from around the globe it's thecube with coverage of kubecon and cloudnativecon north america 2020 virtual brought to you by red hat the cloud native computing foundation and ecosystem partners hello and welcome back to the cube's coverage of kubecon plus cloud nativecon 2020 the virtual edition abinav joshi is here he's the senior product marketing manager for openshift at red hat and tom dean is the senior director of pro product management at cloudera gentlemen thanks for coming on thecube good to see you thank you very much for having us here hey guys i know you would be here it was great to have you and guys i know you're excited about the partnership and i definitely want to get in and talk about that but before we do i wonder if we could just set the tone you know what are you seeing in the market tom let's let's start with you i had a great deep dive a couple of weeks back with anupam singh and he brought me up to speed on what's new with cloudera but but one of the things we discussed was the accelerated importance of data putting data at the core of your digital business tom what are you seeing in the marketplace right now yeah absolutely so um overall we're still seeing a growing demand for uh storing and and processing massive massive amounts of data even in the past few months um where perhaps we see a little bit more variety is on by industry sector is on the propensity to adopt some of the latest and greatest uh technologies that are out there or that we we deliver to the market um so whether perhaps in the retail hospitality sector you may see a little bit more risk aversion around some of the latest tools then you you go to the healthcare industry as an example and you see we see a strong demand for our latest technologies uh with with everything that is that is going on um so overall um still a lot lots of demand around this space so abnormal i mean we just saw in ibm's earnings though the momentum of red hat you know growing in the mid teens and the explosion that we're seeing around containers and and obviously openshift is at the heart of that how the last nine months affected your customers priorities and what are you seeing yeah we've been a lot more busier like in the last few months because there's like a lot of use cases and if you look at the like a lot of the research and so on and we are seeing that from our customers as well that now the customers are actually speeding up the digital transformation right people say that okay kovac 19 has actually uh speeded up the digital transformation for a lot of our customers for the right reasons to be able to help the customers and so on so we are seeing a lot of attraction on like number of verticals and number of use cases beyond the traditional lab dev data analytics aiml messaging streaming edge and so on like lots of use cases in like a lot of different like industry verticals so there's a lot of momentum going on on openshift and the broader that portfolio as well yeah it's ironic the the timing of the pandemic but it sure underscores that this next 10 years is going to be a lot different than the last 10 years okay let's talk about some of the things that are new around data tom cloudera you guys have made a number of moves since acquiring hortonworks a little over two years ago what's new with uh with the cloudera data platform cdp sure so yes our latest therap uh platform is called cbp clara data platform last year we announced the public cloud version of cdp running on aws and then azure and what's new is just two months ago we announced the release of the version of this platform targeted at the data center and that's called cvp private cloud and really the focus of this platform this new version has been around solving some of the pain points that we see around agility or time to value and the ease of use of the platform and to give you some specific examples with our previous technology it could take a customer three months to provision a data warehouse if you include everything from obtaining the infrastructure to provisioning the warehouse loading the data setting security policies uh and fine-tuning the the software now with cbp private cloud we've been able to take those uh three months and turn it into three minutes so significant uh speed up in in that onboarding time and in time to valley and a key piece of this uh that enabled this this speed up was a revamping of the entire stack specifically the infrastructure and service services management layer and this is where the containerization of the platform comes in specifically kubernetes and red hat open shift that is a key piece of the puzzle that enables this uh order of magnitude uh improvement in time right uh now abner you think about uh red hat you think about cloudera of course hortonworks the stalwarts of of of open source you got kind of like birds of a feather how are red hat and cloudera partnering with each other you know what are the critical aspects of that relationship that people should be aware of yeah absolutely that's a very good question yeah so on the openshift side we've had a lot of momentum in the market and we have well over 2000 customers in terms of a lot of different verticals and the use cases that i talked about at the beginning of our conversation in terms of traditional and cloud native app dev databases data analytics like ai messaging and so on right and the value that you have with openshift and the containers kubernetes and devops like part of the solution being able to provide the agility flexibility scalability the cross cloud consistency like so all that that you see in a typical app dev world is directly applicable to fast track the data analytics and the ai projects as well and we've seen like a lot of customers and some of the ones that we can talk about in a public way like iix rbc bank hca healthcare boston children's bmw exxon mobil so all these organizations are being are able to leverage openshift to kind of speed up the ai projects and and help with the needs of the data engineers data scientists and uh and the app dev folks now from our perspective providing the best in class uh you say like experience for the customers at the platform level is key and we have to make sure that the tooling that the customers run on top of it uh gets the best in class the experience in terms of the day zero to day two uh management