and join go-to-market. So we are launching that today. Wind River is one of our flagship partners for that, and I'm excited to be here to talk about that as well. >> Can you guys talk a little bit about the partnership, maybe a little bit about Wind River so the audience gets that context? >> Sure, absolutely, and the theme of the show, Velocity, is what this partnership is all about. We create velocity for operators if they want to adopt Open RAN, right? We simplify it. Wind River as a company has been around for 40 years. We were part of Intel at one point, and now we're independent, owned by a company called Aptiv. And with that we get another round of investment to help continue our acceleration into this market. So, the Dell partnership is about, like I said, velocity, accelerating the adoption. When we talk to operators, they have told us there are many roadblocks that they face, right? Like systems integration, operating at scale. 'Cause when you buy a traditional radio access network solution from a single supplier, it's very easy. It's works, it's been tested. When you break these components apart and disaggregate 'em, as we talked about David, it creates integration points and support issues, right? And what Dell and Wind River have done together is created a cloud infrastructure solution that could host a variety of RAN workloads, and essentially create a two layer cake. What we're, overall, what we're trying to do is create a traditional RAN experience, with the innovation agility and flexibility of Open RAN. And that's really what this partnership does. >> So these work, this workload innovation is interesting to me because you've got now developers, you know, the, you know, what's the telco developer look like, you know, is to be defined, right? I mean it's like this white sheet of paper that can create all this innovation. And to do that, you've got to have, as I said earlier, an ecosystem. But you've got now, I'm interested in your Open RAN agenda and how you see that sort of maturity model taking place. 'Cause today, you got disruptors that are going to lean right in say "Hey, yeah, that's great." The traditional carriers, they have to have a, you know, they have to migrate, they have to have a hybrid world. We know that takes time. So what's that look like in the marketplace today? >> Yeah, so I mean, I can start, right? So from a Dell's perspective, what we see in the market is yes, there is a drive towards, everyone understands the benefits of being open, right? There's the agility piece, the innovation piece. That's a no-brainer. The question is how do we get there? And I think that's where partnerships become critical to get there, right? So we've been working with partners like Wind River to build solutions that make it easier for customers to start adopting some of the foundational elements of an open network. The, one of the purposes in the agenda of building this community is to bring like-minded developers, like you said like we want those guys to come and work with the customers to create new solutions, and come up with something creative, which no one's even thought about, that accelerates your option even quicker, right? So that's exactly what we want to do as well. And that's one of the reasons why we launched the community. >> Yeah, and what we find with a lot of carriers, they are used to buying, like I said, traditional RAN solutions which are provided from a single provider like Erickson or Nokia and others, right? And we break this apart, and you cloudify that network infrastructure, there's usually a skills gap we see at the operator level, right? And so from a developer standpoint, they struggle with having the expertise in order to execute on that. Wind River helps them, working with companies like Dell, simplify that bottom portion of the stack, the infrastructure stack. So, and we lifecycle manage it, we test- we're continually testing it, and integrating it, so that the operator doesn't have to do that. In addition to that, wind River also has a history and legacy of working with different RAN vendors, both disruptors like Mavenir and Parallel Wireless, as well as traditional RAN providers like Samsung, Erickson, and others soon to be announced. So what we're doing on the northbound side is making it easy by integrating that, and on the southbound side with Dell, so that again, instead of four or five solutions that you need to put together, it's simply two. >> And you think about today how we- how you consume telco services are like there's these fixed blocks of services that you can buy, that has to change. It's more like the, the app stores. It's got to be an open marketplace, and that's where the innovation's going to come in, you know, from the developers, you know, top down maybe. I don't know, how do you see that maturity model evolving? People want to know how long it's going to take. So many questions, when will Open RAN be as reliable. Does it even have to be? You know, so many interesting dynamics going on. >> Yeah, and I think that's something we at Dell are also trying to find out, right? So we have been doing a lot of good work here to help our customers move in that direction. The work with Dish is an example of that. But I think we do understand the challenges as well in terms of getting, adopting the technologies, and adopting the innovation that's being driven by Open. So one of the agendas that we have as a company this year is to work with the community to drive this a lot further, right? We want to have customers adopt the technology more broadly with the tier one, tier two telcos globally. And our sales organizations are going to be working together with Wind Rivers to figure out who's the right set of customers to have these conversations with, so we can drop, drive, start driving this agenda a lot quicker than what we've seen historically. >> And where are you having those customer conversations? Is that at the operator level, is it higher, is it both? >> Well, all operators are deploying 5G in preparation for 6G, right? And we're all looking for those killer use cases which will drive top line revenue and not just make it a TCO discussion. And that starts at a very basic level today by doing things like integrating with Juniper, for their cloud router. So instead of at the far edge cell site, having a separate device that's doing the routing function, right? We take that and we cloudify that application, run it on the same server that's hosting the RAN applications, so you eliminate a device and reduce TCO. Now with Aptiv, which is primarily known as an automotive company, we're having lots of conversations, including with Dell and Intel and others about vehicle to vehicle communication, vehicle to anything communication. And although that's a little bit futuristic, there are shorter term use cases that, like, vehicle to vehicle accident avoidance, which are going to be much nearer term than autonomous driving, for example, which will help drive traffic and new revenue streams for operators. >> So, oh, that's, wow. So many other things (Scott laughs) that's just opened up there too. But I want to come back to, sort of, the Open RAN adoption. And I think you're right, there's a lot of questions that that still have to be determined. But my question is this, based on your knowledge so far does it have to be as hardened and reliable, obviously has to be low latency as existing networks, or can flexibility, like the cloud when it first came out, wasn't better than enterprise IT, it was just more flexible and faster, and you could rent it. And, is there a similar dynamic here where it doesn't have to replicate the hardened stack, it can bring in new benefits that drive adoption, what are your thoughts on that? >> Well there's a couple of things on that, because Wind River, as you know, where our legacy and history is in embedded devices like F-15 fighter jets, right? Or the Mars Rover or the James Web telescope, all run Wind River software. So, we know about can't fail ultra reliable systems, and operators are not letting us off the hook whatsoever. It has to be as hardened and locked down, as secure as a traditional RAN environment. Otherwise they will (indistinct). >> That's table stakes. >> That's table stakes that gets us there. And when River, with our legacy and history, and having operator experience running live commercial networks with a disaggregated stack in the tens of thousands of nodes, understand what this is like because they're running live commercial traffic with live customers. So we can't fail, right? And with that, they want their cake and eat it too, right? Which is, I want ultra reliable, I want what I have today, but I want the agility and flexibility to onboard third party apps. Like for example, this JCNR, this Juniper Cloud-Native Router. You cannot do something as simple as that on a traditional RAN Appliance. In an open ecosystem you can take that workload and onboard it because it is an open ecosystem, and that's really one of the true benefits. >> So they want the mainframe, but they want (Scott laughs) the flexibility of the developer cloud, right? >> That's right. >> They want their, have their cake eat it too and not gain weight. (group laughs) >> Yeah I mean David, I come from the public cloud world. >> We all don't want to do that. >> I used to work with a public cloud company, and nine years ago, public cloud was in the same stage, where you would go to a bank, and they would be like, we don't trust the cloud. It's not secure, it's not safe. It was the digital natives that adopted it, and that that drove the industry forward, right? And that's where the enterprises that realized that they're losing business because of all these innovative new companies that came out. That's what I saw over the last nine years in the cloud space. I think in the telco space also, something similar might happen, right? So a lot of this, I mean a lot of the new age telcos are understanding the value, are looking to innovate are adopting the open technologies, but there's still some inertia and hesitancy, for the reasons as Scott mentioned, to go there so quickly. So we just have to work through and balance between both sides. >> Yeah, well with that said, if there's still some inertia, but there's a theme of velocity, how do you help organizations balance that so they trust evolving? >> Yeah, and I think this is where our solution, like infrastructure block, is a foundational pillar to make that happen, right? So if we can take away the concerns that the organizations have in terms of security, reliability from the fundamental elements that build their infrastructure, by working with partners like Wind River, but Dell takes the ownership end-to-end to make sure that service works and we have those telco grade SLAs, then the telcos can start focusing on what's next. The applications and the customer services on the top. >> Customer service customer experience. >> You know, that's an interesting point Gautam brings up, too, because support is an issue too. We all talk about when you break these things apart, it creates integration points that you need to manage, right? But there's also, so the support aspect of it. So imagine if you will, you had one vendor, you have an outage, you call that one vendor, one necktie to choke, right, for accountability for the network. Now you have four or five vendors that you have to work. You get a lot of finger pointing. So at least at the infrastructure layer, right? Dell takes first call support for both the hardware infrastructure and the Wind River cloud infrastructure for both. And we are training and spinning them up to support, but we're always behind them of course as well. >> Can you give us a favorite customer example of- that really articulates the value of the partnership and the technologies that it's delivering to customers? >> Well, Infra Block- >> (indistinct) >> Is quite new, and we do have our first customer which is LG U plus, which was announced yesterday. Out of Korea, small customer, but a very important one. Okay, and I think they saw the value of the integrated system. They don't have the (indistinct) expertise and they're leveraging Dell and Wind River in order to make that happen. But I always also say historically before this new offering was Vodafone, right? Vodafone is a leader in Europe in terms of Open RAN, been very- Yago and Paco have been very vocal about what they're doing in Open RAN, and Dell and Wind River have been there with them every step of the way. And that's what I would say, kind of, led up to where we are today. We learned from engagements like Vodafone and I think KDDI as well. And it got us where we are today and understanding what the operators need and what the impediments are. And this directly addresses that. >> Those are two very different examples. You were talking about TCO before. I mean, so the earlier example is, that's an example to me of a disruptor. They'll take some chances, you know, maybe not as focused on TCO, of course they're concerned about it. Vodafone I would think very concerned about TCO. But I'm inferring from your comments that you're trying to get the industry, you're trying to check the TCO box, get there. And then move on to higher levels of value monetization. The TCO is going to come down to how many humans it takes to run the network, is it not, is that- >> Well a lot of, okay- >> Or is it devices- >> So the big one now, particularly with Vodafone, is energy cost, right? >> Of course, greening the network. >> Two-thirds of the energy consumption in RAN is the the Radio Access Network. Okay, the OPEX, right? So any reductions, even if they're 5% or 10%, can save tens or hundreds of millions of dollars. So we do things creatively with Dell to understand if there's a lot of traffic at the cell site and if it's not, we will change the C state or P state of the server, which basically spins it down, so it's not consuming power. But that's just at the infrastructure layer. Where this gets really powerful is working with the RAN vendors like Samsung and Ericson and others, and taking data from the traffic information there, applying algorithms to that in AI to shut it down and spin it back up as needed. 'Cause the idea is you don't want that thing powered up if there's no traffic on it. >> Well there's a sustainability, ESG, benefit to that, right? >> Yes. >> And, and it's very compute intensive. >> A hundred percent. >> Which is great for Dell. But at the same time, if you're not able to manage that power consumption, the whole thing fails. I mean it's, because there's going to be so much data, and such a intense requirement. So this is a huge issue. Okay, so Scott, you're saying that in the TCO equation, a big chunk is energy consumption? >> On the OPEX piece. Now there's also the CapEx, right? And Open RAN solutions are now, what we've heard from our customers today, are they're roughly at parity. 'Cause you can do things like repurpose servers after the useful life for a lower demand application which helps the TCO, right? Then you have situations like Juniper, where you can take, now software that runs on the same device, eliminating at a whole other device at the cell site. So we're not just taking a server and software point of view, we're taking a whole cell site point of view as it relates to both CapEx and OPEX. >> And then once that infrastructure it really gets adopted, that's when the innovation occurs. The ecosystem comes in. Developers now start to think of new applications that we haven't thought of yet. >> Gautam: Exactly. >> And that's where, that's going to force the traditional carriers to respond. They're responding, but they're doing so very carefully right now, it's understandable why. >> Yeah, and I think you're already seeing some news in the, I mean Nokia's announcement yesterday with the rebranding, et cetera. That's all positive momentum in my opinion, right? >> What'd you think of the logo? >> I love the logo. >> I liked it too. (group laughs) >> It was beautiful. >> I thought it was good. You had the connectivity down below, You need pipes, right? >> Exactly. >> But you had this sort of cool letters, and then the the pink horizon or pinkish, it was like (Scott laughs) endless opportunity. It was good, I thought it was well thought out. >> Exactly. >> Well, you pick up on an interesting point there, and what we're seeing, like advanced carriers like Dish, who has one of the true Open RAN networks, publishing APIs for programmers to build in their 5G network as part of the application. But we're also seeing the network equipment providers also enable carriers do that, 'cause carriers historically have not been advanced in that way. So there is a real recognition that in order for these networks to monetize new use cases, they need to be programmable, and they need to publish standard APIs, so you can access the 5G network capabilities through software. >> Yeah, and the problem from the carriers, there's not enough APIs that the carriers have produced yet. So that's where the ecosystem comes in, is going to >> A hundred percent >> I think there's eight APIs that are published out of the traditional carriers, which is, I mean there's got to be 8,000 for a marketplace. So that's where the open ecosystem really has the advantage. >> That's right. >> That's right. >> That's right. >> Yeah. >> So it all makes sense on paper, now you just, you got a lot of work to do. >> We got to deliver. Yeah, we launched it today. We got to get some like-minded partners and customers to come together. You'll start seeing results coming out of this hopefully soon, and we'll talk more about it over time. >> Dave: Great Awesome, thanks for sharing with us. >> Excellent. Guys, thank you for sharing, stopping by, sharing what's going on with Dell and Wind River, and why the opportunity's in it for customers and the technological evolution. We appreciate it, you'll have to come back, give us an update. >> Our pleasure, thanks for having us. (Group talks over each other) >> All right, thanks guys >> Appreciate it. >> For our guests and for Dave Vellante, I'm Lisa Martin. You're watching theCUBE, Live from MWC23 in Barcelona. theCUBE is the leader in live tech coverage. (upbeat music)

>> John: Hello and welcome to this cube conversation here in Palo Alto, California. I'm John Furrier, host of theCUBE. We got a great conversation on making machine learning easier and more affordable in an era where everybody wants more machine learning and AI. We're featuring Neural Magic with the CEO is also Cube alumni, Brian Steve. CEO, Great to see you Brian. Thanks for coming on this cube conversation. Talk about machine learning. >> Brian: Hey John, happy to be here again. >> John: What a buzz that's going on right now? Machine learning, one of the hottest topics, AI front and center, kind of going mainstream. We're seeing the success of the, of the kind of NextGen capabilities in the enterprise and in apps. It's a really exciting time. So perfect timing. Great, great to have this conversation. Let's start with taking a minute to explain what you guys are doing over there at Neural Magic. I know there's some history there, neural networks, MIT. But the, the convergence of what's going on, this big wave hitting, it's an exciting time for you guys. Take a minute to explain the company and your mission. >> Brian: Sure, sure, sure. So, as you said, the company's Neural Magic and spun out at MIT four plus years ago, along with some people and, and some intellectual property. And you summarize it better than I can cause you said, we're just trying to make, you know, AI that much easier. And so, but like another level of specificity around it is. You know, in the world you have a lot of like data scientists really focusing on making AI work for whatever their use case is. And then the next phase of that, then they're looking at optimizing the models that they built. And then it's not good enough just to work on models. You got to put 'em into production. So, what we do is we make it easier to optimize the models that have been developed and trained and then trying to make it super simple when it comes time to deploying those in production and managing them. >> Brian: You know, we've seen this movie before with the cloud. You start to see abstractions come out. Data science we saw like was like the, the secret art of being like a data scientist now democratization of data. You're kind of seeing a similar wave with machine learning models, foundational models, some call it developers are getting involved. Model complexity's still there, but, but it's getting easier. There's almost like the democratization happening. You got complexity, you got deployment, it's challenges, cost, you got developers involved. So it's like how do you grow it? How do you get more horsepower? And then how do you make developers productive, right? So like, this seems to be the thread. So, so where, where do you see this going? Because there's going to be a massive demand for, I want to do more with my machine learning. But what's the data source? What's the formatting? This kind of a stack develop, what, what are you guys doing to address this? Can you take us through and demystify this, this wave that's hitting, that everyone's seeing? >> Brian: Yeah. Now like you said, like, you know, the democratization of all of it. And that brings me all the way back to like the roots of open source, right? When you think about like, like back in the day you had to build your own tech stack yourself. A lot of people probably probably don't remember that. And then you went, you're building, you're always starting on a body of code or a module that was out there with open source. And I think that's what I equate to where AI has gotten to with what you were talking about the foundational models that didn't really exist years ago. So you really were like putting the layers of your models together in the formulas and it was a lot of heavy lifting. And so there was so much time spent on development. With far too few success cases, you know, to get into production to solve like a business stereo technical need. But as these, what's happening is as these models are becoming foundational. It's meaning people don't have to start from scratch. They're actually able to, you know, the avant-garde now is start with existing model that almost does what you want, but then applying your data set to it. So it's, you know, it's really the industry moving forward. And then we, you know, and, and the best thing about it is open source plays a new dimension, but this time, you know, in the, in the realm of AI. And so to us though, like, you know, I've been like, I spent a career focusing on, I think on like the, not just the technical side, but the consumption of the technology and how it's still way too hard for somebody to actually like, operationalize technology that all those vendors throw at them. So I've always been like empathetic the user around like, you know what their job is once you give them great technology. And so it's still too difficult even with the foundational models because what happens is there's really this impedance mismatch between the development of the model and then where, where the model has to live and run and be deployed and the life cycle of the model, if you will. And so what we've done in our research is we've developed techniques to introduce what's known as sparsity into a machine learning model. It's already been developed and trained. And what that sparsity does is that unlocks by making that model so much smaller. So in many cases we can make a model 90 to 95% smaller, even smaller than that in research. So, and, and so by doing that, we do that in a way that preserves all the accuracy out of the foundational model as you talked about. So now all of a sudden you get this much smaller model just as accurate. And then the even more exciting part about it is we developed a software-based engine called Deep Source. And what that, what the Inference Runtime does is takes that now sparsified model and it runs it, but because you sparsified it, it only needs a fraction of the compute that it, that it would've needed otherwise. So what we've done is make these models much faster, much smaller, and then by pairing that with an inference runtime, you now can actually deploy that model anywhere you want on commodity hardware, right? So X 86 in the cloud, X 86 in the data center arm at the edge, it's like this massive unlock that happens because you get the, the state-of-the-art models, but you get 'em, you know, on the IT assets and the commodity infrastructure. That is where all the applications are running today. >> John: I want to get into the inference piece and the deep sparse you mentioned, but I first have to ask, you mentioned open source, Dave and I with some fellow cube alumnis. We're having a chat about, you know, the iPhone and Android moment where you got proprietary versus open source. You got a similar thing happening with some of these machine learning modules where there's a lot of proprietary things happening and there's open source movement is growing. So is there a balance there? Are they all trying to do the same thing? Is it more like a chip, you know, silicons involved, all kinds of things going on that are really fascinating from a science. What's your, what's your reaction to that? >> Brian: I think it's like anything that, you know, the way we talk about AI you think had been around for decades, but the reality is it's been some of the deep learning models. When we first, when we first started taking models that the brain team was working on at Google and billing APIs around them on Google Cloud where the first cloud to even have AI services was 2015, 2016. So when you think about it, it's really been what, 6 years since like this thing is even getting lift off. So I think with that, everybody's throwing everything at it. You know, there's tons of funded hardware thrown at specialty for training or inference new companies. There's legacy companies that are getting into like AI now and whether it's a, you know, a CPU company that's now building specialized ASEX for training. There's new tech stacks proprietary software and there's a ton of asset service. So it really is, you know, what's gone from nascent 8 years ago is the wild, wild west out there. So there's a, there's a little bit of everything right now and I think that makes sense because at the early part of any industry it really becomes really specialized. And that's the, you know, showing my age of like, you know, the early pilot of the two thousands, you know, red Hat people weren't running X 86 in enterprise back then and they thought it was a toy and they certainly weren't running open source, but you really, and it made sense that they weren't because it didn't deliver what they needed to at that time. So they needed specialty stacks, they needed expensive, they needed expensive hardware that did what an Oracle database needed to do. They needed proprietary software. But what happens is that commoditizes through both hardware and through open source and the same thing's really just starting with with AI. >> John: Yeah. And I think that's a great point before we to call that out because in any industry timing's everything, right? I mean I remember back in the 80s, late 80s and 90s, AI, you know, stuff was going on and it just wasn't, there wasn't enough horsepower, there wasn't enough tech. >> Brian: Yep. >> John: You mentioned some of the processing. So AI is this industry that has all these experts who have been itch scratching that itch for decades. And now with cloud and custom silicon. The tech fundamental at the lower end of the stack, if you will, on the performance side is significantly more performant. It's there you got more capabilities. >> Brian: Yeah. >> John: Now you're kicking into more software, faster software. So it just seems like we're at a tipping point where finally it's here, like that AI moment or machine learning and now data is, is involved. So this is where organizations I see really jumping in with the CEO mandate. Hey team, make ML work for us. Go figure it out. It's got to be an advantage for us. >> Brian: Yeah. >> John: So now they go, okay boss, we will. So what, what do they do? What's the steps does an enterprise take to get machine learning into their organizations? Cause you know, it's coming down from the boards, you know, how does this work for rob? >> Brian: Yeah. Like the, you know, the, what we're seeing is it's like anything, like it's, whether that was source adoption or whether that was cloud adoption, it always starts usually with one person. And increasingly it is the CEO, which realizes they're getting further behind the competition because they're not leaning in, you know, faster. But typically it really comes down to like a really strong practitioner that's inside the organization, right? And, that realizes that the number one goal isn't doing more and just training more models and and necessarily being proprietary about it. It's really around understanding the art of the possible. Something that's grounded in the art of the possible, what, what deep learning can do today and what business outcomes you can deliver, you know, if you can employ. And then there's well proven paths through that. It's just that because of where it's been, it's not that industrialized today. It's very much, you know, you see ML project by ML project is very snowflakey, right? And that was kind of the early days of open source as well. And so, we're just starting to get to the point where it's getting easier, it's getting more industrialized, there's less steps, there's less burdensome on developers, there's less burdensome on, on the deployment side. And we're trying to bring that, that whole last mile by saying, you know what? Deploying deep learning and AI models should be as easy as the as to deploy your application, right? You shouldn't have to take an extra step to deploy an AI model. It shouldn't have to require a new hardware, it shouldn't require a new process, a new DevOps model. It should be as simple as what you're already doing. >> John: What is the best practice for companies to effectively bring an acceptable level of machine learning and performance into their organizations? >> Brian: Yeah, I think like the, the number one start is like what you hinted at before is they, they have to know the use case. They have to, in most cases, you're going to find across every industry you know, that that problem's been tackled by some company, right? And then you have to have the best practice around fine-tuning the models already exist. So fine tuning that existing model. That foundational model on your unique dataset. You, you know, if you are in medical instruments, it's not good enough to identify that it's a medical instrument in the picture. You got to know what type of medical instrument. So there's always a fine tuning step. And so we've created open source tools that make it easy for you to do two things at once. You can fine tune that existing foundational model, whether that's in the language space or whether that's in the vision space. You can fine tune that on your dataset. And at the same time you get an optimized model that comes out the other end. So you get kind of both things. So you, you no longer have to worry about you're, we're freeing you from worrying about the complexity of that transfer learning, if you will. And we're freeing you from worrying about, well where am I going to deploy the model? Where does it need to be? Does it need to be on a device, an edge, a data center, a cloud edge? What kind of hardware is it? Is there enough hardware there? We're liberating you from all of that. Because what you want, what you can count on is there'll always be commodity capability, commodity CPUs where you want to deploy in abundance cause that's where your application is. And so all of a sudden we're just freeing you of that, of that whole step. >> John: Okay. Let's get into deep sparse because you mentioned that earlier. What inspired the creation of deep sparse and how does it differ from any other solutions in the market that are out there? >> Brian: Sure. So, so where unique is it? It starts by, by two things. One is what the industry's pretty good at from the optimization side is they're good at like this thing called quantization, which turns like, you know, big numbers into small numbers, lower precision. So a 32 bit representation of a, of AI weight into a bit. And they're good at like cutting out layers, which also takes away accuracy. What we've figured out is to take those, the industry techniques for those that are best practice, but we combined it with unstructured varsity. So by reducing that model by 90 to 95% in size, that's great because it's made it smaller. But we've taken that when it's the deep sparse engine, when you deploy it that looks at that model and says, because it's so much smaller, I no longer have to run the part of the model that's been essentially sparsified. So what that's done is, it's meant that you no longer need a supercomputer to run models because there's not nearly as much math and processing as there was before the model was optimized. So now what happens is, every CPU platform out there has, has an enormous amount of compute because we've sparsified the rest of it away. So you can pick a, you can pick your, your laptop and you have enough compute to run state-of-the-art models. The second thing that, and you need a software engine to do that cause it ignores the parts of the models. It doesn't need to run, which is what like specialized hardware can't do. The second part is it's then turned into a memory efficiency problem. So it's really around just getting memory, getting the models loaded into the cash of the computer and keeping it there. Never having to go back out to memory. So, so our techniques are both, we reduce the model size and then we only run the part of the model that matters and then we keep it all in cash. And so what that does is it gets us to like these, these low, low latency faster and we're able to increase, you know, the CPU processing by an order magnitude. >> John: Yeah. That low latency is key. And you got developers, you know, co coding super fast. We'll get to the developer angle in a second. I want to just follow up on this, this motivation behind the, the deep sparse because you know, as we were talking earlier before we came on camera about the old days, I mean, not too long ago, virtualization and VMware abstracted away the os from, from the hardware rights and the server virtualization changed the game. >> Brian: Yeah. >> John: And that basically invented cloud computing as we know it today. So, so we see that abstraction. >> Brian: Yeah. >> John: There seems to be a motivation behind abstracting the way the machine learning models away from the hardware. And that seems to be bringing advantages to the AI growth. Can you elaborate on, is that true? And it's, what's your comment? >> Brian: It's true. I think it's true for us. I don't think the industry's there yet, honestly. Cause I think the industry still is of that mindset that if I took, if it took these expensive GPUs to train my model, then I want to run my model on those same expensive GPUs. Because there's often like not a separation between the people that are developing AI and the people that have to manage and deploy at where you need it. So the reality is, is that that's everything that we're after. Like, do we decrease the cost? Yes. Do we make the models smaller? Yes. Do we make them faster? A yes. But I think the