>>Welcome back to the Cube, as a continued coverage here from AWS Reinvent 22. It's day three of our coverage here at the Venetian in Las Vegas, and we're part of the AWS Global Startup Showcase. With me to talk about what Kong's to in that regard is Marco Palladino, who's the, the CTO and the co-founder of Con Marco. Good >>To see you. Well, thanks for having me >>Here. Yeah, I was gonna say, by the way, I, I, you've got a beautiful exhibit down on the show floor. How's the week been for you so far as an exhibitor here? >>It's been very busy. You know, to this year we made a big investment at the WS reinvent. You know, I think this is one of the best conferences in the industry. There is technology developers, but it's also business oriented. So you can learn about all the business outcomes that our, you know, customers or, you know, people are trying to make when, when adopting these new technologies. So it's very good so far. >>Good, good, good to hear. Alright, so in your world, the API world, you know, it used to be we had this, you know, giant elephant. Now we're cutting down the little pieces, right? That's right. We're all going micro now these days. That's right. Talk about that trend a little bit, what you're seeing, and we'll jump in a little deeper as to how you're addressing that. >>Well, I think the industry learned a long time ago that running large code bases is actually quite problematic when it comes to scaling the organization and capturing new opportunities. And so, you know, we're transitioning to microservices because we want to get more opportunities in our business. We want to be able to create new products, fasters, we want to be able to leverage existing services or data that we have built, like an assembly line of software, you know, picking up APIs that other developers are building, and then assemble them together to create new experiences or new products, enter new markets. And so microservices are fantastic for that, except microservices. They also introduce significant concerns on the networking layer, on the API layer. And so this is where Kong specializes by providing API infrastructure to our customers. >>Right. So more about the problems, more about the challenges there, because you're right, it, opportunities always create, you know, big upside and, and I, I don't wanna say downside, but they do introduce new complexities. >>That's right. And introducing new complexity. It's a little bit the biggest enemy of any large organization, right? We want to reduce complexity, we want to move faster, we want to be more agile, and, and we need an API vision to be able to do that. Our teams, you know, I'm speaking with customers here at Reinvent, they're telling me that in the next five years, the organization is going to be creating more APIs than all the APIs they've created up until now. Right? So how do you >>Support, that's a mind boggling number, right? >>It's mind boggling. Yeah, exactly. How do you support that type of growth? And things have been moving so fast. I feel like there is a big dilemma in, you know, with certain organizations where, you know, we have not taught a long term strategy for APIs, whereas we do have a long term strategy for our business, but APIs are running the business. We must have a long term strategy for our APIs, otherwise we're not gonna be able to execute. And that's a big dilemma right now. Yeah. >>So, so how do we get the horse back in front of the cart then? Because it's like you said, it's almost as if we've, we're, we're reprioritizing, you know, incorrectly or inaccurately, right? You're, you're getting a little bit ahead of ourselves. >>Well, so, you know, whenever we have a long-term strategy for pretty much anything in the organization, right? We know what we want to do. We know the outcome that we want to achieve. We work backwards to, you know, determine what are the steps that are gonna bring us there. And, and the responsibility for thinking long term in, in every organization, including for APIs at the end of the day, always falls on the leaders and the should on the shoulders of the leadership and, and to see executives of the organization, right? And so we're seeing, you know, look at aws by the way. Look at Amazon. This conference would not have been possible without a very strong API vision from Amazon. And the CEO himself, Jeff Bezos, everybody talks about wanting to become an API first organization. And Amazon did that with the famous Jeff Bezos mandate today, aws, it's a hundred billion revenue for Amazon. You see, Amazon was not the first organization with, with an e-commerce, but if it was the first one that married a very strong e-commerce business execution with a very strong API vision, and here we are. >>So yeah, here we are putting you squarely in, in, in a pretty good position, right? In terms of what you're offering to the marketplace who has this high demand, you see this trend starting to explode. The hockey sticks headed up a little bit, right? You know, how are you answering that call specifically at how, how are you looking at your client's needs and, and trying to address what they need and when they need it, and how they need it. Because everybody's in a kind of a different place right now. >>Right? That's exactly right. And so you have multiple teams at different stages of their journey, right? With technology, some of them are still working on legacy, some of them are moving to the cloud. Yep. Some of them are working in containers and in microservices and Kubernetes. And so how do you, how do we provide an API vision that can fulfill the needs of the entire organization in such a way that we reduce that type of fragmentation and we don't introduce too much complexity? Well, so at con, we do it by essentially splitting the API platform in three different components. Okay. One is API management. When, whenever we want to expose APIs internally or to an ecosystem of partners, right? Or to mobile, DRA is a service mesh. You know, as we're splitting these microservices into smaller parts, we have a lot of connectivity, all, you know, across all the services that the teams are building that we need to, to manage. >>You know, the network is unreliable. It's by default, not secure, not observable. There is nothing that that works in there. And so how do we make that network reliable without asking our teams to go and build these cross-cut concerns whenever they create a new service. And so we need a service match for that, right? And then finally, we could have the best AP infrastructure in the world, millions of APIs and millions of microservices. Everything is working great. And with no API consumption, all of that would be useless. The value of our APIs and the value of our infrastructure is being driven by the consumption that we're able to drive to all of these APIs. And so there is a whole area of API productivity and discovery and design and testing and mocking that enables the application teams to be successful with APIs, even when they do have a, the proper API infrastructure in place that's made of meshes and management products and so on and so forth. Right. >>Can you gimme some examples? I mean, at least with people that you've been working with in terms of addressing maybe unique needs. Cuz again, as you've addressed, journeys are in different stages now. Some people are on level one, some people are on level five. So maybe just a couple of examples Yeah. Of clients with whom you've been working. Yeah, >>So listen, I I was talking with many organizations here at AWS Reinvent that are of course trying to migrate to the cloud. That's a very common common transformation that pretty much everybody's doing in the world. And, and how do you transition to the cloud by de-risking the migration while at the same time being able to get all the benefits of, of running in the cloud? Well, we think that, you know, we can do that in two, two ways. One, by containerizing our workloads so that we can make them portable. But then we also need to lift and shift the API connectivity in such a way that we can determine how much traffic goes to the legacy and how much traffic goes to the new cloud infrastructure. And by doing that, we're able to deal with some of these transformations that can be quite complex. And then finally, API infrastructure must support every team in the organization. >>And so being able to run on a single cloud, multi-cloud, single cluster, multi cluster VMs containers, that's important and essential because we want the entire organization to be on board. Because whenever we do not do that, then the developers will make short term decisions that are not going to be fitting into the organizational outcomes that we want to achieve. And we look at any outcome that your organization wants to achieve the cloud transformation, improving customer retention, creating new products, being more agile. At the end of the day, there is an API that's powering that outcome. >>Right? Right. Well, and, and there's always a security component, right? That you have to be concerned about. So how are you raising that specter with your clients to make them aware? Because sometimes it, I wouldn't say it's an afterthought, but sometimes it's not the first thought. And, and obviously with APIs and with their integral place, you know, in, in the system now security's gotta be included in that, right? >>API security is perhaps the biggest, biggest request that we're hearing from customers. You know, 83% of the world's internet traffic at the end of the day runs on APIs, right? That's a lot of traffic. As a matter of fact, APIs are the first attack vector for any, you know, malicious store party. Whenever there is a breach, APIs must be secured. And we can secure APIs on different layers of our infrastructure. We can secure APIs at the L four mesh layer by implementing zero trust security, for example, encrypting all the traffic, assigning an identity to every service, removing the concept of trust from our systems because trust is exploitable, right? And so we need to remove the cut zero trust, remove the concept of trust, and then once we have that underlying networking that's being secure and encrypted, we want to secure access to our APIs. >>And so this is the typical authentication, authorization concerns. You know, we can use patterns like op, op or opa open policy agent to create a security layer that does not rely on the team's writing code every time they're creating a new service. But the infrastructure is enforcing the type of layer. So for example, last week I was in Sweden, as a matter of fact speaking with the largest bank in Sweden while our customers, and they were telling us that they are implementing GDPR validation in the service mesh on the OPPA layer across every service that anybody's building. Why? Well, because you can embed the GDPR settings of the consumer into a claim in a gel token, and then you can use OPPA to validate in a blanket way that Jo Token across every service in the mesh, developers don't have to do that. It just comes out of the box like that. And then finally, so networking, security, API security for access and, and management of those APIs. And then finally we have deep inspection of our API traffic. And here you will see more exotic solutions for API security, where we essentially take a subset of our API traffic and we try to inspect it to see if there is anybody doing anything that they shouldn't be doing and, and perhaps block them or, you know, raise, raise, raise the flag, so to speak. >>Well, the answer is probably yes, they are. Somebody's trying to, somebody's trying to, yeah, you're trying to block 'em out. Before I let you go, you've had some announcements leading up here to the show that's just to hit a few of those highlights, if you would. >>Well, you know, Kong is an organization that you know, is very proud of the technology that we create. Of course, we started with a, with the API gateway Con Gateway, which was our first product, the most adopted gateway in the world. But then we've expanded our platform with service mesh. We just announced D B P F support in the service mesh. For example, we made our con gateway, which was already one of the fastest gateway, if not the fastest gateway out there, 30% faster with Con Gateway 3.0. We have shipped an official con operator for Kubernetes, both community and enterprise. And then finally we're doubling down on insomnia, insomnia's, our API productivity application that essentially connects the developers with the APIs that are creating and allows them to create a discovery mechanism for testing, mocking the bagging, those APIs, all of this, we of course ship it OnPrem, but then also on the cloud. And you know, in a cloud conference right now, of course, cloud, right? Right. Is a very important part of our corporate strategy. And our customers are asking us that. Why? Because they don't wanna manage the software, they want the API platform, they don't, don't wanna manage it. >>Well, no, nobody does. And there are a few stragglers, >>A few, a few. And for them there is the on-prem >>Platform. Fine, let 'em go. Right? Exactly. But if you wanna make it a little quick and dirty, hand it off, right? Oh, >>That's exactly right. Yes. >>Let Con do the heavy lifting for you. Hey Marco, thanks for the time. Yeah, thank you so much. We appreciate, and again, congratulations on what appears to be a pretty good show for you guys. Yeah, thank you. Well done. All right, we continue our discussions here at aws. Reinvent 22. You're watching the Cube, the leader in high tech coverage. >>Okay.

>>Mhm. Yes one. >>Hello and welcome. Today's session of the 2021 AWS Global Public Sector Partner Awards. I am pleased to introduce our very next guests. Their names are a lean black S. V. P. Public sector at culebra and Marco Timon are Chief Enterprise Architect at the HQ. D. A. Office of business transformation at the U. S. Army. I'm your host Natalie ehrlich, we're going to be discussing the award for best partner transformation. Best data led migration. Thank you both for joining the program. >>Thank you for having us. >>Thank you. Glad to be here. >>Well, a lien, why is it important to have a data driven migration? >>You know, migrations to the cloud that are simply just a lift and ship does take advantage of the elasticity of the cloud but not really about how to innovate and leverage what truly the AWS cloud has to offer. Um so a data led migration allows agencies to truly innovate and really kind of almost reimagine how they make their mission objectives and how they leverage the cloud, you know, the government has, let's face it mountains of data, right? I mean every single day there's more and more data and you you can't pick up a trade magazine that doesn't talk about how data is the new currency or data is the new oil. Um, so you know, data to have value has to be usable, right? So you to turn your data into knowledge. You really need to have a robust data intelligence platform which allows agencies to find understand and trust or data data intelligence platform like culebra is the system of record for their data no matter where it may reside. Um no strategy is complete without a strong data, governments platform and security and privacy baked in from the very start, data has to be accessible to the average data. Citizen people need to be able to better collaborate to make data driven decisions. Organizations need to be united by data. This is how a technology and platform like cal Ibra really allows agencies to leverage the data as a strategic asset. >>Terrific. Well, why is it more important than ever to do this than ever before? >>Well, you know, there's just the innovation of technology like Ai and Ml truly to be truly leveraged. Um you know, they need to be able to have trust the data that they're using it. If it if the model is trained with only a small set of data, um it's not going to really produce the trusted results they want. ML models deliver faster results at scale, but the results can be only precise when data feeding them is of high quality. And let's say Gardner just came out with a study that said data quality is the number one obstacle for adoption of A. I. Um when good data and good models find a unified scalable platform with superior collaboration capabilities, you're A I. M. L. Opportunities to truly be leveraged and you can truly leverage data as a strategic asset. >>Terrific. Well marco what does the future look like for the army and data >>so and let me play off. Do you think that Allen said so in terms of the future um obviously data's uh as you mentioned the data volumes are growing enormously so. Part of the future has to do with dealing with those data volumes just from a straight >>technological >>perspective. But as the data volumes grow and as we have to react to things that we need to react to the military, we're not just trying to understand the quantity of data but what it is and not just the quality but the nature of it. So understanding authoritative nous. Being able to identify what data we need to solve certain problems or answer certain questions. I mean a major theme in terms of what we're doing with data governance and having a data governance platform and a data catalog is having immediate knowledge of what data is, where what quality and confidence we have in the data. Sometimes it's more important to have data that's approximately correct than truly correct as quickly as possible, you know. So not all data needs to be of perfect quality at all times you need to understand what's authoritative, what the quality is, how current the information is. So as the data volumes grow and grow and grow. Keeping up with that. Not just from the standpoint of can we scale we know how to scale pretty well in terms of containing data volume but keeping up what it is, the knowledge of the data itself, understand authoritative nous quality, providence etcetera, uh that's a whole enterprise to keep keeping up with and that's what we're doing right now with this, with this project. >>Yeah. And I'd like to also follow up with that, how has leveraging palabras data intelligence platform enabled the army to accelerate its overall mission. >>So there's uh there's sort of interplay between, you know, just having a technology does something doesn't mean you're going to use it to do that something, but often having a place to do work of governance, work of knowledge management can be the precipitating functions or the stimulus to do so. So it's not and if you build it they will come. But if you don't have a place to play ball, you're not going to play ball to kind of run with that metaphor. So having technology that can do these things is a precursor to being able to. But then of course we, as an organization have to do it. So the interplay between making a selection of technology and doing the implementation from a technical perspective that plays off of an urgency, we've made the decision to use a technology, so then that helped accelerate getting roles, responsibilities of our ceo of our missionary data. Officers of data Stewart's the folks that have to be doing the work. Um, when you educate system owners in cataloging and giving a central environment, the information is needed. If you say here's a place to put it, then it's very tangible, especially in the military where work is done in a very uh, concrete task based way. If you have a place to do things, then it's easier to tell people to do things. So the technology is great and works for us. But the choice to to move with the technology has then been a productive interplay with with the doing of the things that need to be done to take advantage of the technology, if that makes >>sense? Well, >>yeah, that's really great to hear. I mean, speaking of taking advantage of the technology, a lien can collaborate, help your other public sector customers take advantage of A. I and machine learning. >>Well, people need to be able to collaborate and take advantage of their most strategic asset data to make those data driven decisions. It gives them the agility to be able to act 2020 was a great lesson around the importance of having your data house in order. Let's face it, the pandemic, we watched organizations that, you know, had a strong data governance framework who had looked at and understood where their data were and they were very able to very quickly assess the situation in react and others were not in such a good situation. So, you know, being able to have that data governance framework, being able to have that data quality, being able to have the right information and being able to trust it allows people to be effective and quickly to react to situations >>fascinating. Um do you have any insight on that marco, would you like to weigh in? >>Well, definitely concur. Um I think our strategy, like I said has been to um use the technology to highlight the need to put governance into place and to focus on increasing data quality the data sources. And I would say this has also helped us uh I mean things that we weren't doing before that have to do with just educating the populace, you know all the way from the folks operators of systems to the most senior executives. Being conversant in the principles that we're talking about this whole discipline is a bit arcane and kind of back office and kind of I. T. But it's actually not. If you don't have the data to make, if you don't know where to get the data to make a decision then you're going to make a decision based on incorrect data and and you know that's pretty important in the military to not get wrong. So definitely concur and we're taking that approach as well. >>I'd like to take it one step further. If if you're speaking the same language then so if you have an understanding what the data governments framework is you can understand what the data is, where it is. Sometimes there's duplicate data and there's duplicate data for a reason, but understanding where it came from and what the linage is associated with, it really gives you the power of being able to shop for data and get the right information at the right time and give it the right perspective. And I think that's the power of what has laid the foundation for the work that the army and MArco has done to really set the stage for what they can do in the future. >>Terrific and marco, if you could comment a little bit about data storage ship and how it can positively dry future outcomes. >>Yeah, So um data stewardship for us um has a lot to do with the functional, so the people that were signing as a senior data Stewart's are the senior functional in the respective organizations, logistics, financial management, training, readiness, etcetera. So the idea of the folks who know really everything about those functional domains, um looking at things from the perspective of the data that's needed to support those functions, logistics, human resources, etcetera. Um and being, you know, call it the the most authoritative subject matter experts. So the governance that we're doing is coming much more from a functional perspective than a technical perspective, so that when a when a system is being built, if we're talking about data migration, if we're talking about somebody driving analytics, the knowledge that were associated with the data comes from the functional. So our data stewardship is less about the technical side and more about making sure that the understanding from functional perspective of what the data is for, what the provenance is, not from a technical perspective, but what it means in terms of sources of information, sources of personnel, sources of munitions et cetera um is available to the folks using it. So they basically know what it is. So the emphasis is on that functional infusion of knowledge into the metadata so that then people who are trying to use that day to have a way of understanding what it really is and what the meaning is. And that's what really what data stewardship means from were actually very good at stewarding data. From a technical perspective. We know how to run systems very well. We know how to scale, We're good at that, but making sure that people know what it is and why and when to use it. Um that's where it's maybe we have some catching up to do, which is what this efforts about. >>Terrific. Well, fantastic insights from you both. I really appreciate you taking the time uh to tell all our viewers about this. That was Eileen Black and Marco Timoner and that, of course, was our section for the AWS Global Public Partner Sector Awards. Thanks for watching. I'm your host, Natalie Early. Thank you. >>Yeah. Mm.