right and it's uh and and it's an ecosystem play for us and and and that's the way cloudera is the top isv in the space right when it comes to data analytics and ai and that was our key motivation to partner with cloudera in terms of bringing this joint solution to market and making sure that our customers are successful so the partnership is at all the different levels in the organization say both up and down as well as in the the engineering level the product management level the marketing level the sales level and at the support and services level as well so that way if you look at the customer journey in terms of selecting a solution uh putting it in place and then getting the value out of it so the partnership it actually spans across the entire spectrum yeah and tom you know i wonder if you could add anything there i mean it's not just about the public cloud with containers you're seeing obviously the acceleration of of cloud native principles on-prem in a hybrid you know across clouds it's sort of the linchpin containers really and kubernetes specifically linchpin to enable that what would you add to that discussion yeah as part of the partnership when we were looking for a vendor who could provide us that kubernetes layer we looked at our customer base and if you think about who clara is focused on we really go after that global the global 2000 firms out there these customers have very strict uh security requirements and they're often in these highly regulated uh industries and so when we looked at a customer's base uh we saw a lot of overlap and there was a natural good fit for us there but beyond that just our own technical evaluation of the solutions and also talking to uh to our own customers about who they do they see as a trusted platform that can provide enterprise grade uh features on on a kubernetes layer red hat had a clear leadership in in that front and that combined with our own uh long-standing relationship with our parent company ibm uh it made this partnership a natural good thing for us right and cloudera's always had a good relationship with ibm tom i want to stay with you if i can for a minute and talk about the specific joint solutions that you're providing with with red hat what are you guys bringing to customers in in terms of those solutions what's the business impact where's the value absolutely so the solution is called cbd or color data platform private cloud on red hat openshift and i'll describe three uh the three pillars that make up cbp uh first what we have is the five data analytic experiences and that is meant to cover the end to end data lifecycle in the first release we just came out two months ago we announced the availability of two of those five experiences we have data warehousing for bi analytics as well as machine learning and ai where we offer a collaborative data science data science tools for data scientists to come together do exploratory data analytics but also develop predictive models and push them to production going forward we'll be adding the remaining three uh experiences they include data engineering or transformations on uh on your data uh data flow for streaming analytics and ingest uh as well as operational database for uh real-time surveying of both structure and unstructured data so these five experiences have been re-banked right compared to our prior platform to target these specific use cases and simplify uh these data disciplines the second pillar that i'll talk about is the sdx or uh what what we call the shared data experience and what this is is the ability for these five experiences to have one global data set that they can all access with shared metadata security including fine grain permissions and a suite of governance tools that provide lineage provide auditing and business metadata so by having these shared data experiences our developers our users can build these multi-disciplinary workflows in a very straightforward way without having to create all this custom code and i can stitch you can stitch them together and the last pillar that i'll mention uh is the containerization of of the platform and because of containers because of kubernetes we're now able to offer that next level of agility isolation uh and infrastructure efficiency on the platform so give you a little bit more specific examples on the agility i mentioned going from three months to three minutes in terms of the speed up with i uh with uh containers we can now also give our users the ability to bring their own versions of their libraries and engines without colliding with another user who's sharing the platform that has been a big ask from our customers and last i'll mention infrastructure efficiency by re-architecting our services to running a microservices architecture we can now impact those servers in a much more efficient way we can also auto scale auto suspend bring all this as you mentioned bring all these cloud native concepts on premises and the end result of that is better infrastructure efficiency now our customers can do more with the same amount of hard work which overall uh reduces their their total spend on the solution so that's what we call cbp private cloud great thanks for that i mean wow we've seen really the evolution from the the wild west days of you know the early days of so-called big data ungoverned a lot of shadow data science uh maybe maybe not as efficient as as we'd like and but certainly today taking advantage of some of those capabilities dealing with the noisy neighbor problem enough i wonder if you could comment another question that i have is you know one of the things that jim whitehurst talked about when ibm acquired red hat was the scale that ibm could bring and what i always looked at in that context was ibm's deep expertise in vertical industries so i wonder what are some of the key industry verticals that you guys are targeting and succeeding in i mean yes there's the pandemic has some effects we talked about hospitality obviously airlines have to have to be careful and conserving cash but what are some of the interesting uh tailwinds that you're seeing by industry and some of the the more interesting and popular use cases yeah that's a very good question now in terms of the industry vertical so we are seeing the traction in like a number of verticals right and the top ones being the financial services