most amazing power is that we've turned AI into a docker based microservice. And so like who in the industry wants to deploy their apps the old way on a os without virtualization, without docker, without Kubernetes, without microservices, without service mesh without serverless. You want all those tools for your apps by converting AI models. So they can be run inside a docker container with no apologies around latency and performance cause it's faster. You get the best of that whole world that you just talked about, which is, you know, what we're calling, you know, software delivered AI. So now the AI lives in the same world. Organizations that have gone through that digital cloud transformation with their app infrastructure. AI fits into that world. >> John: And this is where the abstraction concepts matter. When you have these inflection points, the convergence of compute data, machine learning that powers AI, it really becomes a developer opportunity. Because now applications and businesses, when they actually go through the digital transformation, their businesses are completely transformed. There is no IT. Developers are the application. They are the company, right? So AI will be part of whatever business or app will be out there. So there is a application developer angle here. Brian, can you explain >> Brian: Oh completely. >> John: how they're going to use this? Because you mentioned docker container microservice, I mean this really is an insane flipping of the script for developers. >> Brian: Yeah. >> John: So what's that look like? >> Brian: Well speak, it's because like AI's kind of, I mean, again, like it's come so fast. So you figure there's my app team and here's my AI team, right? And they're in different places and the AI team is dragging in specialized infrastructure in support of that as well. And that's not how app developers think. Like they've ran on fungible infrastructure that subtracted and virtualized forever, right? And so what we've done is we've, in addition to fitting into that world that they, that they like, we've also made it simple for them for they don't have to be a machine learning engineer to be able to experiment with these foundational models and transfer learning 'em. We've done that. So they can do that in a couple of commands and it has a simple API that they can either link to their application directly as a library to make difference calls or they can stand it up as a standalone, you know, scale up, scale out inference server. They get two choices. But it really fits into that, you know, you know that world that the modern developer, whether they're just using Python or C or otherwise, we made it just simple. So as opposed to like Go learn something else, they kind of don't have to. So in a way though, it's made it. It's almost made it hard because people expect when we talk to 'em for the first time to be the old way. Like, how do you look like a piece of hardware? Are you compatible with my existing hardware that runs ML? Like, no, we're, we're not. Because you don't need that stack anymore. All you need is a library called to make your prediction and that's it. That's it. >> John: Well, I mean, we were joking on Twitter the other day with someone saying, is AI a pet or a cattle? Right? Because they love their, their AI bots right now. So, so I'd say pet there. But you look at a lot of, there's going to be a lot of AI. So on a more serious note, you mentioned in microservices, will deep sparse have an API for developers? And how does that look like? What do I do? >> Brian: Yeah. >> John: tell me what my, as a developer, what's the roadmap look like? What's the >> Brian: Yeah, it, it really looks, it really can go in both modes. It can go in a standalone server mode where it handles, you know, rest API and it can scale out with ES as the workload comes up and scale back and like try to make hardware do that. Hardware may scale back, but it's just sitting there dormant, you know, so with this, it scales the same way your application needs to. And then for a developer, they basically just, they just, the PIP install de sparse, you know, has one commanded to do an install, and then they do two calls, really. The first call is a library call that the app makes to create the model. And models really already trained, but they, it's called a model create call. And the second command they do is they make a call to do a prediction. And it's as simple as that. So it's, it's AI's as simple as using any other library that the developers are already using, which I, which sounds hard to fathom because it is just so simplified. >> John: Software delivered AI. Okay, that's a cool thing. I believe in it personally. I think that's the way to go. I think there's going to be plenty of hardware options if you look at the advances of cloud players that got more silicon coming out. Yeah. More GPU. I mean, there's more instance, I mean, everything's out there right now. So the question is how does that evolve in your mind? Because that's seems to be key. You have open source projects emerging. What, what path does this take? Is there a parallel mental model that you see, Brian, that is similar? You mentioned open source earlier. Is it more like a VMware virtualization thing or is it more of a cloud thing? Is there Yeah. Is it going to evolve in a, in a trajectory that looks similar to what we might've seen in the past? >> Brian: Yeah, we're, you know, when I, when when I got involved with the company, what I, when I thought about it and I was reasoning about it, like, do you, you know, you want to, like, we all do when you want to join something full-time. I thought about it and said, where will the industry eventually get to? Right? To fully realize the value of, of deep learning and what's plausible as it evolves. And to me, like I, I know it's the old adage of, you know, you know, software, its hardware, cloudy software. But it truly was like, you know, we can solve these problems in software. Like there's nothing special that's happening at the hardware layer and the processing AI. The reality is that it's just early in the industry. So the view that that we had was like, this is eventually the best place where the industry will be, is the liberation of being able to run AI anywhere. Like you're really not democratizing, you democratize the model. But if you can't run the model anywhere you want because these models are getting bigger and bigger with these large language models, then you're kind of not democratizing. And if you got to go and like by a cluster to run this thing on. So the democratization comes by if all of a sudden that model can be consumed anywhere on demand without planning, without provisioning, wherever infrastructure is. And so I think that's with or without Neural Magic, that's where the industry will go and will get to. I think we're the leaders, leaders in getting it there. It's right because we're more advanced on these techniques. >> John: Yeah. And your background too. You've seen OpenStack, pre-cloud, you saw open source grow and still exponentially growing. And so you have the same similar dynamic with machine learning models growing. And they're also segmenting into almost a, an ML stack or foundational model as we talk about. So you're starting to see the formation of tooling inference. So a lot of components coming. It's almost a stack, it's almost a, it literally is like an operating system problem space, you know? How do you run things, how do you link things? How do you bring things together? Is that what's going on here? Is this like a data modeling operating environment kind of red hat type thing going on? Like. >> Brian: Yeah. Yeah. Like I think there is, you know, I thought about that too. And I think there is the role of like distribution, because the industrialization not happening fast enough of this. Like, can I go back to like every customers, every, every user does it in their own kind of way. Like it's not, everyone's a little bit of a snowflake. And I think that's okay. There's definitely plenty of companies that want to come in and say, well, this is the way it's going to be and we industrialize it as long as you do it our way. The reality is technology doesn't get industrialized by one company just saying, do it our way. And so that's why like we've taken the approach through open source by saying like, Hey, you haven't really industrialized it if you said. We made it simple, but you always got to run AI here. Yeah, right. You only like really industrialize it if you break it down into components that are simple to use and they work integrated in the stack the way you want them to. And so to me, that first principles was getting thing into microservices and dockers that could be run on VMware, OpenShare on the cloud in the edge. And so that's the, that's the real part that we're happening with. The other part, like I do agree, like I think it's going to quickly move into less about the model. Less about the training of the model and the transfer learning, you know, the data set of the model. We're taking away the complexity of optimization. Giving liberating deployment to be anywhere. And I think the last mile, John is going to be around the ML ops around that. Because it's easy to think of like soft now that it's just a software problem, we've turned it into a software problem. So it's easy to think of software as like kind of a point release, but that's not the reality, right? It's a life cycle. And it's, and so I think ML very much brings in the what is the lifecycle of that deployment? And, you know, you get into more interesting conversations, to be honest than like, once you've deployed in a docking container is around like model drift and accuracy and the dataset changes and the user changes is how do you become from an ML perspective of where of that sending signal back retraining. And, and that's where I think a lot of the, in more of the innovation's going to start to move there. >> John: Yeah. And software also, the software problem, the software opportunity as well is developer focused. And if you look at the cloud native landscape now, similar stacks developing a lot of components. A lot of things to, to stitch together a lot of things that are automating under the hood. A lot of developer productivity conversations. I think this is going to go down that same road. I want to get your thoughts because developers will set the pace. And this is something that's clear in this next wave developer productivity. They're the defacto standards bodies. They will decide what microservices check, API check. Now, skill gap is going to be a problem because it's relatively new. So model sprawl, model sizes, proprietary versus open. There has to be a way to kind of crunch that down into a, like a DevOps, like just make it, get the developer out of the, the muck. So what's your view? Are we early days like that? Or what's the young kid in college studying CS or whatever degree who comes into this with, with both feet? What are they doing? >> Brian: I'll probably say like the, the non-popular answer to that. A little bit is it's happening so fast that it's going to get kind of boring fast. Meaning like, yeah, you could go to school and go to MIT, right? Sorry. Like, and you could get a hold through end like becoming a model architect, like inventing the next model, right? And the layers and combining 'em and et cetera, et cetera. And then what operators and, and building a model that's bigger than the last one and trains faster, right? And there will be those people, right? That actually, like they're building the engines the same way. You know, I grew up as an infrastructure software developer. There's not a lot of companies that hire those anymore because they're all sitting inside of three big clouds. Yeah. Right? So you better be a good app developer, but I think what you're going to see is before you had to be everything, you had to be the, if you were going to use infrastructure, you had to know how to build infrastructure. And I think the same thing's true around is quickly exiting ML is to be able to use ML in your company, you better be like, great at every aspect of ML, including every intricacy inside of the model and every operation's doing, that's quickly changing. Like, you're going to start with a starting point. You know, in the future you're not going to be like cracking open these GPT models, you're going to just be pulling them off the shelf, fine tuning 'em and go. You don't have to invent it. You don't have to understand it. And I think that's going to be a pivot point, you know, in the industry between, you know, what's the future? What's, what's the future of a, a data scientist? ML engineer researcher look like? >> John: I think that's, the outcome's going to be determined. I mean, you mentioned, you know, doing it yourself what an SRE is for a Google with the servers scale's huge. So yeah, it might have to, at the beginning get boring, you get obsolete quickly, but that means it's progressing. So, The scale becomes huge. And that's where I think it's going to be interesting when we see that scale. >> Brian: Yep. Yeah, I think that's right. I think that's right. And we always, and, and what I've always said, and much the, again, the distribute into my ML team is that I want every developer to be as adept at being able take advantage of ML as non ML engineer, right? It's got to be that simple. And I think, I think it's getting there. I really do. >> John: Well, Brian, great, great to have you on theCUBE here on this cube conversation. As part of the startup showcase that's coming up. You're going to be featured. Or your company would featured on the upcoming ABRA startup showcase on making machine learning easier and more affordable as more machine learning models come in. You guys got deep sparse and some great technology. We're going to dig into that next time. I'll give you the final word right now. What do you see for the company? What are you guys looking for? Give a plug for the company right now. >> Brian: Oh, give a plug that I haven't already doubled in as the plug. >> John: You're hiring engineers, I assume from MIT and other places. >> Brian: Yep. I think like the, the biggest thing is like, like we're on the developer side. We're here to make this easy. The majority of inference today is, is on CPUs already, believe it or not, as much as kind of, we like to talk about hardware and specialized hardware. The majority is already on CPUs. We're basically bringing 95% cost savings to CPUs through this acceleration. So, but we're trying to do it in a way that makes it community first. So I think the, the shout out would be come find the Neural Magic community and engage with us and you'll find, you know, a thousand other like-minded people in Slack that are willing to help you as well as our engineers. And, and let's, let's go take on some successful AI deployments. >> John: Exciting times. This is, I think one of the pivotal moments, NextGen data, machine learning, and now starting to see AI not be that chat bot, just, you know, customer support or some basic natural language processing thing. You're starting to see real innovation. Brian Stevens, CEO of Neural Magic, bringing the magic here. Thanks for the time. Great conversation. >> Brian: Thanks John. >> John: Thanks for joining me. >> Brian: Cheers. Thank you. >> John: Okay. I'm John Furrier, host of theCUBE here in Palo Alto, California for this cube conversation with Brian Stevens. Thanks for watching.

>>Hello. And welcome back to the cubes live coverage here in Seattle for the cubes coverage of AWS marketplace seller conference. Now part of really big move and news, Amazon partner network combines with AWS marketplace to form one organization, the Amazon partner organization, APO where the efficiencies, the next iteration, as they say in Amazon language, where they make things better, simpler, faster, and, and for customers is happening. We're here with Chris Cruz, who's the general manager, worldwide leader of ISV alliances and marketplace, which includes all the channel partners and the buyer and seller relationships all now under one partner organization, bringing together years of work. Yes. If you work with AWS and are a partner and, or sell with them, all kind of coming together, kind of in a new way for the next generation, Chris, congratulations on the new role and the reor. >>Thank you. Yeah, it's very exciting. We're we think it invent, simplifies the process on how we work with our partners and we're really optimistic so far. The feedback's been great. And I think it's just gonna get even better as we kind of work out the final details. >>This is huge news because one, we've been very close to the partner that we've been working with and we talking to, we cover them. We cover the news, the startups from startups, channel partners, big ISVs, big and small from the dorm room to the board room. You guys have great relationships. So check marketplace, the future of procurement, how software will be bought, implemented and deployed is also changed. So you've got the confluence of two worlds coming together, growth in the ecosystem. Yep. NextGen cloud on the horizon for AWS and the customers as digital transformation goes from lift and shift to refactoring businesses. Yep. This is really a seminal moment. Can you share what you talked about on the keynote stage here, around why this is happening now? Yeah. What's the guiding principle. What's the north star where, why what's what's the big news. >>Yeah. And so, you know, a lot of reasons on why we kind of, we pulled the two teams together, but you know, a lot of it kind gets centered around co-sell. And so if you take a look at marketplace where we started off, where it was really a machine image business, and it was a great self-service model and we were working with ISVs that wanted to have this new delivery mechanism on how to bring in at the time was Amazon machine images and you fast forward, we started adding more product types like SAS and containers. And the experience that we saw was that customers would use marketplace for kind of up to a certain limit on a self-service perspective. But then invariably, they wanted by a quantity discount, they wanted to get an enterprise discount and we couldn't do that through marketplace. And so they would exit us and go do a direct deal with a, an ISV. >>And, and so to remedy that we launched private offers, you know, four years ago. And private offers now allowed ISVs to do these larger deals, but do 'em all through marketplace. And so they could start off doing self-service business. And then as a customer graduated up to buying for a full department or an organization, they can now use private offers to execute that larger agreement. And it, we started to do more and more private offers, really kind of coincided with a lot of the initiatives that were going on within Amazon partner network at the time around co-sell. And, and so we started to launch programs like ISV accelerate that really kind of focused on our co-sell relationship with ISVs. And what we found was that marketplace private offers became this awesome way to automate how we co-sell with ISV. And so we kinda had these two organizations that were parallel. We said, you know what, this is gonna be better together. If we put together, it's gonna invent simplify and we can use marketplace private offers as part of that co-sell experience and really feed that automation layer for all of our ISVs as they interacted with native >>Discussions. Well, I gotta give you props, you and Mona work on stage. You guys did a great job and it reminds me of the humble nature of AWS and Amazon. I used to talk to Andy jazzy about this all the time. That reminds me of 2013 here right now, because you're in that mode where Amazon reinvent was in 2013. Yeah. Where you knew it was breaking out. Yeah. Everyone's it was kind of small, but we haven't made it yet. Yeah. But you guys are doing billions of vows in transactions. Yeah. But this event is really, I think the beginning of what we're seeing as the change over from securing and deploying applications in the cloud, because there's a lot of nuanced things I want to get your reaction on one. I heard making your part product as an ISV, more native to AWS's stack. That was one major call out. I heard the other one was, Hey, if you're a channel partner, you can play too. And by the way, there's more choice. There's a lot going on here. That's about to kind of explode in a good way for customers. Yeah. Buyers get more access to assemble their solutions. Yeah. And you got all kinds of like business logic, compensation, integration, and scale. Yeah. This is like unprecedented. >>Yeah. It's, it's exciting to see what's going on. I mean, I think we kind of saw the tipping point probably about two years ago, which, you know, prior to that, you know, we would be working with ISVs and customers and it was really much more of an evangelism role where we were just getting people to try it. Just, just list a product. We think this is gonna be a good idea. And if you're a buyer, it's like just try out a private offer, try out a self, you know, service subscription. And, and what's happened now is there's no longer a lot of that convincing that needs to happen. It's really become accepted. And so a lot of the conversations I have now with ISVs, it's not about, should I do marketplace it's how do I do it better? And how do I really leverage marketplace as part of my co-sell initiatives as, as part of my go to market strategy. >>And so you've, you've really kind of passed this tipping point where marketplaces are now becoming very accepted ways to buy third party software. And so that's really exciting. And, and we see that we, you know, we can really enhance that experience, you know, and what we saw on the machine image side is we had this awesome integrated experience where you would buy it. It was tied right into the EC two control plane. And you could go from buying to deploying in one single motion. SAS is a little bit different, you know, we can do all the buying in a very simple motion, but then deploying it. There's a whole bunch of other stuff that our customers have to do. And so we see all kinds of ways that we can simplify that. You know, recently we launched the ability to put third party solutions outta marketplace, into control tower, which is how we deploy all of our landing zones for AWS. And now it's like, instead of having to go wire that up as you're adding new AWS environments, why not just use that third party solution that you've already integrated to you and have it there as you're span those landing zones through >>Control towers, again, back to humble nature, you guys have dominated the infrastructure as a service layer. You kind of mentioned it. You didn't really kind of highlight it other than saying you're doing pretty good. Yeah. On the IAS or the technology partners as you call or infrastructure as you guys call it. Okay. I can see how the, the, the pan, the control panel is great for those customers. But outside that, when you get into like CRM, you mentioned E R P these business apps, these horizontal and verticals have data they're gonna have SageMaker, they're gonna have edge. They might have, you know, other services that are coming online from Amazon. How do I, as an ISV, get my stuff in there. Yeah. And how do I succeed? And what are you doing to make that better? Cause I know it's kind of new, but not new. Yeah, >>No, it's not. I mean, that's one of the things that we've really invested on is how do we make it really easy to list marketplace? And, you know, again, when we first start started, it was a big, huge spreadsheet that you had to fill out. It was very cumbersome and we've really automated all those aspects. So now we've exposed an API as an example. So you can go straight out of your own build process and you might have your own C I CD pipeline. And then you have a build step at the end. And now you can have that execute marketplace update from your build script, right across that API all the way over to AWS marketplace. So it's taking that effectively, a C CD pipeline from an ISV and extending it all the way to AWS and then eventually to a customer, because now it's just an automated supply chain for that software coming into their environment. And we see that being super powerful. There's nowhere manual steps >>Along. Yeah. I wanna dig into that because you made a comment and I want you to clarify it here in the cube. Some have said, even us on the cube. Oh, marketplace. Just the website's a catalog. Yeah. Feels old school. Yeah. Feels like 1995 database. I'm kind of just, you know, saying no offense sake. And now you're saying, you're now looking at this and, and implementing more of a API based. Why is that relevant? I'm I know the answer. You already set up with APIs, but explain the transition from the mindset of it's a website. Yeah. Buy stuff on a catalog to full blown API layer. Yeah. Services. >>Absolutely. Well, when you look at all AWS services, you know, our customers will interface, you know, they'll interface them through a console initially, but when they're using them in production, they're, it's all about APIs and marketplace, as you mentioned, did start off as a website. And so we've kind of taken the opposite approach. We've got this great website experience, which is great for demand gen and, you know, highlighting those listings. But what we want to do is really have this API service layer that you're interfacing with so that an ISV effectively is not even in our marketplace. They interfacing over APIs to do a variety of their high, you know, value functions, whether it's listing soy, private offers. We don't have that all available through APIs and the same thing on the buyer side. So it's integrating directly into their AWS environment and then they can view all their third party spend within things like our cost management suites. They can look at things like cost Explorer, see third party software, right next to first party software, and have that all integrated this nice as seamless >>For the customer. That's a nice cloud native kind of native experience. I think that's a huge advantage. I'm gonna track that closer. We're we're gonna follow that. I think that's gonna be the killer killer feature. All right. Now let's get to the killer feature and the business logic. Okay. Yeah. All partners all wanna know what's in it for me. Yeah. How do I make more cash? Yeah. How do I compensate my sales people? Yeah. What do you guys don't compete with me? Give me leads. Yeah. Can I get MDF market development funds? Yeah. So take me through the, how you're thinking about supporting the partners that are leaning in that, you know, the parachute will open when they jump outta the plane. Yeah. It's gonna be, they're gonna land safely with you. Yeah. MDF marketing to leads. What are you doing to support the partners to help them serve their >>Customers? It's interesting. Market marketplace has become much more of an accepted way to buy, you know, our customers are, are really defaulting to that as the way to go get that third party software. So we've had some industry analysts do some studies and in what they found, they interviewed a whole cohort of ISVs across various categories within marketplace, whether it was security or network or even line of business software. And what they've found is that on average, our ISVs will see a 24% increased close rate by using marketplace. Right. So when I go talk to a CRO and say, do you want to close, you know, more deals? Yes. Right. And we've got data to show that we're also finding that customers on average, when an ISV sales marketplace, they're seeing an 80% uplift in the actual deal size. And so if your ASP is a hundred K 180 K has a heck of a lot better, right? >>So we're seeing increased deal sizes by going through marketplace. And then the third thing that we've seen, that's a value prop for ISVs is speed of closure. And so on average, what we're finding is that our ISVs are closing deals 40% faster by using marketplace. So if you've got a 10 month sales cycle, shaving four months off of a sales cycle means you're bringing deals in, in an earlier calendar year, earlier quarter. And for ISVs getting that cash flow early is very important. So those are great metrics that we're seeing. And, and, you know, we think that they're only >>Gonna improve and from startups who also want, they don't have a lot of cash ISVs that are rich and doing well. Yeah. They have good, good, good, good, good to market funding. Yeah. You got the range of partners and you know, the next startup could be the next Figma could be in that batch startups. Exactly. Yeah. You don't know the game is changing. Yeah. The next brand could be one of those batch of startups. Yeah. What's the message to the startup community. Yeah. >>I mean, marketplace in a lot of ways becomes a level in effect, right. Because, you know, if, if you look at pre marketplace, if you were a startup, you were having to go generate sales, have a sales force, go compete, you know, kind of hand to hand with these largest ISVs marketplace is really kind of leveling that because now you can both list in marketplace. You have the same advantage of putting that directly in the AWS bill, taking advantage of all the management go features that we offer all the automation that we bring to the table. And so >>A lot of us joint selling >>And joint selling, right? When it goes through marketplace, you know, it's gonna feed into a number of our APN programs like ISV accelerate, our sales teams are gonna get recognized for those deals. And so, you know, it brings nice co-sell behavior to how we work with our, our field sales teams together. It brings nice automation that, you know, pre marketplaces, they would have to go build all that. And that was a heavy lift that really now becomes just kind of table stakes for any kind of ISV selling to an, any of >>Customer. Well, you know, I'm a big fan of the marketplace. I've always have been, even from the early days, I saw this as a procurement game changer. It makes total sense. It's so obvious. Yeah. Not obvious to everyone, but there's a lot of moving parts behind the scenes behind the curtain. So to speak that you're handling. Yeah. What's your message to the audience out there, both the buyers and the sellers. Yeah. About what your mission is, what you're you wake up every day thinking about. Yeah. And what's your promise to them and what you're gonna work on. Cause it's not easy. You're building a, an operating model. That's not a website. It's a full on cloud service. Yeah. What's your promise. And what's >>Your goals. No. And like, you know, ultimately we're trying to do from an Aus market perspective is, is provide that selection experience to the ABUS customer, right? There's the infamous flywheel that Jeff put together that had the concepts of why Amazon is successful. And one are the concepts he points to is the concept of selection. And, and what we mean by that is if you come to Amazon it's is effectively that everything stored. And when you come across, AWS marketplace becomes that selection experience. And so that's what we're trying to do is provide whatever our AWS customers wanna buy, whatever form factor, whatever software type, whatever data type it's gonna be available in AWS marketplace for consumption. And that ultimately helps our customers because now they can get whatever technologies that they need to use alongside Avis. >>And I want, wanna give you props too. You answered the hard question on stage. I've asked Andy EY this on the cube when he was the CEO, Adam Celski last year, I asked him the same question and the answer has been consistent. We have some solutions that people want a AWS end to end, but your ecosystem, you want people to compete yes. And build a product and mostly point to things like snowflake, new Relic. Yeah. Other people that compete with Amazon services. Yeah. You guys want that. You encourage that. Yeah. You're ratifying that same statement. >>Absolutely. Right. Again, it feeds into that selection experience. Right. If a customer wants something, we wanna make sure it's gonna be a great experience. Right. And so a lot of these ISVs are building on top of AWS. We wanna make sure that they're successful. And, you know, while we have a number of our first party services, we have a variety of third party technologies that run very well in a AWS. And ultimately the customer's gonna make their decision. We're customer obsessed. And if they want to go with a third party product, we're absolutely gonna support them in every way shape we can and make sure that's a successful experience for our customers. >>I, I know you referenced two studies check out the website's got buyer and seller surveys on there for Boer. Yeah. I don't want to get into that. I want to just end on one. Yeah. Kind of final note, you got a lot of successful buyers and a lot of successful sellers. The word billions, yes. With an S was and the slide. Can you say the number, how much, how many billions are sold yeah. Through the marketplace. Yeah. And the buyer experience future what's those two things. >>Yeah. So we went on record at reinvent last year, so it's approaching it birthday, but it was the first year that we've in our 10 year history announced how much was actually being sold to the marketplace. And, you know, we are now selling billions of dollars to our marketplace and that's with an S so you can assume, at least it's two, but it's, it's a, it's a large number and it's going >>Very quickly. Yeah. Can't disclose, you know, >>But it's a, it's been a very healthy part of our business. And you know, we look at this, the experience that we >>Saw, there's a lot of headroom. I mean, oh yeah, you have infrastructure nailed down. That's long, you get better, but you have basically growth up upside with these categor other categories. What's the hot categories. You >>Know, we, we started off with infrastructure related products and we've kind of hit critical mass there. Right? We've, there's very few ISVs left that are in that infrastructure related space that are not in our marketplace. And what's happened now is our customers are saying, well, I've been buying infrastructure products for years. I'm gonna buy everything. I wanna buy my line of business software. I wanna buy my vertical solutions. I wanna buy my data and I wanna buy all my services alongside of that. And so there's tons of upside. We're seeing all of these either horizontal business applications coming to our marketplace or vertical specific solutions. Yeah. Which, you know, when we first designed our marketplace, we weren't sure if that would ever happen. We're starting to see that actually really accelerate because customers are now just defaulting to buying everything through their marketplace. >>Chris, thanks for coming on the queue. I know we went a little extra long. There wanted to get that clarification on the new role. Yeah. New organization. Great, great reorg. It makes a lot of sense. Next level NextGen. Thanks for coming on the cube. Okay. >>Thank you for the opportunity. >>All right here, covering the new big news here of AWS marketplace and the AWS partner network coming together under one coherent organization, serving fires and sellers, billions sold the future of how people are gonna be buying software, deploying it, managing it, operating it. It's all happening in the marketplace. This is the big trend. It's the cue here in Seattle with more coverage here at Davis marketplace sellers conference. After the short break.