(upbeat music) >> Well, thank you for joining us here as we continue our Cube Conversation on the AWS startup showcase with Marco Palladino who is the CTO of Kong. And Marco, also a co-founder by the way, Marco, thank you for joining us here on theCUBE. It's good to have you with us. >> Thank you, John, for having me. >> You bet, absolutely. First off, for our visitors and our viewers who might not be too familiar with Kong, tell us a little bit about what you're up to and your core competencies, of which I know are many. >> Yeah, Kong is a cloud connectivity company. We provide the technology software that developers and enterprise organizations all over the world can use to connect securely their software, and their microservices, and their APIs together. So we're really executing here on being the Cisco of L4 and L7. >> Yeah, great analogy. A really good analogy. So when you are talking about microservices, obviously this is a pretty new space, or certainly a growing space, in terms of deployments and different technologies. How come, like where's this come from, basically the whole microservice notion and concept? >> Yeah, it's a very interesting concept. In 2013 and 2014 there was a market transition in the landscape. Docker was released in 2013. Kubernetes was released in 2014. And Docker and Kubernetes together really have unleashed a new era of microservices across pretty much every organization in the world. We know that if we are trying to grow a business we must iterate fast ship, new products faster. We must be reliable. We must be distributed decoupled. And to do that, monolithic applications, which is the previous way of building modern software, monolithic applications, doesn't really scale that well in a distributed world. And so with microservices, running on top of Kubernetes containerized with Docker, we can now decouple our software and run it in a faster, better, more reliable way across pretty much any cloud vendor in the world. And as a result of that, we can enter new markets faster. We can make our users happier by shipping fixes and features faster. And therefore we can grow the business. That's why microservices really have been adopted across the board. >> So let's dive into that a little deeper here in terms of the value proposition, because, just because you could do something obviously isn't what the reason why you should do it. There is value at the end of the day that you're delivering, a new value. So summarize that a little bit for, again, a perspective customer who might be watching right now, somebody that you want to talk to about these new services these new values that they can enjoy. Why they be thinking about Kong? Why should they be thinking about microservices? >> Yeah, you see, every organization in the world is becoming digital. And we've discovered that, a few years ago, with digital transformation 1.0, as I call it. And in that digital transformation, we have realized that in order for us to build a successful software, in order for us to grow our business, we really must be able to innovate quicker. We must be able to create and ship new products faster. We must be able to duplicate our workloads across multiple regions and cloud vendors so that we can target our users with low latency and with the quickest performance we can possibly get. Now, in order to do that the monolithic applications we used to build they don't do that that well. monolithic applications, as they grow, they become huge, hard to move, hard to scale, hard, to deploy, hard to innovate. And we, as an industry, have learned that if we can decouple those large monolithic applications into smaller components, like microservices, we can then ship and innovate faster. Now, of course, on one end, we ship and deploy faster. On the other end, we are introducing something that our monolithic applications never really had at this scale. And that is this massive connectivity across all the services that make up the final application. Being decoupled and being distributed really means that we are connecting them over the network with service connectivity. And if that service connectivity is not working well then the application is not working. So digital transformation 2.0 really is all about taking our digital business and transforming it, by decoupling it and distributing it, in order for us to build a stronger business. >> So you talked about the monolithic application and there's some simplicity to that though, isn't there? Because now we're introducing multiple layers and a lot of complexity in some respects. Which allows us to do a lot of things really well, but it also introduces challenges. So if you were talking to, again, a prospective customer and they said, "Hey, this all sounds well and good, but what if..?" There are a lot of what ifs out there. How do you address the different challenges or the questions that might be raised in terms of trouble that you're inviting by introducing this new complexity into the marketplace? >> Yeah, the key here is to abstract away all the things that we don't need to build for our business. The key is to focus on what drives our business and that's our users, our customers, the applications that we're building. Everything else that's not part of the core business should be delegated as part of the underlying infrastructure. Likewise, today, when we want to enter a new market we just leverage a cloud vendor. We don't go and build a physical data center from scratch. Likewise, when we build new modern applications, we don't want to build the orchestration platforms by ourselves. We don't want to build the connectivity stack by ourselves. But we want to abstract that away so that our teams can focus on what matters for the business. And that's the users, the customers, the application. It's not building the underlying infrastructure which can be given as a service to the application teams as opposed to asking the teams to build it from scratch. And there's going to be challenges, of course, but there's going to be benefits. And as long as the benefits are bigger than the challenges then it's worth while transitioning to microservices if that can help us scale faster and grow faster. And if anything, with COVID last year, we have learned how important it is for every organization to think about digitalizing in a faster way, in order to keep being in business, as a matter of fact, to keep winning against their competitors. And the organizations that can acquire good knowledge of the underlying tooling to allow them to transform this way, those are the organizations that are going to be succeeding moving forward. >> What do you think is the biggest shift in this paradigm then in terms of this legacy system that we had in place, that worked pretty well, to now We have a much more specialized, instead a much more distributed approach, that is providing these new values and certainly great benefits. But in your mind, what's the biggest shift there, you think, in terms of mindset and in terms of actual deployment? >> Well transitioning to microservices really involves three different transformations and that's why sometimes it can be challenging. It requires transforming our software to microservices. By doing so, it requires us to rethink the operations of how we deploy, run, and test our software. And the third aspect, the third component that it transforms it's the cultural component. And now we can build smaller teams that can work in a decoupled asynchronous way. And as long as they expose an API those teams are going to be very well integrated with the rest of the organization. Look at what companies like Amazon, Netflix, or Google have done. And that's a big cultural shift. Like any large transformation, it is not, there is not one secret ingredient. It's an entire mindset that has to change. Now, thankfully for us, this transition is also being driven by bottom up adoption and transformation that's being driven by open source software. So unlike the previous transformations, these ones, if you wish, it's a self service transformation. Open source ecosystem provides us with a self-service ecosystem of a landscape of tools and platform and technologies that the application teams and the infrastructure teams can go ahead and use in order to figure out what's the best formula for them to achieve their success. >> When you have the, so let's just say, you've got your operation in place and you have multiple communications going on amongst microservices, whatever. It's all well and good. Now you want to introducing yet another. And so are there, not concerns, are there challenges there in bringing a newcomer into that environment in terms of testing, in terms of deployment, because of the factors, the variables that come into play here? How one piece works with another piece won't be the same how it works with another piece, right? So how do you handle testing? How do you handle new deployments in this kind of an environment? >> This is perhaps the most critical cultural change and transformation that microservices bring. With a monolithic application, if the monolith was up and running the business was up and running. If the monolith was down the business was done. Simple, easy. It was clear. It's one-to-one clear to understand. With microservices we're effectively making ourselves comfortable of always running in a partially degraded system. Because there is so much more, so many more moving parts running at the same time they cannot possibly be all up and running at any given point in time. Some of them will be running. Some of them will be slow. Some of them will be not executing. And guess what? Our infrastructure is built in such a way that, even when that happens, the customer and the users will never experience any downtime. This is a chance for us to transition to microservices. It's a chance for us to accelerate the innovation in your organization. But also to accelerate the reliability of our applications and also accelerate the security of our applications. And these may sound counterintuitive. Many technology leaders they're like, "Wait, what do you mean by that? How can you transition to microservices and improve the security if you have so many moving parts in your systems running as opposed to a monolith?" But that's an opportunity for us to improve the security. Because now, unlike the monolith, where everything can consume and access everything else, with microservices we can set up a tighter security rules in place to determine what services can consume what other services and in what capacity? In a monolithic world, as long as the code base is accessible, anybody can do anything that the monolith can do. With microservices it's an opportunity for us to lock that down. And even the past year, we've seen how important that is. The reputational of an entire organization can be destroyed by a high profile breach or attack. And so it's very important for us to catch this opportunity so that we can implement zero trust security. We can implement a consistent, non-fragmented layer of security across all of our applications, not just the Kubernetes ones or the containerized ones, but even the virtual machine based ones. And all the connections that we're generating, that's the backbone of every modern architecture, that's the bread and butter of every microservice oriented application. And that connectivity has to be managed, and secure, and observed, and exposed to our partners, developers, and customers. If that connectivity fails, then our business fails. And so today we can not ask the application teams to build that connectivity for us. That's like asking them to go build an application, and as they're doing that, walking to the data center and physically connecting the switches and the routers to the server racks to build the underlying physical connectivity. We don't, we cannot ask them to do that. The connectivity as well has to be abstracted the same way we are abstracting the data center with platforms like Kubernetes. >> So just back again to security. Obviously, you pointed out, we've had some pretty high profile cases here of late. Well, actually it's probably the past four or five years, but certainly of late, state actors taking actions. So that security mindset that you're in right now it does seem counterintuitive to me. That you have multiple doors, right? In the monolithic environment you've got one big one, right? And you just have to crack the code, and you're in. But in this case, you've got a lot of different entry points but you're saying that you're actually, you can batten down that hatch, if you will. You can provide the protective barrier around all of these microservices in an effective way. >> It's an opportunity for us. I'm a big fan of when John Chambers, the ex CEO of Cisco said, "Whenever there is a threat, how can we think of that as an opportunity?" And really microservices gave us the opportunity to implement a new generation security model for all of our applications. That's tight, that cannot be breaked into. And so that zero trust security, OPA, across the entire organization for both North/South and East/West traffic, for both the gateways and the service meshes. That is, for us, the opportunity to secure our applications in a way that could not be secured before in a monolithic world. Microservices not only create a business advantage but they gave us also many, many different chances for us to improve all the other aspects of security and productivity within your organization. And securing it, that's one of the opportunities that we can not miss. >> Well, Marco thank you for the time. Fascinating work, it really is, revolutionary in many respects. And I wish you continued success at Kong. And thank you for joining us here on the startup showcase. >> Thank you so much. >> Great. John was here talking to the Marco Palladino Who is the CTO and co-founder of Kong. We're talking about the service mesh, that landscape. It is new. It is evolving. And it is certainly a fascinating wrinkle to our world. Thanks for joining us here on theCUBE Conversation. I'm John Walls. We'll see you next time. (upbeat music)

(upbeat music) >> Well, thank you for joining us here as we continue our Cube Conversation on the AWS startup showcase with Marco Palladino who is the CTO of Kong. And Marco, also a co-founder by the way, Marco, thank you for joining us here on theCUBE. It's good to have you with us. >> Thank you, John, for having me. >> You bet, absolutely. First off, for our visitors and our viewers who might not be too familiar with Kong, tell us a little bit about what you're up to and your core competencies, of which I know are many. >> Yeah, Kong is a cloud connectivity company. We provide the technology software that developers and enterprise organizations all over the world can use to connect securely their software, and their microservices, and their APIs together. So we're really executing here on being the Cisco of L4 and L7. >> Yeah, great analogy. A really good analogy. So when you are talking about microservices, obviously this is a pretty new space, or certainly a growing space, in terms of deployments and different technologies. How come, like where's this come from, basically the whole microservice notion and concept? >> Yeah, it's a very interesting concept. In 2013 and 2014 there was a market transition in the landscape. Docker was released in 2013. Kubernetes was released in 2014. And Docker and Kubernetes together really have unleashed a new era of microservices across pretty much every organization in the world. We know that if we are trying to grow a business we must iterate fast ship, new products faster. We must be reliable. We must be distributed decoupled. And to do that, monolithic applications, which is the previous way of building modern software, monolithic applications, doesn't really scale that well in a distributed world. And so with microservices, running on top of Kubernetes containerized with Docker, we can now decouple our software and run it in a faster, better, more reliable way across pretty much any cloud vendor in the world. And as a result of that, we can enter new markets faster. We can make our users happier by shipping fixes and features faster. And therefore we can grow the business. That's why microservices really have been adopted across the board. >> So let's dive into that a little deeper here in terms of the value proposition, because, just because you could do something obviously isn't what the reason why you should do it. There is value at the end of the day that you're delivering, a new value. So summarize that a little bit for, again, a perspective customer who might be watching right now, somebody that you want to talk to about these new services these new values that they can enjoy. Why they be thinking about Kong? Why should they be thinking about microservices? >> Yeah, you see, every organization in the world is becoming digital. And we've discovered that, a few years ago, with digital transformation 1.0, as I call it. And in that digital transformation, we have realized that in order for us to build a successful software, in order for us to grow our business, we really must be able to innovate quicker. We must be able to create and ship new products faster. We must be able to duplicate our workloads across multiple regions and cloud vendors so that we can target our users with low latency and with the quickest performance we can possibly get. Now, in order to do that the monolithic applications we used to build they don't do that that well. monolithic applications, as they grow, they become huge, hard to move, hard to scale, hard, to deploy, hard to innovate. And we, as an industry, have learned that if we can decouple those large monolithic applications into smaller components, like microservices, we can then ship and innovate faster. Now, of course, on one end, we ship and deploy faster. On the other end, we are introducing something that our monolithic applications never really had at this scale. And that is this massive connectivity across all the services that make up the final application. Being decoupled and being distributed really means that we are connecting them over the network with service connectivity. And if that service connectivity is not working well then the application is not working. So digital transformation 2.0 really is all about taking our digital business and transforming it, by decoupling it and distributing it, in order for us to build a stronger business. >> So you talked about the monolithic application and there's some simplicity to that though, isn't there? Because now we're introducing multiple layers and a lot of complexity in some respects. Which allows us to do a lot of things really well, but it also introduces challenges. So if you were talking to, again, a prospective customer and they said, "Hey, this all sounds well and good, but what if..?" There are a lot of what ifs out there. How do you address the different challenges or the questions that might be raised in terms of trouble that you're inviting by introducing this new complexity into the marketplace? >> Yeah, the key here is to abstract away all the things that we don't need to build for our business. The key is to focus on what drives our business and that's our users, our customers, the applications that we're building. Everything else that's not part of the core business should be delegated as part of the underlying infrastructure. Likewise, today, when we want to enter a new market we just leverage a cloud vendor. We don't go and build a physical data center from scratch. Likewise, when we build new modern applications, we don't want to build the orchestration platforms by ourselves. We don't want to build the connectivity stack by ourselves. But we want to abstract that away so that our teams can focus on what matters for the business. And that's the users, the customers, the application. It's not building the underlying infrastructure which can be given as a service to the application teams as opposed to asking the teams to build it from scratch. And there's going to be challenges, of course, but there's going to be benefits. And as long as the benefits are bigger than the challenges then it's worth while transitioning to microservices if that can help us scale faster and grow faster. And if anything, with COVID last year, we have learned how important it is for every organization to think about digitalizing in a faster way, in order to keep being in business, as a matter of fact, to keep winning against their competitors. And the organizations that can acquire good knowledge of the underlying tooling to allow them to transform this way, those are the organizations that are going to be succeeding moving forward. >> What do you think is the biggest shift in this paradigm then in terms of this legacy system that we had in place, that worked pretty well, to now We have a much more specialized, instead a much more distributed approach, that is providing these new values and certainly great benefits. But in your mind, what's the biggest shift there, you think, in terms of mindset and in terms of actual deployment? >> Well transitioning to microservices really involves three different transformations and that's why sometimes it can be challenging. It requires transforming our software to microservices. By doing so, it requires us to rethink the operations of how we deploy, run, and test our software. And the third aspect, the third component that it transforms it's the cultural component. And now we can build smaller teams that can work in a decoupled asynchronous way. And as long as they expose an API those teams are going to be very well integrated with the rest of the organization. Look at what companies like Amazon, Netflix, or Google have done. And that's a big cultural shift. Like any large transformation, it is not, there is not one secret ingredient. It's an entire mindset that has to change. Now, thankfully for us, this transition is also being driven by bottom up adoption and transformation that's being driven by open source software. So unlike the previous transformations, these ones, if you wish, it's a self service transformation. Open source ecosystem provider us with a self-service ecosystem of a landscape of tools and platform and technologies that the application teams and the infrastructure teams can go ahead and use in order to figure out what's the best formula for them to achieve their success. >> When you have the, so let's just say, you've got your operation in place and you have multiple communications going on amongst microservices, whatever. It's all well and good. Now you want to introducing yet another. And so are there, not concerns, are there challenges there in bringing a newcomer into that environment in terms of testing, in terms of deployment, because of the factors, the variables that come into play here? How one piece works with another piece won't be the same how it works with another piece, right? So how do you handle testing? How do you handle new deployments in this kind of an environment? >> This is perhaps the most critical cultural change and transformation that microservices bring. With a monolithic application, if the monolith was up and running the business was up and running. If the monolith was down the business was done. Simple, easy. It was clear. It's one-to-one clear to understand. With microservices we're effectively making ourselves comfortable of always running in a partially degraded system. Because there is so much more, so many more moving parts running at the same time they cannot possibly be all up and running at any given point in time. Some of them will be running. Some of them will be slow. Some of them will be not executing. And guess what? Our infrastructure is built in such a way that, even when that happens, the customer and the users will never experience any downtime. This is a chance for us to transition to microservices. It's a chance for us to accelerate the innovation in your organization. But also to accelerate the reliability of our applications and also accelerate the security of our applications. And these may sound counterintuitive. Many technology leaders they're like, "Wait, what do you mean by that? How can you transition to microservices and improve the security if you have so many moving parts in your systems running as opposed to a monolith?" But that's an opportunity for us to improve the security. Because now, unlike the monolith, where everything can consume and access everything else, with microservices we can set up a tighter security rules in place to determine what services can consume what other services and in what capacity? In a monolithic world, as long as the code base is accessible, anybody can do anything that the monolith can do. With microservices it's an opportunity for us to lock that down. And even the past year, we've seen how important that is. The reputational of an entire organization can be destroyed by a high profile breach or attack. And so it's very important for us to catch this opportunity so that we can implement zero trust security. We can implement a consistent, non-fragmented layer of security across all of our applications, not just the Kubernetes ones or the containerized ones, but even the virtual machine based ones. And all the connections that we're generating, that's the backbone of every modern architecture, that's the bread and butter of every microservice oriented application. And that connectivity has to be managed, and secure, and observed, and exposed to our partners, developers, and customers. If that connectivity fails, then our business fails. And so today we can not ask the application teams to build that connectivity for us. That's like asking them to go build an application, and as they're doing that, walking to the data center and physically connecting the switches and the routers to the server racks to build the underlying physical connectivity. We don't, we cannot ask them to do that. The connectivity as well has to be abstracted the same way we are abstracting the data center with platforms like Kubernetes. >> So just back again to security. Obviously, you pointed out, we've had some pretty high profile cases here of late. Well, actually it's probably the past four or five years, but certainly of late, state actors taking actions. So that security mindset that you're in right now it does seem counterintuitive to me. That you have multiple doors, right? In the monolithic environment you've got one big one, right? And you just have to crack the code, and you're in. But in this case, you've got a lot of different entry points but you're saying that you're actually, you can batten down that hatch, if you will. You can provide the protective barrier around all of these microservices in an effective way. >> It's an opportunity for us. I'm a big fan of when John Chambers, the ex CEO of Cisco said, "Whenever there is a threat, how can we think of that as an opportunity?" And really microservices gave us the opportunity to implement a new generation security model for all of our applications. That's tight, that cannot be breaked into. And so that zero trust security, OPA, across the entire organization for both North/South and East/West traffic, for both the gateways and the service meshes. That is, for us, the opportunity to secure our applications in a way that could not be secured before in a monolithic world. Microservices not only create a business advantage but they gave us also many, many different chances for us to improve all the other aspects of security and productivity within your organization. And securing it, that's one of the opportunities that we can not miss. >> Well, Marco thank you for the time. Fascinating work, it really is, revolutionary in many respects. And I wish you continued success at Kong. And thank you for joining us here on the startup showcase. >> Thank you so much. >> Great. John was here talking to the Marco Palladino Who is the CTO and co-founder of Kong. We're talking about the service mesh, that landscape. It is new. It is evolving. And it is certainly a fascinating wrinkle to our world. Thanks for joining us here on theCUBE Conversation. I'm John Walls. We'll see you next time. (upbeat music)

>> live from Boston, Massachusetts. It's the queue covering your red. Have some twenty nineteen brought to you by bread. >> Welcome back to the Cube. Continuing coverage here read. Had some twenty nineteen day three of our three days of covering some nine thousand attendees, great keynotes, great educational sessions and a couple of great guests for you to meddle. And John Walls were joined by Marco Bill Peter, who is the senior vice president of customer experience and engagement at Red Hat. Good to see you, Marco. Thanks for having the job on the keynote stage this morning. And Dr Christoph back, who was the head of infrastructure from Hilty and Christof. Thank you for being here is Well, thankyou. Hailing from from election Stein. And we think you're the first guest alum were to check our database, But But we've set a new record today. So thanks for adding to our having. First off, let's talk about Hilty. I'm sure people don't stay healthy. I've seen them, but this building probably wouldn't be here without you. Have imagined half the city wouldn't be here without you. But just tell folks at home a healthy a little bit about where you fit into the construction. >> Lt was founded in the nineteen forties in principality of a Lichtenstein as and is now today leading supplier for the construction industry. We provide tours, consumables, services and software solutions for professional construction companies. Daddy's from hammer drills, two anchors to calculation software and overall complete services for the industry. That's what hell is doing. >> So you did a very good job this morning on the keynote of painting that picture about about the scope of your work and the necessity of your work, the vitality of it. Because construction projects, as we all know, how very strict deadlines. Sometimes they have unique needs. They have immediate needs emergency needs, and you're in the center of all that. And so your technology is central to your general operation. >> Absolutely yes. I mean, with twenty five thousand or twenty nine thousand employees, twenty five thousand users in our system, basically, everybody's using everyday ASAP or the fast majority of users. We have ten thousand concurrent users every day on our system. That deal with customer requests with orders with quotes, but also, of course, with complaints with repair handling and so on. In >> just a few. Yeah, just >> so Marco, I hear ASAP and, you know, bring me back to when? Oh, well, you know, Lennox was that stuff that sat on little sidelines. We're well past that. You've got so many customers that run their business, you know, mission critical around the globe. Just give give, give us a little bit of background on the partnership with Hilty and Red Hat and Solutions like asap. >> Yes, sure. Yeah. The Department of Hilty goes back to, I think, two thousand seven for me. Personally, I started working with Hilton for another company in ninety three. So I know where the hell did Quite well, actually studied in the same town next to Lichtenstein, Son of the mail. And And it's it's amazing to see the journey kind of two thousand nine going all s ap mission critical on rail and now actually moved to Asa P s for Han. And yes, Hill is one ofthe declines. But it kind of talks that we can handle this mission. Critical applications are mission critical customers and built this good relationship to make sure they have these these courage to actually do this Bold jumps limited The last six months. >> Christoph, you know you've got a broad, you know, roll at the at the company way talked to so many companies on becoming a tech company on becoming a software company. Well, software is critical, but at the end of the day, you know, infrastructure and running your business is core. You know, you're not going to become a fully digital software. You have real stuff in the physical world that lots of people and lots of, you know, physical things that need to go to a little bit about that balance. And now the company has been changing over those last ten years. >> I was excited to be open with you. I was really excited when our executive board a couple of years ago, besides tools, consumables and services also added software into a strategic pillar for Hilty. Um, and while I believe that software will be an interesting pillar for us, well will generate additional revenue, will generate additional sales from early. Also in the consumables and tools and services piece software becomes more and more important when you look at the journey off building a building like this. As you mentioned John, I mean it starts with specifying it starts with the planning on CD, and it ends at the end with with Asset Management. Where are the tours and so on. So it's a complete life cycle through out the building off off throughout the construction of ah building. You >> know, Marco had mentioned that you made this decision to migrate Ohana last year right? Twenty eighteen or or where he might be rated last year? Isn't last year's decision made before that? Talk about that a little bit, if you