like healthcare telco the automotive industry as well as the federal government are some of the key ones right and at the end of the day what what all the customers are looking at doing is be able to improve the experience of their customers with the digital services that they roll out right as part of the pandemic and so on as well and then being able to gain competitive edge right if you can have the services in your platform and make them kind of fresh and relevant and be able to update them on a regular basis that's kind of that's your differentiator these days right and then the next one is yeah if you do all this so you should be able to increase your revenue be able to save cost as well that's kind of a key one that you mentioned right that that a lot of the industries like the hospitality the airlines and so on are kind of working on saving cash right so if you can help them save the cost that's kind of key and then the last one is is being able to automate the business processes right because there's not like a lot of the manual processes so yeah if you can add in like a lot of automation that's all uh good for your business and then now if you look at the individual use cases in these different industry verticals what we're seeing that the use cases cannot vary from the industry to industry like if you look at the financial services the use cases like fraud detection being able to do the risk analysis and compliance being able to improve the customer support and so on are some of the key use cases the cyber security is coming up a lot as well because uh yeah nobody wants to be hacked and so and and so on yeah especially like in these times right and then moving on to healthcare and the life sciences right what we're seeing the use cases on being able to do the data-driven diagnostics and care and being able to do the discovery of drugs being able to say track kobit 19 and be able to tell that okay uh which of my like hospital is going to be full when and what kind of ppe am i going to need at my uh the the sites and so on so that way i can yeah and mobilize like as needed are some of the key ones that we are seeing on the healthcare side uh and then in terms of the automotive industry right that's where being able to speed up the autonomous driving initiatives uh being able to do uh the auto warranty pricing based on the history of the drivers and so on and then being able to save on the insurance cost is a big one that we are seeing as well for the insurance industries and then but more like manufacturing right being able to do the quality assurance uh at the shop floor being able to do the predictive maintenance on machinery and also be able to do the robotics process automation so like lots of use cases that customers are prioritizing but it's very verticalized it kind of varies from the vertical to a vertical but at the end of the day yeah it's all about like improving the customer experience the revenue saving cost and and being able to automate the business processes yeah that's great thank you for that i mean we we heard a lot about automation we were covering ansible fest i mean just think about fraud how much you know fraud detection has changed in the last 10 years it used to be you know so slow you'd have to go go through your financial statements to find fraud and now it's instantaneous cyber security is critical because the adversaries are very capable healthcare is a space where you know it's ripe for change and now of course with the pandemic things are changing very rapidly automotive another one an industry that really hasn't hadn't seen much disruption and now you're seeing with a number of things autonomous vehicles and you know basically software on wheels and insurance great example even manufacturing you're seeing you know a real sea change there so thank you for that description you know very often in the cube we like to look at joint engineering solutions that's a gauge of the substance of a partnership you know sometimes you see these barney deals you know there's a press release i love you you love me okay see you but but so i wonder if you guys could talk about specific engineering that you're doing tom maybe you could start sure yeah so on the on the engineering and product side um we've um for cbp private cloud we've we've changed our uh internal development and testing to run all on uh openshift uh internally uh and as part of that we we have a direct line to red hat engineering to help us solve any issues that that uh we run into so in the initial release we start with support of openshift43 we're just wrapping up uh testing of and we'll begin with openshift46 very soon on another aspect of their partnership is on being able to update our images to account for any security vulnerabilities that are coming up so with the guidance and help from red hat we've been we've standardized our docker images on ubi or the universal based image and that allows us to automatically get many of these security fixes uh into our into our software um the last point that i mentioned here is that it's not just about providing kubernetes uh red hat helps us with the end to end uh solution so there is also the for example bringing a docker registry into the picture or providing a secure vault for storing uh all the secrets so all these uh all these pieces combined make up the uh a strong complete solution actually the last thing i'll mention is is a support aspect which is critical to our customers in this model our customers can bring support tickets to cluberra but as soon as we determine that it may be an issue that uh related to red hat or openshift where we can use their help we have that direct line of communication uh and automated systems in the back end to resolve those support tickets uh quickly for our customers so those are some of the examples of what we're doing on the technical side great thank you uh enough we're out of time but i wonder if we could just close here i mean when we look at our survey data with our data partner etr we see containers container orchestration container management generally and again kubernetes specifically is the the number one area of investment for companies that has the most momentum in terms of where they're putting their efforts it's it's it's right up there and even ahead of ai and machine learning and even ahead of cloud which