(ethereal music) >> Good morning, everyone, and welcome to theCUBE's day-two coverage of Snowflake Summit '22. Lisa Martin here with Dave Vellante. We are live in Las Vegas at Caesar's Forum, looking forward to an action-packed day here on theCUBE. Our first guest joins us, Bill Stratton, the global industry lead, media, entertainment and advertising at Snowflake. Bill, great to have you on the program talking about industry specifics. >> Glad to be here, excited to have a conversation. >> Yeah, the media and entertainment industry has been keeping a lot of us alive the last couple of years, probably more of a dependence on it than we've seen stuck at home. Talk to us about the data culture in the media, entertainment and advertising landscape, how is data being used today? >> Sure. Well, let's start with what you just mentioned, these last couple of years, I think, coming out of the pandemic, a lot of trends and impact to the media industry. I think there were some things happening prior to COVID, right? Streaming services were starting to accelerate. And obviously, Netflix was an early mover. Disney launched their streaming service right before the pandemic, Disney+, with ESPN+ as well. I think then, as the pandemic occurred these last two years, the acceleration of consumers' habits, obviously, of not just unbundling their cable subscription, but then choosing, you know, what services they want to subscribe to, right? I mean, I think we all sort of grew up in this era of, okay, the bundle was the bundle, you had sports, you had news, you had entertainment, whether you watched the channel or not, you had the bundle. And what the pandemic has accelerated is what I call, and I think a lot of folks call, the golden age of content. And really, the golden age of content is about the consumer. They're in control now, they pick and choose what services they want, what they watch, when they watch it. And I think that has extremely, sort of accelerated this adoption on the consumer side, and then it's creating this data ecosystem, as a result of companies like Disney having a direct-to-consumer relationship for the first time. It used to be a Disney or an NBC was a wholesaler, and the cable or satellite company had the consumer data and relationship. Now, the companies that are producing the content have the data and the consumer relationships. It's a fascinating time. >> And they're still coming over the top on the Telco networks, right? >> Absolutely right. >> Telco's playing in this game? >> Yeah, Telco is, I think what the interesting dynamic with Telco is, how do you bundle access, high speed, everybody still needs high speed at their home, with content? And so I think it's a similar bundle, but it takes on a different characteristic, because the cable and Telcos are not taking the content risk. AT&T sold Warner Media recently, and I think they looked at it and said, we're going to stay with the infrastructure, let somebody else do the content. >> And I think I heard, did I hear this right the other day, that Roku is now getting into the content business? >> Roku is getting into it. And they were early mover, right? They said the TVs aren't, the operating system in the television is not changing fast enough for content. So their dongle that you would slide into a TV was a great way to get content on connected televisions, which is the fastest growing platform. >> I was going to say, what are the economics like in this business? Because the bundles were sort of a limiting factor, in terms of the TAM. >> Yeah. >> And now, we get great content, all right, to watch "Better Call Saul", I have to get AMC+ or whatever. >> You know, your comment, your question about the economics and the TAM is an interesting one, because I think we're still working through it. One of the things, I think, that's coming to the forefront is that you have to have a subscription revenue stream. Okay? Netflix had a subscription revenue stream for the last six, eight, 10 years, significantly, but I think you even see with Netflix that they have to go to a second revenue model, which is going to be an ad-supported model, right? We see it in the press these last couple days with Reid Hastings. So I think you're going to see, obviously subscription, obviously ad-supported, but the biggest thing, back to the consumer, is that the consumer's not going to sit through two minutes of advertising to watch a 22 minute show. >> Dave: No way. >> Right? So what's then going to happen is that the content companies want to know what's relevant to you, in terms of advertising. So if I have relevancy in my ad experience, then it doesn't quite feel, it's not intrusive, and it's relevant to my experience. >> And the other vector in the TAM, just one last follow-up, is you see Amazon, with Prime, going consumption. >> Bill: That's right. >> You get it with Prime, it's sort of there, and the movies aren't the best in the world, but you can buy pretty much any movie you want on a consumption basis. >> Yeah. Just to your last quick point, there is, we saw last week, the Boston Red Sox are bundling tickets, season tickets, with a subscription to their streaming service. >> NESN+, I think it is, yeah. So just like Prime, NESN+- >> And it's like 30 bucks a month. >> -just like Prime bundling with your delivery service, you're going to start to see all kinds of bundles happen. >> Dave: Interesting. >> Man, the sky is the limit, it's like it just keeps going and proliferating. >> Bill: It does. >> You talk about, on the ad side for a second, you mentioned the relevance, and we expect that as consumers, we're so demanding, (clears throat) excuse me, we don't have the patience, one of the things I think that was in short supply during COVID, and probably still is, is patience. >> That's right. >> I think with all of us, but we expect that brands know us enough to surf up the content that they think we watched, we watched "Breaking Bad", "Better Call Saul", don't show me other things that aren't relevant to the patterns I've been showing you, the content creators have to adapt quickly to the rising and changing demands of the consumer. >> That's right. Some people even think, as you go forward and consumers have this expectation, like you just mentioned, that brands not only need to understand their own view of the consumer, and this is going to come into the Snowflake points that we talk about in a minute, but the larger view that a brand has about a consumer, not just their own view, but how they consume content, where they consume it, what other brands they even like, that all builds that picture of making it relevant for the consumer and viewer. >> Where does privacy come into the mix? So we want it to be relevant and personalized in a non-creepy way. Talk to us about the data clean rooms that Snowflake launched, >> Bill: That's right. >> and how is that facilitating from a PII perspective, or is it? >> Yeah. Great question. So I think the other major development, in addition to the pandemic, driving people watching all these shows is the fact that privacy legislation is increasing. So we started with California with the CCPA, we had GDPR in Europe, and what we're starting to see is state by state roll out different privacy legislations. At some point, it may be true that we have a federal privacy legislation, and there are some bills that are working through the legislature right now. Hard to tell what's going to happen. But to your question, the importance of privacy, and respecting privacy, is exactly happening at the same time that media companies and publishers need to piece together all the viewing habits that you have. You've probably watched, already this morning, on your PC, on your phone, and in order to bring that experience together a media company has to be able to tie that together, right? Collaborate. So you have collaboration on one side, and then you have privacy on the other, and they're not necessarily, normally, go together, Right? They're opposing forces. So now though, with Snowflake, and our data clean room, we like to call it a data collaboration platform, okay? It's not really what a data warehouse function traditionally has been, right? So if I can take data collaboration, and our clean room, what it does is it brings privacy controls to the participants. So if I'm an advertiser, and I'm a publisher, and I want to collaborate to create an advertising campaign, they both can design how they want to do that privacy-based collaboration, Because it's interesting, one company might have a different perspective of privacy, on a risk profile, than another company. So it's very hard to say one size is going to fit all. So what we at Snowflake do, with our infrastructure, is let you design how you create your own clean room. >> Is that a differentiator for Snowflake, the clean rooms? >> It's absolutely a very big differentiator. Two reasons, or probably two, three reasons, really. One is, it's cross cloud. So all the advertisers aren't going to be in the same cloud, all the publishers aren't going to be in the same cloud. One big differentiator there. Second big differentiator is, we want to be able to bring applications to the data, so our clean room can enable you to create measurement against an ad campaign without moving your data. So bringing measurement to the data, versus sending data to applications then improves the privacy. And then the third one is, frankly, our pricing model. You only pay for Snowflake what you use. So in the advertising world, there's what's called an ad tech tax, there is no ad tech tax for Snowflake, because we're simply a pay-as-you-go service. So it's a very interesting dynamic. >> So what's that stack look like, in your world? So I've pulled up Frank's chart, I took a picture of his, he's called it the new, modern data stack, I think he called it, but it had infrastructure in the bottom, okay, that's AWS, Google, Azure, and then a lot of you, live data, that would be the media data cloud, the workload execution, the specific workload here is media and entertainment, and then application development, that's a new layer of value that you're bringing in, marketplace, which is the whole ecosystem, and then monetization comes from building on top. >> Bill: Yes. >> So I got AWS in there, and other clouds, you got a big chunk of that, where do your customers add value on top of that? >> Yeah. So the way you described it, I think, with Frank's point, is right on. You have the infrastructure. We know that a lot of advertisers, for example, aren't going to use Amazon, because the retailer competes with Amazon, So they want to might be in Google or Azure. And then sort of as you go up the stack, for the data layer that is Snowflake, especially what we call first-party data, is sitting in that Snowflake environment, right? But that Snowflake environment is a distributed environment, so a Disney, who was on stage with me yesterday, she talked about, Jaya talked about their first-party datas in Snowflake, their advertisers' datas in their own Snowflake account, in their own infrastructure. And then what's interesting is is that application layer is coming to the data, and so what we're really seeing is an acceleration of companies building that application natively on Snowflake to do measurement, to do targeting, to do activation. And so, that growth of that final application layer is what we're seeing as the acceleration in the stack. >> So the more data that's in that massive distributed data cloud, the more value your customers can get out of it. And I would imagine you're just looking to tick things off that where customers are going outside of the Snowflake data cloud, let's attack that so they don't have to. >> Yeah, I think these partners, (clears throat) excuse me, and customers, it's an interesting dynamic, because they're customers of ours. But now, because anybody who is already in Snowflake can be their customer, then they're becoming our partner. So it's an interesting dynamic, because we're bringing advertisers to a Disney or an NBCU, because they already have their data in Snowflake. So the network effect that's getting created because of this layer that's being built is accelerated. >> In 2013, right after the second reinvent, I wrote a piece called "How to Compete with the Amazon Gorilla." And it seemed to us pretty obvious at the time, you're not going to win an infrastructure again, you got to build on top of it, you got to build ecosystems within industries, and the data, the connection points, that network effect that you just talked about, it's actually quite thrilling to see you guys building that. >> Well, and I think you know this too, I mean, Amazon's a great partner of ours as well, right? So they're part of our media data cloud, as Amazon, right? So we're making it easier and easier for companies to be able to spin up a clean room in places like AWS, so that they get the privacy controls and the governance that's required as well. >> What do you advise to, say, the next generation of media and advertising companies who may be really early in the data journey? Obviously, there's competition right here in the rear view mirror, but we've seen services that launch and fail, what do you advise to those folks that maybe are early in the journey and how can Snowflake help them accelerate that to be able to launch services they can monetize, and get those consumers watching? >> I think the first thing for a lot of these brands is that they need to really own their data. And what I mean by that is, they need to understand the consumer relationship that they have, they need to take the privacy and the governance very seriously, and they need to start building that muscle. It's almost, it's a routine and a muscle that they just need to continue to kind of build up, because if you think about it, a media company spends two, three hours a day with their customer. You might watch two hours of a streaming show, but how much time do you spend with a single brand a day? Maybe 30 seconds, maybe 10 seconds, right? And so, their need to build the muscle, to be able to collect the data in a privacy-compliant way, build the intelligence off of that, and then leverage the intelligence. We talked about it a few days ago, and you look at a retailer, as a really good example, a retailer is using Snowflake and the retail data cloud to optimize their supply chain. Okay? But their supply chain extends beyond their own infrastructure to the advertising and marketing community, because if I can't predict demand, how do I then connect it to my supply chain? So our media data cloud is helping retailers and consumer product goods companies actually drive demand into their reconstructed supply chain. So they both work together. >> So you have a big focus, obviously, on the monetization piece, of course, that's a great place to start. Where do you see the media data cloud going? >> Yeah. I think we'll start to expand beyond advertising and beyond marketing. There's really important sub-segments of media. Gaming is one. You talk about the pandemic and teenagers playing games on their phones. So we'll have an emphasis around gaming. We'll have an emphasis in sports. Sports is going through a big change in an ecosystem. And there's a big opportunity to connect the dots in those ecosystems as well. And then I think, to what we were just talking about, I think connecting commerce and media is a very important area. And I think the two are still very loosely connected today. It used to be, could I buy the Jennifer Aniston sweater from "Friends", right? That was always the analogy. Now, media and social media, and TikTok and everything else, are combining media and commerce very closely. So I think we'll start to see more focus around that as well. So that adds to your monetization. >> Right, right. And you can NFT that. (Lisa laughs) >> Bill: That's right, there you go, you can mint an NFT on that. >> It's the tip of the iceberg. >> Absolutely. >> There's so much more potential to go. Bill, thank you so much for joining us bright and early this morning, talking about what snowflake is doing in media, entertainment and advertising. Exciting stuff, relevant to all of us, we appreciate your insights and your forward-looking statements. >> Thank you for having me. I enjoyed it. >> Our pleasure. >> Thank you. >> Good >> Bill: Bye now. >> For our guest and Dave Vellante, I'm Lisa Martin, you're up early with us watching theCUBE's day-two coverage of Snowflake Summit '22. We'll be back in a moment with our next guest. (upbeat music)

(upbeat music) >> Okay, we're back with Atri Basu who is Cisco's resident philosopher who also holds a master's in computer science. We're going to have to unpack that a little bit. And Necati Cehreli, who's technical lead at Cisco. Welcome, guys. Thanks for coming on theCUBE. >> Happy to be here. >> Thanks a lot. >> All right, let's get into it. We want you to explain how Cisco validated the Zebrium technology and the proof points that you have that it actually works as advertised. So first Atri, first tell us about Cisco TAC. What does Cisco TAC do? >> So TAC is otherwise it's an acronym for Technical Assistance Center, is Cisco's support arm, the support organization. And the risk of sounding like I'm spouting a corporate line. The easiest way to summarize what TAC does is provide world class support to Cisco customers. What that means is we have about 8,000 engineers worldwide and any of our Cisco customers can either go on our web portal or call us to open a support request. And we get about 2.2 million of these support requests a year. And what these support requests are, are essentially the customer will describe something that they need done some networking goal that they have that they want to accomplish. And then it's TACs job to make sure that that goal does get accomplished. Now, it could be something like they're having trouble with an existing network solution and it's not working as expected or it could be that they're integrating with a new solution. They're, you know, upgrading devices maybe there's a hardware failure anything really to do with networking support and, you know the customer's network goals. If they open up a case for testing for help then TACs job is to respond and make sure the customer's, you know questions and requirements are met. About 44% of these support requests are usually trivial and, you know can be solved within a call or within a day. But the rest of TAC cases really involve getting into the network device, looking at logs. It's a very technical role. It's a very technical job. You need to be conversed with network solutions, their designs, protocols, et cetera. >> Wow. So 56% non-trivial. And so I would imagine you spend a lot of time digging through logs. Is that true? Can you quantify that like, you know, every month how much time you spend digging through logs and is that a pain point? >> Yeah, it's interesting you asked that because when we started on this journey to augment our support engineers workflow with Zebrium solution, one of the things that we did was we went out and asked our engineers what their experience was like doing log analysis. And the anecdotal evidence was that on average an engineer will spend three out of their eight hours reviewing logs either online or offline. So what that means is either with the customer live on a WebEx, they're going to be going over logs, network, state information, et cetera or they're going to do it offline where the customer sends them the logs it's attached to a, you know, a service request and they review it and try to figure out what's going on and provide the customer with information. So it's a very large chunk of our day. You know, I said 8,000 plus engineers and so three hours a day that's 24,000 man hours a day spent on log analysis. Now the struggle with logs or analyzing logs is there by out of necessity, logs are very contrite. They try to pack a lot of information in a very little space. And this is for performance reasons, storage reasons, et cetera, but the side effect of that is they're very esoteric. So they're hard to read if you're not conversant if you're not the developer who wrote these logs or you aren't doing code deep dives. And you're looking at where this logs getting printed and things like that, it may not be immediately obvious or even after a little while it may not be obvious what that log line means or how it correlates to whatever problem you're troubleshooting. So it requires tenure. It requires, you know, like I was saying before it requires a lot of knowledge about the protocol what's expected because when you're doing log analysis what you're really looking for is a needle in a haystack. You're looking for that one anomalous event, that single thing that tells you this shouldn't have happened, and this was a problem right. Now doing that kind of anomaly detection requires you to know what is normal. It requires, you know, what the baseline is. And that requires a very in depth understanding of, you know the state changes for that network solution or product. So it requires time to near and expertise to do well. And it takes a lot of time even when you have that kind of expertise. >> Wow. So thank you, Atri. And Necati, that's almost two days a week for a technical resource. That's not inexpensive. So what was Cisco looking for to sort of help with this and how'd you stumble upon Zebrium? >> Yeah, so, we have our internal automation system which has been running more than a decade now. And what happens is when a customer attach log bundle or diagnostic bundle into the service request we take that from the Sr we analyze it and we represent some kind of information. You know, it can be alerts or some tables, some graph, to the engineer, so they can, you know troubleshoot this particular issue. This is an incredible system, but it comes with its own challenges around maintenance to keep it up to date and relevant with Cisco's new products or a new version of a product, new defects, new issues and all kind of things. And when I mean with those challenges are let's say Cisco comes up with a product today. We need to come together with those engineers. We need to figure out how this bundle works, how it's structured out. We need to select individual logs, which are relevant and then start modeling these logs and get some values out of those logs, using PaaS or some rag access to come to a level that we can consume the logs. And then people start writing rules on top of that abstraction. So people can say in this log I'm seeing this value together with this other value in another log, maybe I'm hitting this particular defect. So that's how it works. And if you look at it, the abstraction it can fail the next time. And the next release when the development or engineer decides to change that log line which you write that rag X or we can come up with a new version which we completely change the services or processes then whatever you have wrote needs to be re-written for the new service. And we see that a lot with products, like for instance, WebEx where you have a very short release cycle that things can change maybe the next week with a new release. So whatever you are writing, especially for that abstraction and for those rules are maybe not relevant with that new release. With that being said we have a incredible rule creation process and governance process around it which starts with maybe a defect. And then it takes it to a level where we have an automation in place. But if you look at it, this really ties to human bandwidth. And our engineers are really busy working on you know, customer facing, working on issues daily and sometimes creating news rules or these PaaS are not their biggest priorities so they can be delayed a bit. So we have this delay between a new issue being identified to a level where we have the automation to detect it next time that some customer faces it. So with all these questions and with all challenges in mind we start looking into ways of actually how we can automate these automation. So these things that we are doing manually how we can move it a bit further and automate. And we had actually a couple of things in mind that we were looking for and this being one of them being this has to be product agnostic. Like if Cisco comes up with a product tomorrow I should be able to take it logs without writing, you know, complex regs, PaaS, whatever and deploy it into this system. So it can embrace our logs and make sense of it. And we wanted this platform to be unsupervised. So none of the engineers need to create rules, you know, label logs, this is bad, this is good. Or train the system like which requires a lot of computational power. And the other most important thing for us was we wanted this to be not noisy at all because what happens with noises when your level of false positives really high your engineers start ignoring the good things between that noise. So they start the next time, you know thinking that this thing will not be relevant. So we want something with a lot more less noise. And ultimately we wanted this new platform or new framework to be easily adaptable to our existing workflow. So this is where we started. We start looking into the, you know first of all, internally, if we can build this thing and also start researching it, and we came up to Zebrium actually Larry, one of the co-founders of Zebrium. We came upon his presentation where he clearly explained why this is different, how this