would please and where Red Hat fit into that? Because that that's that's not a small decision, right? I mean, that's a That's a very calculated and I wouldn't not risky, but it's It's just a big move. Yeah, and so the confidence that you had a CZ well, that red hat was your partner to make that happen. >> Absolutely. I mean, the announcement of SAPI to support Hana as thie only database after twenty twenty five voice one off the factors to push us into that direction, that that was then clear for us that we want to go there. And it was also pretty clear for us that in our size it was not that easy to move in twenty twenty three or something like that in that direction, but that we have to be the first movers to be fully supported by ASAP and >> all >> these Parkins because later on, they will be busy with migrating all the big shots. So Wade took the decision to move first and soon, and that allowed us to be in the focus off all thes attached partners ASAP. But also read had also tell emcee for storage and HP for for servers. That meant that we had confidence that we have full attention from all these providers and partners to help us to migrate. On the other hand, it was clear the the the journey we started in two thousand nine has indicated by Marko that we moved to an open software that we move to commodity hardware. Intel based server hardware was a move that had paid off in the past, and we didn't want to go away from that and move again to a proprietary hardware or software solutions. So it was very clear that we want to do that jointly with red hat on commodity and until based service and That's how we went there, Right? >> So, Christophe, big theme, we hear not only at this show, but almost every show we go to is today customers. It's, you know, the hybrid and multi cloud world I see ASAP at all of the Big Cloud shows that that that we cover well, we're just cloud fit into your over discussion, you know, at your company. And then, you know, we can drill down to the specifics of that sapien red hat. But it's what do you have? A cloud strategy, as it were? >> Oh, yes, you know, we moved fairly soon to Amazon with all our customer facing workload. So when you go to hilton dot com or any of our Web pages, you typically land on a ws powered website because that one gave us the flexibility off operating systems off databases of whatever we needed. That was that was available there with our internal workload. However, So all the software we use Internet eternally toe run the company. We have a world that is split between ASAP, which runs entirely on Red hat, um, and the rest of the workload. Witches to a large degree, windows based workload so there. We decided a few years ago to Movinto Microsoft Azure platform to move the internal workload into Azure as it is mainly Windows based. >> So Marco actually want one a depart from healthy for a second. Just give us a little bit of a broad view. You know, we've talked to you many times. You talked about the stage. You know, the customer experiences a critical piece of red hats mission out there When I talk to customers today, One of the biggest changes they've seen the last few years is I'm managing a lot of stuff that's not in my environment. It's the stuff I'm responsible for it and something goes wrong. I'm absolutely getting a call, but you know, it's not my network. It's not my servers. It's not my piece there, but I have to do all of them, you know, got imagine. That's been a transformation for red Hat in the partnerships, and you're everywhere, so it just gets a little context. Yeah, I >> mean, you described it very well, right? I mean, I think the last two years before, I think it was just like some use cases in the public club. But today. The harder cloud is here, right? And everybody does it right. It's not like just one company from a customer experience to stand behind. Like I mentioned it on the state gets harder. Right? And you gonna have these partnerships, right? One partnership, right. We can talk about the azure. We have people in enrichment, right? Think about it today. And then everything changed with start having on stage here. But we have support people in micro for the last two or three years, right? Same diff ASAP as an example, right? We have people. We actually build a fairly large teeming, involved off to be close of us. Time together. I want to do that speed ASAP. A cloud bead on regular bear closes in general. Yes, That challenges. You mentioned networking, right? It gets tricky, right? And he shifted from, but it's unavoidable, right? It shifted from, like, okay, we own and control the stacked kind of too. Yes, you need to know you're open source after and to have really partnerships. Right? And I think the announcement Microsoft, too have this managed services offering that we do joint. It's That's what we're driving so that we can do this better together with partners. >> Marco is great to hear you that but Christoph, he's not listening. Tell us to reality. You've worked with Red Hat for ten years. You're going to cloud how they doing? How's the ecosystem, the vendors in general? They're all up on stage, holding hands. I mean, it's it's seamless and nobody ever point fingers. I'm sure >> to be very, very honest with you. I mean, I appreciate it last year, hearing that redhead will be offered in Azure. I mean, that was not possible to mention those two company names in one sentence in the past, at least for us as customers, and that that was a bold statement last year that those two parties will suddenly join. That fits very well in our strategy, because we believe internal workload for Hilton should run in in In Azure seeing on last Tuesday, Microsoft and Red Hat shaking hands and movie. Even beyond that one was, for me, them almost the most exciting event here, or the most exciting statement that I saw here during these few days because that reemphasized the close relationship that those two have, and that exactly fits our road map. That's exciting. >> And, you know, we heard that, you know, again from from both CEO Saying customers really kind of brought us together. They made this deal work because we kept hearing that they love us and they love you, and they like us together. So So we got that. We understand that. So? So Marco customers drove that to a certain degree. You've got a customer here who made this big Hana jump, which is you say small guy. You know, I would beg to differ little bit that you had him before the big guys did. But what, like an initiative like that? What is that doing for you? What? Red hat. In terms of carrying that over to other customers. Now, you've learned from one you've seen what they've gone through. What kind of confidence does that give you? What kind of interest does it give you about how to approach this game? >> Absolutely. You know what we learned from give you one example right? If you moved his heart ever closer Christopher Hilty uses systems have twelve terabytes memory. Think about it that fairly large systems and that foot train tried to actually test our softer with that footprint and then even think about the next. Next journey is in if you want to do this in the cloud. What does that mean? If you take a twelve terabyte image and running in a double? Yes. And so that is, since my team also does quality assurance and product security. That's for them as well as in. Okay, we've seen what tilted can do work. How do we actually make this more robust? How do we test you are there? And how do we do that in this journey? It's, I think I'm pretty proud of how we actually learn from these instances, and health is not the only one. It's just one the republic. But yet it's every time. I think that's the only survived is into industry. If you really learn continuously and also applied right. I mean our whole setup involved or we shifted completely and not just from the people. They have theirs. So we have people that do open. Chief. There were people do Lennox and performance, but also from structure. I really be sure that they were set up for success and know what the next they have customers is obviously every casting off. A message we will do will go through a journey license over the next ten years. >> Kristoff obviously being on stage, you know it is good for the company, but coming to Red Hat Summit one. Just give our audience that if they hadn't come to it. Some of the value is, too what you place in some, the activities that have excited you most here this week. >> I mean, one thing is, of course, hearing about latest technologies, new releases, off software, off new possibilities and opportunities for us as customers from Red Hat. But also, it's great to see how on the floor out there other partners customers on DH fingers mingle around the ecosystem that created that was created around open software about, ah, not only operating system, but also about containers about all these those different technologies, which I have an important role for all of us in nineteen the future. >> Sure. Well, good week, that's for sure. Very nice job you get on the Kino stage to both of you and good luck with the partnership on down the road. And again, I would make the difference that way. little guys got in early hilt. He's no small fry in inner world, that's for sure. Thanks for the time, Krystof. Marco. Thank you. Thank you very much. Back with more. We're live here in Boston and we're covering the red hat. Summer twenty nineteen on the

(upbeat music) >> Announcer: Live from San Francisco, It's the Cube. Covering Red Hat Summit 2018. Brought to you buy, Red Hat. >> Okay, welcome back everyone. We're live here in the Cube in San Francisco, California, Monscone West, Cube's exclusive coverage of Red Hat Summit 2018. I'm John Furrier, co-host. With John Troyer, he's my analyst co- host, he's the co -founder of Tech Reckoning Advisory and Community Development Firm. My next guest is Marco Bill-Peter, Senior vice-president of Customer Experience and Engagement at Red Hat. Welcome back to the Cube. Good to see you. So, you guys have a great track record with customer support. You guys use gold standard in open source, you've done it well, very reliable. It's a changing world. You know, Open Shift now, certainly the center piece, west, new acquisition. A lot of things happening with in the portfolio. Cloud native new capabilities are on the horizon. So, you've got to figure it out. So, what's the support strategy? What do you guys do? How are you looking? I'm sure it's challenging but never too much of a challenge for you guys. You're smart, what's the support strategy? >> I think the recipe it is really like not getting stuck in a wave, right? And be open to, you know I think Jim Whitehurst and his keynote talk quite a bit about, you used to do all plan, describe and execute. That thing just doesn't work, right? Because supporting customers on Linux, supporting them when they move to Open Shift or even application, is a whole different piece. So, as a leader you got to be flexible as in okay, here we do it this way, let's put more money in this. Let's say Open Shift support, Open Shift kind of, what's the customer experience there, right? Kind of figure out how it works. There's a lot of things that scare me in the daily business as in like okay, we can't do that. But I think Red Hat is really good in reconfiguring, Jim talked about that in a keynote as well, reconfiguring the organization. And so, we move for example, quality assurance into my organization and combining that with support. All of them give some more opportunities realizing, oh this product maybe not ready yet for the market, right? We can not support that. Or, you augmented with, I wouldn't call it AI capabilities, but more like those capabilities. All of the sudden stuff gets done automatically. >> And multi cloud is again, just like multi vendor environment, but it's a little bit different obviously. But multiple clouds you have different architectures. You guys do some progressive things. What's new, architecturally within the support group? Because you have deals announced here with IBM and Microsoft, one of them is a joint, I think integrated program where guys are teaming up. >> Microsoft is interesting. >> We've teamed last three, four years, right? With he first deal and gone further. You're like funny, right? I've been at Red Hat so long and you put people on premise. It's kind of funny. But it's good, right? And that's where you got to glue together. Sometimes it's people. Sometimes it's also more having the data, right? I mean if you go multi cloud. Difference between multi vendor, multi cloud. Multi vendor, you just call the vendor and tell them hey you handle it. Here, I'll put data, you handle it. Or maybe you do it a bit better. But, multi cloud is, well it's running there, how do you get access to that? Then the whole privacy laws comes in. So you got to be more instrumentation, you know, telemetry-- >> You're using tech to help you guys out. That's what you're referring by AI. >> I actually think that the next ten years you will see support changing quite a bit. >> John: In what way? >> But also you have to staff this up, right? You need to upscale your folks as well as technology. >> That doesn't go away. But I think you've got to go more that you really need deep skills. If you want to support Open Shift you've got to, either you understand it from the middle side, from the application side or from the bottom from the infrastructure. You need both skill sets. So you need really highly skilled people. But one the other hand if it's really like real time and people don't have patience to wait two weeks, especially if you're in the cloud. More and more tooling. I see the vision as in it would be less and less based on the scale but I think it's less people involved more and more automation, tooling. >> You kind of see it now with boss, kind of just tip of the iceberg. But you've got automation built into the culture of Red Hat. You've put coral west. They want to automate everything. >> You see Insights, right? We launched Insights three years ago out of support. They take support data, find out what's really happening, create rules that if you match it the customer systems say you have this and this issue. And now it's in the incentive stage of the strategy as in we can automate it, but you can automate it. you have a problem, you want to have it solved. >> You're presenting a support service. >> Exactly, and eventually, we'll not even tell you, in maybe hindsight we'll tell you, hey, you had this network issue or configured the wrong way, we fixed it have a good day. >> Well it came up in Cooper Netty's conversation we had last week in Copenhagen, we were in Denmark for CubeCon around things Cooper Netty's defacto standing, so great stuff, that's certainly great. Istio service mesh is atopic that's highly discussed. And one of the thing that comes up is the automation the down side is potentially it fixes things. So, you could have a memory leak for instance, that you never know gets fixed. But it just crashes every day and reboots itself. So, the new kinds of instrumentation that's emerging. So this is really the though job. >> Yeah. Yeah. >> How do you get in there-- >> Also have automation-- >> And you as the central provider, right, are pulling in data from across the world and across the customer base. So how do you take that, sift it to be more proactive about decision making and support. >> So we capture all this support data. And you know it's fascinating, we have some AI capabilities, some machine learning capabilities go through there. But it's fascinating, sometimes we see new issues coming up. What we do is then, we go well let's look who is exposed to that, just to get a footprint. And then you actually inform customers, hey, you had this and this issue or you have this. It's really a different, I want to get more proactive or I want to get more automated. With the automation I just want to be, right, so we installed, over the last, I would say 18 months, like a bot, simple bot basically, his name is Edmond. And he works on support cases. And we started slow, very slow. We didn't let it go as in total machine or anything. But now, I gave some stats earlier today. In one used case it's 25 percent faster solving a customer issue using Edmond. And he participates in 11 percent of all support cases. >> Wow. >> Edmond is a busy guy. >> And the game is changing too. I mean in the old days, first lines support, second lines support, offline support, then escalation. These things are older IT mechanisms. With this you're talking about completely doing away with, in essence first line support. But also first line support might come in, from say a Microsoft or an IBM. You've got to be ready for anything. >> Actually I think it's not just first line support. And it's not replacing them. It's helping them. It's really making them faster, right? I think the frustration piece is, like, customer opens his support case, some data is missing, right? So, you have a que it gets to that. Engineering looks and oh, there's data missing. Edmond sees that and says hey, I need this data. Based on all the support cases we fixed similar issues, this is the data we need. So Edmond gets the data ready, engineer looks and in some cases Edmond actually closes it out. >> Closes it out. >> Tells the customer here there's a better solution, do it this way. >> Yeah, that's fascinating. >> I'd love to pull the camera back a little bit, right? You are not the SVP of support. You're the SVP of customer experience and engagement, right? That's an entirely different role in some ways, in that you're responsible for customer success at some level. >> That is correct, yeah. >> Talk a little bit about reconfiguring organization to be that-- >> So I think maybe dive in a little bit on the customer success. So we have a organization, they call technical account. It's part of the customer success organization. That's a human business but it's fascinating, right. We put these claims on clients and have them work together. They understand the business. It's an old business but trust me, having still a human in there understanding, okay this is customer x, y, z. That's the business objective, I talked about this today as well, not to forget, hey this customer actually wants to do whatever, whatever on the like an SIP to actually take that further to actually support case and doing that the team helps quite a bit. And then also the commitment, right? We don't want just to do support cases and then that's why you renew with Red Head, we want to make sure you actually get value out of it and that's why you want to renew. So that's why we configured different. It's bigger, right? It's bigger as in really making sure the product is correct. So that's why quality assurance is in my team, this support. That's why I run internal IT for the engineering team. We run the stuff that we sell actually earlier. And some of my team is like, Marco why do we have to do that? Because we learn and I much rather have you feel the pain than the customer feel the pain. That's why we configure different than, I've been 12 a half years right on this and it's still exciting that we are still able to change around-- >> I think the quality assurance piece is still big too cause you're in there as well. Looking at the QA. >> Yeah. >> Making sure that's good too. You're testing out the products and doing QA all within the mindset of customer experience. >> Exactly, and you've got to move that being agile, is more you see developers actually submitting test cases. Tests, so that's the component testing and the basic tests. What we got to do more, is what you mentioned, if somebody does less with Open Shift to contain all that, that thing together, if some service software defines storage, that thing together to bring together that's the hard drive. So I want to move more and more. That we take used spaces from customers, we'll close it. This is how we do it. X, y, z, customer and apply that. >> At the end of the day it's the same game different playing field. The customer wants choice, best possible solution experience, for them. You guys got to enable that, and then support it, make it happen. >> Yeah. >> And with cloud. >> And you see how, I don't know if you saw the demo yesterday when they show basically I think or Amazon was slower and every traffic that routed. This is reality as well, right? I mean if you look at one press release we did yesterday, I just find it a fascinating story. They're kitchen appliances. I don't know if you saw that. But they have over a million kitchen appliances or cooking appliances connected to the internet. It's a German, Swiss company when they got to upgrade the system so they get recipes done, they actually spin up instances in Alibaba in Asia and I think in Amazon in the U.S. They spin it up, they scale out all the appliances connect then they shrink it together. How do you support these customers a whole different case. >> That's great for the customer. >> Yeah. >> But more of a challenge for you guys. >> Then again with preparation of the right integration testing before, with the right set up that we know this is what the customer is doing this weekend. Amadeus as well, talked at the keynote, we worked long time with Amadeus. >> You're a smart team. >> As part of your customer role, you were involved with the Innovation awards. They were up on stage this morning. What struck me was they were both about time to value. And speed of deployment as well as scale. Often these were global companies, we had Amadeus on yesterday, spanning the globe. Huge number of transactions. Anything stand out to you in those Innovation Awards this year? Perhaps, that's been different in previous years? I think that the scale is actually interesting that you say. I think we have much quicker now. I think that's awesome, technology matures. I think we used to have more smaller work projects in getting to a certain scale. But I just goes faster. I think the controlled piece is probably a bit more accepted. This whole containerization is not magic anymore. I think a lot is being moved, is coming from the development side but also from the Linux side. So I think there's a less struggle of that. But I do still see some cultural struggles. You talk to customers, maybe not the Innovation Award winners. but even them they say, hey it took us a long time to convince internal structures, how we change things around. >> Talk about the open source role because you mentioned, before we came on how you guys are all in the open, an open source. Is there like a project that you're part of that supports centric? Is there certain things you're picking out over the source? As you guys do the QA and build you own stuff. >> Yeah we do a lot. We submit a lot to open. There's very few. We don't share data. We can't share customer data for obvious reasons. But tooling, most of the tooling we share if it's data collectors. We re an open source road. There' not much that we don't, there's nothing proprietary. Engineers, that's why they're coming to write. That's the configuration. They want to see, hey how does this stuff get applied. They own the packages, then some stuff is shared. If it's tied to the customer portal, the AI pieces maybe the open source parts of it but-- >> What's it like this year, for the folks who are watching who couldn't make it? What's the vibe here at Red Hat Summit 2018? What's the hallway conversations like? What's some of the dinners? What are you talking about? What's the chatter? >> I think the big chatter for me is kind of like this Open Shift, containers, agile development. You know the agile development comes back and back and really like how do we do this right? And tech connects obviously, how do you take application develop them or how do you take applications put them in a container. And then you see these demos. With multi cloud. >> New applications is not stand alone Linux anymore. >> Yeah. We have containers and tend to be able to run public cloud or multi cloud on premise. The options are endless. And I think that's the strengths from Red Hat. We prove that with Linux we can have a solid API. We don't screw up the applications. And if we can guarantee that across the four footprints, that's Paul's vision five, six years ago. I think we are there. >> You talked about a bit of cultural shift. How can Red Hat help it's customers come up to speed? That's a little bit...but be more agile. >> It's a good example. I think we do a lot of these sessions. I actually think that our sales motion, they are pretty aware with open sources, what the culture is. They do a lot of these sessions with customers. Jim Whitehurst is actually awesome. When he comes to clients. We did a C level event at a bank, based in Zurich and it was in a Swiss bank. And I think that they got like 140 C level, CIO groups. And Jim did a talk about the open organization about breaking down the barriers. I think that's a role that we play. Well some is Red Hat's role, but we go to do that stuff. Because we can share part of it in how we are configured, how we are different. >> I think that kind of thing is high on every CIO's list of agendas. >> And everything in the open is proving that open is winning. Open beats closed pretty much every time and is now pretty standard operating wise we're starting to see but operational wise, not just for software development. >> I actually think that from practice and how to run the company. Some stuff is transparency, right? If you work in a company that you're not transparent with your associates, can you really do this in 2018? >> No. >> And so I think those are elements that I think we do well to have had. And we got to keep internal as well, reminding ourselves, these core principles from open source are really important. >> Hiring, so you're bringing new Red Hatters in? >> At the rate we are hiring it's actually big concerns. How do we maintain this culture, right. This talk is not always polite but it's the way we function. >> You guys are humble. You're playing the long game, I love that about you. So congratulations Marco. Thanks for coming on the Cube show. >> Thanks very much. >> Thanks. >> It's the Cube Live here in San Francisco for Red Hat Summit 2018 here in Moscone West. I'm John Furrier and John Troyer. Stay with us for more live coverage after this short break. (upbeat music)

>> Narrator: Live, from Boston, Massachusetts. It's theCUBE, covering Red Hat Summit 2017. Brought to you by Red Hat. (light techno music) >> Welcome back to theCUBE's coverage of the Red Hat Summit here in beautiful Boston, Massachusetts. I am your host, Rebecca Knight. I'm here with my co-host, Dave Vellante. Joining us is Marco Bill-Peter. He is the vice president of customer experience and engagement at Red Hat. Thank you so much for joining us. >> Thank you for having me. >> So I want to start out by talking about your management philosophy, and your philosophy really of what you do. Because that is just so core to the Red Hat experience for customers. I noticed that you changed the name, it's no longer Customer Support, it is Customer Experience and Engagement. Do you want to talk a little bit about why you made that switch? >> Yeah, I think, well yes, it would be awesome, yeah. I mean the name reflects actually, in my opinion, also the business model from Red Hat, which is you take an open-source development model, you develop the products, you actually sell them as a subscription. But there's no license behind it, which is the amazing business model, right? That there's no lock-in, right? The customer can buy it, they can use it, if we don't provide the value, then shame on us, they can move on. And so that's where the customer experience has to be good. That they really see, hey, I got something from Red Hat, I'm coming back, I will renew this. And the engagement is as well, it's the part, one is like, experience was great and engagement is, we got to engage them, right? Because shame on us if we didn't engage a customer in during their journey during that year of subscription life that they have, or three-year, whatever it is. That is the, I think it's the business model, but it's also my philosophy, which is, I used to work for proprietary companies and running support or customer success functions. There you make the money on the license and the maintenance is, basically, I always call it janitorial services. This way our model is different, so that's why I also report through Paul Cormier, it's integrated in technologies. It's maybe not the philosophy, but it's our philosophy really, the customer, it's not a joke, it's customer is in the center. And if they are successful, yes, they will come back, they will buy more, they will renew. But it's an honest model. And so that's why we changed the name for various reasons. I also, we looked at other functions at Red Hat, and said, "Which one is really about customer experience?" And security, for example, is in my team, right? It's not just support, because security is a big element of our value that we provide. And so that's why we expanded the team, changed the name to kind of reflect that. >> You mention Paul Cormier and we're actually, he's going to be joining us later today too. And he was talking about how the design process is really led by customers in this new era of cloud computing. >> Marco: Yeah. >> Talk a little bit about what it's like to collaborate with customers in these products. >> Yeah, it's really good. I can give you an example from the innovation award winners this week. We have, like, for example, British Columbia, the government of British Columbia, and they start on this journey and they wanted to create this, I would say, exchange for partners that they have, companies to kind of provide some services and make it easier for them. And they started on a journey and it didn't go well. And shame on whoever was involved in that. But then we got involved. I was like, okay, that's what you're trying to solve. We got one of my guys actually flew out there, spent a few weeks out there. He was like, oh, this is the problem, let's build it differently. Then Ashesh Badani from the open-shift team got involved and we realized, okay, what they're trying to do is this, change the product, adjust it. There's one example from last year's innovation award winner. We had Betfair. It's a horse-race company in England. Same thing, they wanted to really innovate a data center in a complete, like, software-defined way. And having that collaboration with us directly and the upstream communities, but then also with partners, like in that case it was a software-defined network provider, to get involved and really build the solutions. It's a whole different way. And if you go back a few years when we just did Linux, that maybe didn't really happen because it was more Linux was driven by the community. And now I think it's honestly good to see because it's customers involved, there's still a lot of partners involved and there is a strong community and that whole thing working together is pretty cool to see. >> There's a saying that I like. It's customer satisfaction is one thing, customer loyalty is everything. And you live in a world where customer loyalty really is everything. Describe, you mentioned before your previous company, what's the innovation experience and total customer experience like now and how do you innovate versus the way a traditional company might innovate in customer experience? >> I think traditional companies, they innovate around, since it's a maintenance budget, we've got to save costs, right? That's their take. It's like, save costs, deflect cases, deflect customers basically, right? And our model is the opposite, right? If I start deflecting customers that's kind of the negative of engagement, right? So pushing back customers that actually they see value if they have interaction so that's where we look at it completely different. We innovate around, you know, like two years ago when we talked about we presented Red Hat insights, the tooling that basically out of our customer support cases we provide back to our customers a connection that they know, hey, this might happen. That's one piece that, you saw it probably at the keynote today, it's integrated in our products now, right? And so that's one piece we innovate around. Support is not seen as an afterthought. It's like, how do we build this into our tooling? So insights was a good example as an innovation. We did a lot of workflow changes, which sounds very technical but really to provide more value back to the customers. So it's called a knowledge center support approach that you really basically take what customers provide you, rephrase that and provide it back as a documentation. If you run into this situation, and it can be a support situation, but it can also an innovation situation, they want to build something new, you provide that back in the form of documentation customer form. >> One of the other things that's changed in the last two years is this explosion of artificial intelligence, some people call it cognitive, and we saw the kickoff video this morning how, and we talked about this a little bit a couple of years ago, how you're going to use data to improve customer experiences and now we're here. How are you using data and insights and analytics to improve the customer service? >> Analytics, I think, we started in a way in a traditional way, right? You have data and then you've got to figure out the data and then you kind of just create rules out of it. If this and this happen you do this. Call it AI, sounds cool, but basically it's rules matching. This happens, that. Now I think it's detecting the trends more automatically that's more done in, I would say, in more real AI. That's where we are. I would say the last year we spent more time figuring out, hey, how do we, instead of trying manually find the trends to actually automatically find them. And I think there is, I just gave an interview a few weeks in Japan where AI is a really hot topic, I think we're just scratching the surface. You saw it in autonomous driving but I think in support there's so much more to do in this area as well. >> When I asked you, you know, about juxtaposing Red Hat versus, say, a traditional software company it would seem like cutting costs was in conflict with innovating for customer experience but when I hear you speak about AI, is it possible there's a relationship between the two? That you can actually improve customer service and cut costs? >> Absolutely, but you want to do it in a good way. You want to do it in a way that it provides value back to the customer. If you do it an way, hey, we've cut out this and this things, then the customer just gets a lousy experience. That doesn't, I think that doesn't even work for a traditional company or proprietary company any more. That whole old, no, there's other companies that do autodeflection, right? But I think if you actually optimize the experience in a way that also the customer sees, hey, this is actually great value, right? If you just optimize things and the customer experience is great, you might actually create a situation where customer doesn't see value, right? Like in the old days, we had a lot of customers saying, hey, I never had the support case, why should I pay you guys? So, you know, obviously you can talk about that's great you didn't have a support case, but a customer paying a few millions and they only had one support case is a tough recovery. Today, not AI, but a lot of data is we can tell the customer, yeah, you had one support case, but look at all the tools you used in the customer portal, all the interactions you have. We have a nice dashboard we can present back to a customer. And, I'll give you, if you have a minute >> Yeah, please. a story quickly of a CTO from a bank that, a few years we met, and then he said, "Oh, Red Hat, you guys are good." That was in a bar. "You guys are good but, you know, I don't really need "your support, Marco. "My guys, they know how to do things." And so, I was like, okay. So in the evening I went back to the hotel, looked at the dashboard and then realized his story was maybe not as realistic. Next day, I see him again and show him the dashboard. And support was involved. There was documentation in use. I showed him back, I was like look at this, this is the value we provide you. And out of that came a whole different discussion, as in we do it annually now, and we looked at this data and he sees trends. He sees like, "Oh, my Latin America bank, "they still use this and this. "My North America team does that and that." And it's a whole different discussion. It's awesome, right, that they realize from the data we have there is a lot of value that he can change his operation. That's a short example. >> I want to talk to you about security. You mentioned this earlier in our conversation. The era of cloud computing is maturing and we are seeing now customers caring more about compliance and governance and management. What are the big concerns that you're hearing from customers? >> Obviously the big concern is still the traditional vulnerabilities, right? If there is a security hole, how do we fix it? How quickly fix it? Do we have the right data that we provide back as a customer realizes, is this a security hole I need to worry about or not. And that's what we do, we kind of focus on, we have a pretty large security team, I think, for the size we are because of the open-source model. So they're involved in a lot of the communities. So we provide fast response and we also provide response not just with a security fix but also with information about, hey, this is why you should worry or this is why you shouldn't worry. 