is obviously larger maybe more mature but i wonder if you can add anything and bring us home with this segment yeah absolutely and i think uh so uh one thing i want to add is like in terms of the engineering level right we also have like between cloudera and red hat the partnership and the sales and the go to market levels as well because once you build the uh the integration it yeah it has to be built out in the customer environments as well right so that's where we have the alignment um at the marketing level as well as the sales level so that way we can like jointly go in and do the customer workshops and make sure the solutions are getting deployed the right way right uh and also we have a partnership at the professional services level as well right where um the experts from both the orgs are kind of hand in hand to help the customers right and then at the end of the day if you need help with support and that's what tom talked about that we have the experts on the support side as well yeah and then so to wrap things up right uh so all the industry research and the customer conversation that we are having are kind of indicating that the organizations are actually increasing the focus on digital uh transformation with the data and ai being a key part of it and that's where this strategic partnership between cloudera and and red hat is going to play a big role to help our mutual customers uh through that our transition and be able to achieve the key goals that they set for their business great well guys thanks so much for taking us through the partnership and the integration work that you guys are doing with customers a great discussion really appreciate your time yeah thanks a lot dave really appreciate it really enjoyed the conversation all right keep it right there everybody you're watching thecube's coverage of cubecon plus cloud nativecon north america the virtual edition keep it right there we'll be right back

>> Announcer: From around the globe, it's theCUBE with coverage of KubeCon + CloudNativeCon Europe 2020 Virtual brought to you by Red Hat, the Cloud Native Computing Foundation and Ecosystem partners. >> Welcome back I'm Stu Miniman, this is theCUBE's coverage of KubeCon + CloudNativeCon Europe 2020, the virtual event. Of course, when we talk about Cloud Native we talk about Kubernetes there's a lot that's happening to modernize the infrastructure but a very important thing that we're going to talk about today is also what's happening up the stack, what sits on top of it and some of the new use cases and applications that are enabled by all of this modern environment and for that we're going to talk about artificial intelligence and machine learning or AI and ML as we tend to talk in the industry, so happy to welcome to the program. We have two first time guests joining us from Red Hat. First of all, we have Abhinav Joshi and Tushar Katarki they are both senior managers, part of the OpenShift group. Abhinav is in the product marketing and Tushar is in product management. Abhinav and Tushar thank you so much for joining us. >> Thanks a lot, Stu, we're glad to be here. >> Thanks Stu and glad to be here at KubeCon. >> All right, so Abhinav I mentioned in the intro here, modernization of the infrastructure is awesome but really it's an enabler. We know... I'm an infrastructure person the whole reason we have infrastructure is to be able to drive those applications, interact with my data and the like and of course, AI and ML are exciting a lot going on there but can also be challenging. So, Abhinav if I could start with you bring us inside your customers that you're talking to, what are the challenges, the opportunities? What are they seeing in this space? Maybe what's been holding them back from really unlocking the value that is expected? >> Yup, that's a very good question to kick off the conversation. So what we are seeing as an organization they typically face a lot of challenges when they're trying to build an AI/ML environment, right? And the first one is like a talent shortage. There is a limited amount of the AI, ML expertise in the market and especially the data scientists that are responsible for building out the machine learning and the deep learning models. So yeah, it's hard to find them and to be able to retain them and also other talents like a data engineer or app DevOps folks as well and the lack of talent can actually stall the project. And the second key challenge that we see is the lack of the readily usable data. So the businesses collect a lot of data but they must find the right data and make it ready for the data scientists to be able to build out, to be able to test and train the machine learning models. If you don't have the right kind of data to the predictions that your model is going to do in the real world is only going to be so good. So that becomes a challenge as well, to be able to find and be able to wrangle the right kind of data. And the third key challenge that we see is the lack of the rapid availability of the compute infrastructure, the data and machine learning, and the app dev tools for the various personas like a data scientist or data engineer, the software developers and so on that can also slow down the project, right? Because if all your teams are waiting on the infrastructure and the tooling of their choice to be provisioned on a recurring basis and they don't get it in a timely manner, it can stall the projects. And then the next one is the lack of collaboration. So you have all these kinds of teams that are involved in the AI project, and they have to collaborate with each other because the work one of the team does has a dependency on a different team like say for example, the data scientists are responsible for building the machine learning models and then what they have to do is they have to work with the app dev teams to make sure the models get integrated as part of the app dev processes and ultimately rolled out into the production. So if all these teams are operating in say silos and there is lack of collaboration between the teams, so this can stall the projects as well. And finally, what we see is the data scientists they typically start the machine learning modeling on their individual PCs or laptops and they don't focus on the operational aspects of the solution. So what this means is when the IT teams have to roll all this