works and it immediately clicked in and we said, okay, this is exactly what we were looking for. We dive deeper. We checked the block posts where Zebrium guys really explain everything very clearly there. They're really open about it. And most importantly, there is a button in their system. And so what happens usually with AI ML vendors is they have this button where you fill in your details and a sales guys call you back and you know, explains the system here. They were like, this is our trial system. We believe in the system you can just sign up and try it yourself. And that's what we did. We took one of our Cisco live DNA Center, wireless platforms. We start streaming logs out of it. And then we synthetically, you know, introduce errors like we broke things. And then we realized that Zebrium was really catching the errors perfectly. And on top of that, it was really quiet unless you are really breaking something. And the other thing we realized was during that first trial is Zebrium was actually bringing a lot of context on top of the logs. During those failures, we worked with couple of technical leaders and they said, "Okay if this failure happens I'm expecting this individual log to be there." And we found out with Zebrium apart from that individual log there were a lot of other things which gives a bit more context around the root cause, which was great. And that's where we wanted to take it to the next level. Yeah. >> Okay. So, you know, a couple things to unpack there. I mean, you have the dart board behind you which is kind of interesting, 'cause a lot of times it's like throwing darts at the board to try to figure this stuff out. But to your other point, Cisco actually has some pretty rich tools with AppD and doing observability and you've made acquisitions like thousand eyes. And like you said, I'm presuming you got to eat your own dog food or drink your own champagne. And so you've got to be tools agnostic. And when I first heard about Zebrium, I was like wait a minute. Really? I was kind of skeptical. I've heard this before. You're telling me all I need is plain text and a timestamp. And you got my problem solved. So, and I understand that you guys said, okay let's run a POC. Let's see if we can cut that from, let's say two days a week down to one day, a week. In other words, 50%, let's see if we can automate 50% of the root cause analysis. And so you funded a POC. How did you test it? You put, you know, synthetic, you know errors and problems in there, but how did you test that, it actually works Necati? >> Yeah. So we wanted to take it to the next level which is meaning that we wanted to back test is with existing SaaS. And we decided, you know, we chose four different products from four different verticals, data center security, collaboration, and enterprise networking. And we find out SaaS where the engineer put some kind of log in the resolution summary. So they closed the case. And in the summary of the SR, they put "I identified these log lines and they led me to the root cause" and we ingested those log bundles. And we tried to see if Zebrium can surface that exact same log line in their analysis. So we initially did it with archery ourself and after 50 tests or so we were really happy with the results. I mean, almost most of them we saw the log line that we were looking for but that was not enough. And we brought it of course to our management and they said, "Okay, let's try this with real users" because the log being there is one thing but the engineer reaching to that log is another take. So we wanted to make sure that when we put it in front of our users, our engineers, they can actually come to that log themselves because, you know, we know this platform so we can, you know make searches and find whatever we are looking for but we wanted to do that. So we extended our pilots to some selected engineers and they tested with their own SaaS. Also due some back testing for some SaaS which are closed in the past or recently. And with a sample set of, I guess, close to 200 SaaS we find out like majority of the time, almost 95% of the time the engineer could find the log they were looking for in Zebrium's analysis. >> Yeah. Okay. So you were looking for 50%, you got the 95%. And my understanding is you actually did it with four pretty well known Cisco products, WebEx client, DNA Center Identity services, engine ISE, and then UCS. Unified pursuit. So you use actual real data and that was kind of your proof point, but Atri, so that sounds pretty impressive. And have you put this into production now and what have you found? >> Well, yes, we've launched this with the four products that you mentioned. We're providing our TAC engineers with the ability, whenever a support bundle for that product gets attached to the support request. We are processing it, using sense and then providing that sense analysis to the TAC engineer for their review. >> So are you seeing the results in production? I mean, are you actually able to reclaim that time that people are spending? I mean, it was literally almost two days a week down to you know, a part of a day, is that what you're seeing in production and what are you able to do with that extra time and people getting their weekends back? Are you putting 'em on more strategic tasks? How are you handling that? >> Yeah. So what we're seeing is, and I can tell you from my own personal experience using this tool that troubleshooting any one of the cases, I don't take more than 15 to 20 minutes to go through the Zebrium report. And I know within that time either what the root causes or I know that Zebrium doesn't have the information that I need to solve this particular case. So we've definitely seen, well it's been very hard to measure exactly how much time we've saved per engineer, right? Again, anecdotally, what we've heard from our users is that out of those three hours that they were spending per day, we're definitely able to reclaim at least one of those hours and what even more importantly, you know, what the kind of feedback that we've gotten in terms of I think one statement that really summarizes how Zebrium's impacted our workflow was from one of our users. And they said, "Well, you know, until you provide us with this tool, log analysis was a very black and white affair, but now it's become really colorful." And I mean, if you think about it log analysis is indeed black and white. You're looking at it on a terminal screen where the background is black and the text is white, or you're looking at it as a text where the background is white and the text is black, but what they're really trying to say is there are hardly any visual cues that help you navigate these logs which are so esoteric, so dense, et cetera. But what Zebrium does is it provides a lot of color and context to the whole process. So now you're able to quickly get to, you know using their Word Cloud, using their interactive histogram, using the summaries of every incident. You're very quickly able to summarize what might be happening and what you need to look into. Like, what are the important aspects of this particular log bundle that might be relevant to you? So we've definitely seen that. A really great use case that kind of encapsulates all of this was very early on in our experiment. There was this support request that had been escalated to the business unit or the development team. And the TAC engineer had really, they had an intuition about what was going wrong because of their experience because of, you know the symptoms that they'd seen. They kind of had an idea but they weren't able to convince the development team because they weren't able to find any evidence to back up what they thought was happening. And it was entirely happenstance that I happened to pick up that case and did an analysis using Zebrium. And then I sat down with a TAC engineer and we were very quickly within 15 minutes we were able to get down to the exact sequence of events that highlighted what the customer thought was happening, evidence of what the sorry not the customer what the TAC engineer thought was a root cause. And then we were able to share that evidence with our business unit and, you know redirect their resources so that we could chase down what the problem was. And that that really shows you how that color and context helps in log analysis. >> Interesting. You know, we do a fair amount of work in theCUBE in the RPA space, the robotic process automation and the narrative in the press when our RPA first started taking off was, oh, it's, you know machines replacing humans, or we're going to lose jobs. And what actually happened was people were just eliminating mundane tasks and the employees actually very happy about it. But what my question to you is was there ever a reticence amongst your team? Like, oh, wow, I'm going to lose my job if the machine's going to replace me or have you found that people were excited about this and what's been the reaction amongst the team? >> Well, I think, you know, every automation and AI project has that immediate gut reaction of you're automating away our jobs and so forth. And there is initially there's a little bit of reticence but I mean, it's like you said once you start using the tool, you realize that it's not your job, that's getting automated away. It's just that your job's becoming a little easier to do and it's faster and more efficient. And you're able to get more done in less time. That's really what we're trying to accomplish here. At the end of the day, Zebrium will identify these incidents. They'll do the correlation, et cetera. But if you don't understand what you're reading then that information's useless to you. So you need the human you need the network expert to actually look at these incidents, but what we are able to skin away or get rid of is all of is all the fat that's involved in our process like without having to download the bundle, which, you know when it's many gigabytes in size and now we're working from home with the pandemic and everything, you're, you know pulling massive amounts of logs from the corporate network onto your local device that takes time and then opening it up, loading it in a text editor that takes time. All of these things are we're trying to get rid of. And instead we're trying to make it easier and quicker for you to find what you're looking for. So it's like you said, you take away the mundane you take away the difficulties and the slog but you don't really take away the work the work still needs to be done. >> Yeah, great. Guys, thanks so much appreciate you sharing your story. It's quite, quite fascinating. Really. Thank you for coming on. >> Thanks for having us. >> You're very welcome. >> Excellent. >> Okay. In a moment, I'll be back to wrap up with some final thoughts. This is Dave Vellante and you're watching theCUBE. (upbeat music)

>>The cube presents, Coon and cloud native con Europe, 2022. Brought to you by red hat, the cloud native computing foundation and its ecosystem partners. >>Welcome to Licia Spain and cube con cloud native con 2022 Europe. I'm Keith Townsend, along with Paul Gillon senior editor, enterprise architecture for Silicon angle. We're gonna talk to some amazing folks. Day two coverage of Q con cloud native con Paul. We did the wrap up yesterday. Great. A great back and forth about what en Rico about yesterday's, uh, session. What are you looking for to today? >>I'm looking for, uh, to understand better, uh, how Kubernetes is being put into production, the types of applications that are being built on top of it. Yesterday, we talked a lot about infrastructure today. I think we're gonna talk a little bit more about applications, including with our first guest. >>Yeah, I was speaking our first guest. We have ish Degan CPO chief product officer at Hazelcast Hazelcast has been on the program before, but you, this is your first time in the queue, correct? >>It, it is Keith. Yeah. Well, >>Welcome to been Cuban. So we're talking data, which is always a fascinating topic. Containers are, have been known for not being supportive of stateful applications. At least you shouldn't hold the traditional thought. You shouldn't hold stateful data in containers. Tell me about the relationship between Hazel cast and containers we're at Cuan. >>Yeah, so a little bit about, uh, Hazelcast. We are a real time data platform and, uh, we are not a database, but a data platform because we basically allow, uh, data at rest as well as data in motion. So you can imagine that if you're writing an application, you can basically query and join a data coming in events, as well as data, which might have been persisted. So you can do both stream processing as well as, you know, low latency data access. And, and this platform of course, is supported on all the clouds. And we kind of delegate the orchestration of this kind of scale out system to Kubernetes. Um, and you know, that provides a resiliency and many things which go along with that. >>So you say you don't, you're not a database platform. What are you used for to manage the data? >>So we are, uh, we are memory first. So we are, you know, we started with low latency applications, but then we realized that real time has really become a business term. It's it's more of a business SLA mm-hmm, <affirmative>, it's really the, we see the opportunity, the punctuated change, which is happening in the market today is about real time data access to real time. I mean, there are real time applications. Our customers are building around real time offers, um, realtime thread detection. I mean, just imagine, you know, one of our customers like B and P par bars, they have, they basically originate a loan while the customer is banking. So you are in an ATM machine and you swipe your card and you are asking for, you know, taking 50 euros out. And at that point they can actually originate a custom loan offer based on your existing balance you're existing request and your credit score in that moment. So that's a value moment for them and they actually saw 400% loan origination go up because of that, because nobody's gonna be thinking about a credit, uh, line of credit after they're done banking. So it's in that value moment and we allow basically our data platform allows you to have fast access to data and also process incoming streams. So not before they get stored, but as they're coming in. >>So if I'm a developer and cuon is definitely a conference for developer and I, I come to the booth and I hear <inaudible>, that's the end value. I, I hear what I can do with my application. I guess the question is, how do I get there? I mean, uh, if it's not a database, how do I make a call from a container to, from my microservice to Hazel cath? Like, do I think of this as a, uh, a CNI or, or C CSI? How do I access >>PA care? Yeah. So, so we, uh, you know, we are, our server is actually built in Java. So a lot of the application which get written on top of the data platform are basically accessing through Java APIs. Or as you have a.net shop, you can actually use.net API. So we are basically an API first platform and SQL is basically the polyglot way of accessing data, both streaming data, as well as it store data. So most of the application developers, a lot of it is run done in microservices, and they're doing these fast get inputs for data. So they, they have a key, they want to get to a customer, they give a customer ID. And the beauty is that, um, while they're processing the events, they can actually enrich it because you need contextual information as well. So going back to the ATM example, you know, at that event happened, somebody swiped the card and ask for 50 euros, and now you want more information like credit score information, all that needs to be combined in that, in that value moment. >>So we allow you to do those joins and, you know, the contextual information is very important. So you see a lot of streaming platform out there, which just do streaming, but if you're an application developer, like you asked, you have to basically do call out to a streaming platform to get, um, to do streaming analytics and then do another call to get the context of that. You know, what is the credit score for this customer? But whereas in our case, because the data platform supports both streaming as well as data at rest, you can do that in one call and, you know, you don't want to have the operational complexity to stand out. Two different scale out servers is, is, is, is humongous, right? I mean, you want to build your business application. So, >>So you are querying data streaming data and data rest yes. In the same query >>Yes. In the same query. And we are memory first. So what happens is that we store a lot of the hot data in memory. So we have a scale out Ram based server. So that's where you get the low latency from. In fact, last year we did a benchmark. We were able to process a billion events a second, uh, with 99% of the latency under 30 milliseconds. So that kind of processing and that kind of power is, and, and the most important thing is determinism. I mean, you know, there's a lot of, um, if you look at real time, what real time is, is about this predictable latency at scale, because ultimately your, your adhering to a business SLA is not about milliseconds or microsecond. It's what your business needs. If your business needs that you need to deny or, uh, approve a credit credit card transaction in 50 milliseconds, that's your business SLA, and you need that predictability for every transaction. >>So talk to us about how how's this packaged in consumed. Cause I'm hearing a, a bunch of server Ram I'm hearing numbers that we're trying to adapt away from at this conference. We don't wanna see the onlay. We just want to use it. >>Yeah. So, so we kind of take a bit that, that complexity of managing this scale out, um, uh, uh, cluster, which actually utilizes Rams from each server. And then, you know, if you, you can configure it so that the hard set of data is in Ram, but the data, which is, you know, not so hard can actually go into a tiered storage model. So we are memory first. So, but what you are doing is you're doing simple, it's an API. So you do basically a crud, right? You create records, you read them through SQL. So for you, it's, it's, it's kind of like how you access that database. And we also provide you, you know, real time is also a journey. I mean, a lot of customers, you know, you don't want to rip their existing system and deploy another kind of scale out platform. Right? So we, we see a lot of these use cases where they have a database and we can sit in between the database, a system of record and the application. So we are kind of in between there. So that's, that's the journey you can take to real time. >>How does Kubernetes, uh, containers and Kubernetes change the game for real time analytics? >>Yeah. So, uh, Kubernetes does change it because what's hap first of all, we service most of the operational workloads. So it's, it's more on the, a lot of our customers. We have most, most of the big banks credit card companies in financial services and retail. Those are the two big sectors for us. And first of all, you know, a lot of these operational workloads are moving to the cloud and with move to the cloud, they're actually taking their existing applications and, and moving to, you know, one of the providers and to kind of orchestrate this scale out platform, which does auto scaling, that's where the benefit comes from mm-hmm <affirmative>. And it also gives them the freedom of choice. So, you know, the Kubernetes is, you know, a standard which goes across cloud providers. So that gives them the benefit that they can actually take their application. And if they want, they can actually move it to a different, a different cloud provider because we take away the orchestration complexity, you know, in that abstraction layer. >>So what happens when I need to go really fast? I mean, I, I, I need, uh, I'm looking at bare metal and I'm looking at really scaling a, a, a homogeneous application in a single data center set of data centers. Is there a bare metal play here? >>Yes. There, there, there are some very, very, uh, like if you want microsecond latency, mm-hmm, <affirmative>, um, you know, we have customers who actually store two to four terabytes in Ram and, and they can actually stand up. Um, you know, again, it depends on what kind of deployment you want. You can either scale up or scale out, scaling up is expensive, you know, because those boxes are not cheap, but if you have a requirement like that, where there is sub millisecond or microphone latency requirement, you could actually store the entire data set. I mean, a lot of the operational data sets are under four terabytes. So it's not uncommon that you could actually take the entire operational transactional data set, actually move, move that to a pure Ram. But, uh, I think now we, we also see that these operational workloads are also, there's a need for analytics to be done on top as well. >>I mean, we, going back to the example I gave you, so this, this, uh, customer is not only doing stream crossing, they're also influencing a machine learning algorithm in that same, in the same kind of cycle in the life cycle. So they might have trained a machine learning or algorithm on a data lake somewhere, but once they're ready, they're actually influencing the ML algorithm in our kind of life cycle right there. So, you know, that that really brings analytics and transactions kind of together because after all transactions are where the real, you know, insights are. >>Yeah. I'm, I'm struggling a little bit with this, with these two different use cases where I have transactional basically a transactional database or transactional data platform alongside a analytics platform. Those are two, like they're two different things. I have a, you know, I, I have spinning rust for one, and then I have memory and, and MBME for another. Uh, and that requires tuning requires DBAs. It requires a lot of overhead, there seems to be some type of secret sauce going on here. >>Yeah. Yeah. So, I mean, you know, we, we basically say that if you are, if you have a business case where you want to make a decision, you know, you, the only chance to succeed is where you are not making a decision tomorrow based on today's data. Right? I mean, the only way to act on that data is today. So the act is a keyword here. We actually let you generate a realtime offer. We, we let you do credit card fraud detection. In that moment, the analytics is about knowing less about acting on it. Right? Most of our applications are machine critical. They're acting on real time. I think when you talk about like the data lakes there, there's actually a real time there as well, but it's about knowing, and we believe that the operational side is where, you know, that value moment is there, you know, what good is, is to know about something tomorrow, you know, if something wrong happened, I mean, it, yeah, so there's a latency squeeze there as well, but we are on, on more on the kind of transaction and operational side. >>I gotcha. Yeah. So help me understand, like integrations. A lot of the, the, when I think of transactions, I'm thinking of SAP, Oracle, where the process is done, or some legacy banking or not legacy or new modern banking app, how does the data get from one platform to a, to Hazel cast so I can make those >>Decisions? Yeah. So we have, uh, this, the streaming engine, we have has a whole bunch of connectors to a lot of data sources. So in fact, most of our use cases already have data sources underneath there, their databases there's KA connectors, you know, joining us because if you look at it, events is, are comprised of transactions. So something, a customer did, uh, a credit card swipe, right. And also events events could be machine or IOT. So it's really unique connectivity and data ingestion before you can process that. So we have, uh, a whole suite of connectors to kind of bring data in, in our platform. >>We've been talking a lot, these last couple of days about, uh, about the edge and about moving processing capability closer to the edge. How do you enable that? >>Yeah. So edge is actually very, very relevant because of what's happening is that, um, you know, if you, if you look at like a edge deployment use case, um, you know, we have a use case where data is being pushed from these different edge devices to cloud data warehouse. Right. But just imagine that you want to be filtering data at the, at, at where it is being originated from, and you wanna push only relevant data to, to maybe a central data lake where you might want to do, you know, train your machine learning models. Mm-hmm <affirmative> so that at the edge, we are actually able to process that data. So Hazel cast will allow you to actually write a data pipeline and do stream processing so that you might want to just push, you know, a part or a subset of data, which applies by the rules. Uh, so there's, there's a big, um, uh, I think edge is, you know, there's a lot of data being generated and you don't want like garbage and garbage out there's there's, there is there's filtration done at the edge. So that only the relevant data lands in a data, data lake or something like that. >>Well, Monash, we really appreciate you stopping by realtime data is an exciting area of coverage for the queue overall from Valencia Spain, I'm Keith Townsend, along with Paul Gillon, and you're watching the queue, the leader in high tech coverage.