'Cause sometimes the press creates this frenziness about, hey, pick your favorite name of a security hole, Heartbleed, et cetera. And for some customers it doesn't really matter because in their environment this is not a real scenario, right? And so we provide the patch, we provide data or documentation, but then also tooling that they can figure out are we exposed or not. That's one of the things. The other problem is in containers, right? You have these containers. You build the containers from everywhere. To actually realize, hey, is this container also compliant with security is a big topic. We just released, released, or we will release this week, it's not a secret, the container catalog, with actually a scoring that actually says, yes, this container is quality A, B. Kind of a freshness score. >> Ah, a ratings system. >> Yeah, rating. And this is a huge effort for every company and we do it as well, as in how do we keep these containers updated, right? Because you, if you build a container from application to middleware down to the operating system, you've got to worry about a lot of security. >> It's a fresh date. >> Yeah, it's like expiration date. >> A sell-by date. >> And that's what we do actually. We have an expiration date but depending on security hole, that just changes. >> So we were talking to John Hodgson who was at the keynote as well and he was telling us, and he mentioned this in the keynote, that he went from 17 developers in 2009 to 1,600 today. He was talking about that a lot of them are kids right out of college and we were talking about how you have to treat millennials differently and give them flexible time. And, Rebecca, you were talking about a new way to work at the beginning of our segments today. So how is that new way to work affect the customer experience and what are you guys doing in that regard? >> I mean obviously, like you say, right, there's a whole new generation coming and I actually think the new generation, they're actually a pretty sensitive customer experience. I think they're growing up in a different age of like digital age so, and social media as well, so actually I was worried that the new generation maybe, but I have to say, I think contrary, right? So that's good. So I don't think customer experience will suffer. What will change is, like, you can't have people, you know, we don't have it, but like call centers, if you have a little farm for everybody. That just ain't going to fly anymore, right? And so that's where we got to adjust. We don't do call centers, but we got to just adjust, like, how is the office done, where is it, and things like that. But it's, I think, I'm not too worried about that. >> And mobile is huge for you guys obviously, right? The mobile trend. And there's a lot of talk in Silicon Valley about, you know, what's next beyond mobile. How we, this is not how we're going to interface with our two thumbs in the future. It's going to be voice and, you have to be careful not to over-rotate either, right, because you could ruin the customer experience. Do you do that type of advanced, you know, research in total customer experience? >> Yeah, we do research. We actually also research how do they interact with us. You know, mobile is always a topic but our customers aren't engaging as mobile. You know, like it's, I mean-- >> You say they're not? >> They're not, no. >> They're out there. >> You know, our portal is all mobile enabled so you could go with it, but mostly it's still laptops, notebooks, et cetera, that they're using to engage us. So we haven't really invested a lot in that, but we invest in the digital experience, right, so make it easier, provide the tooling, don't force a customer to jump through, like, hoops to find something out. Give them the tool to find out. They want to self-solve a lot, right? Which goes back in the old discussion, is that deflection? But if a self-solve tool helps you, I think customers see, hey, this is value from Red Hat. >> If they can do it themselves, yeah. >> Marco: If they can and they can learn something, right? That part is good. >> Well thank you so much for joining us, Marco Bill-Peter, who is the vice-president, customer experience and engagement, at Red Hat. I'm Rebecca Knight, for Dave Vellante, thank you so much for joining us and we'll be back after this break. (light techno music)

>>from >>around the globe, >>it's the >>Cube with digital coverage of IBM. Think 2021 brought to you by IBM >>Well, good to have you here on the cube. We continue our conversations here as part of the IBM think initiative. I'm john Walsh your host here on the cube, joined today by Marco Novakovich, who is the co founder and Ceo of an stana, which is an IBM company they specialize in enterprise observe ability for cloud native applications. And Merkel joins us all the way from Germany near cologne Germany. Merkel good to see you today. How are you doing >>and good. Hi john nice to be here, >>you bet, thank you for taking the time today. Well, first off, let's just let's just give some definitions here. Enterprise observe ability. Um what is that? What are we talking about here? >>Yes. So observe ability is basically the next generation of monitoring, which means it provides data from a system from an application to the outside so that people from the outside can basically judge what's happening inside of an application. So think about your a big e commerce provider and you are, you have your shop application and it doesn't work, observe ability, gives you the ability to really deep dive and see all the relevant metrics, logs, uh, and, and application flows to understand why something is not working as you would expect. >>So if I'm just listening to this, I think, okay, I'm I'm monitoring my applications already right. I've got a PM and force and, and I kind of know what things are going on, what's happening, where the hiccups are all that, how, what is the enhancement here than in terms of observe ability taking it sounds like you're kind of taking a P. M. To a much higher level. >>Absolutely. I mean that's essentially how you can think about it and, and, and we see three things that really make us and stana and enterprise observe ability different. And number one is automation. So the way we gather this information is fully automated, so you don't have to configure anything. We get inside of your code, we analyze the flow up the application, we get the errors, the logs and the metrics fully automatic. And the second is getting context. One of the problems with monitoring is that you have all these monitoring data silos. So you have metrics on the one side locks in a different tool. What we build is a real context. So we tie those data automatically together so that you get real information out of all the data. And and and the third is that we provide actions. So basically use ai to figure out what the problem is and then automate things. Is it a problem resolution restarting a container or resizing your cloud? That's what we suggest automatically out of all the contacts and data that we've gathered. >>So you talk about automation context intelligence, you combine all that in one big bundle here, then basically um that's a big bundle, right? I'm not a giant vacuum if you will. You're ingesting all this information, you're looking for performance metrics. So you're trying to find problems um what's the complexity of tying all that together instead of keeping those functions separate? Um you know what or and what's the benefit to having all that kind of under one roof then? >>Yeah. So from the complexity point of view for the end customer, it's really easy because we do it automated for us as a vendor building this, it's super complex but we wanted to make it very easy for the user and I would say the benefit is that you get, we call it the mean time to repair, like the time from a problem to resolve the problem gets significantly reduced because normally you have to do that correlation of data manually and now with that context you get this automated by a machine and we even suggest you these intelligent actions to fix the problem. >>So so I'm sorry go ahead. >>Yeah. And by the way, one of the things why IBM acquired us and why we are so excited working together with IBM is the combination of that functionality with something like what's in a I ops because as I said, we are suggesting an action and the next step is really fully automating uh this action with something like what's new Ai Ops and the automation functionality that IBM has so that the end users are not only gets the information what to do the machine even does and fix the problem automatically. >>Mm Well, I'm wondering to just about about the kind of the volume that we're dealing with these days in terms of software capabilities and data, uh you've got obviously a lot more inputs, right, a lot more interaction going on, a lot more capabilities. Uh You've got apps uh they're kind of broken down the microservices now, so I mean you've got you got a lot more action basically, right, You've got a lot more going on and and um and what's the challenge to not only keeping up with that, but also building for the future for building for different kinds of capabilities and different kinds of interactions that maybe we can't even predict right now. >>Absolutely, yeah. So uh I'm 20 years in that space. And when I started, as you said, it was a very simple system. Right? You had an application server like web sphere, maybe a DB two database. So that was your applications like today. Applications are broken down and hundreds of little services that communicate with each other. And you can imagine if, if something breaks down in a system where you have two or three components, it's maybe not easy, but it's handled by a human to figure out what the problem is, if you have 1000 pieces that are somehow interconnected and something is broken. It is really hard to figure that out. And that's essentially the problem uh that we have to solve with the contacts with the automation, with ai to figure out how all these things are tied together and then analyze automatically for the user where issues are happening. And and and by the way, that's that's also when you look into the future, I think things will get more and more complicated. You can see now that people break down from micro service into functions. We get more serverless. We got to get more into a hybrid cloud environment where you operate on premise and in multiple clouds. So things get more complex, not less complex. From an architectural perspective, >>you bring up clouds to is this diagnostic I mean or do you work with a an exclusive cloud provider or you open for business? Basically >>we are open for business but but we have to support the different cloud technologies. So we support all the big public cloud vendors from, from IBM to amazon google Microsoft. But on the other hand, we see with enterprises Maybe there is 10 20 of the workload in the public cloud, but the rest is still on premises. And there's also a lot of legacy. So you have to bring all this together in one view and in one context. And that's one of the things we do. We not only support the modern cloud native applications, we also support the legacy on premise world, so that we can bring that together and that helps customer to migrate. Right? Because if they understand the workload in the on premise world, it's easier to transform that into a cloud native world. But it also gives an end to end view from the end user to we we always say from mobile to mainframe, right from a mobile app down to the mainframe application. We can give you an end to end view. >>Yeah, you talk about legacy uh in this case it may be cloud services that people use but there but you know, a lot of these legacy applications right to that are running that that are, they're still very useful and still highly functional, but at some point they're not going to be so would it be easier for you or what do you do in terms of talking with your clients in terms of what do they leave behind? What do they bring with them? How what kind of transition time frames should they be thinking about? Because I don't think you want to be supporting forever. Right. I mean, you you want to be evolving into newer, more efficient services and solutions and so you've got to bring them along too. I would think. Right. >>Yeah. But to be really honest, I think there are two ways of thinking. One is as as a vendor, you would love to support only the new technologies and don't have to support all the legacy technologies. But on the other hand, the reality is especially in bigger enterprises, you will find everything in every word. Right? And so if you want to give a holistic D view into the application stacks, you have to support also the older legacy parts because they are part of the business critical systems of the customer. And yes, we suggest to upgrade and go into a cloud native world. But being realistic, I think for the next decade We will have to live with a world where you have legacy and new things working together. I think that's just the reality. And in 10 years, what is new today is legacy then? Right. So we'll always, we will always live in a kind of hybrid world between legacy and and new things. >>Yeah, you got this technological continuum going on right. That you know that you know what's new and shiny today is going to be, you know, old hat in five years. But that's the beauty of it all. Obviously you talked about Ai Ops. Um, I mean let's go into that relationship a little bit if you would. I mean eventually what is observe ability set you up to do in terms of uh your artificial intelligence operations and what are the capabilities now that you're providing in terms of the observe ability solutions that Ai Ops can benefit from? >>So the way I think about these two categories is that observe abilities, the system of record. That's where all the data is collected and and put into context. So that's what we do as in stana is we take all the data metrics, locks, traces, profiles and put it into a system of record by the way in in in very high granularity. It's very important. So we, we do not sample. We have second granularity metrics. So very high quality data in that system of record where Ai ops is the system of action. This is a system where it takes the data that we have applies machine learning, statistical analytics etcetera on it to figure out for example root cause of problems or even predict problems in the future and then suggests actions. Right? What the next thing that AI does is it suggests or automates an action that you need to do to for example scale up the system, scale down the system scaling down because you want to safe cost for example these are all things that are happening in the system of action which is the IOP space >>when I think about what you're talking about in terms of observe ability. I think well who needs it? Everybody is probably the answer to that. Um Can you give us maybe just a couple of examples of some clients that you've worked with in terms of of particular needs that they had and then how you applied your observe ability platform to provide them with these kinds of solutions? >>Yeah I I remember a big e commerce vendor in the U. S. Approaching us. Uh last october they were approaching the black friday right where where they sell a lot of goods and and they had performance issues but they only had issues with certain types of customers and with their existing APM solution. They couldn't figure out where the problem is because existing solutions sample, which means if you have 1000 customers you only see one of them as an example because the other 999 are not in your in your sample. And so they used us because we don't sample with us. If you have they have more than a billion requests today. You see every of the one billion requests and offer a few days they had all the problems figure out. And that's what that was. One of the things that we really do differently is providing all the needed data, not sampling and then giving the context around the problem so that you can solve issues like performance issues on your e commerce system easily. So they switched and you can imagine switching the system before black friday, you only do that if it's really needed. So they were really under pressure and so they switched their A P. M. Tool to in stana to be able to to fulfill the big demand they have on these black friday days. >>All right. So uh I I before I let you go you were just saying they had a high degree of confidence. How are you sweating? That went out because that was not a small thing at all. I would I >>assume. Uh Yes, it's not a small thing. And to be honest also it's very hard to predict the traffic on black Fridays. Right? Uh And and in this case I remember our SRE team, they had almost 20 times the traffic of the normal day during that black friday. And we because we don't sample, we need to make sure that we can handle and process all these traces. But we did, we did pretty well. So I have high confidence in our platform that we can really handle big amounts of data. We have >>one >>of the biggest companies in the world, the biggest companies in these worlds. They use our tool to monitor billions of requests. So I think we have proven that it works. >>You know, I say you're smiling to about it. So I think it obviously it did work. It >>did work. But yeah, I'm sweating still. Yeah. >>Never let them see you sweat merkel. I think you're very good at that and obviously very good at enterprise observe ability. It's an interesting concept, certainly putting it well under practice and thanks for the time today to talk about it here as part of IBM think to, to share your company's success story. Thank you. Marco. >>Thanks for having me, john >>All right. We're talking about enterprise observe ability here. I P. M. Thank the initiative continues here on the cube. I'm john Walton. Thank you for joining us. >>Yeah. Mhm. >>Yeah.

>>from around the globe. It's the Cube with digital coverage of AWS reinvent 2020 sponsored by Intel and AWS. Yeah, welcome back to the cubes. Ongoing coverage of AWS reinvent virtual Cuba's Gone Virtual along with most events these days are all events and continues to bring our digital coverage of reinvent With me is Rahul Pathak, who is the vice president of analytics at AWS A Ro. It's great to see you again. Welcome. And thanks for joining the program. >>They have Great co two and always a pleasure. Thanks for having me on. >>You're very welcome. Before we get into your leadership discussion, I want to talk about some of the things that AWS has announced. Uh, in the early parts of reinvent, I want to start with a glue elastic views. Very notable announcement allowing people to, you know, essentially share data across different data stores. Maybe tell us a little bit more about glue. Elastic view is kind of where the name came from and what the implication is, >>Uh, sure. So, yeah, we're really excited about blue elastic views and, you know, as you mentioned, the idea is to make it easy for customers to combine and use data from a variety of different sources and pull them together into one or many targets. And the reason for it is that you know we're really seeing customers adopt what we're calling a lake house architectural, which is, uh, at its core Data Lake for making sense of data and integrating it across different silos, uh, typically integrated with the data warehouse, and not just that, but also a range of other purpose. Both stores like Aurora, Relation of Workloads or dynamodb for non relational ones. And while customers typically get a lot of benefit from using purpose built stores because you get the best possible functionality, performance and scale forgiven use case, you often want to combine data across them to get a holistic view of what's happening in your business or with your customers. And before glue elastic views, customers would have to either use E. T. L or data integration software, or they have to write custom code that could be complex to manage, and I could be are prone and tough to change. And so, with elastic views, you can now use sequel to define a view across multiple data sources pick one or many targets. And then the system will actually monitor the sources for changes and propagate them into the targets in near real time. And it manages the anti pipeline and can notify operators if if anything, changes. And so the you know the components of the name are pretty straightforward. Blues are survivalists E T Elling data integration service on blue elastic views about our about data integration their views because you could define these virtual tables using sequel and then elastic because it's several lists and will scale up and down to deal with the propagation of changes. So we're really excited about it, and customers are as well. >>Okay, great. So my understanding is I'm gonna be able to take what's called what the parlance of materialized views, which in my laypersons terms assumes I'm gonna run a query on the database and take that subset. And then I'm gonna be ableto thio. Copy that and move it to another data store. And then you're gonna automatically keep track of the changes and keep everything up to date. Is that right? >>Yes. That's exactly right. So you can imagine. So you had a product catalog for example, that's being updated in dynamodb, and you can create a view that will move that to Amazon Elasticsearch service. You could search through a current version of your catalog, and we will monitor your dynamodb tables for any changes and make sure those air all propagated in the real time. And all of that is is taken care of for our customers as soon as they defined the view on. But they don't be just kept in sync a za long as the views in effect. >>Let's see, this is being really valuable for a person who's building Looks like I like to think in terms of data services or data products that are gonna help me, you know, monetize my business. Maybe, you know, maybe it's a simple as a dashboard, but maybe it's actually a product. You know, it might be some content that I want to develop, and I've got transaction systems. I've got unstructured data, may be in a no sequel database, and I wanna actually combine those build new products, and I want to do that quickly. So So take me through what I would have to do. You you sort of alluded to it with, you know, a lot of e t l and but take me through in a little bit more detail how I would do that, you know, before this innovation. And maybe you could give us a sense as to what the possibilities are with glue. Elastic views? >>Sure. So, you know, before we announced elastic views, a customer would typically have toe think about using a T l software, so they'd have to write a neat L pipeline that would extract data periodically from a range of sources. They then have to write transformation code that would do things like matchup types. Make sure you didn't have any invalid values, and then you would combine it on periodically, Write that into a target. And so once you've got that pipeline set up, you've got to monitor it. If you see an unusual spike in data volume, you might have to add more. Resource is to the pipeline to make a complete on time. And then, if anything changed in either the source of the destination that prevented that data from flowing in the way you would expect it, you'd have toe manually, figure that out and have data, quality checks and all of that in place to make sure everything kept working but with elastic views just gets much simpler. So instead of having to write custom transformation code, you right view using sequel and um, sequel is, uh, you know, widely popular with data analysts and folks that work with data, as you well know. And so you can define that view and sequel. The view will look across multiple sources, and then you pick your destination and then glue. Elastic views essentially monitors both the source for changes as well as the source and the destination for any any issues like, for example, did the schema changed. The shape of the data change is something briefly unavailable, and it can monitor. All of that can handle any errors, but it can recover from automatically. Or if it can't say someone dropped an important table in the source. That was part of your view. You can actually get alerted and notified to take some action to prevent bad data from getting through your system or to prevent your pipeline from breaking without your knowledge and then the final pieces, the elasticity of it. It will automatically deal with adding more resource is if, for example, say you had a spiky day, Um, in the markets, maybe you're building a financial services application and you needed to add more resource is to process those changes into your targets more quickly. The system would handle that for you. And then, if you're monetizing data services on the back end, you've got a range of options for folks subscribing to those targets. So we've got capabilities like our, uh, Amazon data exchange, where people can exchange and monetize data set. So it allows this and to end flow in a much more straightforward way. It was possible before >>awesome. So a lot of automation, especially if something goes wrong. So something goes wrong. You can automatically recover. And if for whatever reason, you can't what happens? You quite ask the system and and let the operator No. Hey, there's an issue. You gotta go fix it. How does that work? >>Yes, exactly. Right. So if we can recover, say, for example, you can you know that for a short period of time, you can't read the target database. The system will keep trying until it can get through. But say someone dropped a column from your source. That was a key part of your ultimate view and destination. You just can't proceed at that point. So the pipeline stops and then we notify using a PS or an SMS alert eso that programmatic action can be taken. So this effectively provides a really great way to enforce the integrity of data that's going between the sources and the targets. >>All right, make it kindergarten proof of it. So let's talk about another innovation. You guys announced quicksight que, uh, kind of speaking to the machine in my natural language, but but give us some more detail there. What is quicksight Q and and how doe I interact with it. What What kind of questions can I ask it >>so quick? Like you is essentially a deep, learning based semantic model of your data that allows you to ask natural language questions in your dashboard so you'll get a search bar in your quick side dashboard and quick site is our service B I service. That makes it really easy to provide rich dashboards. Whoever needs them in the organization on what Q does is it's automatically developing relationships between the entities in your data, and it's able to actually reason about the questions you ask. So unlike earlier natural language systems, where you have to pre define your models, you have to pre define all the calculations that you might ask the system to do on your behalf. Q can actually figure it out. So you can say Show me the top five categories for sales in California and it'll look in your data and figure out what that is and will prevent. It will present you with how it parse that question, and there will, in line in seconds, pop up a dashboard of what you asked and actually automatically try and take a chart or visualization for that data. That makes sense, and you could then start to refine it further and say, How does this compare to what happened in New York? And we'll be able to figure out that you're tryingto overlay those two data sets and it'll add them. And unlike other systems, it doesn't need to have all of those things pre defined. It's able to reason about it because it's building a model of what your data means on the flight and we pre trained it across a variety of different domains So you can ask a question about sales or HR or any of that on another great part accused that when it presents to you what it's parsed, you're actually able toe correct it if it needs it and provide feedback to the system. So, for example, if it got something slightly off you could actually select from a drop down and then it will remember your selection for the next time on it will get better as you use it. >>I saw a demo on in Swamis Keynote on December 8. That was basically you were able to ask Quick psych you the same question, but in different ways, you know, like compare California in New York or and then the data comes up or give me the top, you know, five. And then the California, New York, the same exact data. So so is that how I kind of can can check and see if the answer that I'm getting back is correct is ask different questions. I don't have to know. The schema is what you're saying. I have to have knowledge of that is the user I can. I can triangulate from different angles and