out into a production kind of deployment, so they get challenged to take all the work that has been done by the individuals and then be able to make sense out of it, be able to make sure that it can be seamlessly brought up in a production environment in a consistent way, be it on-premises, be it in the cloud or be it say at the edge. So these are some of the key challenges that we see that the organizations are facing, as they say try to take the AI projects from pilot to production. >> Well, some of those things seem like repetition of what we've had in the past. Obviously silos have been the bane of IT moving forward and of course, for many years we've been talking about that gap between developers and what's happening in the operation side. So Tushar, help us connect the dots, containers, Kubernetes, the whole DevOps movement. How is this setting us up to actually be successful for solutions like AI and ML? >> Sure Stu I mean, in fact you said it right like in the world of software, in the world of microservices, in the world of app modernization, in the world of DevOps in the past 10, 15 years, but we have seen this evolution revolution happen with containers and Kubernetes driving more DevOps behavior, driving more agile behavior so this in fact is what we are trying to say here can ease up the cable to EIML also. So the various containers, Kubernetes, DevOps and OpenShift for software development is directly applicable for AI projects to make them move agile, to get them into production, to make them more valuable to organization so that they can realize the full potential of AI. We already touched upon a few personas so it's useful to think about who the users are, who the personas are. Abhinav I talked about data scientists these are the people who obviously do the machine learning itself, do the modeling. Then there are data engineers who do the plumbing who provide the essential data. Data is so essential to machine learning and deep learning and so there are data engineers that are app developers who in some ways will then use the output of what the data scientists have produced in terms of models and then incorporate them into services and of course, none of these things are purely cast in stone there's a lot of overlap you could find that data scientists are app developers as well, you'll see some of app developers being data scientist later data engineer. So it's a continuum rather than strict boundaries, but regardless what all of these personas groups of people need or experts need is self service to that preferred tools and compute and storage resources to be productive and then let's not forget the IT, engineering and operations teams that need to make all this happen in an easy, reliable, available manner and something that is really safe and secure. So containers help you, they help you quickly and easily deploy a broad set of machine learning tools, data tools across the cloud, the hybrid cloud from data center to public cloud to the edge in a very consistent way. Teams can therefore alternatively modify, change a shared container images, machine learning models with (indistinct) and track changes. And this could be applicable to both containers as well as to the data by the way and be transparent and transparency helps in collaboration but also it could help with the regulatory reasons later on in the process. And then with containers because of the inherent processes solution, resource control and protection from threat they can also be very secure. Now, Kubernetes takes it to the next level first of all, it forms a cluster of all your compute and data resources, and it helps you to run your containerized tools and whatever you develop on them in a consistent way with access to these shared compute and centralized compute and storage and networking resources from the data center, the edge or the public cloud. They provide things like resource management, workload scheduling, multi-tendency controls so that you can be a proper neighbors if you will, and quota enforcement right? Now that's Kubernetes now if you want to up level it further if you want to enhance what Kubernetes offers then you go into how do you write applications? How do you actually make those models into services? And that's where... and how do you lifecycle them? And that's sort of the power of Helm and for the more Kubernetes operators really comes into the picture and while Helm helps in installing some of this for a complete life cycle experience. A kubernetes operator is the way to go and they simplify the acceleration and deployment and life cycle management from end-to-end of your entire AI, ML tool chain. So all in all organizations therefore you'll see that they need to dial up and define models rapidly just like applications that's how they get ready out of it quickly. There is a lack of collaboration across teams as Abhinav pointed out earlier, as you noticed that has happened still in the world of software also. So we're talking about how do you bring those best practices here to AI, ML. DevOps approaches for machine learning operations or many analysts and others have started calling as MLOps. So how do you kind of bring DevOps to machine learning, and fosters better collaboration between teams, application developers and IT operations and create this feedback loop so that the time to production and the ability to take more machine learning into production and ML-powered applications into production increase is significant. So that's kind of the, where I wanted shine the light on what you were referring to earlier, Stu. >> All right, Abhinav of course one of the good things about OpenShift is you have quite a lot of customers that have deployed the solution over the years, bring us inside some of your customers what are they doing for AI, ML and help us understand really what differentiates OpenShift in the marketplace for this solution set. >> Yeah, absolutely that's a very good question as well and we're seeing a lot of traction in terms of all kinds of industries, right? Be it the financial services like healthcare, automotive, insurance, oil and gas, manufacturing and so on. For a wide variety of use cases and what we are seeing is at the end of the day like all these deployments are focused on helping improve the customer experience, be able to automate the business processes and then