>> From theCUBE Studios in Palo Alto, in Boston bringing you Data Driven Insights from theCUBE and ETR. This is Breaking Analysis with Dave Vellante. >> In the pre-cloud era hardware companies would run benchmarks, showing how database and or application performance ran better on their systems relative to competitors or previous generation boxes. And they would make a big deal out of it. And the independent software vendors, you know they'd do a little golf clap if you will, in the form of a joint press release it became a game of leaprog amongst hardware competitors. That was pretty commonplace over the years. The Dell Snowflake Deal underscores that the value proposition between hardware companies and ISVs is changing and has much more to do with distribution channels, volumes and the amount of data that lives On-Prem in various storage platforms. For cloud native ISVs like Snowflake they're realizing that despite their Cloud only dogma they have to grit their teeth and deal with On-premises data or risk getting shut out of evolving architectures. Hello and welcome to this week's Wikibon Cube Insights powered by ETR. In this breaking analysis, we unpack what little is known about the Snowflake announcement from Dell Technologies World and discuss the implications of a changing Cloud landscape. We'll also share some new data for Cloud and Database platforms from ETR that shows Snowflake has actually entered the Earth's orbit when it comes to spending momentum on its platform. Now, before we get into the news I want you to listen to Frank's Slootman's answer to my question as to whether or not Snowflake would ever architect the platform to run On-Prem because it's doable technically, here's what he said, play the clip >> Forget it, this will only work in the Public Cloud. Because it's, this is how the utility model works, right. I think everybody is coming through this realization, right? I mean, excuses are running out at this point. You know, we think that it'll, people will come to the Public Cloud a lot sooner than we will ever come to the Private Cloud. It's not that we can't run a private Cloud. It's just diminishes the potential and the value that we bring. >> So you may be asking yourselves how do you square that circle? Because basically the Dell Snowflake announcement is about bringing Snowflake to the private cloud, right? Or is it let's get into the news and we'll find out. Here's what we know at Dell Technologies World. One of the more buzzy announcements was the, by the way this was a very well attended vet event. I should say about I would say 8,000 people by my estimates. But anyway, one of the more buzzy announcements was Snowflake can now run analytics on Non-native Snowflake data that lives On-prem in a Dell object store Dell's ECS to start with. And eventually it's software defined object store. Here's Snowflake's clark, Snowflake's Clark Patterson describing how it works this past week on theCUBE. Play the clip. The way it works is I can now access Non-native Snowflake data using what materialized views, external tables How does that work? >> Some combination of the, all the above. So we've had in Snowflake, a capability called External Tables, which you refer to, it goes hand in hand with this notion of external stages. Basically there's a through the combination of those two capabilities, it's a metadata layer on data, wherever it resides. So customers have actually used this in Snowflake for data lake data outside of Snowflake in the Cloud, up until this point. So it's effectively an extension of that functionality into the Dell On-Premises world, so that we can tap into those things. So we use the external stages to expose all the metadata about what's in the Dell environment. And then we build external tables in Snowflake. So that data looks like it is in Snowflake. And then the experience for the analyst or whomever it is, is exactly as though that data lives in the Snowflake world. >> So as Clark explained, this capability of External tables has been around in the Cloud for a while, mainly to suck data out of Cloud data lakes. Snowflake External Tables use file level metadata, for instance, the name of the file and the versioning so that it can be queried in a stage. A stage is just an external location outside of Snowflake. It could be an S3 bucket or an Azure Blob and it's soon will be a Dell object store. And in using this feature, the Dell looks like it lives inside of Snowflake and Clark essentially, he's correct to say to an analyst that looks exactly like the data is in Snowflake, but uh, not exactly the data's read only which means you can't do what are called DML operations. DML stands for Data Manipulation Language and allows for things like inserting data into tables or deleting and modifying existing data. But the data can be queried. However, the performance of those queries to External Tables will almost certainly be slower. Now users can build things like materialized views which are going to speed things up a bit, but at the end of the day, it's going to run faster than the Cloud. And you can be almost certain that's where Snowflake wants it to run, but some organizations can't or won't move data into the Cloud for a variety of reasons, data sovereignty, compliance security policies, culture, you know, whatever. So data can remain in place On-prem, or it can be moved into the Public Cloud with this new announcement. Now, the compute today presumably is going to be done in the Public Cloud. I don't know where else it's going to be done. They really didn't talk about the compute side of things. Remember, one of Snowflake's early innovations was to separate compute from storage. And what that gave them is you could more efficiently scale with unlimited resources when you needed them. And you could shut off the compute when you don't need us. You didn't have to buy, and if you need more storage you didn't have to buy more compute and vice versa. So everybody in the industry has copied that including AWS with Redshift, although as we've reported not as elegantly as Snowflake did. RedShift's more of a storage tiering solution which minimizes the compute required but you can't really shut it off. And there are companies like Vertica with Eon Mode that have enabled this capability to be done On-prem, you know, but of course in that instance you don't have unlimited elastic compute scale on-Prem but with solutions like Dell Apex and HPE GreenLake, you can certainly, you can start to simulate that Cloud elasticity On-prem. I mean, it's not unlimited but it's sort of gets you there. According to a Dell Snowflake joint statement, the companies the quote, the companies will pursue product integrations and joint go to market efforts in the second half of 2022. So that's a little vague and kind of benign. It's not really clear when this is going to be available based on that statement from the two first, but, you know, we're left wondering will Dell develop an On-Prem compute capability and enable queries to run locally maybe as part of an extended apex offering? I mean, we don't know really not sure there's even a market for that but it's probably a good bet that again, Snowflake wants that data to land in the Snowflake data Cloud kind of makes you wonder how this deal came about. You heard Sloop on earlier Snowflake has always been pretty dogmatic about getting data into its native snowflake format to enable the best performance as we talked about but also data sharing and governance. But you could imagine that data architects they're building out their data mesh we've reported on this quite extensively and their data fabric and those visions around that. And they're probably telling Snowflake, Hey if you want to be a strategic partner of ours you're going to have to be more inclusive of our data. That for whatever reason we're not putting in your Cloud. So Snowflake had to kind of hold its nose and capitulate. Now the good news is it further opens up Snowflakes Tam the total available market. It's obviously good marketing posture. And ultimately it provides an on ramp to the Cloud. And we're going to come back to that shortly but let's look a little deeper into what's happening with data platforms and to do that we'll bring in some ETR data. Now, let me just say as companies like Dell, IBM, Cisco, HPE, Lenovo, Pure and others build out their hybrid Clouds. The cold hard fact is not only do they have to replicate the Cloud Operating Model. You will hear them talk about that a lot, but they got to do that. So it, and that's critical from a user experience but in order to gain that flywheel momentum they need to build a robust ecosystem that goes beyond their proprietary portfolios. And, you know, honestly they're really not even in the first inning most companies and for the likes of Snowflake to sort of flip this, they've had to recognize that not everything is moving into the Cloud. Now, let's bring up the next slide. One of the big areas of discussion at Dell Tech World was Apex. That's essentially Dell's nascent as a service offering. Apex is infrastructure as a Service Cloud On-prem and obviously has the vision of connecting to the Cloud and across Clouds and out to the Edge. And it's no secret that database is one of the most important ingredients of infrastructure as a service generally in Cloud Infrastructure specifically. So this chart here shows the ETR data for data platforms inside of Dell accounts. So the beauty of ETR platform is you can cut data a million different ways. So we cut it. We said, okay, give us the Cloud platforms inside Dell accounts, how are they performing? Now, this is a two dimensional graphic. You got net score or spending momentum on the vertical axis and what ETR now calls Overlap formally called Market Share which is a measure of pervasiveness in the survey. That's on the horizontal axis that red dotted line at 40% represents highly elevated spending on the Y. The table insert shows the raw data for how the dots are positioned. Now, the first call out here is Snowflake. According to ETR quote, after 13 straight surveys of astounding net scores, Snowflake has finally broken the trend with its net score dropping below the 70% mark among all respondents. Now, as you know, net score is measured by asking customers are you adding the platform new? That's the lime green in the bar that's pointing from Snowflake in the graph and or are you increasing spend by 6% or more? That's the forest green is spending flat that's the gray is you're spend decreasing by 6% or worse. That's the pinkish or are you decommissioning the platform bright red which is essentially zero for Snowflake subtract the reds from the greens and you get a net score. Now, what's somewhat interesting is that snowflakes net score overall in the survey is 68 which is still huge, just under 70%, but it's net score inside the Dell account base drops to the low sixties. Nonetheless, this chart tells you why Snowflake it's highly elevated spending momentum combined with an increasing presence in the market over the past two years makes it a perfect initial data platform partner for Dell. Now and in the Ford versus Ferrari dynamic. That's going on between the likes of Dell's apex and HPE GreenLake database deals are going to become increasingly important beyond what we're seeing with this recent Snowflake deal. Now noticed by the way HPE is positioned on this graph with its acquisition of map R which is now part of HPE Ezmeral. But if these companies want to be taken seriously as Cloud players, they need to further expand their database affinity to compete ideally spinning up databases as part of their super Clouds. We'll come back to that that span multiple Clouds and include Edge data platforms. We're a long ways off from that. But look, there's Mongo, there's Couchbase, MariaDB, Cloudera or Redis. All of those should be on the short list in my view and why not Microsoft? And what about Oracle? Look, that's to be continued on maybe as a future topic in a, in a Breaking Analysis but I'll leave you with this. There are a lot of people like John Furrier who believe that Dell is playing with fire in the Snowflake deal because he sees it as a one way ticket to the Cloud. He calls it a one way door sometimes listen to what he said this past week. >> I would say that that's a dangerous game because we've seen that movie before, VMware and AWS. >> Yeah, but that we've talked about this don't you think that was the right move for VMware? >> At the time, but if you don't nurture the relationship AWS will take all those customers ultimately from VMware. >> Okay, so what does the data say about what John just said? How is VMware actually doing in Cloud after its early missteps and then its subsequent embracing of AWS and other Clouds. Here's that same XY graphic spending momentum on the Y and pervasiveness on the X and the same table insert that plots the dots and the, in the breakdown of Dell's net score granularity. You see that at the bottom of the chart in those colors. So as usual, you see Azure and AWS up and to the right with Google well behind in a distant third, but still in the mix. So very impressive for Microsoft and AWS to have both that market presence in such elevated spending momentum. But the story here in context is that the VMware Cloud on AWS and VMware's On-Prem Cloud like VMware Cloud Foundation VCF they're doing pretty well in the market. Look, at HPE, gaining some traction in Cloud. And remember, you may not think HPE and Dell and VCF are true Cloud but these are customers answering the survey. So their perspective matters more than the purest view. And the bad news is the Dell Cloud is not setting the world on fire from a momentum standpoint on the vertical axis but it's above the line of zero and compared to Dell's overall net score of 20 you could see it's got some work to do. Okay, so overall Dell's got a pretty solid net score to you know, positive 20, as I say their Cloud perception needs to improve. Look, Apex has to be the Dell Cloud brand not Dell reselling VMware. And that requires more maturity of Apex it's feature sets, its selling partners, its compensation models and it's ecosystem. And I think Dell clearly understands that. I think they're pretty open about that. Now this includes partners that go beyond being just sellers has to include more tech offerings in the marketplace. And actually they got to build out a marketplace like Cloud Platform. So they got a lot of work to do there. And look, you've got Oracle coming up. I mean they're actually kind of just below the magic 40% in the line which is pro it's pretty impressive. And we've been telling you for years, you can hate Oracle all you want. You can hate its price, it's closed system all of that it's red stack shore. You can say it's legacy. You can say it's old and outdated, blah, blah, blah. You can say Oracle is irrelevant in trouble. You are dead wrong. When it comes to mission critical workloads. Oracle is the king of the hill. They're a founder led company that knows exactly what it's doing and they're showing Cloud momentum. Okay, the last point is that while Microsoft AWS and Google have major presence as shown on the X axis. VMware and Oracle now have more than a hundred citations in the survey. You can see that on the insert in the right hand, right most column. And IBM had better keep the momentum from last quarter going, or it won't be long before they get passed by Dell and HP in Cloud. So look, John might be right. And I would think Snowflake quietly agrees that this Dell deal is all about access to Dell's customers and their data. So they can Hoover it into the Snowflake Data Cloud but the data right now, anyway doesn't suggest that's happening with VMware. Oh, by the way, we're keeping an eye close eye on NetApp who last September ink, a similar deal to VMware Cloud on AWS to see how that fares. Okay, let's wrap with some closing thoughts on what this deal means. We learned a lot from the Cloud generally in AWS, specifically in two pizza teams, working backwards, customer obsession. We talk about flywheel all the time and we've been talking today about marketplaces. These have all become common parlance and often fundamental narratives within strategic plans investor decks and customer presentations. Cloud ecosystems are different. They take both competition and partnerships to new heights. You know, when I look at Azure service offerings like Apex, GreenLake and similar services and I see the vendor noise or hear the vendor noise that's being made around them. I kind of shake my head and ask, you know which movie were these companies watching last decade? I really wish we would've seen these initiatives start to roll out in 2015, three years before AWS announced Outposts not three years after but Hey, the good news is that not only was Outposts a wake up call for the On-Prem crowd but it's showing how difficult it is to build a platform like Outposts and bring it to On-Premises. I mean, Outpost isn't currently even a rounding era in the marketplace. It really doesn't do much in terms of database support and support of other services. And, you know, it's unclear where that that is going. And I don't think it has much momentum. And so the Hybrid Cloud Vendors they've had time to figure it out. But now it's game on, companies like Dell they're promising a consistent experience between On-Prem into the Cloud, across Clouds and out to the Edge. They call it MultCloud which by the way my view has really been multi-vendor Chuck, Chuck Whitten. Who's the new co-COO of Dell called it Multi-Cloud by default. (laughing) That's really, I think an accurate description of that. I call this new world Super Cloud. To me, it's different than MultiCloud. It's a layer that runs on top of hyperscale infrastructure kind of hides the underlying complexity of the Cloud. It's APIs, it's primitives. And it stretches not only across Clouds but out to the Edge. That's a big vision and that's going to require some seriously intense engineering to build out. It's also going to require partnerships that go beyond the portfolios of companies like Dell like their own proprietary stacks if you will. It's going to have to replicate the Cloud Operating Model and to do that, you're going to need more and more deals like Snowflake and even deeper than Snowflake, not just in database. Sure, you'll need to have a catalog of databases that run in your On-Prem and Hybrid and Super Cloud but also other services that customers can tap. I mean, can you imagine a day when Dell offers and embraces a directly competitive service inside of apex. I have trouble envisioning that, you know not with their historical posture, you think about companies like, you know, Nutanix, you know, or Cisco where they really, you know those relationships cooled quite quickly but you know, look, think about it. That's what AWS does. It offers for instance, Redshift and Snowflake side by side happily and the Redshift guys they probably hate Snowflake. I wouldn't blame them, but the EC Two Folks, they love them. And Adam SloopesKy understands that ISVs like Snowflake are a key part of the Cloud ecosystem. Again, I have a hard time envisioning that occurring with Dell or even HPE, you know maybe less so with HPE, but what does this imply that the Edge will allow companies like Dell to a reach around on the Cloud and somehow create a new type of model that begrudgingly accommodates the Public Cloud but drafts of the new momentum of the Edge, which right now to these companies is kind of mostly telco and retail. It's hard to see that happening. I think it's got to evolve in a more comprehensive and inclusive fashion. What's much more likely is companies like Dell are going to substantially replicate that Cloud Operating Model for the pieces that they own pieces that they control which admittedly are big pieces of the market. But unless they're able to really tap that ecosystem magic they're not going to be able to grow much beyond their existing install bases. You take that lime green we showed you earlier that new adoption metric from ETR as an example, by my estimates, AWS and Azure are capturing new accounts at a rate between three to five times faster than Dell and HPE. And in the more mature US and mere markets it's probably more like 10 X and a major reason is because of the Cloud's robust ecosystem and the optionality and simplicity of transaction that that is bringing to customers. Now, Dell for its part is a hundred billion dollar revenue company. And it has the capability to drive that kind of dynamic. If it can pivot its partner ecosystem mindset from kind of resellers to Cloud services and technology optionality. Okay, that's it for now? Thanks to my colleagues, Stephanie Chan who helped research topics for Breaking Analysis. Alex Myerson is on the production team. Kristen Martin and Cheryl Knight and Rob Hof, on editorial they helped get the word out and thanks to Jordan Anderson for the new Breaking Analysis branding and graphics package. Remember these episodes are all available as podcasts wherever you listen. All you do is search Breaking Analysis podcasts. You could check out ETR website @etr.ai. We publish a full report every week on wikibon.com and siliconangle.com. You want to get in touch. @dave.vellente @siliconangle.com. You can DM me @dvellante. You can make a comment on our LinkedIn posts. This is Dave Vellante for the Cube Insights powered by ETR. Have a great week, stay safe, be well. And we'll see you next time. (upbeat music)

(upbeat music) >> Welcome, everyone, to theCUBE's presentation of the AWS Startup Showcase. And this episode two of season two is called "Data as Code," the ongoing series covering exciting new startups in the AWS ecosystem. I'm John Furrier, your host of theCUBE. Today, we're excited to be joined by Jon Dahl, who is the co-founder and CEO of MUX, a hot new startup building cloud video for developers, video with data. John, great to see you. We did an interview on theCube Conversation. Went into big detail of the awesomeness of your company and the trend that you're on. Welcome back. >> Thank you, glad to be here. >> So, video is everywhere, and video for pivot to video, you hear all these kind of terms in the industry, but now more than ever, video is everywhere and people are building with it, and it's becoming part of the developer experience in applications. So people have to stand up video into their code fast, and data is code, video is data. So you guys are specializing this. Take us through that dynamic. >> Yeah, so video clearly is a growing part of how people are building applications. We see a lot of trends of categories that did not involve video in the past making a major move towards video. I think what Peloton did five years ago to the world of fitness, that was not really a big category. Now video fitness is a huge thing. Video in education, video in business settings, video in a lot of places. I think Marc Andreessen famously said, "Software is eating the world" as a pretty, pretty good indicator of what the internet is actually doing to the economy. I think there's a lot of ways in which video right now is eating software. So categories that we're not video first are becoming video first. And that's what we help with. >> It's not obvious to like most software developers when they think about video, video industries, it's industry shows around video, NAB, others. People know, the video folks know what's going on in video, but when you start to bring it mainstream, it becomes an expectation in the apps. And it's not that easy, it's almost a provision video is hard for a developer 'cause you got to know the full, I guess, stack of video. That's like low level and then kind of just basic high level, just play something. So, in between, this is a media stack kind of dynamic. Can you talk about how hard it is to build video for developers? How is it going to become easier? >> Yeah, I mean, I've lived this story for too long, maybe 13 years now, when I first build my first video stack. And, you know, I'll sometimes say, I think it's kind of a miracle every time a video plays on the internet because the internet is not a medium designed for video. It's been hijacked by video, video is 70% of internet traffic today in an unreliable, sort of untrusted network space, which is totally different than how television used to work or cable or things like that. So yeah, so video is hard because there's so many problems from top to bottom that need to be solved to make video work. So you have to worry about video compression encoding, which is a complicated topic in itself. You have to worry about delivering video around the world at scale, delivering it at low cost, at low latency, with good performance, you have to worry about devices and how every device, Android, iOS, web, TVs, every device handles video differently and so there's a lot of work there. And at the end of the day, these are kind of unofficial standards that everyone's using. So one of the miracles is like, if you want to watch a video, somehow you have to get like Apple and Google to agree on things, which is not always easy. And so there's just so many layers of complexity that are behind it. I think one way to think about it is, if you want to put an image online, you just put an image online. And if you want to put video online, you build complex software, and that's the exact problem that MUX was started to help solve. >> It's interesting you guys have almost creating a whole new category around video infrastructure. And as you look at, you mentioned stack, video stack. I'm looking at a market where the notion of a media stack is developing, and you're seeing these verticals having similar dynamics with cloud. And if you go back to the early days of cloud computing, what was the developer experience or entrepreneurial experience, you had to actually do a lot of stuff before you even do anything, provision a server. And this has all kind of been covered in great detail in the glory of Agile and whatnot. It was expensive, and you had that actually engineer before you could even stand up any code. Now you got video that same thing's happening. So the developers have two choices, go do a bunch of stuff complex, building their own infrastructure, which is like building a data center, or lean in on MUX and say, "Hey, thank you for doing all that years of experience building out the stacks to take that hard part away," but using APIs that they have. This is a developer focused problem that you guys are solving. >> Yeah, that's right. my last company was a company called Zencoder, that was an API to video encoding. So it was kind of an API to a small part of what MUX does today, just one of those problems. And I think the thing that we got right at Zencoder, that we're doing again here at MUX, was building four developers first. So our number one persona is a software developer. Not necessarily a video expert, just we think any developer should be able to build with video. It shouldn't be like, yeah, got to go be a specialist to use this technology, because it should become just of the internet. Video should just be something that any developer can work with. So yeah, so we build for developers first, which means we spend a lot of time thinking about API design, we spend a lot of time thinking about documentation, transparent pricing, the right features, great support and all those kind of things that tend to be characteristics of good developer companies. >> Tell me about the pipe lining of the products. I'm a developer, I work for a company, my boss is putting pressure on me. We need video, we have all this library, it's all stacking up. We hired some people, they left. Where's the video, we've stored it somewhere. I mean, it's a nightmare, right? So I'm like, okay, I'm cloud native, I got an API. I need to get my product to market fast, 'cause that is what Agile developers want. So how do you describe that acceleration for time to market? You mentioned you guys are API first, video first. How do these customers get their product into the market as fast as possible? >> Yeah, well, I mean the first thing we do is we put what we think is probably on average, three to four months of hard engineering work behind a single API call. So if you want to build a video platform, we tell our customers like, "Hey, you can do that." You probably need a team, you probably need video experts on your team so hire them or train them. And then it takes several months just to kind of to get video flowing. One API call at MUX gives you on-demand video or live video that works at scale, works around the world with good performance, good reliability, a rich feature set. So maybe just a couple specific examples, we worked with Robin Hood a few years ago to bring video into their newsfeed, which was hugely successful for them. And they went from talking to us for the first time to a big launch in, I think it was three months, but the actual code time there was like really short. I want to say they had like a proof of concept up and running in a couple days, and then the full launch in three months. Another customer of ours, Bandcamp, I think switched from a legacy provider to MUX in two weeks in band. So one of the big advantages of going a little bit higher in the abstraction layer than just building it yourself is that time to market. >> Talk about this notion of video pipeline 'cause I know I've heard people I talk about, "Hey, I just want to get my product out there. I don't want to get stuck in the weeds on video pipeline." What does that mean for folks that aren't understanding the nuances of video? >> Yeah, I mean, it's all the steps that it takes to publish video. So from ingesting the video, if it's live video from making sure that you have secure, reliable ingest of that live feed potentially around the world to the transcoding, which is we talked a little bit about, but it is a, you know, on its own is a massively complicated problem. And doing that, well, doing that well is hard. Part of the reason it's hard is you really have to know where you're publishing too. And you might want to transcode video differently for different devices, for different types of content. You know, the pipeline typically would also include all of the workflow items you want to do with the video. You want to thumbnail a video, you want clip, create clips of the video, maybe you want to restream the video to Facebook or Twitter or a social platform. You want to archive the video, you want it to be available for downloads after an event. If it's just a, if it's a VOD upload, if it's not live in the first place. You have all those things and you might want to do simulated live with the video. You might want to actually record something and then play it back as a live stream. So, the pipeline Ty typically refers to everything from the ingest of the video to the time that the bits are delivered to a device. >> You know, I hear a lot of people talking about video these days, whether it's events, training, just want peer to peer experience, video is powerful, but customers want to own their own platform, right? They want to have the infrastructure as a service. They kind of want platform as a service, this is cloud talk now, but they want to have their own capability to build it out. This allows them to get what they want. And so you see this, like, is it SaaS? Is it platform? People want customization? So kind of the general purpose video solution does it really exist or doesn't? I mean, 'cause this is the question. Can I just buy software and work or is it going to be customized always? How do you see that? Because this becomes a huge discussion point. Is it a SaaS product or someone's going to make a SaaS product? >> Yeah, so I think one of the most important elements of designing any software, but especially when you get into infrastructure is choosing an abstraction level. So if you think of computing, you can go all the way down to building a data center, you can go all the way down to getting a colo and racking a server like maybe some of us used to do, who are older than others. And that's one way to run a server. On the other extreme, you have just think of the early days of cloud competing, you had app engine, which was a really fantastic, really incredible product. It was one push deploy of, I think Python code, if I remember correctly, and everything just worked. But right in the middle of those, you had EC2, which was, EC2 is basically an API to a server. And it turns out that that abstraction level, not Colo, not the full app engine kind of platform, but the API to virtual server was the right abstraction level for maybe the last 15 years. Maybe now some of the higher level application platforms are doing really well, maybe the needs will shift. But I think that's a little bit of how we think about video. What developers want is an API to video. They don't want an API to the building blocks of video, an API to transcoding, to video storage, to edge caching. They want an API to video. On the other extreme, they don't want a big application that's a drop in white label video in a box like a Shopify kind of thing. Shopify is great, but developers don't want to build on top of Shopify. In the payments world developers want Stripe. And that abstraction level of the API to the actual thing you're getting tends to be the abstraction level that developers want to build on. And the reason for that is, it's the most productive layer to build on. You get maximum flexibility and also maximum velocity when you have that API directly to a function like video. So, we like to tell our customers like you, you own your video when you build on top of MUX, you have full control over everything, how it's stored, when it's stored, where it goes, how it's published, we handle all of the hard technology and we give our customers all of the flexibility in terms of designing their products. >> I want to get back some use case, but you brought that up I might as well just jump to my next point. I'd like you to come back and circle back on some references 'cause I know you have some. You said building on infrastructure that you own, this is a fundamental cloud concept. You mentioned API to a server for the nerds out there that know that that's cool, but the people who aren't super nerdy, that means you're basically got an interface into a server behind the scenes. You're doing the same for video. So, that is a big thing around building services. So what wide range of services can we