then look and see if that's correct. Is that is that how you verify or there are other ways? >>Eso That's one way to verify. You could definitely ask the same question a couple of different ways and ensure you're seeing the same results. I think the third option would be toe, uh, you know, potentially click and drill and filter down into that data through the dash one on, then the you know, the other step would be at data ingestion Time. Typically, data pipelines will have some quality controls, but when you're interacting with Q, I think the ability to ask the question multiple ways and make sure that you're getting the same result is a perfectly reasonable way to validate. >>You know what I like about that answer that you just gave, and I wonder if I could get your opinion on this because you're you've been in this business for a while? You work with a lot of customers is if you think about our operational systems, you know things like sales or E r. P systems. We've contextualized them. In other words, the business lines have inject context into the system. I mean, they kind of own it, if you will. They own the data when I put in quotes, but they do. They feel like they're responsible for it. There's not this constant argument because it's their data. It seems to me that if you look back in the last 10 years, ah, lot of the the data architecture has been sort of generis ized. In other words, the experts. Whether it's the data engineer, the quality engineer, they don't really have the business context. But the example that you just gave it the drill down to verify that the answer is correct. It seems to me, just in listening again to Swamis Keynote the other day is that you're really trying to put data in the hands of business users who have the context on the domain knowledge. And that seems to me to be a change in mindset that we're gonna see evolve over the next decade. I wonder if you could give me your thoughts on that change in the data architecture data mindset. >>David, I think you're absolutely right. I mean, we see this across all the customers that we speak with there's there's an increasing desire to get data broadly distributed into the hands of the organization in a well governed and controlled way. But customers want to give data to the folks that know what it means and know how they can take action on it to do something for the business, whether that's finding a new opportunity or looking for efficiencies. And I think, you know, we're seeing that increasingly, especially given the unpredictability that we've all gone through in 2020 customers are realizing that they need to get a lot more agile, and they need to get a lot more data about their business, their customers, because you've got to find ways to adapt quickly. And you know, that's not gonna change anytime in the future. >>And I've said many times in the The Cube, you know, there are industry. The technology industry used to be all about the products, and in the last decade it was really platforms, whether it's SAS platforms or AWS cloud platforms, and it seems like innovation in the coming years, in many respects is coming is gonna come from the ecosystem and the ability toe share data we've We've had some examples today and then But you hit on. You know, one of the key challenges, of course, is security and governance. And can you automate that if you will and protect? You know the users from doing things that you know, whether it's data access of corporate edicts for governance and compliance. How are you handling that challenge? >>That's a great question, and it's something that really emphasized in my leadership session. But the you know, the notion of what customers are doing and what we're seeing is that there's, uh, the Lake House architectural concept. So you've got a day late. Purpose build stores and customers are looking for easy data movement across those. And so we have things like blue elastic views or some of the other blue features we announced. But they're also looking for unified governance, and that's why we built it ws late formation. And the idea here is that it can quickly discover and catalog customer data assets and then allows customers to define granular access policies centrally around that data. And once you have defined that, it then sets customers free to give broader access to the data because they put the guardrails in place. They put the protections in place. So you know you can tag columns as being private so nobody can see them on gun were announced. We announced a couple of new capabilities where you can provide row based control. So only a certain set of users can see certain rose in the data, whereas a different set of users might only be able to see, you know, a different step. And so, by creating this fine grained but unified governance model, this actually sets customers free to give broader access to the data because they know that they're policies and compliance requirements are being met on it gets them out of the way of the analyst. For someone who can actually use the data to drive some value for the business, >>right? They could really focus on driving value. And I always talk about monetization. However monetization could be, you know, a generic term, for it could be saving lives, admission of the business or the or the organization I meant to ask you about acute customers in bed. Uh, looks like you into their own APs. >>Yes, absolutely so one of quick sites key strengths is its embed ability. And on then it's also serverless, so you could embed it at a really massive scale. And so we see customers, for example, like blackboard that's embedding quick side dashboards into information. It's providing the thousands of educators to provide data on the effectiveness of online learning. For example, on you could embed Q into that capability. So it's a really cool way to give a broad set of people the ability to ask questions of data without requiring them to be fluent in things like Sequel. >>If I ask you a question, we've talked a little bit about data movement. I think last year reinvent you guys announced our A three. I think it made general availability this year. And remember Andy speaking about it, talking about you know, the importance of having big enough pipes when you're moving, you know, data around. Of course you do. Doing tearing. You also announced Aqua Advanced Query accelerator, which kind of reduces bringing the computer. The data, I guess, is how I would think about that reducing that movement. But then we're talking about, you know, glue, elastic views you're copying and moving data. How are you ensuring you know, maintaining that that maximum performance for your customers. I mean, I know it's an architectural question, but as an analytics professional, you have toe be comfortable that that infrastructure is there. So how does what's A. W s general philosophy in that regard? >>So there's a few ways that we think about this, and you're absolutely right. I think there's data volumes were going up, and we're seeing customers going from terabytes, two petabytes and even people heading into the exabyte range. Uh, there's really a need to deliver performance at scale. And you know, the reality of customer architectures is that customers will use purpose built systems for different best in class use cases. And, you know, if you're trying to do a one size fits all thing, you're inevitably going to end up compromising somewhere. And so the reality is, is that customers will have more data. We're gonna want to get it to more people on. They're gonna want their analytics to be fast and cost effective. And so we look at strategies to enable all of this. So, for example, glue elastic views. It's about moving data, but it's about moving data efficiently. So What we do is we allow customers to define a view that represents the subset of their data they care about, and then we only look to move changes as efficiently as possible. So you're reducing the amount of data that needs to get moved and making sure it's focused on the essential. Similarly, with Aqua, what we've done, as you mentioned, is we've taken the compute down to the storage layer, and we're using our nitro chips to help with things like compression and encryption. And then we have F. P. J s in line to allow filtering an aggregation operation. So again, you're tryingto quickly and effectively get through as much data as you can so that you're only sending back what's relevant to the query that's being processed. And that again leads to more performance. If you can avoid reading a bite, you're going to speed up your queries. And that Awkward is trying to do. It's trying to push those operations down so that you're really reducing data as close to its origin as possible on focusing on what's essential. And that's what we're applying across our analytics portfolio. I would say one other piece we're focused on with performance is really about innovating across the stack. So you mentioned network performance. You know, we've got 100 gigabits per second throughout now, with the next 10 instances and then with things like Grab it on to your able to drive better price performance for customers, for general purpose workloads. So it's really innovating at all layers. >>It's amazing to watch it. I mean, you guys, it's a It's an incredible engineering challenge as you built this hyper distributed system. That's now, of course, going to the edge. I wanna come back to something you mentioned on do wanna hit on your leadership session as well. But you mentioned the one size fits all, uh, system. And I've asked Andy Jassy about this. I've had a discussion with many folks that because you're full and and of course, you mentioned the challenges you're gonna have to make tradeoffs if it's one size fits all. The flip side of that is okay. It's simple is you know, 11 of the Swiss Army knife of database, for example. But your philosophy is Amazon is you wanna have fine grained access and to the primitives in case the market changes you, you wanna be able to move quickly. So that puts more pressure on you to then simplify. You're not gonna build this big hairball abstraction layer. That's not what he gonna dio. Uh, you know, I think about, you know, layers and layers of paint. I live in a very old house. Eso your That's not your approach. So it puts greater pressure on on you to constantly listen to your customers, and and they're always saying, Hey, I want to simplify, simplify, simplify. We certainly again heard that in swamis presentation the other day, all about, you know, minimizing complexity. So that really is your trade office. It puts pressure on Amazon Engineering to continue to raise the bar on simplification. Isn't Is that a fair statement? >>Yeah, I think so. I mean, you know, I think any time we can do work, so our customers don't have to. I think that's a win for both of us. Um, you know, because I think we're delivering more value, and it makes it easier for our customers to get value from their data way. Absolutely believe in using the right tool for the right job. And you know you talked about an old house. You're not gonna build or renovate a house of the Swiss Army knife. It's just the wrong tool. It might work for small projects, but you're going to need something more specialized. The handle things that matter. It's and that is, uh, that's really what we see with that, you know, with that set of capabilities. So we want to provide customers with the best of both worlds. We want to give them purpose built tools so they don't have to compromise on performance or scale of functionality. And then we want to make it easy to use these together. Whether it's about data movement or things like Federated Queries, you can reach into each of them and through a single query and through a unified governance model. So it's all about stitching those together. >>Yeah, so far you've been on the right side of history. I think it serves you well on your customers. Well, I wanna come back to your leadership discussion, your your leadership session. What else could you tell us about? You know, what you covered there? >>So we we've actually had a bunch of innovations on the analytics tax. So some of the highlights are in m r, which is our managed spark. And to do service, we've been able to achieve 1.7 x better performance and open source with our spark runtime. So we've invested heavily in performance on now. EMR is also available for customers who are running and containerized environment. So we announced you Marnie chaos on then eh an integrated development environment and studio for you Marco D M R studio. So making it easier both for people at the infrastructure layer to run em are on their eks environments and make it available within their organizations but also simplifying life for data analysts and folks working with data so they can operate in that studio and not have toe mess with the details of the clusters underneath and then a bunch of innovation in red shift. We talked about Aqua already, but then we also announced data sharing for red Shift. So this makes it easy for red shift clusters to share data with other clusters without putting any load on the central producer cluster. And this also speaks to the theme of simplifying getting data from point A to point B so you could have central producer environments publishing data, which represents the source of truth, say into other departments within the organization or departments. And they can query the data, use it. It's always up to date, but it doesn't put any load on the producers that enables these really powerful data sharing on downstream data monetization capabilities like you've mentioned. In addition, like Swami mentioned in his keynote Red Shift ML, so you can now essentially train and run models that were built in sage maker and optimized from within your red shift clusters. And then we've also automated all of the performance tuning that's possible in red ships. So we really invested heavily in price performance, and now we've automated all of the things that make Red Shift the best in class data warehouse service from a price performance perspective up to three X better than others. But customers can just set red shift auto, and it'll handle workload management, data compression and data distribution. Eso making it easier to access all about performance and then the other big one was in Lake Formacion. We announced three new capabilities. One is transactions, so enabling consistent acid transactions on data lakes so you can do things like inserts and updates and deletes. We announced row based filtering for fine grained access control and that unified governance model and then automated storage optimization for Data Lake. So customers are dealing with an optimized small files that air coming off streaming systems, for example, like Formacion can auto compact those under the covers, and you can get a 78 x performance boost. It's been a busy year for prime lyrics. >>I'll say that, z that it no great great job, bro. Thanks so much for coming back in the Cube and, you know, sharing the innovations and, uh, great to see you again. And good luck in the coming here. Well, >>thank you very much. Great to be here. Great to see you. And hope we get Thio see each other in person against >>I hope so. All right. And thank you for watching everybody says Dave Volonte for the Cube will be right back right after this short break

>> Thank you for coming to this talk. My name is Amir Safavi-Naeini I'm an Assistant Professor in Applied Physics at Stanford University. And today I'm going to talk about a platform that we've been developing here that allows for quantum and classical information processing using photons and phonons or mechanical motion. So first I'd like to start off, with a picture of the people who did the work. These are graduate students and postdocs in my group. In addition, I want to say that a lot of the work especially on polling of the Lithium niobate was done in collaboration with Martin Fejer's group and in particular Dr.Langrock and Jata Mishra and Marc Jankowski Now our goal is to realize a platform, for quantum coherent information processing, that enables functionality which currently does not exist in other platforms that are available. So in particular we want to have, a very low loss non-linearity that is strong and can be dispersion engineered, to be made broadband. We'd like to make circuits that are programmable and reconfigurable, and that necessitates having efficient modulation and switching. And we'd also really like to have a platform that can leverage some of the advances with superconducting circuits to enable sort of large scale programmable dynamics between many different oscillators on a chip. So, in the next few years what we're really hoping to demonstrate are few photon, optical nonlinear effects by pushing the strength of these non-linearities and reducing the amount of loss. And we also want to demonstrate these coupled, sort of qubit and many oscillators systems. Now the Material system, that we think will enable a lot of these advances is based on lithium niobate, so lithium niobate is a fair electric crystal. It's used very widely in optical components and in acousto optics and then surface acoustic wave devices. It's a fair electric crystal, that has sort of a built-in polarization. And that enables, a lot of effects, which are very useful including the piezoelectric effect, electro- optic effects. And it has a very large K2 optical non-linearity. So it allows for three wave mixing. It also has some effects that are not so great for example, pyroelectricity but because it's very, established material system there's a lot of tricks on how to deal with some of the less attractive parts of it of this material. Now most, Surface Acoustic Wave, or optical devices that you would find are based on kind of bulk lithium niobate crystals that either use surface acoustic waves that propagate on a surface or, you know, bulk waves propagating through a whole crystal, or have a very weak weakly guided low index contrast waveguide that's patterned in the lithium niobate. This was the case until just a little over a decade ago. And this work from ETH Zurich came showing that thin-film lithium niobate can be, bonded and patterned. And Photonic circuits very similar to assigning circuits made from three fives or Silicon can be implemented in this material system. And this really led to a lot of different efforts from different labs. I would say the major breakthrough came, just a few years ago from Marko Loncar, where they demonstrate that high quality factors are possible to realize in this platform. And so they showed resonators with quality factors in the tens of billions corresponding to, line widths of tens of megahertz or losses of, just a few, DB per meter. And so that really changed the picture and you know a little bit after that in collaboration with Martin Fejer's group at Stanford they were able to demonstrate polling and so very large this version engineered nonlinear effects and these types of waveguides. And, and so that showed that, sort of very new types of circuits can be possible on this platform Now our approach is very similar. So we have a thin film of lithium niobate and this time it's on Sapphire instead of oxide or some polymer. and sometimes we put oxide on top. Some Silicon oxide on top, and we can also put electrodes these electrodes can be made out of a superconductor like niobium or aluminum or they can be gold depending on what we're trying to do. The sort of important thing here is that the large index contrast means that, light is guided in a very highly confined waveguide. And it supports bends with small bending radii. And that means we can have resonators that are very small. So the mode volume for the photonic resonators can be very small and as is well known. The interaction rate scale is, one over squared of mode volume. And so we're talking about an enhancement of around six orders of magnitude in the interaction length interaction lengths, over systems using sort of bulk components. And this is in a circuit that's sort of sub millimeter in size and its made on this platform. Now interaction length is important but also quality factor is very important. So when you make these things smaller you don't want to make them much less here. That's, you know, you can look at, for example a second harmonic generation efficiency in these types of resonances and that scales as Q, to the power of three essentially. So you need to achieve, you win a lot by going to low loss circuits. Now loss and non-linearity or sort of material and waveguide properties that we can engineer, but design of these circuits, careful design of these circuits is also very important. For example, you know, because these are highly confined waves and dielectric wave guides they can, you can support several different orders of modes especially if you're working for a broad band light waves that span, you know, an octave. And now when you try to couple light in and out of these structures, you have to be very careful that you're only picking up the polarizations that you care about, and you're not inducing extra loss channels effectively reducing the queue, even though there's no material loss if you're these parasitic coupling, can lead to lower Q. so the design is very important. This plot demonstrates, you know, the types of extrinsic to intrinsic coupling that are needed to achieve very high efficiency SHG, which is unrelated to optical parametric oscillation. And, you know, you, so you sort of have to work in a regime where the extrinsic couplings are much larger than the intrinsic couplings. And this is generally true for any type of quantum operation that you want to do. So just just low material loss itself isn't enough to design is also very important. In terms of where we are, on these three important aspects like getting large G large Q and large cap up. So we've been able to achieve high Q in, in these structures. This is a Q a of a couple million, we've also been able to you can see from a broad transmission spectrum through a grading coupler you can see a very evenly spaced modes showing that we're only coupling to one mode family. And we can see that the depth of the modes is also very large, you know, 90% or more. And that means that our extrinsic coupling in intrinsic coupling is also very large. So we've been able to kind of engineer these devices and to achieve this in terms of the interaction, I won't go over it too much but, you know, in collaboration with Marty Feres group we were able to pull both lithium niobate on insulator and lithium niobate on Sapphire. We'll be able to see a very efficient, sort of high slope proficiency second harmonic generation, you know achieving approaching 5000% per watt centimeters squared for 1560 to 780 conversion. So this is all work in progress. And so for now, I'd like to talk a little bit about the integration of acoustic and mechanical components. So, first of all why would we want to integrate mechanical components? Well, there's lots of cases where, for example you want to have an extremely high extinction switching functionality. That's very difficult to do with electro optics because they need to control the phase, extremely efficiently with extreme precision. You would need very large, long resonators and or large voltages becomes very difficult to achieve you know, 60 DB types of, switching. Mechanical systems. On the other hand, they can have very small mode volumes and can give you 60 DB switching without too many complications. Of course the drawback is that they're slower, but for a lot of applications, that doesn't matter too much. So in terms of being able to make integrate memes, switching and tuning with this platform, here's a device that achieves that so that each of these beams is actuated through the Piezoelectric effect and lithium niobate via this pair of electrodes that we put a voltage across. And when you put a voltage across these have been designed to leverage one of the off diagonal terms in the piezoelectric tensor, which causes bending. And so this bending generates a very large displacement in the center of this beam, in this beam, you might notice is composed of a grading, and this grading effectively generates it's photonic crystal cavity. So it generates a localize optical mode in the center which is very sensitive to these displacements. And what we're able to see in this system is that you know, just a few millivolts so 50 millivolts here shifts the resonance frequency by much more than a line width just a few millivolts is enough to shift by a line width. And so to achieve switching we can also tune this resonance across the full telecom band and these types of devices whether in waveguide resonator form can be extremely useful for sort of phase control in a large scale system, where you might want to have many many face switches on a chip to control phases with, with low loss, because these wave guides are shorter. You have lower loss propagating across them. Now, these interactions are fairly low frequency. When we go to higher frequency, we can use the electro-optic effect. And even the electro-optic effect even though it's very widely used, and well-known on a Photonic circuit like these lithium Niobate for tying circuits has, interesting consequences and device opportunities that don't exist on the bulk devices. So for example, let's look at single sideband modulation. This is what an electro-optic sort of standard electro optics, single sideband modulator looks like you, you take your light, you split into two parts, and then you modulate each of these arms. You modulate them out of phase with an RFC tone that's out of phase. And so now you generate side bands on both and now because they're modulating out of phase when they are recombined and on the output splitter and this mock sender interferometer you end up dropping one of the side bands and then the pump and you end up with a shifted side pan. So that's possible you can do single side band modulation with an electronic device but the caveat is that this is now fundamentally lossy. So, you know, you have generated, this other side band via modulation, and the sideband is simply being lost due to interference. So it's their, It's getting combined, it's getting scattered away because there's no mode that it can get connected to. So actually you know, this is going kind of an efficiency less than 3DB usually much less than 3DB. And that's fine if you just have one of these single sideband modulators because you can always amplify, you can send more power but if you're talking about a system and you have many of these and you can't put amplifiers everywhere then, or you're working with quantum information where loss is particularly bad. This is not an option. Now, when you use resonators, you have another option. So here's a device that tries to demonstrate this. This is two resonators that are brought into the near-field of each other. So they're coupled with each other over here where they're, which causes a splitting. And now when we tune the DC voltage was tuned one of these resonators by sort of changing the effective half lengths And one of these resonators tunes, the frequency, we can see an We should see an anti crossing between the two modes and at the center of this splitting this is versus voltage, a splitting at the center at this voltage, let's say here it's around 15 volts. We can see two residences two dips, when we probed the line field going through. And now if we send in the pump resonant, with one of these, and we modulate at this difference frequency we generate this red side band but we actually don't generate the blue side band because there's no optical density of state. So the, so because there's this other side may has just not generated. This system is now much more efficient. In fact, so in Marco Loncar has give they've demonstrated. You can get a hundred percent conversion. And we've also demonstrated this in a similar experiment showing that you can get very large sideband suppression. So, you know more than 30 DB suppression of the side bands with respect to the sideband that you care about It's also interesting that these interactions now preserve quantum coherence. And this is one path to creating links between superconducting microwave systems and optical components. Because now the microwave signal that's scattered here preserves its coherence. So we've also been able to do acoustic optic interactions at these high frequencies. This is a, this is an acoustic optic modulator that operates at a few gigahertz. Basically you generate electric field here which generates a propagating wave inside this transducer made out of lithium niobate. These are aluminum electrodes on top. The phonons are focused down into a small phononic waveguides that guides mechanical waves. And then these are brought into this crystal area where the sound and the Mo and the light are both convert confined to wavelength skill mode volume and they interact very strongly with each other. And the strong interaction leads to very efficient, effective electro-optic modulation. So here we've been able to see, with just a few microwatts of power, many, many side bands being generated. So this is a fact that they like tropic much later where the VPI is, a few thousands of a volt instead of, you know, several volts, which is sort of the off the shelf, electro-optic modulator that you would find. And importantly, we've been able to combine these, photonic and phononic circuits into the same platform. So this is a lithium niobate on same Lithium niobate on Sapphire platform. This is an acoustic transducer that generates mechanical waves that propagate in this lithium niobate waveguide. You can see them here and we can make phononic circuits now. so this is a ring resonate. It's a ring resonator for phonons. So we send sound waves through. And when it's resonance, when its frequency hits the ring residences, we see peaks. and this is, this is cheeks in the drop port coming out. And what's really nice about this platform is that we actually don't need to unlike unlike many memes platforms where you have to have released steps that are usually not compatible with, you know other devices here, there's no release steps. So the phonons are guided in that thin lithium niobate layer. The high Q of these mechanical modes shows that these mechanical resonances can be very coherent oscillators. And so we've also worked towards integrating these with very non-linear microwave circuits to create strongly interacting phonons and phonon circuits. So this is a example of an experiment we did over a year ago, where we have sort of a superconducting Qubit circuit with mechanical resonances made out of lithium niobate shunting the Qubit capacitor to ground. So now vibrations of this mechanical oscillator generate a voltage across these electrodes that couples to the Qubits voltage. And so now you have an interaction between this qubit and the mechanical oscillator, and we can see that in the spectrum of the qubit as we tune it across the frequency band. And we see splittings every time the qubit frequency approaches the mechanical resonance frequency. And infact this coupling is so large, that we were able to observe for the first time, the phonon spectrum. So we can detune this qubit away from the mechanical resonance. And now you have a dispersive shift on the qubit which is proportional to the number of phonons. And because number of photons is quantized. We can actually see, the different phonon levels in the qubit spectrum. Moving forward, we've been trying to, also understand what the sources of loss are in the system. And we've been able to do this by demonstrating by fabricating very large rays in these mechanical oscillators and looking at things like, their quality factor versus frequency. This is an example of a measurement that shows a jump in the quality factor when we enter the frequency band where we expect our phononic band gap for this period, periodic material is this jump you know, in principle,if loss were only due to clamping only due to acoustic waves leaking out in these out of these ends, then this change in quality factor quality factor should go to essentially infinite or should be ex exponential losses should be exponentially suppress with the length So these, but it's not. And that means we're actually limited by other loss channels. And we've been able to determine that these are two level systems and the lithium niobate by looking at the temperature dependence of these losses and seeing that they fit very well sort of standard models that exist for the effects of two level systems on microwave and mechanical resonances. We've also started experimenting with different materials. In fact, we've been able to see that, for example, going to lithium niobate, that's dope with magnesium oxide changes or reduces significantly the effect of the two level systems. And this is a really exciting direction of research that we're pursuing. So we're understanding these materials. So with that, I'd like to thank the sponsors. NTTResearch, of course, a lot of this work was funded by DARPA, ONR, RAO, DOE very generous funding from David and Lucile Packard foundation and others that are shown here. So thank you.