be able to help them increase the revenue, serve their customers better, and also be able to save costs. If you go to openshift.com/ai-ml it's got like a lot of customer stories in there but today I will not touch on three of the customers we have in terms of the different industries. The first one is like Royal Bank of Canada. So they are a top global financial institution based out of Canada and they have more than 17 million clients globally. So they recently announced that they build out an AI-powered private cloud platform that was based on OpenShift as well as the NVIDIA DGX AI compute system and this whole solution is actually helping them to transform the customer banking experience by being able to deliver an AI-powered intelligent apps and also at the same time being able to improve the operational efficiency of their organization. And now with this kind of a solution, what they're able to do is they're able to run thousands of simulations and be able to analyze millions of data points in a fraction of time as compared to the solution that they had before. Yeah, so like a lot of great work going on there but now the next one is the ETCA healthcare. So like ETCA is one of the leading healthcare providers in the country and they're based out of the Nashville, Tennessee. And they have more than 184 hospitals as well as more than 2,000 sites of care in the U.S. as well as in the UK. So what they did was they developed a very innovative machine learning power data platform on top of our OpenShift to help save lives. The first use case was to help with the early detection of sepsis like it's a life-threatening condition and then more recently they've been able to use OpenShift in the same kind of stack to be able to roll out the new applications that are powered by machine learning and deep learning let say to help them fight COVID-19. And recently they did a webinar as well that had all the details on the challenges they had like how did they go about it? Like the people, process and technology and then what the outcomes are. And we are proud to be a partner in the solution to help with such a noble cause. And the third example I want to share here is the BMW group and our partner DXC Technology what they've done is they've actually developed a very high performing data-driven data platform, a development platform based on OpenShift to be able to analyze the massive amount of data from the test fleet, the data and the speed of the say to help speed up the autonomous driving initiatives. And what they've also done is they've redesigned the connected drive capability that they have on top of OpenShift that's actually helping them provide various use cases to help improve the customer experience. With the customers and all of the customers are able to leverage a lot of different value-add services directly from within the car, their own cars. And then like last year at the Red Hat Summit they had a keynote as well and then this year at Summit, they were one of the Innovation Award winners. And we have a lot more stories but these are the three that I thought are actually compelling that I should talk about here on theCUBE. >> Yeah Abhinav just a quick follow up for you. One of the things of course we're looking at in 2020 is how has the COVID-19 pandemic, people working from home how has that impacted projects? I have to think that AI and ML are one of those projects that take a little bit longer to deploy, is it something that you see are they accelerating it? Are they putting on pause or are new project kicking off? Anything you can share from customers you're hearing right now as to the impact that they're seeing this year? >> Yeah what we are seeing is that the customers are now even more keen to be able to roll out the digital (indistinct) but we see a lot of customers are now on the accelerated timeline to be able to say complete the AI, ML project. So yeah, it's picking up a lot of momentum and we talk to a lot of analyst as well and they are reporting the same thing as well. But there is the interest that is actually like ramping up on the AI, ML projects like across their customer base. So yeah it's the right time to be looking at the innovation services that it can help improve the customer experience in the new virtual world that we live in now about COVID-19. >> All right, Tushar you mentioned that there's a few projects involved and of course we know at this conference there's a very large ecosystem. Red Hat is a strong contributor to many, many open source projects. Give us a little bit of a view as to in the AI, ML space who's involved, which pieces are important and how Red Hat looks at this entire ecosystem? >> Thank you, Stu so as you know technology partnerships and the power of open is really what is driving the technology world these days in any ways and particularly in the AI ecosystem. And that is mainly because one of the machine learning is in a bootstrap in the past 10 years or so and a lot of that emerging technology to take advantage of the emerging data as well as compute power has been built on the kind of the Linux ecosystem with openness and languages like popular languages like Python, et cetera. And so what you... and of course tons of technology based in Java but the point really here is that the ecosystem plays a big role and open plays a big role and that's kind of Red Hat's best cup of tea, if you will. And that really has plays a leadership role in the open ecosystem so if we take your question and kind of put it into two parts, what is the... what we are doing in the community and then what we are doing in terms of partnerships themselves, commercial partnerships, technology partnerships we'll take it one step at a time. In terms of the community itself, if you step back to the three years, we worked with other vendors and users, including Google and NVIDIA and H2O and other Seldon, et cetera, and both startups and big companies to develop this Kubeflow ecosystem. The Kubeflow is upstream community that is focused on developing MLOps as we talked about earlier end-to-end machine learning on top of Kubernetes. So Kubeflow right now is in 1.0 it happened a few months ago now it's actually at 1.1 you'll see that coupon here and then so that's the Kubeflow community in addition to that we are augmenting that with the Open Data Hub community which is something