expect beyond MUX? If I'm going to have an API to video, what could I do possibly? >> What sort of experience could you build? >> Yes, I got a team of developers saying I'm all in API to video, I don't want to do all that transit got straight there, I want to build experiences, video experiences on my app. >> Yeah, I mean, I think, one way to think about it is that, what's the range of key use cases that people do with video? We tend to think about six at MUX, one is kind of the places where the content is, the prop. So one of the things that use video is you can create great video. Think of online courses or fitness or entertainment or news or things like that. That's kind of the first thing everyone thinks of, when you think video, you think Netflix, and that's great. But we see a lot of really interesting uses of video in the world of social media. So customers of ours like Visco, which is an incredible photo sharing application, really for photographers who really care about the craft. And they were able to bring video in and bring that same kind of Visco experience to video using MUX. We think about B2B tools, videos. When you think about it, all video is, is a high bandwidth way of communicating. And so customers are as like HubSpot use video for the marketing platform, for business collaboration, you'll see a lot of growth of video in terms of helping businesses engage their customers or engage with their employees. We see live events obviously have been a massive category over the last few years. You know, we were all forced into a world where we had to do live events two years ago, but I think now we're reemerging into a world where the online part of a conference will be just as important as the in-person component of a conference. So that's another big use case we see. >> Well, full disclosure, if you're watching this live right now, it's being powered by MUX. So shout out, we use MUX on theCUBE platform that you're experiencing in this. Actually in real time, 'cause this is one application, there's many more. So video as code, is data as code is the theme, that's going to bring up the data ops. Video also is code because (laughs) it's just like you said, it's just communicating, but it gets converted to data. So data ops, video ops could be its own new category. What's your reaction to that? >> Yeah, I mean, I think, I have a couple thoughts on that. The first thought is, video is a way that, because the way that companies interact with customers or users, it's really important to have good monitoring and analytics of your video. And so the first product we ever built was actually a product called MUX video, sorry, MUX data, which is the best way to monitor a video platform at scale. So we work with a lot of the big broadcasters, we work with like CBS and Fox Sports and Discovery. We work with big tech companies like Reddit and Vimeo to help them monitor their video. And you just get a huge amount of insight when you look at robust analytics about video delivery that you can use to optimize performance, to make sure that streaming works well globally, especially in hard to reach places or on every device. That's we actually build a MUX data platform first because when we started MUX, we spent time with some of our friends at companies like YouTube and Netflix, and got to know how they use data to power their video platforms. And they do really sophisticated things with data to ensure that their streams well, and we wanted to build the product that would help everyone else do that. So, that's one use. I think the other obvious use is just really understanding what people are doing with their video, who's watching what, what's engaging, those kind of things. >> Yeah, data is definitely there. You guys mentioned some great brands that are working with you guys, and they're doing it because of the developer experience. And I'd like you to explain, if you don't mind, in your words, why is the MUX developer experience so good? What are some of the results you're seeing from your customers? What are they saying to you? Obviously when you win, you get good feedback. What are some of the things that they're saying and what specific develop experiences do they like the best? >> Yeah, I mean, I think that the most gratifying thing about being a startup founder is when your customers like what you're doing. And so we get a lot of this, but it's always, we always pay attention to what customers say. But yeah, people, the number one thing developers say when they think about MUX is that the developer experience is great. I think when they say that, what they mean is two things, first is it's easy to work with, which helps them move faster, software velocity is so important. Every company in the world is investing and wants to move quickly and to build quickly. And so if you can help a team speed up, that's massively valuable. The second thing I think when people like our developer experience is, you know, in a lot of ways that think that we get out of the way and we let them do what they want to do. So well, designed APIs are a key part of that, coming back to abstraction, making sure that you're not forcing customers into decisions that they actually want to make themselves. Like, if our video player only had one design, that that would not be, that would not work for most developers, 'cause developers want to bring their own design and style and workflow and feel to their video. And so, yeah, so I think the way we do that is just think comprehensively about how APIs are designed, think about the workflows that users are trying to accomplish with video, and make sure that we have the right APIs, make sure they're the right information, we have the right webhooks, we have the right SDKs, all of those things in place so that they can build what they want. >> We were just having a conversation on theCUBE, Dave Vellante and I, and our team, and I'd love to get you a reaction to this. And it's more and more, a riff real quick. We're seeing a trend where video as code, data as code, media stack, where you're starting to see the emergence of the media developer, where the application of media looks a lot like kind of software developer, where the app, media as an app. It could be a chat, it could be a peer to peer video, it could be part of an event platform, but with all the recent advances, in UX designers, coders, the front end looks like an emergence of these creators that are essentially media developers for all intent and purpose, they're coding media. What's your reaction to that? How do you see that evolving? >> I think the. >> Or do you agree with it? >> It's okay. >> Yeah, yeah. >> Well, I think a couple things. I think one thing, I think this goes along through saying, but maybe it's disagreement, is that we don't think you should have to be an expert at video or at media to create and produce or create and publish good video, good audio, good images, those kind of things. And so, you know, I think if you look at software overall, I think of 10 years ago, the kind of DevOps movement, where there was kind of a movement away from specialization in software where the same software developer could build and deploy the same software developer maybe could do front end and back end. And we want to bring that to video as well. So you don't have to be a specialist to do it. On the other hand, I do think that investments and tooling, all the way from video creation, which is not our world, but there's a lot of amazing companies out there that are making it easier to produce video, to shoot video, to edit, a lot of interesting innovations there all the way to what we do, which is helping people stream and publish video and video experiences. You know, I think another way about it is, that tool set and companies doing that let anyone be a media developer, which I think is important. >> It's like DevOps turning into low-code, no-code, eventually it's just composability almost like just, you know, "Hey Siri, give me some video." That kind of thing. Final question for you why I got you here, at the end of the day, the decision between a lot of people's build versus buy, "I got to get a developer. Why not just roll my own?" You mentioned data center, "I want to build a data center." So why MUX versus do it yourself? >> Yeah, I mean, part of the reason we started this company is we have a pretty, pretty strong opinion on this. When you think about it, when we started MUX five years ago, six years ago, if you were a developer and you wanted to accept credit cards, if you wanted to bring payment processing into your application, you didn't go build a payment gateway. You just probably used Stripe. And if you wanted to send text messages, you didn't build your own SMS gateway, you probably used Twilio. But if you were a developer and you wanted to stream video, you built your own video gateway, you built your own video application, which was really complex. Like we talked about, you know, probably three, four months of work to get something basic up and running, probably not live video that's probably only on demand video at that point. And you get no benefit by doing it yourself. You're no better than anyone else because you rolled your own video stack. What you get is risk that you might not do a good job, maybe you do worse than your competitors, and you also get distraction where you've just taken, you take 10 engineers and 10 sprints and you apply it to a problem that doesn't actually really give you differentiated value to your users. So we started MUX so that people would not have to do that. It's fine if you want to build your own video platform, once you get to a certain scale, if you can afford a dozen engineers for a VOD platform and you have some really massively differentiated use case, you know, maybe, live is, I don't know, I don't have the rule of thumb, live videos maybe five times harder than on demand video to work with. But you know, in general, like there's such a shortage of software engineers today and software engineers have, frankly, are in such high demand. Like you see what happens in the marketplace and the hiring markets, how competitive it is. You need to use your software team where they're maximally effective, and where they're maximally effective is building differentiation into your products for your customers. And video is just not that, like very few companies actually differentiate on their video technology. So we want to be that team for everyone else. We're 200 people building the absolute best video infrastructure as APIs for developers and making that available to everyone else. >> John, great to have you on with the showcase, love the company, love what you guys do. Video as code, data as code, great stuff. Final plug for the company, for the developers out there and prospects watching for MUX, why should they go to MUX? What are you guys up to? What's the big benefit? >> I mean, first, just check us out. Try try our APIs, read our docs, talk to our support team. We put a lot of work into making our platform the best, you know, as you dig deeper, I think you'd be looking at the performance around, the global performance of what we do, looking at our analytics stack and the insight you get into video streaming. We have an emerging open source video player that's really exciting, and I think is going to be the direction that open source players go for the next decade. And then, you know, we're a quickly growing team. We're 60 people at the beginning of last year. You know, we're one 50 at the beginning of this year, and we're going to a add, we're going to grow really quickly again this year. And this whole team is dedicated to building the best video structure for developers. >> Great job, Jon. Thank you so much for spending the time sharing the story of MUX here on the show, Amazon Startup Showcase season two, episode two, thanks so much. >> Thank you, John. >> Okay, I'm John Furrier, your host of theCUBE. This is season two, episode two, the ongoing series cover the most exciting startups from the AWS Cloud Ecosystem. Talking data analytics here, video cloud, video as a service, video infrastructure, video APIs, hottest thing going on right now, and you're watching it live here on theCUBE. Thanks for watching. (upbeat music)

from the cube studios in palo alto in boston bringing you data-driven insights from the cube and etr this is breaking analysis with dave vellante you know we've often said that the next 10 years in cloud computing won't be like the last ten cloud has firmly planted its footprint on the other side of the chasm with the momentum of the entire multi-trillion dollar tech business behind it both sellers and buyers are leaning in by adopting cloud technologies and many are building their own value layers on top of cloud in the coming years we expect innovation will continue to coalesce around the three big u.s clouds plus alibaba in apac with the ecosystem building value on top of the hardware saw tooling provided by the hyperscalers now importantly we don't see this as a race to the bottom rather our expectation is that the large public cloud players will continue to take cost out of their platforms through innovation automation and integration while other cloud providers and the ecosystem including traditional companies that buy it mine opportunities in their respective markets as matt baker of dell is fond of saying this is not a zero sum game welcome to this week's wikibon cube insights powered by etr in this breaking analysis we'll update you on our latest projections in the cloud market we'll share some new etr survey data with some surprising nuggets and drill into this the important cloud database landscape first we want to take a look at what people are talking about in cloud and what's been in the recent news with the exception of alibaba all the large cloud players have reported earnings google continues to focus on growth at the expense of its profitability google reported that it's cloud business which includes applications like google workspace grew 45 percent to five and a half billion dollars but it had an operating loss of 890 billion now since thomas curion joined google to run its cloud business google has increased head count in its cloud business from 25 000 25 000 people now it's up to 40 000 in an effort to catch up to the two leaders but playing catch up is expensive now to put this into perspective let's go back to aws's revenue in q1 2018 when the company did 5.4 billion so almost exactly the same size as google's current total cloud business and aws is growing faster at the time at 49 don't forget google includes in its cloud numbers a big chunk of high margin software aws at the time had an operating profit of 1.4 billion that quarter around 26 of its revenues so it was a highly profitable business about as profitable as cisco's overall business which again is a great business this is what happens when you're number three and didn't get your head out of your ads fast enough now in fairness google still gets high marks on the quality of its technology according to corey quinn of the duck bill group amazon and google cloud are what he called neck and neck with regard to reliability with microsoft azure trailing because of significant disruptions in the past these comments were made last week in a bloomberg article despite some recent high-profile outages on aws not surprisingly a microsoft spokesperson said that the company's cloud offers industry-leading reliability and that gives customers payment credits after some outages thank you turning to microsoft and cloud news microsoft's overall cloud business surpassed 22 billion in the december quarter up 32 percent year on year like google microsoft includes application software and sas offerings in its cloud numbers and gives little nuggets of guidance on its azure infrastructure as a service business by the way we estimate that azure comprises about 45 percent of microsoft's overall cloud business which we think hit a 40 billion run rate last quarter microsoft guided in its earning call that recent declines in the azure growth rates will reverse in q1 and that implies sequential growth for azure and finally it was announced that the ftc not the doj will review microsoft's announced 75 billion acquisition of activision blizzard it appears ftc chair lena khan wants to take this one on herself she of course has been very outspoken about the power of big tech companies and in recent a recent cnbc interview suggested that the u.s government's actions were a meaningful contributor back then to curbing microsoft's power in the 90s i personally found that dubious just ask netscape wordperfect novell lotus and spc the maker of harvard presentation graphics how effective the government was in curbing microsoft power generally my take is that the u s government has had a dismal record regulating tech companies most notably ibm and microsoft and it was market forces company hubris complacency and self-inflicted wounds not government intervention these were far more effective than the government now of course if companies are breaking the law they should be punished but the u.s government hasn't been very productive in its actions and the unintended consequences of regulation could be detrimental to the u.s competitiveness in the race with china but i digress lastly in the news amazon announced earnings thursday and the company's value increased by 191 billion dollars on friday that's a record valuation gain for u.s stocks aws amazon's profit engine grew 40 percent year on year for the quarter it closed the year at 62 billion dollars in revenue and at a 71 billion dollar revenue run rate aws is now larger than ibm which without kindrel is at a 67 billion dollar run rate just for context ibm's revenue in 2011 was 107 billion dollars now there's a conversation going on in the media and social that in order to continue this growth and compete with microsoft that aws has to get into the sas business and offer applications we don't think that's the right strategy for amp from for amazon in the near future rather we see them enabling developers to compete in that business finally amazon disclosed that 48 of its top 50 customers are using graviton 2 instances why is this important because aws is well ahead of the competition in custom silicon chips is and is on a price performance curve that is far better than alternatives especially those based on x86 this is one of the reasons why we think this business is not a race to the bottom aws is being followed by google microsoft and alibaba in terms of developing custom silicon and will continue to drive down their internal cost structures and deliver price performance equal to or better than the historical moore's law curves so that's the recent news for the big u.s cloud providers let's now take a look at how the year ended for the big four hyperscalers and look ahead to next year here's a table we've shown this view before it shows the revenue estimates for worldwide is and paths generated by aws microsoft alibaba and google now remember amazon and alibaba they share clean eye ass figures whereas microsoft and alphabet only give us these nuggets that we have to interpret and we correlate those tidbits with other data that we gather we're one of the few outlets that actually attempts to make these apples to apples comparisons there's a company called synergy research there's another firm that does this but i really can't map to their numbers their gcp figures look far too high and azure appears somewhat overestimated and they do include other stuff like hosted private cloud services but it's another data point that you can use okay back to the table we've slightly adjusted our gcp figures down based on interpreting some of alphabet's statements and other survey data only alibaba has yet to announce earnings so we'll stick to a 2021 market size of about 120 billion dollars that's a 41 growth rate relative to 2020 and we expect that figure to increase by 38 percent to 166 billion in 2022 now we'll discuss this a bit later but these four companies have created an opportunity for the ecosystem to build what we're calling super clouds on top of this infrastructure and we're seeing it happen it was increasingly obvious at aws re invent last year and we feel it will pick up momentum in the coming months and years a little bit more on that later now here's a graphical view of the quarterly revenue shares for these four companies notice that aws has reversed its share erosion and is trending up slightly aws has accelerated its growth rate four quarters in a row now it accounted for 52 percent of the big four hyperscaler revenue last year and that figure was nearly 54 in the fourth quarter azure finished the year with 32 percent of the hyper scale revenue in 2021 which dropped to 30 percent in q4 and you can see gcp and alibaba they're neck and neck fighting for the bronze medal by the way in our recent 2022 predictions post we said google cloud platform would surpass alibaba this year but given the recent trimming of our numbers google's got some work to do for that prediction to be correct okay just to put a bow on the wikibon market data let's look at the quarterly growth rates and you'll see the compression trends there this data tracks quarterly revenue growth rates back to 20 q1 2019 and you can see the steady downward trajectory and the reversal that aws experienced in q1 of last year now remember microsoft guided for sequential growth and azure so that orange line should trend back up and given gcp's much smaller and big go to market investments that we talked about we'd like to see an acceleration there as well the thing about aws is just remarkable that it's able to accelerate growth at a 71 billion run rate business and alibaba you know is a bit more opaque and likely still reeling from the crackdown of the chinese government we're admittedly not as close to the china market but we'll continue to watch from afar as that steep decline in growth rate is somewhat of a concern okay let's get into the survey data from etr and to do so we're going to take some time series views on some of the select cloud platforms that are showing spending momentum in the etr data set you know etr uses a metric we talked about this a lot called net score to measure that spending velocity of products and services netscore basically asks customers are you spending more less or the same on a platform and a vendor and then it subtracts the lesses from the moors and that yields a net score this chart shows net score for five cloud platforms going back to january 2020. note in the table that the table we've inserted inside that chart shows the net score and shared n the latter metric indicates the number of mentions in the data set and all the platforms we've listed here show strong presence in the survey that red dotted line at 40 percent that indicates spending is at an elevated level and you can see azure and aws and vmware cloud on aws as well as gcp are all nicely elevated and bounding off their october figures indicating continued cloud momentum overall but the big surprise in these figures is the steady climb and the steep bounce up from oracle which came in just under the 40 mark now one quarter is not necessarily a trend but going back to january 2020 the oracle peaks keep getting higher and higher so we definitely want to keep watching this now here's a look at some of the other cloud platforms in the etr survey the chart here shows the same time series and we've now brought in some of the big hybrid players notably vmware cloud which is vcf and other on-prem solutions red hat openstack which as we've reported in the past is still popular in telcos who want to build their own cloud we're also starting to see hpe with green lake and dell with apex show up more and ibm which years ago acquired soft layer which was really essentially a bare metal hosting company and over the years ibm cobbled together its own public cloud ibm is now racing after hybrid cloud using red hat openshift as the linchpin to that strategy now what this data tells us first of all these platforms they don't have the same presence in the data set as do the previous players vmware is the one possible exception but other than vmware these players don't have the spending velocity shown in the previous chart and most are below the red line hpe and dell are interesting and notable in that they're transitioning their early private cloud businesses to dell gr sorry hpe green lake and dell apex respectively and finally after years of kind of staring at their respective navels in in cloud and milking their legacy on-prem models they're finally building out cloud-like infrastructure for their customers they're leaning into cloud and marketing it in a more sensible and attractive fashion for customers so we would expect these figures are going to bounce around for a little while for those two as they settle into a groove and we'll watch that closely now ibm is in the process of a complete do-over arvin krishna inherited three generations of leadership with a professional services mindset now in the post gerschner gerstner era both sam palmisano and ginny rometty held on far too long to ibm's service heritage and protected the past from the future they missed the cloud opportunity and they forced the acquisition of red hat to position the company for the hybrid cloud remedy tried to shrink to grow but never got there krishna is moving faster and with the kindred spin is promising mid-single-digit growth which would be a welcome change ibm is a lot of work to do and we would expect its net score figures as well to bounce around as customers transition to the future all right let's take a look at all these different players in context these are all the clouds that we just talked about in a two-dimensional view the vertical axis is net score or spending momentum and the horizontal axis is market share or presence or pervasiveness in the data set a couple of call-outs that we'd like to make here first the data confirms what we've been saying what everybody's been saying aws and microsoft stand alone with a huge presence many tens of billions of dollars in revenue yet they are both well above the 40 line and show spending momentum and they're well ahead of gcp on both dimensions second vmware while much smaller is showing legitimate momentum which correlates to its public statements alibaba the alibaba in this survey really doesn't have enough sample to make hardcore conclusions um you can see hpe and dell and ibm you know similarly they got a little bit more presence in the data set but they clearly have some work to do what you're seeing there is their transitioning their legacy install bases oracle's the big surprise look what oracle was in the january survey and how they've shot up recently now we'll see if this this holds up let's posit some possibilities as to why it really starts with the fact that oracle is the king of mission critical apps now if you haven't seen video on twitter you have to check it out it's it's hilarious we're not going to run the video here but the link will be in our post but i'll give you the short version some really creative person they overlaid a data migration narrative on top of this one tooth guy who speaks in spanish gibberish but the setup is he's a pm he's a he's a a project manager at a bank and aws came into the bank this of course all hypothetical and said we can move all your apps to the cloud in 12 months and the guy says but wait we're running mission critical apps on exadata and aws says there's nothing special about exadata and he starts howling and slapping his knee and laughing and giggling and talking about the 23 year old senior engineer who says we're going to do this with microservices and he could tell he was he was 23 because he was wearing expensive sneakers and what a nightmare they encountered migrating their environment very very very funny video and anyone who's ever gone through a major migration of mission critical systems this is gonna hit home it's funny not funny the point is it's really painful to move off of oracle and oracle for all its haters and its faults is really the best environment for mission critical systems and customers know it so what's happening is oracle's building out the best cloud for oracle database and it has a lot of really profitable customers running on-prem that the company is migrating to oracle cloud infrastructure oci it's a safer bet than ripping it and putting it into somebody else's cloud that doesn't have all the specialized hardware and oracle knowledge because you can get the same integrated exadata hardware and software to run your database in the oracle cloud it's frankly an easier and much more logical migration path for a lot of customers and that's possibly what's happening here not to mention oracle jacks up the license price nearly doubles the license price if you run on other clouds so not only is oracle investing to optimize its cloud infrastructure it spends money on r d we've always talked about that really focused on mission critical applications but it's making it more cost effective by penalizing customers that run oracle elsewhere so this possibly explains why when the gartner magic quadrant for cloud databases comes out it's got oracle so well positioned you can see it there for yourself oracle's position is right there with aws and microsoft and ahead of google on the right-hand side is gartner's critical capabilities ratings for dbms and oracle leads in virtually all of the categories gartner track this is for operational dvms so it's kind of a narrow view it's like the red stack sweet spot now this graph it shows traditional transactions but gartner has oracle ahead of all vendors in stream processing operational intelligence real-time augmented transactions now you know gartner they're like old name framers and i say that lovingly so maybe they're a bit biased and they might be missing some of the emerging opportunities that for example like snowflake is pioneering but it's hard to deny that oracle for its business is making the right moves in cloud by optimizing for the red stack there's little question in our view when it comes to mission critical we think gartner's analysis is correct however there's this other really exciting landscape emerging in cloud data and we don't want it to be a blind spot snowflake calls it the data cloud jamactagani calls it data mesh others are using the term data fabric databricks calls it data lake house so so does oracle by the way and look the terminology is going to evolve and most of the action action that's happening is in the cloud quite frankly and this chart shows a select group of database and data warehouse companies and we've filtered the data for aws azure and gcp customers accounts so how are these accounts or companies that were showing how these vendors were showing doing in aws azure and gcp accounts and to make the cut you had to have a minimum of 50 mentions in the etr survey so unfortunately data bricks didn't make it just not enough presence in the data set quite quite yet but just to give you a sense snowflake is represented in this cut with 131 accounts aws 240 google 108 microsoft 407 huge [ __ ] 117 cloudera 52 just made the cut ibm 92 and oracle 208. again these are shared accounts filtered by customers running aws azure or gcp the chart shows a net score lime green is new ads forest green is spending more gray is flat spending the pink is spending less and the bright red is defection again you subtract the red from the green and you get net score and you can see that snowflake as we reported last week is tops in the data set with a net score in the 80s and virtually no red and even by the way single digit flat spend aws google and microsoft are all prominent in the data set as is [ __ ] and snowflake as i just mentioned and they're all elevated over the 40 mark cloudera yeah what can we say once they were a high flyer they're really not in the news anymore with anything compelling other than they just you know took the company private so maybe they can re-emerge at some point with a stronger story i hope so because as you can see they actually have some new additions and spending momentum in the green just a lot of customers holding steady and a bit too much red but they're in the positive territory at least with uh plus 17 percent unlike ibm and oracle and this is the flip side of the coin ibm they're knee-deep really chest deep in the middle of a major transformation we've said before arvind krishna's strategy and vision is at least achievable prune the portfolio i.e spin out kindrel sell watson health hold serve with the mainframe and deal with those product cycles shift the mix to software and use red hat to win the day in hybrid red hat is working for ibm's growing well into the double digits unfortunately it's not showing up in this chart with little database momentum in aws azure and gcp accounts zero new ads not enough acceleration and spending a big gray middle in nearly a quarter of the base in the red ibm's data and ai business only grew three percent this last quarter and the word database wasn't even mentioned once on ibm's earnings call this has to be a concern as you can see how important database is to aws microsoft google and the momentum it's giving companies like snowflake and [ __ ] and others which brings us to oracle with a net score of minus 12. so how do you square the momentum in oracle cloud spending and the strong ratings and databases from gartner with this picture good question and i would say the following first look at the profile people aren't adding oracle new a large portion of the base 25 is reducing spend by 6 or worse and there's a decent percentage of the base migrating off oracle with a big fat middle that's flat and this accounts for the poor net score overall but what etr doesn't track is how much is being spent rather it's an account based model and oracle is heavily weighted toward big spenders running mission critical applications and databases oracle's non-gaap operating margins are comparable to ibm's gross margins on a percentage basis so a very profitable company with a big license and maintenance in stall basin oracle has focused its r d investments into cloud erp database automation they've got vertical sas and they've got this integrated hardware and software story and this drives differentiation for the company but as you can see in this chart it has a legacy install base that is constantly trying to minimize its license costs okay here's a little bit of different view on the same data we expand the picture with the two dimensions of net score on the y-axis and market share or pervasiveness on the horizontal axis and the table insert is how the data gets plotted y and x respectively not much to add here other than to say the picture continues to look strong for those companies above the 40 line that are focused and their focus and have figured out a clear cloud strategy and aren't necessarily dealing with a big install base the exception of course is is microsoft and the ones below the line definitely have parts of their portfolio which have solid momentum but they're fighting the inertia of a large install base that moves very slowly again microsoft had the advantage of really azure and migrating those customers very quickly okay so let's wrap it up starting with the big three cloud players aws is accelerating and innovating great example is custom silicon with nitro and graviton and other chips that will help the company address concerns related to the race to the bottom it's not a race to zero aws we believe will let its developers go after the sas business and for the most part aws will offer solutions that address large vertical markets think call centers the edge remains a wild card for aws and all the cloud players really aws believes that in the fullness of time all workloads will run in the public cloud now it's hard for us to imagine the tesla autonomous vehicles running in the public cloud but maybe aws will redefine what it means by its cloud microsoft well they're everywhere and they're expanding further now into gaming and the metaverse when he became ceo in 2014 many people said that satya should ditch xbox just as an aside the joke among many oracle employees at the time was that safra katz would buy her kids and her nieces and her nephews and her kids friends everybody xbox game consoles for the holidays because microsoft lost money for everyone that they shipped well nadella has stuck with it and he sees an opportunity to expand through online gaming communities one of his first deals as ceo was minecraft now the acquisition of activision will make microsoft the world's number three gaming company by revenue behind only 10 cent and sony all this will be powered by azure and drive more compute storage ai and tooling now google for its part is battling to stay relevant in the conversation luckily it can afford the massive losses it endures in cloud because the company's advertising business is so profitable don't expect as many have speculated that google is going to bail on cloud that would be a huge mistake as the market is more than large enough for three players which brings us to the rest of the pack cloud ecosystems generally and aws specifically are exploding the idea of super cloud that is a layer of value that spans multiple clouds hides the underlying complexity and brings new value that the cloud players aren't delivering that's starting to bubble to the top and legacy players are staying close to their customers and fighting to keep them spending and it's working dell hpe cisco and smaller predominantly on-plan prem players like pure storage they continue to do pretty well they're just not as sexy as the big cloud players the real interesting activity it's really happening in the ecosystem of companies and firms within industries that are transforming to create their own digital businesses virtually all of them are running a portion of their offerings on the public cloud but often connecting to on-premises workloads and data think goldman sachs making that work and creating a great experience across all environments is a big opportunity and we're seeing it form right before our eyes don't miss it okay that's it for now thanks to my colleague stephanie chan who helped research this week's topics remember these episodes are all available as podcasts wherever you listen just search breaking analysis podcast check out etr's website at etr dot ai and also we publish a full report every week on wikibon.com and siliconangle.com you can get in touch with me email me at david.velante siliconangle.com you can dm me at divalante or comment on my linkedin post this is dave vellante for the cube insights powered by etr have a great week stay safe be well and we'll see you next time [Music] you