hello everybody and thank you for joining us today for the virtual verdict of BBC 2020 today's breakout session is entitled the shortest path to Vertica best practices for data warehouse migration ETL I'm Jeff Healey I'll leave verdict and marketing I'll be your host for this breakout session joining me today are Marco guesser and Mauricio lychee vertical product engineer is joining us from yume region but before we begin I encourage you to submit questions or comments or in the virtual session don't have to wait just type question in a comment in the question box below the slides that click Submit as always there will be a Q&A session the end of the presentation will answer as many questions were able to during that time any questions we don't address we'll do our best to answer them offline alternatively visit Vertica forums that formed at vertical comm to post your questions there after the session our engineering team is planning to join the forums to keep the conversation going also reminder that you can maximize your screen by clicking the double arrow button and lower right corner of the sides and yes this virtual session is being recorded be available to view on demand this week send you a notification as soon as it's ready now let's get started over to you mark marco andretti oh hello everybody this is Marco speaking a sales engineer from Amir said I'll just get going ah this is the agenda part one will be done by me part two will be done by Mauricio the agenda is as you can see big bang or piece by piece and the migration of the DTL migration of the physical data model migration of et I saw VTL + bi functionality what to do with store procedures what to do with any possible existing user defined functions and migration of the data doctor will be by Maurice it you want to talk about emeritus Rider yeah hello everybody my name is Mauricio Felicia and I'm a birth record pre-sales like Marco I'm going to talk about how to optimize that were always using some specific vertical techniques like table flattening live aggregated projections so let me start with be a quick overview of the data browser migration process we are going to talk about today and normally we often suggest to start migrating the current that allows the older disease with limited or minimal changes in the overall architecture and yeah clearly we will have to port the DDL or to redirect the data access tool and we will platform but we should minimizing the initial phase the amount of changes in order to go go live as soon as possible this is something that we also suggest in the second phase we can start optimizing Bill arouse and which again with no or minimal changes in the architecture as such and during this optimization phase we can create for example dog projections or for some specific query or optimize encoding or change some of the visual spools this is something that we normally do if and when needed and finally and again if and when needed we go through the architectural design for these operations using full vertical techniques in order to take advantage of all the features we have in vertical and this is normally an iterative approach so we go back to name some of the specific feature before moving back to the architecture and science we are going through this process in the next few slides ok instead in order to encourage everyone to keep using their common sense when migrating to a new database management system people are you often afraid of it it's just often useful to use the analogy of how smooth in your old home you might have developed solutions for your everyday life that make perfect sense there for example if your old cent burner dog can't walk anymore you might be using a fork lifter to heap in through your window in the old home well in the new home consider the elevator and don't complain that the window is too small to fit the dog through this is very much in the same way as Narita but starting to make the transition gentle again I love to remain in my analogy with the house move picture your new house as your new holiday home begin to install everything you miss and everything you like from your old home once you have everything you need in your new house you can shut down themselves the old one so move each by feet and go for quick wins to make your audience happy you do bigbang only if they are going to retire the platform you are sitting on where you're really on a sinking ship otherwise again identify quick wings implement published and quickly in Vertica reap the benefits enjoy the applause use the gained reputation for further funding and if you find that nobody's using the old platform anymore you can shut it down if you really have to migrate you can still go to really go to big battle in one go only if you absolutely have to otherwise migrate by subject area use the group all similar clear divisions right having said that ah you start off by migrating objects objects in the database that's one of the very first steps it consists of migrating verbs the places where you can put the other objects into that is owners locations which is usually schemers then what do you have that you extract tables news then you convert the object definition deploy them to Vertica and think that you shouldn't do it manually never type what you can generate ultimate whatever you can use it enrolls usually there is a system tables in the old database that contains all the roads you can export those to a file reformat them and then you have a create role and create user scripts that you can apply to Vertica if LDAP Active Directory was used for the authentication the old database vertical supports anything within the l dubs standard catalogued schemas should be relatively straightforward with maybe sometimes the difference Vertica does not restrict you by defining a schema as a collection of all objects owned by a user but it supports it emulates it for old times sake Vertica does not need the catalog or if you absolutely need the catalog from the old tools that you use it it usually said it is always set to the name of the database in case of vertical having had now the schemas the catalogs the users and roles in place move the take the definition language of Jesus thought if you are allowed to it's best to use a tool that translates to date types in the PTL generated you might see as a mention of old idea to listen by memory to by the way several times in this presentation we are very happy to have it it actually can export the old database table definition because they got it works with the odbc it gets what the old database ODBC driver translates to ODBC and then it has internal translation tables to several target schema to several target DBMS flavors the most important which is obviously vertical if they force you to use something else there are always tubes like sequel plots in Oracle the show table command in Tara data etc H each DBMS should have a set of tools to extract the object definitions to be deployed in the other instance of the same DBMS ah if I talk about youth views usually a very new definition also in the old database catalog one thing that you might you you use special a bit of special care synonyms is something that were to get emulated different ways depending on the specific needs I said I stop you on the view or table to be referred to or something that is really neat but other databases don't have the search path in particular that works that works very much like the path environment variable in Windows or Linux where you specify in a table an object name without the schema name and then it searched it first in the first entry of the search path then in a second then in third which makes synonym hugely completely unneeded when you generate uvl we remained in the analogy of moving house dust and clean your stuff before placing it in the new house if you see a table like the one here at the bottom this is usually corpse of a bad migration in the past already an ID is usually an integer and not an almost floating-point data type a first name hardly ever has 256 characters and that if it's called higher DT it's not necessarily needed to store the second when somebody was hired so take good care in using while you are moving dust off your stuff and use better data types the same applies especially could string how many bytes does a string container contains for eurozone's it's not for it's actually 12 euros in utf-8 in the way that Vertica encodes strings and ASCII characters one died but the Euro sign thinks three that means that you have to very often you have when you have a single byte character set up a source you have to pay attention oversize it first because otherwise it gets rejected or truncated and then you you will have to very carefully check what their best science is the best promising is the most promising approach is to initially dimension strings in multiples of very initial length and again ODP with the command you see there would be - I you 2 comma 4 will double the lengths of what otherwise will single byte character and multiply that for the length of characters that are wide characters in traditional databases and then load the representative sample of your cells data and profile using the tools that we personally use to find the actually longest datatype and then make them shorter notice you might be talking about the issues of having too long and too big data types on projection design are we live and die with our projects you might know remember the rules on how default projects has come to exist the way that we do initially would be just like for the profiling load a representative sample of the data collector representative set of already known queries from the Vertica database designer and you don't have to decide immediately you can always amend things and otherwise follow the laws of physics avoid moving data back and forth across nodes avoid heavy iOS if you can design your your projections initially by hand encoding matters you know that the database designer is a very tight fisted thing it would optimize to use as little space as possible you will have to think of the fact that if you compress very well you might end up using more time in reading it this is the testimony to run once using several encoding types and you see that they are l e is the wrong length encoded if sorted is not even visible while the others are considerably slower you can get those nights and look it in look at them in detail I will go in detail you now hear about it VI migrations move usually you can expect 80% of everything to work to be able to live to be lifted and shifted you don't need most of the pre aggregated tables because we have live like regain projections many BI tools have specialized query objects for the dimensions and the facts and we have the possibility to use flatten tables that are going to be talked about later you might have to ride those by hand you will be able to switch off casting because vertical speeds of everything with laps Lyle aggregate projections and you have worked with molap cubes before you very probably won't meet them at all ETL tools what you will have to do is if you do it row by row in the old database consider changing everything to very big transactions and if you use in search statements with parameter markers consider writing to make pipes and using verticals copy command mouse inserts yeah copy c'mon that's what I have here ask you custom functionality you can see on this slide the verticals the biggest number of functions in the database we compare them regularly by far compared to any other database you might find that many of them that you have written won't be needed on the new database so look at the vertical catalog instead of trying to look to migrate a function that you don't need stored procedures are very often used in the old database to overcome their shortcomings that Vertica doesn't have very rarely you will have to actually write a procedure that involves a loop but it's really in our experience very very rarely usually you can just switch to standard scripting and this is basically repeating what Mauricio said in the interest of time I will skip this look at this one here the most of the database data warehouse migration talks should be automatic you can use you can automate GDL migration using ODB which is crucial data profiling it's not crucial but game-changing the encoding is the same thing you can automate at you using our database designer the physical data model optimization in general is game-changing you have the database designer use the provisioning use the old platforms tools to generate the SQL you have no objects without their onus is crucial and asking functions and procedures they are only crucial if they depict the company's intellectual property otherwise you can almost always replace them with something else that's it from me for now Thank You Marco Thank You Marco so we will now point our presentation talking about some of the Vertica that overall the presentation techniques that we can implement in order to improve the general efficiency of the dot arouse and let me start with a few simple messages well the first one is that you are supposed to optimize only if and when this is needed in most of the cases just a little shift from the old that allows to birth will provide you exhaust the person as if you were looking for or even better so in this case probably is not really needed to to optimize anything in case you want optimize or you need to optimize then keep in mind some of the vertical peculiarities for example implement delete and updates in the vertical way use live aggregate projections in order to avoid or better in order to limit the goodbye executions at one time used for flattening in order to avoid or limit joint and and then you can also implement invert have some specific birth extensions life for example time series analysis or machine learning on top of your data we will now start by reviewing the first of these ballots optimize if and when needed well if this is okay I mean if you get when you migrate from the old data where else to birth without any optimization if the first four month level is okay then probably you only took my jacketing but this is not the case one very easier to dispute in session technique that you can ask is to ask basket cells to optimize the physical data model using the birth ticket of a designer how well DB deal which is the vertical database designer has several interfaces here I'm going to use what we call the DB DB programmatic API so basically sequel functions and using other databases you might need to hire experts looking at your data your data browser your table definition creating indexes or whatever in vertical all you need is to run something like these are simple as six single sequel statement to get a very well optimized physical base model you see that we start creating a new design then we had to be redesigned tables and queries the queries that we want to optimize we set our target in this case we are tuning the physical data model in order to maximize query performances this is why we are using my design query and in our statement another possible journal tip would be to tune in order to reduce storage or a mix between during storage and cheering queries and finally we asked Vertica to produce and deploy these optimized design in a matter of literally it's a matter of minutes and in a few minutes what you can get is a fully optimized fiscal data model okay this is something very very easy to implement keep in mind some of the vertical peculiarities Vaska is very well tuned for load and query operations aunt Berta bright rose container to biscuits hi the Pharos container is a group of files we will never ever change the content of this file the fact that the Rose containers files are never modified is one of the political peculiarities and these approach led us to use minimal locks we can add multiple load operations in parallel against the very same table assuming we don't have a primary or unique constraint on the target table in parallel as a sage because they will end up in two different growth containers salad in read committed requires in not rocket fuel and can run concurrently with insert selected because the Select will work on a snapshot of the catalog when the transaction start this is what we call snapshot isolation the kappa recovery because we never change our rows files are very simple and robust so we have a huge amount of bandages due to the fact that we never change the content of B rows files contain indiarose containers but on the other side believes and updates require a little attention so what about delete first when you believe in the ethica you basically create a new object able it back so it appeared a bit later in the Rose or in memory and this vector will point to the data being deleted so that when the feed is executed Vertica will just ignore the rules listed in B delete records and it's not just about the leak and updating vertical consists of two operations delete and insert merge consists of either insert or update which interim is made of the little insert so basically if we tuned how the delete work we will also have tune the update in the merge so what should we do in order to optimize delete well remember what we said that every time we please actually we create a new object a delete vector so avoid committing believe and update too often we reduce work the work for the merge out for the removal method out activities that are run afterwards and be sure that all the interested projections will contain the column views in the dedicate this will let workers directly after access the projection without having to go through the super projection in order to create the vector and the delete will be much much faster and finally another very interesting optimization technique is trying to segregate the update and delete operation from Pyrenean third workload in order to reduce lock contention beliefs something we are going to discuss and these contain using partition partition operation this is exactly what I want to talk about now here you have a typical that arouse architecture so we have data arriving in a landing zone where the data is loaded that is from the data sources then we have a transformation a year writing into a staging area that in turn will feed the partitions block of data in the green data structure we have at the end those green data structure we have at the end are the ones used by the data access tools when they run their queries sometimes we might need to change old data for example because we have late records or maybe because we want to fix some errors that have been originated in the facilities so what we do in this case is we just copied back the partition we want to change or we want to adjust from the green interior a the end to the stage in the area we have a very fast operation which is Tokyo Station then we run our updates or our adjustment procedure or whatever we need in order to fix the errors in the data in the staging area and at the very same time people continues to you with green data structures that are at the end so we will never have contention between the two operations when we updating the staging area is completed what we have to do is just to run a swap partition between tables in order to swap the data that we just finished to adjust in be staging zone to the query area that is the green one at the end this swap partition is very fast is an atomic operation and basically what will happens is just that well exchange the pointer to the data this is a very very effective techniques and lot of customer useless so why flops on table and live aggregate for injections well basically we use slot in table and live aggregate objection to minimize or avoid joint this is what flatten table are used for or goodbye and this is what live aggregate projections are used for now compared to traditional data warehouses better can store and process and aggregate and join order of magnitudes more data that is a true columnar database joint and goodbye normally are not a problem at all they run faster than any traditional data browse that page there are still scenarios were deficits are so big and we are talking about petabytes of data and so quickly going that would mean be something in order to boost drop by and join performances and this is why you can't reduce live aggregate projections to perform aggregations hard loading time and limit the need for global appear on time and flux and tables to combine information from different entity uploading time and again avoid running joint has query undefined okay so live aggregate projections at this point in time we can use live aggregate projections using for built in aggregate functions which are some min Max and count okay let's see how this works suppose that you have a normal table in this case we have a table unit sold with three columns PIB their time and quantity which has been segmented in a given way and on top of this base table we call it uncle table we create a projection you see that we create the projection using the salad that will aggregate the data we get the PID we get the date portion of the time and we get the sum of quantity from from the base table grouping on the first two columns so PID and the date portion of day time okay what happens in this case when we load data into the base table all we have to do with load data into the base table when we load data into the base table we will feel of course big injections that assuming we are running with k61 we will have to projection to projections and we will know the data in those two projection with all the detail in data we are going to load into the table so PAB playtime and quantity but at the very same time at the very same time and without having to do nothing any any particular operation or without having to run any any ETL procedure we will also get automatically in the live aggregate projection for the data pre aggregated with be a big day portion of day time and the sum of quantity into the table name total quantity you see is something that we get for free without having to run any specific procedure and this is very very efficient so the key concept is that during the loading operation from VDL point of view is executed again the base table we do not explicitly aggregate data or we don't have any any plc do the aggregation is automatic and we'll bring the pizza to be live aggregate projection every time we go into the base table you see the two selection that we have we have on in this line on the left side and you see that those two selects will produce exactly the same result so running select PA did they trying some quantity from the base table or running the select star from the live aggregate projection will result exactly in the same data you know this is of course very useful but is much more useful result that if we and we can observe this if we run an explained if we run the select against the base table asking for this group data what happens behind the scene is that basically vertical itself that is a live aggregate projection with the data that has been already aggregating loading phase and rewrite your query using polite aggregate projection this happens automatically you see this is a query that ran a group by against unit sold and vertical decided to rewrite this clearly as something that has to be collected against the light aggregates projection because if I decrease this will save a huge amount of time and effort during the ETL cycle okay and is not just limited to be information you want to aggregate for example another query like select count this thing you might note that can't be seen better basically our goodbyes will also take advantage of the live aggregate injection and again this is something that happens automatically you don't have to do anything to get this okay one thing that we have to keep very very clear in mind Brassica what what we store in the live aggregate for injection are basically partially aggregated beta so in this example we have two inserts okay you see that we have the first insert that is entered in four volts and the second insert which is inserting five rules well in for each of these insert we will have a partial aggregation you will never know that after the first insert you will have a second one so better will calculate the aggregation of the data every time irin be insert it is a key concept and be also means that you can imagine lies the effectiveness of bees technique by inserting large chunk of data ok if you insert data row by row this technique live aggregate rejection is not very useful because for every goal that you insert you will have an aggregation so basically they'll live aggregate injection will end up containing the same number of rows that you have in the base table but if you everytime insert a large chunk of data the number of the aggregations that you will have in the library get from structure is much less than B base data so this is this is a key concept you can see how these works by counting the number of rows that you have in alive aggregate injection you see that if you run the select count star from the solved live aggregate rejection the query on the left side you will get four rules but actually if you explain this query you will see that he was reading six rows so this was because every of those two inserts that we're actively interested a few rows in three rows in India in the live aggregate projection so this is a key concept live aggregate projection keep partially aggregated data this final aggregation will always happen at runtime okay another which is very similar to be live aggregate projection or what we call top K projection we actually do not aggregate anything in the top case injection we just keep the last or limit the amount of rows that we collect using the limit over partition by all the by clothes and this again in this case we create on top of the base stable to top gay projection want to keep the last quantity that has been sold and the other one to keep the max quantity in both cases is just a matter of ordering the data in the first case using the B time column in the second page using quantity in both cases we fill projection with just the last roof and again this is something that we do when we insert data into the base table and this is something that happens automatically okay if we now run after the insert our select against either the max quantity okay or be lost wanted it okay we will get the very last you see that we have much less rows in the top k projections okay we told at the beginning that basically we can use for built-in function you might remember me max sum and count what if I want to create my own specific aggregation on top of the lid and customer sum up because our customers have very specific needs in terms of live aggregate projections well in this case you can code your own live aggregate production user-defined functions so you can create the user-defined transport function to implement any sort of complex aggregation while loading data basically after you implemented miss VPS you can deploy using a be pre pass approach that basically means the data is aggregated as loading time during the data ingestion or the batch approach that means that the data is when that woman is running on top which things to remember on live a granade projections they are limited to be built in function again some max min and count but you can call your own you DTF so you can do whatever you want they can reference only one table and for bass cab version before 9.3 it was impossible to update or delete on the uncle table this limit has been removed in 9.3 so you now can update and delete data from the uncle table okay live aggregate projection will follow the segmentation of the group by expression and in some cases the best optimizer can decide to pick the live aggregates objection or not depending on if depending on the fact that the aggregation is a consistent or not remember that if we insert and commit every single role to be uncoachable then we will end up with a live aggregate indirection that contains exactly the same number of rows in this case living block or using the base table it would be the same okay so this is one of the two fantastic techniques that we can implement in Burtka this live aggregate projection is basically to avoid or limit goodbyes the other which we are going to talk about is cutting table and be reused in order to avoid the means for joins remember that K is very fast running joints but when we scale up to petabytes of beta we need to boost and this is what we have in order to have is problem fixed regardless the amount of data we are dealing with so how what about suction table let me start with normalized schemas everybody knows what is a normalized scheme under is no but related stuff in this slide the main scope of an normalized schema is to reduce data redundancies so and the fact that we reduce data analysis is a good thing because we will obtain fast and more brides we will have to write into a database small chunks of data into the right table the problem with these normalized schemas is that when you run your queries you have to put together the information that arrives from different table and be required to run joint again jointly that again normally is very good to run joint but sometimes the amount of data makes not easy to deal with joints and joints sometimes are not easy to tune what happens in in the normal let's say traditional data browser is that we D normalize the schemas normally either manually or using an ETL so basically we have on one side in this light on the left side the normalized schemas where we can get very fast right on the other side on the left we have the wider table where we run all the three joints and pre aggregation in order to prepare the data for the queries and so we will have fast bribes on the left fast reads on the Left sorry fast bra on the right and fast read on the left side of these slides the probability in the middle because we will push all the complexity in the middle in the ETL that will have to transform be normalized schema into the water table and the way we normally implement these either manually using procedures that we call the door using ETL this is what happens in traditional data warehouse is that we will have to coach in ETL layer in order to round the insert select that will feed from the normalized schema and right into the widest table at the end the one that is used by the data access tools we we are going to to view store to run our theories so this approach is costly because of course someone will have to code this ETL and is slow because someone will have to execute those batches normally overnight after loading the data and maybe someone will have to check the following morning that everything was ok with the batch and is resource intensive of course and is also human being intensive because of the people that will have to code and check the results it ever thrown because it can fail and introduce a latency because there is a get in the time axis between the time t0 when you load the data into be normalized schema and the time t1 when we get the data finally ready to be to be queried so what would be inverter to facilitate this process is to create this flatten table with the flattened T work first you avoid data redundancy because you don't need the wide table on the normalized schema on the left side second is fully automatic you don't have to do anything you just have to insert the data into the water table and the ETL that you have coded is transformed into an insert select by vatika automatically you don't have to do anything it's robust and this Latin c0 is a single fast as soon as you load the data into the water table you will get all the joints executed for you so let's have a look on how it works in this case we have the table we are going to flatten and basically we have to focus on two different clauses the first one is you see that there is one table here I mentioned value 1 which can be defined as default and then the Select or set using okay the difference between the fold and set using is when the data is populated if we use default data is populated as soon as we know the data into the base table if we use set using Google Earth to refresh but everything is there I mean you don't need them ETL you don't need to code any transformation because everything is in the table definition itself and it's for free and of course is in latency zero so as soon as you load the other columns you will have the dimension value valued as well okay let's see an example here suppose here we have a dimension table customer dimension that is on the left side and we have a fact table on on the right you see that the fact table uses columns like o underscore name or Oh the score city which are basically the result of the salad on top of the customer dimension so Beezus were the join is executed as soon as a remote data into the fact table directly into the fact table without of course loading data that arise from the dimension all the data from the dimension will be populated automatically so let's have an example here suppose that we are running this insert as you can see we are running be inserted directly into the fact table and we are loading o ID customer ID and total we are not loading made a major name no city those name and city will be automatically populated by Vertica for you because of the definition of the flood table okay you see behave well all you need in order to have your widest tables built for you your flattened table and this means that at runtime you won't need any join between base fuck table and the customer dimension that we have used in order to calculate name and city because the data is already there this was using default the other option was is using set using the concept is absolutely the same you see that in this case on the on the right side we have we have basically replaced this all on the school name default with all underscore name set using and same is true for city the concept that I said is the same but in this case which we set using then we will have to refresh you see that we have to run these select trash columns and then the name of the table in this case all columns will be fresh or you can specify only certain columns and this will bring the values for name and city reading from the customer dimension so this technique this technique is extremely useful the difference between default and said choosing just to summarize the most important differences remember you just have to remember that default will relate your target when you load set using when you refresh end and in some cases you might need to use them both so in some cases you might want to use both default end set using in this example here we'll see that we define the underscore name using both default and securing and this means that we love the data populated either when we load the data into the base table or when we run the Refresh this is summary of the technique that we can implement in birth in order to make our and other browsers even more efficient and well basically this is the end of our presentation thank you for listening and now we are ready for the Q&A session you