that extends the capabilities of the Kubeflow community to also add some of the data pipelining stuff and some of the data stuff that I talked about and forms a reference architecture on how to run some of this on top of OpenShift. So the Open Data Hub community also has a great way of including partners from a technology partnership perspective and then tie that with something that I mentioned earlier, which is the idea of Kubernetes operators. Now, if you take a step back as I mentioned earlier, Kubernetes operators help manage the life cycle of the entire application or containerized application including not only the configuration on day one but also day two activities like update and backups, restore et cetera whatever the application needs. Afford proper functioning that a "operator" needs for it to make sure so anyways, the Kubernetes operators ecosystem is also flourishing and we haven't faced that with the OperatorHub.io which is a community marketplace if you will, I don't call it marketplace a community hub because it's just comprised of community operators. So the Open Data Hub actually can take community operators and can show you how to run that on top of OpenShift and manage the life cycle. Now that's the reference architecture. Now, the other aspect of it really is as I mentioned earlier is the commercial aspect of it. It is from a customer point of view, how do I get certified, supported software? And to that extent, what we have is at the top of the... from a user experience point of view, we have certified operators and certified applications from the AI, ML, ISV community in the Red Hat marketplace. And from the Red Hat marketplace is where it becomes easy for end users to easily deploy these ISVs and manage the complete life cycle as I said. Some of the examples of these kinds of ISVs include startups like H2O although H2O is kind of well known in certain sectors PerceptiLabs, Cnvrg, Seldon, Starburst et cetera and then on the other side, we do have other big giants also in this which includes partnerships with NVIDIA, Cloudera et cetera that we have announced, including our also SaaS I got to mention. So anyways these provide... create that rich ecosystem for data scientists to take advantage of. A TEDx Summit back in April, we along with Cloudera, SaaS Anaconda showcased a live demo that shows all these things to working together on top of OpenShift with this operator kind of idea that I talked about. So I welcome people to go and take a look the openshift.com/ai-ml that Abhinav already referenced should have a link to that it take a simple Google search might download if you need some of that, but anyways and the other part of it is really our work with the hardware OEMs right? And so obviously NVIDIA GPUs is obviously hardware, and that accelerations is really important in this world but we are also working with other OEM partners like HP and Dell to produce this accelerated AI platform that turnkey solutions to run your data-- to create this open AI platform for "private cloud" or the data center. The other thing obviously is IBM, IBM Cloud Pak for Data is based on OpenShift that has been around for some time and is seeing very good traction, if you think about a very turnkey solution, IBM Cloud Pak is definitely kind of well ahead in that and then finally Red Hat is about driving innovation in the open-source community. So, as I said earlier, we are doing the Open Data Hub which that reference architecture that showcases a combination of upstream open source projects and all these ISV ecosystems coming together. So I welcome you to take a look at that at opendatahub.io So I think that would be kind of the some total of how we are not only doing open and community building but also doing certifications and providing to our customers that assurance that they can run these tools in production with the help of a rich certified ecosystem. >> And customer is always key to us so that's the other thing that the goal here is to provide our customers with a choice, right? They can go with open source or they can go with a commercial solution as well. So you want to make sure that they get the best in cloud experience on top of our OpenShift and our broader portfolio as well. >> All right great, great note to end on, Abhinav thank you so much and Tushar great to see the maturation in this space, such an important use case. Really appreciate you sharing this with theCUBE and Kubecon community. >> Thank you, Stu. >> Thank you, Stu. >> Okay thank you and thanks a lot and have a great rest of the show. Thanks everyone, stay safe. >> Thanks you and stay with us for a lot more coverage from KubeCon + CloudNativeCon Europe 2020, the virtual edition I'm Stu Miniman and thank you as always for watching theCUBE. (soft upbeat music plays)

(pulsing music) (digital music) >> Welcome back to the Seaport in Boston. You're watching theCUBE's coverage of Red Hat Summit 2022. A lot of bummed out Bruins fans, but a lot of happy Celtics fans. We're optimistic for tonight, Boston's crazy sports town, but we're talking tech, we're talking open source. Dr. Amanda Purnell is here. She's the director of data and analytics innovation at the US Department of Veteran Affairs, and Ben Cushing is the chief architect for federal health and life sciences at Red Hat. Folks, welcome to theCUBE, thank for coming on. >> Thank you for having us. >> So glad to be here. >> So we heard your keynote this morning, project Arches. Now you were telling us just briefly about your previous life as a clinician. >> That's right. >> That's really interesting, because you know what the outcome has to be. So talk about that project in your perspective. What the goals were and how you actually got it done. >> I could tell the long view. I'm a psychologist by training. I spent the first 10 years of my VA career providing care to veterans. So engaging in healthcare behavior change, providing training to providers and really trying to understand what is the care pathway for veterans, what's the experience of veterans along each of those touchpoints, and it became clear to me over time that there were opportunities for us to improve the transitions of care and provide better information at the right time to improve those decisions that are being made at the point of care. Ben and I were just talking before we began today, part