>> From theCUBE Studios in Palo Alto in Boston, bringing you data-driven insights from theCUBE and ETR. This is "Breaking Analysis" with Dave Vellante. >> When Facebook changed its name to Meta last fall, it catalyzed a chain reaction throughout the tech industry. Software firms, gaming companies, chip makers, device manufacturers, and others have joined in hype machine. Now, it's easy to dismiss the metaverse as futuristic hyperbole, but do we really believe that tapping on a smartphone, or staring at a screen, or two-dimensional Zoom meetings are the future of how we work, play, and communicate? As the internet itself proved to be larger than we ever imagined, it's very possible, and even quite likely that the combination of massive processing power, cheap storage, AI, blockchains, crypto, sensors, AR, VR, brain interfaces, and other emerging technologies will combine to create new and unimaginable consumer experiences, and massive wealth for creators of the metaverse. Hello, and welcome to this week's Wiki Bond Cube Insights, powered by ETR. In this "Breaking Analysis" we welcome in cyber expert, hacker gamer, NFT expert, and founder of ORE System, Nick Donarski. Nick, welcome, thanks so much for coming on theCUBE. >> Thank you, sir, glad to be here. >> Yeah, okay, so today we're going to traverse two parallel paths, one that took Nick from security expert and PenTester to NFTs, tokens, and the metaverse. And we'll simultaneously explore the complicated world of cybersecurity in the enterprise, and how the blockchain, crypto, and NFTs will provide key underpinnings for digital ownership in the metaverse. We're going to talk a little bit about blockchain, and crypto, and get things started there, and some of the realities and misconceptions, and how innovations in those worlds have led to the NFT craze. We'll look at what's really going on in NFTs and why they're important as both a technology and societal trend. Then, we're going to dig into the tech and try to explain why and how blockchain and NFTs are going to lay the foundation for the metaverse. And, finally, who's going to build the metaverse. And how long is it going to take? All right, Nick, let's start with you. Tell us a little bit about your background, your career. You started as a hacker at a really, really young age, and then got deep into cyber as a PenTester. You did some pretty crazy stuff. You have some great stories about sneaking into buildings. You weren't just doing it all remote. Tell us about yourself. >> Yeah, so I mean, really, I started a long time ago. My dad was really the foray into technology. I wrote my first program on an Apple IIe in BASIC in 1989. So, I like to say I was born on the internet, if you will. But, yeah, in high school at 16, I incorporated my first company, did just tech support for parents and teachers. And then in 2000 I transitioned really into security and focused there ever since. I joined Rapid7 and after they picked up Medis boy, I joined HP. I was one of their founding members of Shadowlabs and really have been part of the information security and the cyber community all throughout, whether it's training at various different conferences or talking. My biggest thing and my most awesome moments as various things of being broken into, is really when I get to actually work with somebody that's coming up in the industry and who's new and actually has that light bulb moment of really kind of understanding of technology, understanding an idea, or getting it when it comes to that kind of stuff. >> Yeah, and when you think about what's going on in crypto and NFTs and okay, now the metaverse it's you get to see some of the most innovative people. Now I want to first share a little bit of data on enterprise security and maybe Nick get you to comment. We've reported over the past several years on the complexity in the security business and the numerous vendor choices that SecOps Pros face. And this chart really tells that story in the cybersecurity space. It's an X,Y graph. We've shown it many times from the ETR surveys where the vertical axis, it's a measure of spending momentum called net score. And the horizontal axis is market share, which represents each company's presence in the data set, and a couple of points stand out. First, it's really crowded. In that red dotted line that you see there, that's 40%, above that line on the net score axis, marks highly elevated spending momentum. Now, let's just zoom in a bit and I've cut the data by those companies that have more than a hundred responses in the survey. And you can see here on this next chart, it's still very crowded, but a few call-outs are noteworthy. First companies like SentinelOne, Elastic, Tanium, Datadog, Netskope and Darktrace. They were all above that 40% line in the previous chart, but they've fallen off. They still have actually a decent presence in the survey over 60 responses, but under that hundred. And you can see Auth0 now Okta, big $7 billion acquisition. They got the highest net score CrowdStrike's up there, Okta classic they're kind of enterprise business, and Zscaler and others above that line. You see Palo Alto Networks and Microsoft very impressive because they're both big and they're above that elevated spending velocity. So Nick, kind of a long-winded intro, but it was a little bit off topic, but I wanted to start here because this is the life of a SecOps pro. They lack the talent in a capacity to keep bad guys fully at bay. And so they have to keep throwing tooling at the problem, which adds to the complexity and as a PenTester and hacker, this chaos and complexity means cash for the bad guys. Doesn't it? >> Absolutely. You know, the more systems that these organizations find to integrate into the systems, means that there's more components, more dollars and cents as far as the amount of time and the engineers that need to actually be responsible for these tools. There's a lot of reasons that, the more, I guess, hands in the cookie jar, if you will, when it comes to the security architecture, the more links that are, or avenues for attack built into the system. And really one of the biggest things that organizations face is being able to have engineers that are qualified and technical enough to be able to support that architecture as well, 'cause buying it from a vendor and deploying it, putting it onto a shelf is good, but if it's not tuned properly, or if it's not connected properly, that security tool can just hold up more avenues of attack for you. >> Right, okay, thank you. Now, let's get into the meat of the discussion for today and talk a little bit about blockchain and crypto for a bit. I saw sub stack post the other day, and it was ripping Matt Damon for pedaling crypto on TV ads and how crypto is just this big pyramid scheme. And it's all about allowing criminals to be anonymous and it's ransomware and drug trafficking. And yes, there are definitely scams and you got to be careful and lots of dangers out there, but these are common criticisms in the mainstream press, that overlooked the fact by the way that IPO's and specs are just as much of a pyramid scheme. Now, I'm not saying there shouldn't be more regulation, there should, but Bitcoin was born out of the 2008 financial crisis, cryptocurrency, and you think about, it's really the confluence of software engineering, cryptography and game theory. And there's some really powerful innovation being created by the blockchain community. Crypto and blockchain are really at the heart of a new decentralized platform being built out. And where today, you got a few, large internet companies. They control the protocols and the platform. Now the aspiration of people like yourself, is to create new value opportunities. And there are many more chances for the little guys and girls to get in on the ground floor and blockchain technology underpins all this. So Nick, what's your take, what are some of the biggest misconceptions around blockchain and crypto? And do you even pair those two in the same context? What are your thoughts? >> So, I mean, really, we like to separate ourselves and say that we are a blockchain company, as opposed to necessarily saying(indistinct) anything like that. We leverage those tools. We leverage cryptocurrencies, we leverage NFTs and those types of things within there, but blockchain is a technology, which is the underlying piece, is something that can be used and utilized in a very large number of different organizations out there. So, cryptocurrency and a lot of that negative context comes with a fear of something new, without having that regulation in place, without having the rules in place. And we were a big proponent of, we want the regulation, right? We want to do right. We want to do it by the rules. We want to do it under the context of, this is what should be done. And we also want to help write those rules as well, because a lot of the lawmakers, a lot of the lobbyists and things, they have a certain aspect or a certain goal of when they're trying to get these things. Our goal is simplicity. We want the ability for the normal average person to be able to interact with crypto, interact with NFTs, interact with the blockchain. And basically by saying, blockchain in quotes, it's very ambiguous 'cause there's many different things that blockchain can be, the easiest way, right? The easiest way to understand blockchain is simply a distributed database. That's really the core of what blockchain is. It's a record keeping mechanism that allows you to reference that. And the beauty of it, is that it's quote unquote immutable. You can't edit that data. So, especially when we're talking about blockchain, being underlying for technologies in the future, things like security, where you have logging, you have keeping, whether you're talking about sales, where you may have to have multiple different locations (indistinct) users from different locations around the globe. It creates a central repository that provides distribution and security in the way that you're ensuring your data, ensuring the validation of where that data exists when it was created. Those types of things that blockchain really is. If you go to the historical, right, the very early on Bitcoin absolutely was made to have a way of not having to deal with the fed. That was the core functionality of the initial crypto. And then you had a lot of the illicit trades, those black markets that jumped onto it because of what it could do. The maturity of the technology though, of where we are now versus say back in 97 is a much different world of blockchain, and there's a much different world of cryptocurrency. You still have to be careful because with any fed, you're still going to have that FUD that goes out there and sells that fear, uncertainty and doubt, which spurs a lot of those types of scams, and a lot of those things that target end users that we face as security professionals today. You still get mailers that go out, looking for people to give their social security number over during tax time. Snail mail is considered a very ancient technology, but it still works. You still get a portion of the population that falls for those tricks, fishing, whatever it might be. It's all about trying to make sure that you have fear about what is that change. And I think that as we move forward, and move into the future, the simpler and the more comfortable these types of technologies become, the easier it is to utilize and indoctrinate normal users, to be able to use these things. >> You know, I want to ask you about that, Nick, because you mentioned immutability, there's a lot of misconceptions about that. I had somebody tell me one time, "Blockchain's Bs," and they say, "Well, oh, hold on a second. They say, oh, they say it's a mutable, but you can hack Coinbase, whatever it is." So I guess a couple of things, one is that the killer app for blockchain became money. And so we learned a lot through that. And you had Bitcoin and it really wasn't programmable through its interface. And then Ethereum comes out. I know, you know a lot about Ether and you have solidity, which is a lot simpler, but it ain't JavaScript, which is ubiquitous. And so now you have a lot of potential for the initial ICO's and probably still the ones today, the white papers, a lot of security flaws in there. I'm sure you can talk to that, but maybe you can help square that circle about immutability and security. I've mentioned game theory before, it's harder to hack Bitcoin and the Bitcoin blockchain than it is to mine. So that's why people mine, but maybe you could add some context to that. >> Yeah, you know it goes to just about any technology out there. Now, when you're talking about blockchain specifically, the majority of the attacks happen with the applications and the smart contracts that are actually running on the blockchain, as opposed to necessarily the blockchain itself. And like you said, the impact for whether that's loss of revenue or loss of tokens or whatever it is, in most cases that results from something that was a phishing attack, you gave up your credentials, somebody said, paste your private key in here, and you win a cookie or whatever it might be, but those are still the fundamental pieces. When you're talking about various different networks out there, depending on the blockchain, depends on how much the overall security really is. The more distributed it is, and the more stable it is as the network goes, the better or the more stable any of the code is going to be. The underlying architecture of any system is the key to success when it comes to the overall security. So the blockchain itself is immutable, in the case that the owner are ones have to be trusted. If you look at distributed networks, something like Ethereum or Bitcoin, where you have those proof of work systems, that disperses that information at a much more remote location, So the more disperse that information is, the less likely it is to be able to be impacted by one small instance. If you look at like the DAO Hack, or if you look at a lot of the other vulnerabilities that exist on the blockchain, it's more about the code. And like you said, solidity being as new as it is, it's not JavaScript. The industry is very early and very infantile, as far as the developers that are skilled in doing this. And with that just comes the inexperience and the lack of information that you don't learn until JavaScript is 10 or 12 years old. >> And the last thing I'll say about this topic, and we'll move on to NFTs, but NFTs relate is that, again, I said earlier that the big internet giants have pretty much co-opted the platform. You know, if you wanted to invest in Linux in the early days, there was no way to do that. You maybe have to wait until red hat came up with its IPO and there's your pyramid scheme folks. But with crypto it, which is again, as Nick was explaining underpinning is the blockchain, you can actually participate in early projects. Now you got to be careful 'cause there are a lot of scams and many of them are going to blow out if not most of them, but there are some, gems out there, because as Nick was describing, you've got this decentralized platform that causes scaling issues or performance issues, and people are solving those problems, essentially building out a new internet. But I want to get into NFTs, because it's sort of the next big thing here before we get into the metaverse, what Nick, why should people pay attention to NFTs? Why do they matter? Are they really an important trend? And what are the societal and technological impacts that you see in this space? >> Yeah, I mean, NFTs are a very new technology and ultimately it's just another entry on the blockchain. It's just another piece of data in the database. But how it's leveraged in the grand scheme of how we, as users see it, it can be the classic idea of an NFT is just the art, or as good as the poster on your wall. But in the case of some of the new applications, is where are you actually get that utility function. Now, in the case of say video games, video games and gamers in general, already utilize digital items. They already utilize digital points. As in the case of like Call of Duty points, those are just different versions of digital currencies. You know, World of Warcraft Gold, I like to affectionately say, was the very first cryptocurrency. There was a Harvard course taught on the economy of WOW, there was a black market where you could trade your end game gold for Fiat currencies. And there's even places around the world that you can purchase real world items and stay at hotels for World of Warcraft Gold. So the adoption of blockchain just simply gives a more stable and a more diverse technology for those same types of systems. You're going to see that carry over into shipping and logistics, where you need to have data that is single repository for being able to have multiple locations, multiple shippers from multiple global efforts out there that need to have access to that data. But in the current context, it's either sitting on a shipping log, it's sitting on somebody's desk. All of those types of paper transactions can be leveraged as NFTs on the blockchain. It's just simply that representation. And once you break the idea of this is just a piece of art, or this is a cryptocurrency, you get into a world where you can apply that NFT technology to a lot more things than I think most people think of today. >> Yeah, and of course you mentioned art a couple of times when people sold as digital art for whatever, it was 60, 65 million, 69 million, that caught a lot of people's attention, but you're seeing, I mean, there's virtually infinite number of applications for this. One of the Washington wizards, tokenized portions of his contract, maybe he was creating a new bond, that's really interesting use cases and opportunities, and that kind of segues into the latest, hot topic, which is the metaverse. And you've said yourself that blockchain and NFTs are the foundation of the metaverse, they're foundational elements. So first, what is the metaverse to you and where do blockchain and NFTs, fit in? >> Sure, so, I mean, I affectionately refer to the metaverse just a VR and essentially, we've been playing virtual reality games and all the rest for a long time. And VR has really kind of been out there for a long time. So most people's interpretation or idea of what the metaverse is, is a virtual reality version of yourself and this right, that idea of once it becomes yourself, is where things like NFT items, where blockchain and digital currencies are going to come in, because if you have a manufacturer, so you take on an organization like Nike, and they want to put their shoes into the metaverse because we, as humans, want to individualize ourselves. We go out and we want to have that one of one shoe or that, t-shirt or whatever it is, we're going to want to represent that same type of individuality in our virtual self. So NFTs, crypto and all of those digital currencies, like I was saying that we've known as gamers are going to play that very similar role inside of the metaverse. >> Yeah. Okay. So basically you're going to take your physical world into the metaverse. You're going to be able to, as you just mentioned, acquire things- I loved your WOW example. And so let's stay on this for a bit, if we may, of course, Facebook spawned a lot of speculation and discussion about the concept of the metaverse and really, as you pointed out, it's not new. You talked about why second life, really started in 2003, and it's still around today. It's small, I read recently, it's creators coming back into the company and books were written in the early 90s that used the term metaverse. But Nick, talk about how you see this evolving, what role you hope to play with your company and your community in the future, and who builds the metaverse, when is it going to be here? >> Yeah, so, I mean, right now, and we actually just got back from CES last week. And the Metaverse is a very big buzzword. You're going to see a lot of integration of what people are calling, quote unquote, the metaverse. And there was organizations that were showing virtual office space, virtual malls, virtual concerts, and those types of experiences. And the one thing right now that I don't think that a lot of organizations have grasp is how to make one metaverse. There's no real player one, if you will always this yet, There's a lot of organizations that are creating their version of the metaverse, which then again, just like every other software and game vendor out there has their version of cryptocurrency and their version of NFTs. You're going to see it start to pop up, especially as Oculus is going to come down in price, especially as you get new technologies, like some of the VR glasses that look more augmented reality and look more like regular glasses that you're wearing, things like that, the easier that those technologies become as in adopting into our normal lifestyle, as far as like looks and feels, the faster that stuff's going to actually come out to the world. But when it comes to like, what we're doing is we believe that the metaverse should actually span multiple different blockchains, multiple different segments, if you will. So what ORE system is doing, is we're actually building the underlying architecture and technologies for developers to bring their metaverse too. You can leverage the ORE Systems NFTs, where we like to call our utility NFTs as an in-game item in one game, or you can take it over and it could be a t-shirt in another game. The ability for having that cross support within the ecosystem is what really no one has grasp on yet. Most of the organizations out there are using a very classic business model. Get the user in the game, make them spend their money in the game, make all their game stuff as only good in their game. And that's where the developer has you, they have you in their bubble. Our goal, and what we like to affectionately say is, we want to bring white collar tools and technology to blue collar folks, We want to make it simple. We want to make it off the shelf, and we want to make it a less cost prohibitive, faster, and cheaper to actually get out to all the users. We do it by supporting the technology. That's our angle. If you support the technology and you support the platform, you can build a community that will build all of the metaverse around them. >> Well, and so this is interesting because, if you think about some of the big names, we've Microsoft is talking about it, obviously we mentioned Facebook. They have essentially walled gardens. Now, yeah, okay, I could take Tik Tok and pump it into Instagram is fine, but they're really siloed off. And what you're saying is in the metaverse, you should be able to buy a pair of sneakers in one location and then bring it to another one. >> Absolutely, that's exactly it. >> And so my original kind of investment in attractiveness, if you will, to crypto, was that, the little guy can get an early, but I worry that some of these walled gardens, these big internet giants are going to try to co-op this. So I think what you're doing is right on, and I think it's aligned with the objectives of consumers and the users who don't want to be forced in to a pen. They want to be able to live freely. And that's really what you're trying to do. >> That's exactly it. You know, when you buy an item, say a Skin in Fortnite or Skin in Call of Duty, it's only good in that game. And not even in the franchise, it's only good in that version of the game. In the case of what we want to do is, you can not only have that carry over and your character. So say you buy a really cool shirt, and you've got that in your Call of Duty or in our case, we're really Osiris Protocol, which is our proof of concept video game to show that this all thing actually works, but you can actually go in and you can get a gun in Osiris Protocol. And if we release, Osiris Protocol two, you'll be able to take that to Osiris Protocol two. Now the benefit of that is, is you're going to be the only one in the next version with that item, if you haven't sold it or traded it or whatever else. So we don't lock you into a game. We don't lock you into a specific application. You own that, you can trade that freely with other users. You can sell that on the open market. We're embracing what used to be considered the black market. I don't understand why a lot of video games, we're always against the skins and mods and all the rest. For me as a gamer and coming up, through the many, many years of various different Call of Duties and everything in my time, I wish I could still have some this year. I still have a World of Warcraft account. I wasn't on, Vanilla, Burning Crusade was my foray, but I still have a character. If you look at it that way, if I had that wild character and that gear was NFTs, in theory, I could actually pass that onto my kid who could carry on that character. And it would actually increase in value because they're NFT back then. And then if needed, you could trade those on the open market and all the rest. It just makes gaming a much different thing. >> I love it. All right, Nick, hey, we're out of time, but I got to say, Nick Donarski, thanks so much for coming on the program today, sharing your insights and really good luck to you and building out your technology platform and your community. >> Thank you, sir, it's been an absolute pleasure. >> And thank you for watching. Remember, all these episodes are available as podcasts, just search "Breaking Analysis Podcast", and you'll find them. I publish pretty much every week on siliconangle.com and wikibond.com. And you can reach me @dvellante on Twitter or comment on my LinkedIn posts. You can always email me david.vellante@siliconangle.com. And don't forget, check out etr.plus for all the survey data. This is Dave Vellante for theCUBE Insights, powered by ETR, happy 2022 be well, and we'll see you next time. (upbeat 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 the cube covering Fortinet security summit brought to you by Fortinet. >>Welcome back to the cube. Lisa Martin here at the Fordham het championship security summit. Napa valley has been beautiful and gracious to us all day. We're very pleased to be here. I'm very pleased to welcome a first-timer to the cube. Rupesh Chuck Chuck Xi, VP a T and T cybersecurity and edge solutions at, at and T cybersecurity. Refresh. Welcome. >>Thank you. Thank you so much for having me, Lisa, I'm looking forward to our conversation today. >>Me too. First of all, it's we're in Napa we're outdoors. It's beautiful venue, no complaints, right? We're at a golf PGA tournament. Very exciting. Talk to me about the at and T Fordanet relationship. Give me, give me an, a good insight into the partnership. >>Sure, sure. So, as you said, you know, beautiful weather in California, Napa it's my first time. Uh, so it's kind of a new experience for me going back to your question in terms of the relationship between eight P and T and Ford in that, uh, a long lasting, you know, 10 plus years, you know, hand in hand in terms of the product, the technology, the capabilities that we are brought together in the security space for our customers. So a strategic relationship, and I'm so thrilled to be here today as a, Fordanet invited us to be part of the championship. Tommy, >>Talk to me. So your role VP of, and T cybersecurity and edge solutions, give me an, a deep dive into what's in your purview. >>Sure, sure. So I, uh, sort of, you know, run the PNL or the profit and loss center for product management for all of at and T cybersecurity and ed solutions and the whole concept behind putting the teams together is the convergence in networking and security. Um, so, you know, we are supporting the entire customer continuum, whether it's a fortune 50, the fortune 1000 to mid-market customers, to small businesses, to, you know, government agencies, you know, whether it's a local government agency or a school district or a federal agency, et cetera. And my team and I focus on bringing new product and capabilities to the marketplace, you know, working with our sales team from an enablement perspective, go to market strategy. Um, and the whole idea is about, uh, you know, winning in the marketplace, right? So delivering growth and revenue to the business, >>Competitive differentiation. So we've seen so much change in the last year and a half. I know that's an epic understatement, but we've also seen the proliferation at the edge. What are some of the challenges that you're seeing and hearing from customers where that's concerned >>As you stated, right. There's a lot happening in the edge. And sometimes the definition for edge varies when you talk with different people, uh, the way we look at it is, you know, definitely focused on the customer edge, right? So if you think about many businesses, whether I am a, a quick serve restaurant or I'm a banking Institute or a financial services or an insurance agency, or I'm a retail at et cetera, you know, lots of different branches, lots of different transformation taking place. So one way of approaching it is that when you think about the customer edge, you see a lot of virtualization, software driven, a lot of IOT endpoints, et cetera, taking place. So the cyber landscape becomes more important. Now you're connecting users, devices, capabilities, your point of sale system to a multi-cloud environment, and that, you know, encryption of that data, the speed at which it needs to happen, all of that is very important. And as we think ahead with 5g and edge compute and what that evolution revolution is going to bring, it's going to get even more excited because to me, those are kind of like in a playgrounds of innovation, but we want to do it right and keep sort of, you know, cyber and security at the core of it. So we can innovate and keep the businesses safe. >>How do you help customers to kind of navigate edge cybersecurity challenges and them not being synonymous? >>That's a great, great question. You know, every day I see, you know, different teams, different agendas, different kinds of ways of approaching things. And