>>LA from Las Vegas. It's the cube covering AWS reinvent 2019 brought to you by Amazon web services and along with its ecosystem partners. >>Welcome back to the queue from Las Vegas. We are live at AWS reinvent 19 Lisa Martin with John furrier. We've been having lots of great conversations. John, we're about to have another one cause we always love to talk about customer proof in the putting. Please welcome a couple of guests. We have Rafael, director of analytics and data management from triple a life. Welcome. Thanks for having me. Really appreciate it. Our pleasure. And from Burkle anchor Jane, the SVP of cloud platforms. Welcome. Thank you. Thank you so much. Pleasure to be here. So here we are in this, I can't see of people around us as, as growing exponential a by the hour here, but awkward. Let's start with you give her audience an understanding of Merkel, who you are and what you do. >>Yeah, absolutely. So Marco is a global performance marketing agency. We are part of a dental agent network and a, it's almost about 9,000 to 10,000 people worldwide. It's a global agency. What differentiates Merkel from rest of the other marketing agencies is our deep roots and data driven approach. We embrace technology. It's embedded in all our, all our solutions that we take to market. Um, and that's what we pride ourselves with. So, um, that's basically a high level pitch about Merkel. What differentiates us, my role, uh, I lead the cloud transformation for Merkel. Um, uh, basically think of my team as the think tanks who bring in the new technology, come up with a new way of rolling out solutions product I solutions, uh, disruptive solutions, which helps our clients and big fortune brands such as triple life insurance, uh, to transform their marketing ecosystem. >>So let's go ahead and dig. A lot of folks probably know AAA life, but, but Raphael, give us a little bit of an overview. This is a 50 year old organization. >>So we celebrate our 50th 50 year anniversary this year. Actually, we're founded in 1969. So everybody life insurance, we endeavor to be the provider of choice for a AAA member. Tell them to protect what matters most to them. And we offer a diverse set of insurance products across just about every channel. Um, and um, we engage with Merkel, uh, earlier, the, um, in 2018 actually to, to, uh, to build a nice solution that allows us to even better serve the needs of the members. Uh, my role, I am the, I lead our analytics and data management work. So helping us collect data and manage better and better leverage it to support the needs of members. >>So a trip, I can't even imagine the volumes of data that you're dealing with, but it's also, this is people's data, right? This is about insurance, life insurance, the volume of it. How have you, what were some of the things that you said? All right guys, we need to change how we're managing the data because we know there's probably a lot more business value, maybe new services that we can get our on it or eyes >>on it. >>So, so that was, that was it. So as an organization, uh, I want to underscore what you said. We make no compromises when it comes to the safety of our, of our members data. And we take every step possible to ensure that it is managed in a responsible and safe way. But we knew that on, on the platform that we had prior to this, we weren't, we weren't as italics. We wanted to be. We would find that threaten processes would take spans of weeks in order to operate or to run. And that just didn't allow us to provide the member experience that we wanted. So we built this new solution and this solution updates every day, right? There's no longer multi-week cycle times and tumbler processes happen in real time, which allows us to go to market with more accurate and more responsive programs to our members. >>Can you guys talk about the Amazon and AWS solution? How you guys using Amazon's at red shift? Can he says, you guys losing multiple databases, give us a peek into the Amazon services that you guys are taking advantage of that anchor. >>Yeah, please. Um, so basically when we were approached by AAA life to kind of come in and you know, present ourselves our credentials, one thing that differentiated there in that solution page was uh, bringing Amazon to the forefront because cloud, you know, one of the issue that Ravel and his team were facing were scalability aspect. You know, the performance was, was not up to the par, I believe you guys were um, on a two week cycle. That data was a definition every two weeks. And how can we turn that around and know can only be possible to, in our disruptive technologies that Amazon brings to the forefront. So what we built was basically it's a complete Amazon based cloud native architecture. Uh, we leveraged AWS with our chip as the data warehouse platform to integrate basically billions and billions of rows from a hundred plus sources that we are bringing in on a daily basis. >>In fact, actually some of the sources are the fresh on a real time basis. We are catching real time interactions of users on the website and then letting Kimberly the life make real time decisions on how we actually personalize their experience. So AWS, Redshift, you know, definitely the center's centerpiece. Then we are also leveraging a cloud native ELT technology extract load and transform technology called. It's a third party tool, but again, a very cloud native technology. So the whole solution leverage is Python to some extent. And then our veil can talk about AI and machine learning that how they are leveraging AWS ecosystem there. >>Yeah. So that was um, so, uh, I anchor said it right. One thing that differentiated Merkel was that cloud first approach, right? Uh, we looked at it what a, all of the analysts were saying. We went to all the key vendors in this space. We saw the, we saw the architecture is, and when Merkel walked in and presented that, um, that AWS architecture, it was great for me because if nausea immediately made sense, there was no wizardry around, I hope this database scales. I was confident that Redshift and Lambda and dynamo would this go to our use cases. So it became a lot more about are we solving the right business problem and less about do we have the right technologies. So in addition to what Ankur mentioned, we're leveraging our sort of living RNR studio, um, in AWS as well as top low frat for our machine learning models and for business intelligence. >>And more recently we've started transition from R to a Python as a practitioner on the keynote today. Slew a new thing, Sage maker studio, an IDE for machine learning framework. I mean this is like a common set. Like finally, I couldn't have been more excited right? That, that was my Superbowl moment. Um, I was, I was as I was, we were actually at dinner yesterday and I was mentioning Tonker, this is my wishlist, right? I want AWS to make a greater investment in that end user data scientists experience in auto ML and they knocked it out of the park. Everything they announced today, I was just, I was texting frat. Wow, this is amazing. I can't wait to go home. There's a lot of nuances to, and a lot of these announcements, auto ML for instance. Yeah. Really big deal the way they did it. >>And again, the ID who would've thought, I mean this is duh, why didn't we think about this sooner? Yeah. With auto ML that that focus on transparency. Right. And then I think about a year ago we went to market and we ended up not choosing any solutions because they hadn't solved for once you've got a model built, how do you effectively migrated from let's say an analyst who might not have the, the ML expertise to a data science team and the fact that AWS understood out of the gate that you need that transparent all for it. I'm really excited for that. What do you think the impacts are going to be more uptake on the data science side? What do you think the impact of this and the, so I think for, I think we're going to see, um, that a lot of our use cases are going to part a lot less effort to spin up. >>So we're going to see much more, much faster pilots. We're going to have a much clearer sense of is this worth it? Is this something we should continue to invest in and to me we should drive and I expect that a lot, much larger percentage of my team, the analysts are going to be involved in data and data science and machine learning. So I'm really excited about that. And also the ability to inquire, to integrate best practices into what we're doing out of the gate. Right? So software engineers figured out profiling, they figured out the bugging and these are things that machine learners are picking up. Now the fact that you're front and center is really excited. Superbowl moment. You can be like the new England Patriots, 17 straight AFC championship games. Boston. Gosh, I could resist. Uh, they're all Seattle. They're all Seattle here and Amazon. I don't even bring Seattle Patriots up here and Amazon, >>we are the ESPN of tech news that we have to get in as far as conversation. But I want to kind of talk a little bit, Raphael about the transformation because presumably in, in every industry, especially in insurance, there are so many born in the cloud companies that are a lot, they're a lot more agile and they are chasing what AAA life and your competitors and your peers are doing. What your S establishing with the help of anchor and Merkel, how does this allow you to actually take the data that you had, expand it, but also extract insights from maybe competitive advantages that you couldn't think about before? >>Yeah, so I think, uh, so as an organization, even though we're 50 years old, one of the things that drew me to the company and it's really exciting is it's unrelated to thrusting on its laurels, right? I think there's tremendous hunger and appetite within our executive group to better serve our members and to serve more members. And what this technology is allowed is the technology is not a limiting factor. It's an enabling factors. We're able to produce more models, more performant models, process more of IO data, build more features. Um, we've managed to do away with a lot of the, you know, if you take it and you look at it this way and squeeze it and maybe it'll work and systematize more aspects of our reporting and our campaign development and our model development and the observability, the visibility of just the ability to be agile and have our data be a partner to what we're trying to accomplish. That's been really great. >>You talked about the significant reduction in cycle times. If we go back up to the executive suite from a business differentiation perspective, is the senior leadership at AAA understanding what this cloud infrastructure is going to enable their business to achieve? >>Absolutely. So, so our successes here I think have been instrumental in encouraging our organization to continue to invest in cloud. And uh, we're an active, we're actively considering and discussing additional cloud initiatives, especially around the areas of machine learning and AI. >>And the auger question for you in terms of, of your expertise, in your experience as we look at how cloud is changing, John, you know, educate us on cloud cloud, Tuto, AI machine learning. What are, as, as these, as businesses, as industries have the opportunity to for next gen cloud, what are some of the next industries that you think are really prime to be completely transformed? >>Um, I'm in that are so many different business models. If you look around, one thing I would like to actually touch upon what we are seeing from Merkel standpoint is the digital transformation and how customers in today's world they are, you know, how brands are engaging with their customers and how customers are engaging with the brands. Especially that expectations customer is at the center stage here they are the ones who are driving the whole customer engagement journey, right? How all I am browsing a catalog of a particular brand on my cell phone and then I actually purchased right then and there and if I have an issue I can call them or I can go to social media and log a complaint. So that's whole multi channel, you know, aspect of this marketing ecosystem these days. I think cloud is the platform which is enabling that, right? >>This cannot happen without cloud. I'm going to look at, Raphael was just describing, you know, real time interaction, real time understanding the behavior of the customer in real time and engaging with them based on their need at that point of time. If you have technologies like Sage maker, if you have technologies like AWS Redship you have technologies like glue, Kinesis, which lets you bring in data from all these disparate sources and give you the ability to derive some insights from that data in that particular moment and then interact with the customer right then and there. That's exactly what we are talking about. And this can only happen through cloud so, so that's my 2 cents are where they are, what we from Merkel standpoint, we are looking into the market. That's what we are helping our brands through to >>client. I completely agree. I think that the change from capital and operation, right to no longer house to know these are all the sources and all the use cases and everything that needs to happen before you start the project and the ability to say, Hey, let's get going. Let's deliver value in the way that we've had and continue to have conversations and deliver new features, new stores, a new functionality, and at the same time, having AWS as a partner who's, who's building an incremental value. I think just last week I was really excited with the changes they've made to integrate Sage maker with their databases so you can score from the directly from the database. So it feels like all these things were coming together to allow us as a company to better off on push our aims and exciting time. >>It is exciting. Well guys, I wish we had more time, but we are out of time. Thank you Raphael and anchor for sharing with Merkel and AAA. Pleasure. All right. Take care. Or John furrier. I am Lisa Martin and you're watching the cube from Vegas re-invent 19 we'll be right back.

>> live from San Francisco, celebrating 10 years of high tech coverage. It's the Cube covering Veum World 2019. Brought to you by VM Wear and its ecosystem partners. >> Welcome back to San Francisco, everybody. My name is David Lantz. I'm here with my co host John Troia. This is Day three of V M World 2019 2 sets. >> This is >> our 10th year at the M. World Cube is the leader in live enterprise tech coverage. Marry on Blondie is here. He's the C m o and chief evangelist that 10 tree by DDN Yes, sir. He's joined by Graham Breezes The Field CTO at 10 Tree also by DDN Recent acquisition jets Great to see you. >> Likewise, as they say, we're back. I like I like to call it a hibernation in the sense that people may have not known where did Ian or 10 Trias and Tension by Dede and, as the name implies, were acquired a year ago at the M World August 31st of 2018. And in the year since, we've been ableto invest in engineering support, my joining the company in marketing to take this solution, we've been able to save thousands of customers millions of man hours and bring it to a larger number of users. Way >> first saw 10 tree, we said, Wow, this is all about simplification. And Jonah Course you remember that when you go back to the early early Dick Cube days of of'em World, very complex storage was a major challenge. 10 Tree was all about simplifying that. Of course, we know DDN as well is the high performance specialist and have worked with those guys for a number of years. But take >> us >> back Married to the original vision of 10 Cherie. Is that original vision still alive? How was it evolved? >> Well, I'd say that it's, ah, number one reason why we're a part of the DD and family of brands because, as, ah, portfolio company, they're looking good. Bring technologies. I'm the marketing guy for our enterprise or virtual ization audience, and the product sets that cover high performance computing have their own audience. So for me, I'm focused on that. Graham's also focused on that, and, uh, really what continues to make us different today is the fact we were designed to learn from the beginning to understand how virtual machines end to end work with infrastructure. And that's really the foundation of what makes us different today. The same thing, right? >> So from the very beginning we were we were built to understand the work clothes that we service in the data center. So and that was virtual machines. We service those on multiple hyper visors today in terms of being able to understand those workloads intrinsically gives us a tremendous capability. Thio place. I owe again understanding that the infrastructure network storage, hyper visor, uh, weaken view that end end in terms of a latent a graph and give customers and insight into the infrastructure how it's performing. I would say that we're actually extending that further ways in terms of additional workload that we're gonna be able to take on later this year. >> So I know a lot >> of storage admits, although I I only play one on >> TV, but, uh, no, consistently >> throughout the years, right? 10 tree user experiences that is the forefront there. And in fact, they they often some people have said, You know what? I really want to get something done. I grab my tent Reeboks and so it can't talk. Maybe some examples of one example of why the user experience how the user experiences differ or why, why it's different. >> I'll start off by saying that I had a chance being new to the company just two weeks to meet a lot of 10 tree users. And prior to taking the job, I talkto us some folks behind the scenes, and they all told me the same thing. But what I was so interested to hear is that if they didn't have 10 tree, they'd otherwise not have the time to do the automation work, the research work, the strategy work or even the firefighting that's vital to their everyday operations. Right? So it's like, of course, I don't need to manage it. If I did, I wouldn't be able to do all these other things. And I think that's it. Rings true right that it's hard to quantify that time savings because people say, 0 1/2 of it. See, that's really not much of the greater scheme of things. I don't know. 1/2 50. Working on strategic program is a huge opportunity. Let's see >> the value of 10 tree to our end users and we've heard from a lot of them this week actually spent a fantastic event hearing from many of our passionate consumers. From the very beginning. We wanted to build a product that ultimately customers care about, and we've seen that this week in droves. But I would say the going back to what they get out of it. It's the values and what they don't have to do, so they don't have to carve up ones. They don't have to carve up volumes. All they have to do is work with the units of infrastructure that air native to their environment, v ems. They deal with everything in their environment from our virtual machine perspective, virtual machines, one thing across the infrastructure. Again, they can add those virtual machines seamlessly. They can add those in seconds they don't have toe size and add anything in terms of how am I gonna divide up the storage coming in a provisional I Oh, how am I going to get the technical pieces right? Uh, they basically just get place v EMS, and we have a very simplistic way to give them Ah, visualization into that because we understand that virtual machine and what it takes to service. It comes right back to them in terms of time savings that are tremendous in terms of that. >> So let's deal with the elephant in the room. So, so 10 tree. We've talked about all the great stuff in the original founding vision. But then I ran into some troubles, right? And so what? How do you deal with that with customers in terms of just their perception of what what occurred you guys did the eye poets, et cetera, take us through how you're making sure customers are cool with you guys. >> I'm naturally, glass is half full kind of guy from previous, uh, times on the Cube. The interesting thing is, not a lot of people actually knew. Maybe we didn't create enough brand recognition in the past for people to even know that there was a transition. There were even some of our customers. And Graham, you can pile on this that because they don't manage the product every day because they don't have to. It's kind of so easy they even for gotten a lot about it on don't spend a lot of time. I'd say that the reason why we are able to continue. Invest today a year after the acquisition is because retaining existing customers was something that was very successful, and to a lot of them, you can add comments. It wasn't easy to switch to something. They could just switch to something else because there's no other product, does these automatic things and provides the predictive modeling that they're used to. So it's like what we switched to so they just kept going, and to them, they've given us a lot of great feedback. Being owned by the largest private storage company on planet Earth has the advantages of strong source of supply. Great Leverett reverse logistics partnerships with suppliers as a bigger company to be able to service them. Long >> trial wasn't broke, so you didn't need to fix it. And you were ableto maintain obviously a large portion of that customer base. And what was this service experience like? And how is that evolving? And what is Dede and bring to the table? >> So, uh, boy DD and brings so many resources in terms of bringing this from the point when they bought us last year. A year ago today, I think we transition with about 40 people in the company. We're up about 200 now, so Ah, serious investment. Obviously, that's ah have been a pretty heavy job in terms of building that thing back up. Uh, service and support we've put all of the resource is the stated goal coming across the acquisition was they have, ah, 10. Tree support tender by DNC would be better than where 10 tree support was. We fought them on >> rate scores, too. So it's hard to go from there. Right? And >> I would say what we've been doing on that today. I mean, in terms of the S L. A's, I think those were as good as they've ever been from that perspective. So we have a big team behind us that are working really hard to make sure that the customer experience is exactly what we want. A 10 tree experience to be >> So big messages at this This show, of course, multi cloud kubernetes solving climate change, fixing the homeless problem in San Francisco. I'm not hearing that from you guys. What's what's your key message to the VM world? >> Well, I personally believe that there's a lot of opportunity to invest in improving operations that are already pretty darn stable, operating these environments, talking to folks here on the floor. These new technologies you're talking about are certainly gonna change the way we deploy things. But there's gonna be a lot of time left Still operating virtualized server infrastructure and accelerating VD I deployments to just operationalized things better. We're hoping that folks choose some new technologies out there. I mean, there's a bill was a lot of hype in past years. About what technology to choose. We're all flash infrastructure, but well, I'd liketo for the say were intelligent infrastructure. We have 10 and 40 get boards were all flash, but that's not what you choose this. You choose this because you're able to take their operations and spend more your time on the apse because you're not messing around with that low level infrastructure. I think that there's a renaissance of, of, of investment and opportunity to innovate in that space into Graham's point about going further up the stack. We now have data database technology that we can show gives database administrators the direct ability to self service their own cloning, their own, staging their own operations, which otherwise would be a complex set of trouble tickets internally to provision the environment. Everyone loves to self service. That's really big. I think our customers love. It's a self service aspect. I see the self service and >> the ability to d'oh again, not have to worry about all the things that they don't have to do in terms of again not having to get into those details. A cz Morrow mentioned in terms of the database side, that's, ah, workload, the workload intelligence that we've already had for virtual machines. We can now service that database object natively. We're going to do sequel server later this year, uh, being ableto again, being able to see where whether or not they've got a host or a network or a storage problem being able to see where those the that unit they're serving, having that inside is tremendously powerful. Also being able the snapshot to be able to clone to be able thio manage and protect that database in a native way. Not having to worry about, you know, going into a console, worrying about the underlying every structure, the ones, the volumes, all the pieces that might people people would have to get involved with maybe moving from, like, production to test and those kinds of things. So it's the simplicity, eyes all the things that you really don't have to do across the getting down in terms of one's the volumes, the sizing exercises one of our customers put it. Best thing. You know, I hear a lot of things back from different customer. If he says the country, the sentry box is the best employee has >> I see that way? Reinvest, Reinvest. I haven't heard a customer yet that talks about reducing staff. Their I t staff is really, really critical. They want to invest up Kai throw buzzword out there, Dev. Ops. You didn't mention that it's all about Dev ops, right? And one thing that's interesting here is were or ah, technology that supports virtual environments and how many software developers use virtual environments to write, test and and basically developed programmes lots and being able to give those developers the ability to create new machines and be very agile in the way they do. Their test of is awesome and in terms of just taking big amounts of data from a nap, if I can circling APP, which is these virtual machines be ableto look at that on the infrastructure and more of her copy data so that I can do stuff with that data. All in the flying virtualization we think of Dev Ops is being very much a cloud thing. I'd say that virtual ization specifically server virtualization is the perfect foundation for Dav ops like functionality. And what we've been able to do is provide that user experience directly to those folks up the stacks of the infrastructure. Guy doesn't have to touch it. I wanted to pull >> a couple of threads together, and I think because we talked about the original vision kind of E m r centric, VM centric multiple hyper visors now multi cloud here in the world. So what >> are you seeing >> in the customers? Is that is it? Is it a multi cloud portfolio? What? What are you seeing your customers going to in the future with both on premise hybrid cloud public. So where does where does 10 tree fit into the storage portfolio? >> And they kind of >> fit all over the map. I think in terms of the most of the customers that we have ultimately have infrastructure on site and in their own control. We do have some that ultimately put those out in places that are quote unquote clouds, if you will, but they're not in the service. Vendor clouds actually have a couple folks, actually, that our cloud providers. So they're building their own clouds to service customers using market. What >> differentiates service is for serving better d our offerings because they can offer something that's very end end for that customer. And so there's more. They monetize it. Yeah, and I think those type of customers, like the more regional provider or more of a specialty service provider rather than the roll your own stuff, I'd say that Generally speaking, folks want tohave a level of abstraction as they go into new architecture's so multi cloud from a past life I wrote a lot about. This is this idea that I don't have to worry about which cloud I'm on to do what I'm doing. I want to be able to do it and then regards of which clouded on it just works. And so I think that our philosophy is how we can continue to move up the stack and provide not US access to our analytics because all that analytic stuff we do in machine learning is available via a P I We have ah v r o plug in and all that sort of stuff to be able allow that to happen. But when we're talking now about APS and how those APS work across multiple, you know, pieces of infrastructure, multiple V EMS, we can now develop build a composite view of what those analytics mean in a way that really now gives them new inside test. So how can I move it over here? Can I move over here? What's gonna happen if I move it over here over there? And I think that's the part that should at least delineate from your average garden variety infrastructure and what we like to call intelligent infrastructure stopping that can, Actually that's doing stuff to be able to give you that data because there's always a way you could do with the long way. Just nobody has time to do with the long way, huh? No. And I would actually say that you >> know what you just touched on, uh, going back to a fundamental 10 tree. Different churches, getting that level of abstraction, right is absolutely the key to what we do. We understand that workload. That virtual machine is the level of abstraction. It's the unit infrastructure within a virtual environment in terms of somebody who's running databases. Databases are the unit of infrastructure that they want to manage. So we line exactly to the fundamental building blocks that they're doing in those containers, certainly moving forward. It's certainly another piece we're looking. We've actually, uh I think for about three years now, we've been looking pretty hard of containers. We've been waiting to see where customers were at. Obviously Of'em were put. Put some things on the map this week in terms of that they were pretty excited about in terms of looking in terms of how we would support. >> Well, it certainly makes it more interesting if you're gonna lean into it with someone like Vienna where behind it. I mean, I still think there are some questions, but I actually like the strategy of because if I understand it correctly of Visa, the sphere admin is going to see the spear. But ah ah, developers going to see kubernetes. So >> yeah, that's kind of cool. And we just want to give people an experience, allows them to self service under the control of the I T department so that they can spend less time on infrastructure. Just the end of the I haven't met a developer that even likes infrastructure. They love to not have to deal with it at all. They only do it out. It assessed even database folks They love infrastructural because they had to think about it. They wanted to avoid the pitfalls of bad infrastructure infrastructures Code is yeah, way we believe in that >> question. Go to market. Uh, you preserve the 10 tree name so that says a lot. What's to go to market like? How are you guys structuring the >> organizational in terms of, ah, parent company perspective or a wholly owned subsidiary of DDN? So 10 tree by DDN our go to market model is channel centric in the sense that still a vast majority of people who procure I t infrastructure prefer to use an integrator or reseller some sort of thing. As far as that goes, what you'll see from us, probably more than you did historically, is more work with some of the folks in the ecosystem. Let's say in the data protection space, we see a rubric as an example, and I think you can talk to some of that scene where historically 10 Tree hadn't really done. It's much collaboration there, but I think now, given the overall stability of the segment and people knowing exactly where value could be added, we have a really cool joint story and you're talking about because your team does that. >> Yeah, so I would certainly say, you know, in terms of go to market Side, we've been very much channel lead. Actually, it's been very interesting to go through this with the channel folks. It's a There's also a couple other pieces I mentioned you mentioned some of the cloud provider. Some of those certainly crossed lines between whether they're MSP is whether they are resellers, especially as we go to our friends across the pond. Maybe that's the VM it'll Barcelona discussion, but some of those were all three, right? So there are customer their service providers there. Ah ah, channel partner if you want terms of a resellers. So, um, it's been pretty interesting from that perspective. I think the thing is a lot of opportunity interview that Certainly, uh, I would say where we're at in terms of, we're trying to very much. Uh, we understand customers have ecosystems. I mean, Marco Mitchem, the backup spaces, right? Uh, customers. We're doing new and different things in there, and they want us to fit into those pieces. Ah, and I'd certainly say in the world that we're in, we're not tryingto go solve and boil the ocean in terms of all the problems ourselves we're trying to figure out are the things that we can bring to the table that make it easier for them to integrate with us And maybe in some new and novel, right, >> So question So what's the number one customer problem that when you guys hear you say, that's our wheelhouse, we're gonna crush the competition. >> I'll let you go first, >> So I'd say, you know, if they have a virtualized environment, I mean, we belong there. Vermin. Actually, somebody said this bed is the best Earlier again. Today in the booze is like, you know, the person who doesn't have entries, a person who doesn't know about 10 tree. If they have a virtual environment, you know, the, uh I would say that this week's been pretty interesting. Lots of customer meetings. So it's been pretty, pretty awesome, getting a lot of things back. But I would say the things that they're asking us to solve our not impossible things. They're looking for evolution's. They're looking for things in terms of better insights in their environment, maybe deeper insights. One of the things we're looking to do with the tremendous amount of data we've got coming back, Um, got almost a million machines coming back to us in terms of auto support data every single night. About 2.3 trillion data points for the last three years, eh? So we're looking to make that data that we've gotten into meaningful consumable information for them. That's actionable. So again, again, what can we see in a virtual environment, not just 10 tree things in terms of storage of those kinds of things, but maybe what patches they have installed that might be affecting a network driver, which might affect the certain configuration and being able to expose and and give them some actionable ways to go take care of those problems. >> All right, we gotta go marry. I'll give you. The last word >> stated simply if you are using virtual, is a Shinto abstract infrastructure. As a wayto accelerate your operations, I run the M where, if you have ah 100 virtual machine, 150 virtual machines, you could really benefit from maybe choosing a different way to do that. Do infrastructure. I can't say the competition doesn't work. Of course, the products work. We just want hope wanted hope that folks could see that doing it differently may produce a different outcome. And different outcomes could be good. >> All right, Mario Graham, Thanks very much for coming to the cubes. Great. Thank you so much. All right. Thank you for watching John Troy a day Volante. We'll be back with our next guest right after this short break. You're watching the cube?