what I tell customers and even my own teams is that, look, we have to have a, a blueprint and architecture, a vision, you know, what are the business outcomes that we want to achieve? What the customer wants to achieve. And then start to look at that kind of technology kind of convergence that is taking place, and especially in the security and the networking space, significant momentum on the convergence and utilize that convergence to create kind of full value stack solutions that can be scaled, can be delivered. So you are not just one and done, but it's a continuous innovation and improvement. And in the security space, you need that, right. It's never going to be one and done. No >>We've seen so much change in the last year. We've seen obviously this rapid pivot to work from home that was overnight for millions and millions of people. We're still in that too. A fair amount. There's a good amount of people that are still remote, and that probably will be permanently there's. Those that are going to be hybrid threat landscape bloated. I was looking at and talking with, um, 40 guard labs and the, the nearly 11 X increase in the last 12 months in ransomware is insane. And the ransomware as a business has exploded. So security is a board level conversation for businesses I assume in any. >>Absolutely. Absolutely. I agree with you, it's a board level conversation. Security is not acknowledged the problem about picking a tool it's about, you know, the business risk and what do we need to do? Uh, you mentioned a couple of interesting stats, right? So we've seen, uh, you know, two things I'll share. One is we've seen, you know, 440 petabytes of data on the at and T network in one average business day. So 440 petabytes of data. Most people don't know what it is. So you can imagine the amount of information. So you can imagine the amount of security apparatus that you need, uh, to Tofino, protect, and defend and provide the right kind of insights. And then the other thing that VOC and along the same lines of what you were mentioning is significant, you know, ransomware, but also significant DDoSs attacks, right? So almost like, you know, we would say around 300% plus said, DDoSs mitigations that we did from last year, you know, year over year. >>So a lot of focus on texting the customer, securing the end points, the applications, the data, the network, the devices, et cetera. Uh, the other two points that I want to mention in this space, you know, again, going back to all of this is happening, right? So you have to focus on this innovation at the, at the speed of light. So, you know, artificial intelligence, machine learning, the software capabilities that are more, forward-looking have to be applied in the security space ever more than ever before, right. Needs these do, we're seeing alliances, right? We're seeing this sort of, you know, crowdsourcing going on of action on the good guys side, right? You see the national security agencies kind of leaning in saying, Hey, let's together, build this concept of a D because we're all going to be doing business. Whether it's a public to public public, to private, private, to private, all of those different entities have to work together. So having security, being a digital trust, >>Do you think that the Biden administrations fairly recent executive order catalyst of that? >>I give it, you know, the president and the, the administration, a lot of, you know, kudos for kind of, and then taking it head on and saying, look, we need to take care of this. And I think the other acknowledgement that it is not just hunting or one company or one agency, right? It's the whole ecosystem that has to come together, not just national at the global level, because we live in a hyper connected world. Right. And one of the things that you mentioned was like this hybrid work, and I was joking with somebody the other day that, and really the word is location, location, location, thinking, network security, and networking. The word is hybrid hybrid hybrid because you got a hybrid workforce, the hybrid cloud, you have a hybrid, you have a hyper-connected enterprise. So we're going to be in this sort of, you know, hybrid for quite some time are, and it has to >>Be secure and an org. And it's, you know, all the disruption of folks going to remote work and trying to get connected. One beyond video conference saying, kids are in school, spouse working, maybe kids are gaming. That's been, the conductivity alone has been a huge challenge. And Affordanet zooming a lot there with links to us, especially to help that remote environment, because we know a lot of it's going to remain, but in the spirit of transformation, you had a session today here at the security summit, talked about transformation, formation plan. We talk about that word at every event, digital transformation, right? Infrastructure transformation, it security. What context, where you talking about transformation in it today? What does it transformation plan mean for your customers? >>That's a great question because I sometimes feel, you know, overused term, right? Then you just take something and add it. It's it? Transformation, network, transformation, digital transformation. Um, but what we were talking today in, in, in the morning was more around and sort of, you know, again, going back to the network security and the transformation that the customers have to do, we hear a lot about sassy and the convergence we are seeing, you know, SD van takeoff significantly from an adoption perspective application, aware to experiences, et cetera, customers are looking at doing things like internet offload and having connectivity back into the SAS applications. Again, secure connectivity back into the SAS applications, which directly ties to their outcomes. Um, so the, the three tenants of my conversation today was, Hey, make sure you have a clear view on the business outcomes that you want to accomplish. Now, the second was work with a trusted advisor and at and T and in many cases is providing that from a trusted advisor perspective. And third, is that going back to the one and done it is not a one and done, right? This is a, is a continuous process. So sometimes we have to be thinking about, are we doing it in a way that we will always be future ready, will be always be able to deal with the security threats that we don't even know about today. So yeah, >>You bring up the term future ready. And I hear that all the time. When you think of man, we really weren't future ready. When the pandemic struck, there was so much that wasn't there. And when I was talking with 49 earlier, I said, you know, how much, uh, has the pandemic been a, uh, a catalyst for so much innovation? I imagine it has been the same thing that >>Absolutely. And, you know, I remember, you know, early days, February, March, where we're all just trying to better understand, right? What is it going to be? And the first thing was, Hey, we're all going to work remote, is it a one week? Is it a two week thing? Right? And then if you're like the CIO or the CSO or other folks who are worried about how am I going to give the productivity tools, right. Businesses in a one customer we work with, again, tobacco innovation was said, Hey, I have 20,000 call center agents that I need to take remote. How do you deliver connectivity and security? Because that call center agent is the bloodline for that business interacting with their end customers. So I think, you know, it is accelerated what would happen over 10 years and 18 months, and it's still unknown, right? So we're still discovering the future. >>There's a, there will be more silver linings to come. I think we'll learn to pick your brain on, on sassy adoption trends. One of the things I noticed in your abstract of your session here was that according to Gardner, the convergence of networking and security into the sassy framework is the most vigorous technology trend. And coming out of 2020, seeing that that's a big description, most vigorous, >>It's a big, big description, a big statement. And, uh, we are definitely seeing it. You know, we saw some of that, uh, in the second half of last year, as the organizations were getting more organized to deal with, uh, the pandemic and the change then coming into this year, it's even more accelerated. And what I mean by that is that, you know, I look at sort of, you know, three things, right? So one is going back to the hybrid work, remote work, work from anywhere, right. So how do you continue to deliver a differentiated experience, highly secure to that workforce? Because productivity, human capital very important, right? The second is that there's a back and forth on the branch transformation. So yes, you know, restaurants are opening back up. Retailers are opening back up. So businesses are thinking about how do I do that branch transformation? And then the third is explosive business IOT. So the IOT end points, do you put into manufacturing, into airports in many industries, we continue to see that. So when you think about sassy and the framework, it's about delivering a, a framework that allows you to protect and secure all of those endpoints at scale. And I think that trend is real. I've seen customer demand, we've signed a number of deals. We're implementing them as we speak across all verticals, healthcare, retail, finance, manufacturing, transportation, government agencies, small businesses, mid-sized businesses. >>Nope, Nope. Not at all. Talk to me about, I'm curious, you've been at, at and T a long time. You've seen a lot of innovation. Talk, talk to me about your perspectives on seeing that, and then what to you think as a silver lining that has come out of the, the acceleration of the last 18 months. >>She and I, I get the question, you know, I've been with at and T long time. Right. And I still remember the day I joined at T and T labs. So it was one of my kind of dream coming out of engineering school. Every engineer wants to go work for a brand that is recognized, right. And I, I drove from Clemson, South Carolina to New Jersey Homedale and, uh, I'm still, you know, you can see I'm still having the smile on my face. So I've, you know, think innovation is key. And that's what we do at, at and T I think the ability to, um, kind of move fast, you know, I think what the pandemic has taught us is the speed, right? The speed at which we have to move the speed at which we have to collaborate the speed at which we have to deliver, uh, to agility has become, you know, the differentiator for all of us. >>And we're focusing on that. I also feel that, uh, you know, there have been times where, you know, product organizations, technology organizations, you know, we struggle with jumping this sort of S-curve right, which is, Hey, I'm holding onto something. Do I let go or not? Let go. And I think the pandemic has taught us that you have to jump the S-curve, you have to accelerate because that is where you need to be in, in a way, going back to the sassy trend, right. It is something that is real, and it's going to be there for the next three to five years. So let's get ready. >>I call that getting comfortably uncomfortable, no businesses safe if they rest on their laurels these days. I think we've learned that, speaking of speed, I wanna, I wanna get kind of your perspective on 5g, where you guys are at, and when do you think it's going to be really impactful to, you know, businesses, consumers, first responders, >>The 5g investments are happening and they will continue to happen. And if you look at what's happened with the network, what at and T has announced, you know, we've gotten a lot of kudos for whatever 5g network for our mobile network, for our wireless network. And we are starting to see that, that innovation and that innovation as we anticipated is happening for the enterprise customers first, right? So there's a lot of, you know, robotics or warehouse or equipment that needs to sort of, you know, connect at a low latency, high speed, highly secure sort of, you know, data movements, compute edge that sits next to the, to the campus, you know, delivering a very different application experience. So we're seeing that, you know, momentum, uh, I think on the consumer side, it is starting to come in and it's going to take a little bit more time as the devices and the applications catch up to what we are doing in the network. And if you think about, you know, the, the value creation that has happened on, on the mobile networks is like, if you think about companies like Uber or left, right, did not exist. And, uh, many businesses, you know, are dependent on that network. And I think, uh, it will carry on. And I think in the next year or two, we'll see firsthand the outcomes and the value that it is delivering you go to a stadium at and T stadium in Dallas, you know, 5g enabled, you know, that the experience is very different. >>I can't wait to go to a stadium again and see it came or live music. Oh, that sounds great. Rubbish. Thank you so much for joining me today, talking about what a T and T is doing with 49, the challenges that you're helping your customers combat at the edge and the importance of really being future. Ready? >>Yes. Thank you. Thank you so much. Really appreciate you having me. Thanks for 49 to invite us to be at this event. Yes. >>Thank you for refresh talk. She I'm Lisa Martin. You're watching the cube at the 40 net championship security summits.

>>Mhm >>Yes, welcome to this cube conversation. I'm lisa martin excited about this conversation. It's combining my background in life sciences with technology. Please welcome Mike Tarsa Lee, the chief scientific officer at Tetra Science. Mike I'm so excited to talk to you today. >>Thank you lisa and thank you very much to the cube for hosting us. >>Absolutely. So we talk about cloud and data all the time. This is going to be a very interesting conversation especially because we've seen events of the last what are we on 14 months and counting have really accelerated the need for drug discovery and really everyone's kind of focused on that. But I want you to talk with our audience about Tetra science, Who you guys are, what you do and you were founded in 2014. You just raised 80 million in series B but give us an idea of who you are and what you do. >>Got it. Tetro Science, what are we? We are digital plumbers and that may seem funny but really we are taking the world of data and we are trying to resolve it in such a way that people can actually pipe it from the data sources they have in a vendor agnostic way to the data targets in which they need to consume that data. So bringing that metaphor a little bit more to life sciences, let's say that you're a chemist and you have a mass spec and an NMR and some other piece of technology and you need all of those to speak the same language. Right? Generally speaking, all of these are going to be made by different vendors. They're all going to have different control software and they're all going to have slightly different ways of sending their data in. Petro Science takes those all in. We bring them up to the cloud or cloud native solution. We harmonize them, we extract the data first and then we actually put it into what we call our special sauce are intermediate data schema to harmonize it. So you have sort of like a picture and a diagram of what the prototypical mass spec or H P. L. C. Or cell counting data should look like. And then we build pipelines to export that data over to where you need it. So if you need it to live in an L. N. Or a limb system or in a visualization tool like spot fire tableau. We got you covered. So again we're trying to pipe things from left to right from sources to targets and we're trying to do it with scientific context. >>That was an outstanding description. Data plumbers who have secret sauce and never would have thought I would have heard that when I woke up this morning. But I'm going to unpack this more because one of the things that I read in the press release that just went out just a few weeks ago announcing the series B funding, it said that that picture science is pioneering a $300 billion dollar Greenfield data market and operating this is what got my attention without a direct cloud native and open platform competitor. Why is that? >>That's right. If you look at the way pharma data is handled today, even those that long tend to be either on prem solutions with a sort of license model or a distribution into a company and therefore maintenance costs, professional services, etcetera. Or you're looking at somebody who is maybe cloud but their cloud second, you know, they started with their on prem journey and they said we should go and build out some puppies, we should go to the cloud migrate. However, we're cloud first cloud native. So that's one first strong point. And the second is that in terms of data harmonization and in terms of looking at data in a vendor agnostic way, um many companies claim to do it. But the real hard test of this, the metal, what will say is when you can look at this with the Scientific contextual ization we offer. So yes, you can collect the data and put it on a cloud. Okay great. Yes. You may be able to do an extract, transform and load and move it to somewhere else. Okay. But can you actually do that from front to back while retaining all the context of the data while keeping all of the metadata in the right place? With veracity, with G XP readiness, with data fidelity and when it gets over to the other side can somebody say oh yeah that's all the data from all the H. P. L. C. S we control. I got it. I see where it is. I see where to go get it, I see who created it. I see the full data train and validation landscape and I can rebuild that back and I can look back to the old raw source files if I need to. Um I challenge someone to find another direct company that's doing that today. >>You talk about that context and the thing that sort of surprises me is with how incredibly important scientific discovery is and has been for since the beginning of time. Why is why has nobody come out in the last seven years and tried to facilitate this for life sciences organizations. >>Right. I would say that people have tried and I would say that there are definitely strides being made in the open source community, in the data science community and inside pharma and biotech themselves on these sort of build motif, right. If you are inside of a company and you understand your own ontology and processes while you can probably design an application or a workflow using several different tools in order to get that data there. But will it be generally useful to the bioscience community? One thing we pride ourselves on is when we product eyes a connector we call or an integration, we actually do it with a many different companies, generic cases in mind. So we say, OK, you have an h p l C problem over at this top pharma, you have an HPC problem with this biotech and you have another one of the C R. O. Okay. What are the common points between all of those? Can we actually distill that down to a workflow? Everyone's going to need, for example a compliance workflow. So everybody needs compliance. Right. So we can actually look into an empower or a unicorn operation and we can say, okay, did you sign off on that? Did it come through the right way? Was the data corrupted etcetera? That's going to be generically useful to everybody? And that's just one example of something we can do right now for anybody in bio pharma. >>Let's talk about the events of the last 14 months or so mentioned 10 X revenue growth in 2020. Covid really really highlighted the need to accelerate drug discovery and we've seen that. But talk to me about some of the things that Tetra science has seen and done to facilitate that. >>Yeah, this past 14 months. I mean um I will say that the global pandemic has been a challenge for everyone involved ourselves as well. We've basically gone to a full remote workforce. Um We have tried our very best to stay on top of it with remote collaboration tools with vera, with GIT hub with everything. However, I'll say that it's actually been some of the most successful time in our company's history because of that sort of lack of any kind of friction from the physical world. Right? We've really been able to dig down and dig deep on our integrations are connections, our business strategy. And because of that, we've actually been able to deliver a lot of value to customers because, let's be honest, we don't actually have to be on prem from what we're doing since we're not an on prem solution and we're not an original equipment manufacturer, we don't have to say, okay, we're going to go plug the thing in to the H. P. L. C. We don't have to be there to tune the specific wireless protocols or you're a W. S. Protocols, it can all be done remotely. So it's about building good relationships, building trust with our colleagues and clients and making sure we're delivering and over delivering every time. And then people say great um when I elect a Tetra solution, I know what's going right to the cloud, I know I can pick my hosting options, I know you're going to keep delivering more value to me every month. Um Thanks, >>I like that you make it sound simple and that actually you bring up a great point though that the one of the many things that was accelerated this last year Plus is the need to be remote that need to be able to still communicate, collaborate but also the need to establish and really foster those relationships that you have with existing customers and partners as everybody was navigating very, very different challenges. I want to talk now about how you're helping customers unlock the problem that is in every industry data silos and point to point integration where things can talk to each other, Talk to me about how you're helping customers like where do they start with? Touch? Where do you start that? Um kind of journey to unlock data value? >>Sure. Journey to unlock data value. Great question. So first I'll say that customers tend to come to us, it's the oddest thing and we're very lucky and very grateful for this, but they tend to have heard about what we've done with other companies and they come to us they say listen, we've heard about a deployment you've done with novo Nordisk, I can say that for example because you know, it's publicly known. Um so they'll say, you know, we hear about what you've done, we understand that you have deep expertise in chromatography or in bio process. And they'll say here's my really sticky problem. What can you do here? And invariably they're going to lay out a long list of instruments and software for us. Um we've seen lists that go up past 2000 instruments. Um and they'll say, yeah, they'll say here's all the things we need connected, here's four or five different use cases. Um we'll bring you start to finish, we'll give you 20 scientists in the room to talk through them and then we to get somewhere between two and four weeks to think about that problem and come back and say here's how we might solve that. Invariably, all of these problems are going to have a data silos somewhere, there's going to be in Oregon where the preclinical doesn't see the biology or the biology doesn't see the screening etcetera. So we say, all right, give us one scientist from each of those, hence establishing trust, establishing input from everybody. And collaboratively we'll work with, you will set up an architecture diagram, will set up a first version of a prototype connector, will set up all this stuff they need in order to get moving, we'll deliver value upfront before we've ever signed a contract and will say, is this a good way to go for you? And they'll say either no, no, thank you or they'll say yes, let's go forward, let's do a pilot a proof of concept or let's do a full production rollout. And invariably this data silos problem can usually be resolved by again, these generic size connectors are intermediate data schema, which talks and moves things into a common format. Right? And then also by organizationally, since we're already connecting all these groups in this problem statement, they tend to continue working together even when we're no longer front and center, right? They say, oh we set up that thing together. Let's keep thinking about how to make our data more available to one another. >>Interesting. So culturally, within the organization it sounds like Tetra is having significant influences their, you know, the collaboration but also data ownership. Sometimes that becomes a sticky situation where there are owners and they want to read retain that control. Right? You're laughing? You've been through this before. I'd like to understand a little bit more though about the conversation because typically we're talking about tech but we're also talking about science. Are you having these technical conversations with scientists as well as I. T. What is that actual team from the customer perspective look >>like? Oh sure. So the technical conversation and science conversation are going on sometimes in parallel and sometimes in the same threat entirely. Oftentimes the folks who reach out to us first tend to be the scientists. They say I've got a problem, you know and and my research and and I. T. Will probably hear about this later. But let's go. And then we will invariably say well let's bring in your R. And D. I. T. Counterparts because we need them to help solve it right? But yes we are usually having those conversations in parallel at first and then we unite them into one large discussion. And we have varied team members here on the Tetris side we have me from science along with multiple different other PhD holders and pharma lifers in our business who actually can look at the scientific use cases and recommend best practices for that and visualizations. We also have a lot of solutions architects and delivery engineers who can look at it from the how should the platform assemble the solution and how can we carry it through? Um And those two groups are three groups really unite together to provide a unified front and to help the customer through and the customer ends up providing the same thing as we do. So they'll give us on the one call, right? Um a technical expert, a data and QA person and a scientist all in one group and they'll say you guys work together to make sure that our orders best represented here. Um And I think that that's actually a really productive way to do this because we end up finding out things and going deeper into the connector than we would have otherwise. >>It's very collaborative, which is I bet those are such interesting conversations to be a part of it. So it's part of the conversation there helping them understand how to establish a common vision for data across their organization. >>Yes, that that tends to be a sort of further reaching conversation. I'll say in the initial sort of short term conversation, we don't usually say you three scientists or engineers are going to change the fate of the entire orig. That's maybe a little outside of our scope for now. But yes, that first group tends to describe a limited solution. We help to solve that and then go one step past and then they'll nudge somebody else in the Oregon. Say, do you see what Petra did over here? Maybe you could use it over here in your process. And so in that way we sort of get this cultural buy in and then increased collaboration inside a single company. >>Talk to me about some customers that you've worked with it. Especially love to know some of the ones that you've helped in the last year where things have been so incredibly dynamic in the market. But give us an insight into maybe some specific customers that work with you guys. >>Sure. I'd love to I'll speak to the ones that are already on our case studies. You can go anytime detector science dot com and read all of these. But we've worked with Prelude therapeutics for example. We looked at a high throughput screening cascade with them and we were able to take an instrument that was basically unloved in a corner at T. Can liquid handler, hook it up into their Ln. And their screening application and bring in and incorporate data from an external party and do all of that together and merge it so they could actually see out the other side a screening cascade and see their data in minutes as opposed to hours or days. We've also worked as you've seen the press release with novo Nordisk, we worked on automating much of their background for their chromatography fleet. Um and finally we've also worked with several smaller biotechs in looking at sort of in stan shih ation, they say well we've just started we don't have an L. N. We don't have a limbs were about to buy these 50 instruments. Um what can you do with us and we'll actually help them to scope what their initial data storage and harmonization strategy should even be. Um so so we're really man, we're at everywhere from the enterprise where its fleets of thousands of instruments and we're really giving data to a large amount of scientists worldwide, all the way down to the small biotech with 50 people who were helping add value there. >>So big range there in terms of the data conversation, I'm curious has have you seen it change in the last year plus with respect to elevating to the C suite level or the board saying we've got to be able to figure this out because as we saw, you know, the race for the Covid 19 vaccine for example. Time to value and and to discovery is so critical. Is that C suite or board involved in having conversations with you guys? >>It's funny because they are but they are a little later. Um we tend to be a scientist and user driven um solution. So at the beginning we get a power user, an engineer or a R and D I. T. Person in who really has a problem to solve. And as they are going through and developing with us, eventually they're going to need either approval for the time, the resources or the budget and then they'll go up to their VP or their CIA or someone else at the executive level and say, let's start having more of this conversation. Um, as a tandem effort, we are starting to become involved in some thought leadership exercises with some larger firms. And we are looking at the strategic aspect through conferences, through white papers etcetera to speak more directly to that C suite and to say, hey, you know, we could fit your industry for dato motif. And then one other thing you said, time to value. So I'll say that the Tetro science executive team actually looks at that as a tract metric. So we're actually looking at driving that down every single week. >>That's outstanding. That's a hard one to measure, especially in a market that is so dynamic. But that time to value for your customers is critical. Again, covid sort of surfaced a number of things and some silver linings. But that being able to get hands on the day to make sure that you can actually pull insights from it accelerate facilitate drug discovery. That time to value there is absolutely critical. >>Yeah. I'll say if you look at the companies that really, you know, went first and foremost, let's look at Moderna right? Not our customer by the way, but we'll look at Madonna quickly as an example as an example are um, everything they do is automated, right? Everything they do is cloud first. Everything they do is global collaboration networks, you know, with harmonized data etcetera. That is the model we believe Everyone's going to go to in the next 3-5 years. If you look at the fact that Madonna went from sequence to initial vaccine in what, 50, 60 days, that kind of delivery is what the market will become accustomed to. And so we're going to see many more farmers and biotechs move to that cloud first. Distributed model. All data has to go in somewhere centrally. Everyone has to be able to benefit from it. And we are happy to help them get >>Well that's that, you know, setting setting a new record for pace is key there, but it's also one of those silver linings that has come out of this to show that not only was that critical to do, but it can be done. We have the technology, we have the brain power to be able to put those all user would harmonize those together to drive this. So give me a last question. Give me an insight into some of the things that are ahead for Tetra science the rest of this year. >>Oh gosh, so many things. One of the nice parts about having funding in the bank and having a dedicated team is the ability to do more. So first of course our our enterprise pharma and BioPharma clients, there are plenty more use cases, workflows, instruments. We've just about scratch the surface but we're going to keep growing and growing our our integrations and connectors. First of all right we want to be like a netflix for connectors. You know we just want you to come and say look do they have the connector? No well don't worry. They're going to have it in a month or two. Um so that we can be basically the almost the swiss army knife for every single connector you can imagine. Then we're going to be developing a lot more data apps so things that you can use to derive value from your data out. And then again, we're going to be looking at helping to educate everybody. So how is cloud useful? Why go to the system with harmonization? How does this influence your compliance? How can you do bi directional communication? There's lots of ways you can use. Once you have harmonized centralized data, you can do things with it to influence your order and drive times down again from days and weeks, two minutes and seconds. So let's get there. And I think we're going to try doing that over the next year. >>That's awesome. Never a dull moment. And I, you should partner with your marketing folks because we talked about, you talked about data plumbing the secret sauce and becoming the netflix of connectors. These are three gems that you dropped on this this morning mike. This has been awesome. Thank you for sharing with us what teacher science is doing, how you're really helping to fast track a lot of the incredibly important research that we're all really um dependent on and helping to heal the world through data. It's been a pleasure talking with you. >>Haley says I'm a real quickly. It's a team effort. The entire Tetro science team deserves credit for this. I'm just lucky enough to be able to speak to you. So thank you very much for the opportunity. >>And she about cheers to the whole touch of science team. Keep up the great work guys. Uh for mike Roselli, I'm lisa martin. You're watching this cube conversation. >>Mhm.