>> Voiceover: Live from Boston, Massachusetts, it's the CUBE. Covering your Red Hat Summit 2019. Brought to you by Red Hat. >> And welcome back to our coverage here on the CUBE Red Hat Summit 2019. We're at the BCEC in Beantown, Boston, Massachusetts playing host this week to some 9000 strong attendees, pack keynotes. Just a great three days of programming here and educational sessions. Stu Miniman and I'm John Walls. We're joined by Mike Piech, who's the VP and general manager of Middleware at Red Hat. Mike, good to see you today. >> Great to be back. >> And Mark Little, VP of engineering Middleware at Red Hat. Mark, Good to see you as well, sir. >> You too. >> Yeah. First of, let's just talk about your ideas at the show here. Been here for a few days. As we've seen on the keynote stage, wide variety of first off, announcements and great case studies, great educational sessions. But your impressions of what's going on and some of the announcements we've heard about this week. >> Well, sure. I mean definitely some very big announcements with RHEL 8 and OpenShift 4. So as Middleware we're a little bit more in sort of gorilla mode here while some of the bigger announcements take a lot of the limelight. But nevertheless those announcements and the advances that they represent are very important for us as Middleware. Particularly OpenShift 4 as sort of the next layer up from OpenShift which the developers sort of touch and feel and live and breathe on a daily basis. We are the immediate beneficiaries of much of the advances in OpenShift and so that's something that, we as the Middleware guys sort of make real for the enterprise application developer. >> I'd say, probably for me, building on that in a way, one of the biggest announcements, one of the biggest surprises is gotta be the first keynote where we had Satya from Microsoft on stage with Jim announcing the collaboration that we're doing. I never believed that would ever happen and that's, that's fantastic. Has a benefit for Middleware as well but just for Red Hat as a whole. Who would've thought it? >> John: Who would have thought it, right? Yeah, we actually just had Marco Bill-Peter on and he was talking about, he's like "Look, we've actually had some of our support people up in Redmond now for a couple of years." And we had Chris Wright on earlier and he says "You know, sometimes we got to these shows and you get the big bang announcement. It's like, well, really we're working incrementally along the way and open source you can watch it. Sure sometimes you get the new chipset or there's a new this or that. But you know, it's very very small things." So in the spirit of that, maybe, you know, give us the updates since last time we got together. What's happening in the Middleware space as you said. If we build up the stack, you know, we got RHEL 8, we got OpenShift 4 and you're sitting on top. >> Yeah. Well one aspect that's an event like this makes clear in almost a reverse sort of way. We put a lot of effort particularly in Mark's team in getting to a much more frequent and more incremental release cycle and style, right. So getting away from sort of big bang releases every year, couple of years, to a much more agile incremental again sort of regime of rolling out functionality. Now, one of the downsides of that is that you don't have these big grand product announcements to make a big deal about in the same way as RHEL just did with 8 for example. So we need to rethink how we sort of (Laughs) >> absence the sort of big .0 releases, you know how we sort of batch up interesting news and roll it out at a large event like this. Now one of the things that we have been working on is our application environment narrative. Right now, the whole idea of the story here is that many people talk about Cloud-Native and about having lot's of different capabilities and services in a cloud environment. And as we've sort of gone through the, particularly the last year or so, it's really become apparent from what our customers tell us and from what we really see as the opportunities in the cloud-native world. The value that we bring is engineering all these pieces together, right? So that it's not simply a list of these disparate, disconnected, independent services but rather Middleware in the world of cloud native re-imagined. It is capabilities that when engineered together in the right way they make for this comprehensive, unified, cohesive environment within which our customers can develop applications and run those applications. And for the developer, you get developer productivity and then at runtime, you're getting operational reliability. So there really is a sort of a dual-sided value proposition there. And this notion of Middleware engineered together for the cloud is what the application environment idea is all about. >> Yeah. I'd add kinda one of the things that ties into that which has been big for us at least at summit this year is an effort that we kicked off or we announced two months ago called Quakers and as you all know a lot of what we do within Middleware, within Red Hat is based on Java and Java is still the dominant language in the enterprise but it's been around for 20 years. It developed in a pre-cloud era and that made lots of assumptions on the way in which the Java language and the JVM on which it runs would develop which aren't necessarily that conducive for running, in a cloud environment, a hybrid cloud environment and certainly public cloud environment based on Linux containers and Kubernetes. So, we've been working for a number of years in the upstream open JDK community to try and make Java much more cloud-native itself. And Quakers kind of builds on that. It essentially is what we call a kub-native approach where we optimize all of the Middleware stack upfront to work really really well in Kubernetes and specifically on OpenShift. And it's all Java though, that's the important thing. And now if people look into this they'll find that we're showing performance figures and memory utilization that is on a per with some of the newer languages like Go for instance, very very fast. Typically your boot time has gone from seconds to tens of milliseconds. And people who have seen it demonstrated have literally been blown away cause it allows them to leverage the skills that they've had invested in their employees to learn Java and move to the cloud without telling them "You guys are gonna have to learn a completely new language and start from scratch" >> All right, so Mark, if I get it right cause we've been at the Kubernetes show for a bunch of years but this is, you're looking at kinda the application side of what's happening in those Kubernetes environment >> Mark: Yeah. So many times we've talked about the platforms and the infrastructure down but it's the the art piece on top. Super important. I know down the DevZone people were buzzing around all the Quaker stuff. What else for people that are you know, looking at that kinda cloud-native containerization space? What other areas that they should be looking at when it comes to your space? >> Well, again, tying into the up environment thing, hopefully, you know, you'll have heard of knative and Istio. So knative is, to put it in a quick sentence is essentially an enabler for serverless if you like. It's where we're spinning containers really really quickly based on events. But really any serverless platform lives and dies based on the services in which your business logic can then rely upon. Do I have a messaging service there? Do I have a transaction service or a database service? So, we've been working with, with Google on knative and with Microsoft on knative to ensure that we have a really good story in OpenShift but tying it into our Middleware suite as well. So, many of our Middleware products are now knative enabled if you like. The second thing is, as I mentioned, Istio which is a sidecar approach. I won't go into details on that but again Istio the aim behind that is to remove from the application developer some of the non-functional business logic that they had to put in there like "How do I use a messaging service? How do I secure this endpoint and push it down the infrastructure?" So the security servers, the messaging servers, the cashing servers et cetera. They move out of the business logic and they move into Istio. But from our point of view, it's our security servers that we've been working on for years, it's our transactional servers that we've been working on for years. So, these are bullet-proof implementations that we have just made more cloud-native by embedding them in a way in Istio and like I said, enabling them with knative. >> I think we'd mentioned that Chris Wright was on earlier and one of the things he talked about was, this new data-eccentric focus and how, that's at the core so much of what enterprise is doing these days. The fact that whenever speed is distributed, they are and you've got so many data inputs come in from, so to a unified user trying to get their data the way they wanna see it. You might want it for a totally other reason, right? I'm just curious, how does that influence or how has that influenced your work in terms of making sure that transport goes smoothly? Because you do have so much more to work with in a much more complex environment for multiple uses that are unique, right? >> (Mike) Yeah. >> It's not all the same. >> Huge, huge impact for sure. The whole idea of decomposing an application into a much larger number of much smaller pieces than was done in the past has many benefits probably one of the most significant being the ability to make small changes, small incremental changes and afford a much more trial and error approach to innovation versus more macro-level planning waterfall as they call it. But one of the implications of that is now you have a large number of entities. Whether they be big or small, there's a large number of them running within the estate. And there's the orchestration of them and the interconnection of them for sure but it's a n-squared relationship, right. The more these entities you have, the more potential connections between each of them you have to somehow structure and manage and ensure are being done securely and so on. So that has really driven the need for new ways of tying things together, new ways essentially of integration. It has definitely amplified the need for disciplines, EPI management for example. It has driven a lot of increase demand for an event-driven approach where you're streaming in realtime and distributing events to many receivers and dealing with things asynchronously and not depending on round-trip times for everything to be consistent and so on. So, there's just a myriad of implications there that are very detailed technical-level drive some of the things that we're doing now. >> Yeah, I'll just add that in terms of data itself, you've probably heard this a number of times, data is king. Everything we do is based on data in one way or another, So we as Red Hat as a whole and Middleware specifically, we've had a very strong data strategy for a long time. Just as you've got myriad types of data, you can't assume that one way of storing that data is gonna be right for every type of data that you've got. So, we've worked through the integration efforts on ensuring that no sequel data stores, relational data stores^, in-memory data caching and even the messaging services as a whole is a way of sto^ring data in transit, that allows you to, in some ways it allows you to actually look at it in an event-driven way and make intelligent decisions. So that's a key part of what anybody should do if they are in the enterprise space. That's certainly what we're doing because at the end of the day people are building these apps to use that data. >> Well, gentlemen, I know you have another engagement. We're gonna cut you loose but I do wanna say you're the first guests to get applause. (guests laugh) >> From across all the way there. People at home can't hear but, so congratulations. You've been well received already. >> I think they're clearly tuned in to the renaissance of the job in here. >> Yes. >> Thank you both. >> Thanks for the time. >> Mark: Thanks so much. >> We appreciate that. Back with more, we are watching a Red Hat summer 2019 coverage live on the CUBE. (Upbeat music)

(dynamic music) >> Hi, I'm Peter Burris, and welcome to another CUBEConversation. This is another very special CUBEConversation in that it's part of a product launch. Today, BMC has come on to theCUBE to launch Helix, a new approach to thinking about cognitive services management. And we're, over the course of the next 20 minutes or so, gonna present some of the salient features of Helix and how it solves critical business problems. And at the end of the segment, at the end of this video segment, we're gonna then go into a CrowdChat and give you, the community, an opportunity to express your thoughts, ask your questions, and get the information that you need from us analysts, from BMC, and also from your peers about what you need to do to exploit cognitive systems management in your business. Now this is a very real problem, this is not something that's being made up. The reality is we're looking at a lot of data-first technologies that are transforming the way business works. Technologies like AI, and machine learning, and deep learning, technologies like big data, having an enormous impact about how businesses behave. These technologies invoke much greater complexity at the application at the systems level and Wikibon strongly believes that we do not understand how businesses can pursue these technologies and these richer applications without finding ways to apply elements of them directly into the IT service management stack. And the reason why is if you don't have high-quality, lower-cost, speedy automation inside how you run your service management overall platform, then it's going to create uncertainty up hiring stack and that's awful for digital business. So to better understand and take us through this launch today, we've got some great guests. And it starts, obviously, with the esteemed Nayaki Nayyar who is the President of the Digital Services Management business unit at BMC, CUBE alum. Nayaki, thanks very much for being here. >> Thank you, Peter, really excited to be here and look forward to our conversation. We are too excited about the launch of BMC Helix and happy to share the details with you. >> So let's start with the why. Obviously, there's a... You know, I've articulated kind of a generalization of some of the challenges that businesses face but it goes deeper than that. Take us through some of the key issues that your customers are facing as they think about this transition to a new way of running their business. >> So, let's put ourselves in the customers' shoes. Then you look at what their journey looks like. Customers are evolving from the online world into the digital world and what we see is, what we call, cognitive world. And the way their journey looks like, especially as customers are entering into the digital world, there are proliferation of clouds. They don't have just one cloud, they have private clouds, hybrid clouds, managed clouds, we call it multi-cloud. So they're entering into a multi-cloud world. In addition, there's also proliferation of devices. It's not just phones that we have to worry about now. As IoT's getting more and more relevant and prevalent, how you help customers manage all the devices and how you provide the service through not just one channel but channel of our customers' or consumers' preference. It could be a Slack as a channel, SMS as a channel, Skype as a channel. So across this multi-cloud, multi-device, and multi-channel, this explosion of technology that is happening in every customer's landscape, and to address this explosion, is where AIML, chatbots, and virtual agents really play a role for them to handle the complexities. So the automation that AIML, chatbots, and virtual agents bring to help customers address these multi-cloud, multi-channel, multi-device world is what we call how we have them evolve from ITSM to cognitive services management. >> Let's talk about that a little bit. We'll get into exactly what you're announcing in a second but historically when we thought about service management we thought about devices. What you're really describing, this transition is, again that notion of how all of these different elements come together in, sometimes, very unique ways and that's what's driving the need for the cognitive. It's not just, you can do multiple clouds, multi-devices, multiple channels, it's your business can put them together in ways that serve your business' needs the best. And now we need a service management capability that can attend to those resources. >> Absolutely. So if you go 10, 15 years back, BMC had a great portfolio. We had Remedy Service Management Suite. We also had Discovery to help customers discover the on-prem assets and provide its service to remedy service management. That's what we had, we were very successful. ITSM, as a category, was created for that whole space. But in this new world of multi-cloud, right, where customers have private clouds, managed clouds, hybrid clouds, multi-devices where IoT is becoming more and more relevant, and multi-channel, customers now have to discover these assets. We call it Discovery as-a-Service but now they can discover the assets across AWS, Azure, OpenStack, and Cloud Foundry and evolve into providing service from reactive to proactive service, and that's what we call Remedy as-a-Service, and then extend that service beyond IT to also lines of business. Now you wanna also provide that service to HR, and procurement, and also various lines of business. And the most important thing is how you provide that experience to your end-users and your end-customers is what we call Digital Workplace-as-a-Service where now customers can consume that service in channel of their preference. They can consume that service through mobile device, of course through web, but also Slack, SMS, chatbots, and virtual agents. So that's what we are combining all of that, that entire suite, we are containerizing that suite using Dockers and Kubernetes so that now customers can run in their choice of cloud. They can run it in AWS cloud, Azure cloud, or in BMC cloud. This whole suite is what we call BMC Helix and helps our customers evolve from ITSM to what we call cognitive services management. >> So that's what BMC's announcing today. >> Yes. >> It's this notion of BMC Helix. >> Yes. >> And it's predicated on the idea, if I can, also of, not only you're going to use these technologies to manage new stuff, we have to bring the old stuff forward. Additionally, we're gonna see a mix of labor, or people, and automation as companies find the right mix for them. >> Right. >> And so we wanna bring and sustain these practices and these approaches forward. Nobody likes a forced migration, especially not in an IT organization. >> Right. >> So that's how we see Helix. if I got this right. >> Yes. >> Helix is gonna help customers bring their existing assets, existing practices, modernize them using some of the new technologies and that's how we get to this new cognitive vision. >> Absolutely. The investments customers have already made in their on-prem assets, in their managing their IT assets, that same concepts come into this new multi-cloud, multi-device, and multi-channel world but now it extends beyond that. It extends beyond just IT to also lines of business and also all these, what we call, omni-channel experiences that you can provide. And this whole suite is, what we call, 3 C's, Helix stands for 3 C's. Everything as a service, Remedy as-a-Service, Discovery as-a-Service, Business Workplace as-a-Service, containerized so that customers can run this in the choice of their cloud, they can run in AWS cloud, Azure cloud, or our cloud with cognitive capabilities, with AIML, and chatbots. And that's how we help them evolve from that existing implementations to this whole new world as they enter into the cognitive world. >> Exciting stuff. >> Absolutely. We are very excited about it. We've been working with a lot of customers already, and we have made really, really good traction. >> So let's do this, Nayaki, let's take a look at a product video that kinda describes how this all comes together in a relatively simple, straightforward way. >> Absolutely. (upbeat music) >> Hi, Peter Burris again, welcome back. We're talking more about BMC's Helix announcement. Great product video. Once again, we're here with Nayaki Nayyar, but we're also being joined by Vidhya Srinivasan who's in Marketing within the Digital Services Management unit at BMC. Thank you very much for joining us in theCUBE. >> Great to be here, thank you. >> So we've heard a lot about the problems, we've heard a lot about BMC Helix as a solution, but obviously it's more than just the technology. There's things that customers have to think about, about how these technologies, how service management, cognitive service management's going to be impacting the business. As businesses become more digital, technology and related services get dragged more deeply into functions. So, Nayaki, tell us a little bit more about how the outcomes within business, the capabilities of businesses are gonna change as a consequence of applying these technologies. >> Absolutely, Peter. So if you look at, traditionally, IT service management was a very reactive process. Every ticket that came in was manually created, assigned, and routed. That was a very reactive process. But as we enter into this cognitive world and you apply intelligence, AIML, you evolve into what we call a proactive and predictive. Before an issue actually happens, you want to resolve that issue. And that's what we call the cognitive services management. And the real business outcomes, you put yourself in a customer's shoes who's providing this service and evolving into this proactive, predictive, and cognitive world, they wanna provide that service at the highest accuracy, at the highest speed, and the lowest cost. That's what is gonna become competitive advantage for every company indifferent of the industry. They could be in a telco, they could be in high-tech, or pharmaceutical. It doesn't matter which industry they are in, how they provide this service at the highest accuracy, highest speed, and lowest cost is gonna be fundamentally a competitive advantage for these customers. >> And when we talk about accuracy, again we're not just talking about accuracy in a technology context. We're talking about accuracy in terms of a brand promise, perhaps. >> Absolutely. >> Or a service promise, or a product promise. >> Yes. >> That's the context. We wanna make sure that the customer is getting what they expect fast, with accuracy, and at low cost. >> Right, every time you tweet or you're SMS-ing your service provider, you expect that response to be at the highest accuracy, at the speed, and the cost. >> So when we start talking about multi-channel, Vidhya, what we're really saying is that this is not just your, you know, this is not just service management for the traditional technology service desk. We're talking about service management for other personas, other individuals, other consumers as well. Take us through that a little bit. >> Yeah, that's right. So we actually take a very holistic approach, right, across the enterprise. So we have end-users who are, at the end of the day, the key subscribers or consumers of our service and we wanna make sure they're very happy with what we provide. We have the agents which kinda goes to the IT persona that people know about in the service desk. But then, as Nayaki said earlier, it's also about extending to a lines of business so you have HR agents, right, people who support HR requests, people who support facilities or procurement request. So making sure that the agent persona is able to do everything that they need to do at the most efficiency level that they can so that they can meet their SLAs to their end consumers is a big part of what Helix, BMC Helix and cognitive service management can provide. And ultimately, when you think about this transformation and where they wanna go, there's a lot of custom applications and custom needs that businesses have. So really thinking about the developer persona and how you actually embed and build intelligent applications through our cognitive microservices that BMC Helix provides is a big part of that value proposition we provide. So as you navigate through this journey and become a cognitive enterprise, how do you make sure that all of these personas throughout your enterprise is able to deliver and get value out of this is what BMC Helix provides for the whole enterprise. >> So the whole concept of incorporating these cognitive capabilities into a service management stack allows us to not only envision, in a traditional way, more complex applications but actually extend this out to new classes of users because we are masking a lot of the complexity and a lot of the uncertainty associated with how this stuff works from that customer. >> That's correct. >> For end-users, for agents, and for developers, and consumers, and customers too. >> Great. >> That's good. >> So you know what... Great conversation. But let's hear what a customer has to say about it, shall we? >> Absolutely, okay. >> My name is Marco Jongen. I work for a company called DSM. And I'm the Director for Service Management within the Global Business Services department. Royal DSM is a global science-based company active in health, nutrition, and materials. And by connecting our unique competencies in life science and in material sciences, DSM is driving economic prosperity, environmental progress, and social advance to create sustainable value for all stakeholders simultaneously. The Global Business Service department is serving the 20,000 employees of DSM spread over 200 locations globally. We are handling, annually, about 600,000 tickets, and we are supporting four business functions: finance, HR, procurement, and IT. We started together with BMC on a shared services transformation across IT, HR, finance, and procurement. And we created a unified ticketing system and a self-service portal using the Remedy system and the Digital Workplace environment. And with this, we are now able to handle all functions in one unified ticketing tool and giving visibility to all our employees with questions related to finance, HR, purchasing, and IT. We were still have and involved with BMC in bringing this product to the next level and we are very excited in the work we have done with BMC so far. >> That was great to hear Royal DSM is transforming its shared services organization with cognitive services management. But, Nayaki, there's no such thing as an easy transformation especially one of this magnitude. We're talking about digital business which is, we're using data assets differently, it's affecting virtually every feature of business today. And now we've got a technology set that's gonna have potentially an enormous impact on IT but everything that IT is being, or everywhere that IT is being employed. That kind of a transformation is not something that people do lightly. They expect their suppliers to help them out. So what is BMC gonna do to ensure that customers are successful as they go through this transformation to cognitive services management? >> Absolutely, Peter. I always say these transformations are not one-month, two-month transformations. These are multi-year transformations and it's a journey that customers go through. We partner very closely with customers in this journey, assessing their requirements, understanding what their future looks like, and helping them every step of the way. Especially in service management, this change, this transformation that is happening, is gonna be very disruptive to their end-to-end processes. Today, all service desks are manned by individuals. Every ticket that comes in gets manually created, assigned, and routed. But if you fast forward into the future world in the next two to three years, that service desk function, which is especially level zero, level one, level two, service desk function, will completely get replaced by bots or virtual agents. It could be 50-50, 70-30, you can pick what the percentage-- >> Whatever the business needs. >> Right? But it is coming. And it is very important for customers to see that change and that transformation that is happening and to be ready for it. And that's where we are working very closely with them in making sure it's not just a system transformation. It's also the people side and the process that have to change. And companies who can do that, what we call cognitive service management using bots and virtual agents at the highest accuracy, highest speed, and the lowest cost, I keep coming back to that because that is what is gonna give them the highest competitive advantage. >> Lot to think about. >> Absolutely. >> Exciting future, crucial for IT if it's gonna succeed moving forward, but even if the business choose to use cloud, you're going to need to be able to discover and sustain service management at a very, very high level. >> Absolutely. How we discover, how we help them discover, how we help them provide that service proactively, predictively, and provide that experience through omni-channel experiences, what this whole thing brings together for our customers. >> Excellent, this has been a great conversation. Nayaki Nayyar, President of BMC's Digital Services Management business unit. Thank you very much for being here on theCUBE and working with us to help announce Helix. Now don't forget folks, that immediately after this, we'll be running the CrowdChat. And in that CrowdChat, your peers, BMC experts, us analysts will be participating to help answer your questions, share experience, identify simpler ways of doing more complex things. So join us in the CrowdChat. Once again, Nayaki, thank you very much. >> Thank you, Peter, and thank you everyone. Thank you all.