of the core of the development of Arches was beginning with human-centered design. We wanted to interview and better understand what was the experience across the VA of many different stakeholders and trying to access meaningful information, understand in that moment what do I need to make a decision with a veteran or what do I need to make a decision with my care team and how can I improve the quality of care for veterans? And so, hundreds of interviews later, it became clear to us that we wanted to help those individuals already working for the VA to continue to improve excellence of care and one of those ways that we're trying to do that is using technology to make life easier for our veterans and for our clinicians. >> I always like to say, they say, "Follow the money." I like to follow the data. And you said something in your keynote about nurses have to have access to information and it just gets to an architectural question, because as a caregiver, you have to get insights and data and you need it fast, 'cause you're saving lives, but a lot of times, architectures are very centralized. They're monolithic and you have to beg, borrow, steal, break through blockers to get to the data that you need. How do you square that circle in today's world? Maybe you could talk about that, and then specific to Arches, how you dealt with that. >> I can dive into that a little bit. I have to say, Amanda had touched on this during the keynote, VA was one of the first, if not the first, healthcare organizations in the world to actually adopt electronic health records and because of that, they just have this incredibly rich amount of historical data and the challenge, as you pointed out, is gaining access to it. So there are a number of programs within VA designed specifically for that. And they are bringing data not just from the data warehouses, but also data from the electronic health records that are running inside of VA right now, and then also third party community data sets, as well as applications that run inside the VA. Now the value here really happens when you produce insights. Data by itself is useless. >> Lot of data out there. They're plentiful. >> You need to create knowledge and then you need that knowledge to inform your process that comes next. Those actions are really what matters. All of healthcare is process and activity and data is really just a historical record. I mean, all data that we look at is happening in the past and then as we're reading it, we're producing knowledge, again, to inform our process. Arches, the program itself, is right in that space at the knowledge layer of actually taking that data and turning it into actual insight and something that is usable and insightful for clinicians to affect the ability to deliver better care and also to actually improve their own working experience. A lot of the models that are getting built out are specifically designed to help their workflow, help them reach better outcomes for the veterans, but also for themselves, because if we can care for the providers, it'll certainly help them care for the patients even more so. >> So how does it work? I mean, from the provider's perspective, how was their life improved by Arches? >> That's a great question. We want to make it easier to access the information. So as Ben noted, the average person providing care in the field doesn't know how to code, doesn't know how to pull a unique request for an individual data point, and what we're trying to do with Arches is provide a user interface that allows for both a non-technical person and a very technical person to access information, and then what gets provisioned in front of a provider is something that is farther abstracted from the underlying data layer and more like here's a specific insight. So I use the example in my keynote of chronic kidney disease. So what's provisioned to the provider in that moment is this person is at higher risk for chronic kidney disease based on this basic information. So it's surfacing just the right amount of information to allow for that care pathway to be improved, but the physician doesn't need to see all of the layers of code underneath. They need to trust that it's worthwhile, but they don't need to know all the background abstractions. >> So it's a self-service, essentially, infrastructure in that sense. You're hiding the underlying complexities. You gave an example in your keynote of an individual who realized that they were under counting the probability of a potential disease for African Americans. >> Yes. >> I believe she just rewrote the algorithm. >> She did. >> Describe that process, because in a lot of organizations, injecting that new algorithm may have required new data sources, would take an act of the Pope to do. How did it work in Arches? >> This is what I get excited about with Arches is that we have the opportunity to empower enthusiastic people like Dr. Joshi to discover an insight and she's a talented informaticist, so she could do the technical work and provision a container for her to work in, for her to do the data analysis, the underground stuff that we're not letting the average provider have to cope with. We were able to provision the tools that she need, the environment that she needed to be able to test and develop the new insight, confirm that they're there and then begin to validate that and test it in other facilities. So our thinking is, how do we bring the resources to the users rather than saying to the users, "This is what's available. Good luck." (chuckles) >> So we've been talking a lot about, I'm sorry, go ahead. >> I want to add on to that. What we're actually experiencing inside of healthcare right now is the emergence of of learning health systems. >> Yes. >> And this is a great example of that. The terrifying number is, it takes 17 years for new knowledge that gets created with healthcare research, whether it's NIH or VA or elsewhere, it takes 17 years for those practices to make their way into practice. Generally the way that happens is through the education of new staff. And so the dissemination of that knowledge is just so freaking slow that we cannot move nimbly enough to take on that new knowledge and actually implement it in clinical space. What Amanda's describing is something that now happens in months. New knowledge getting produced and then actually getting disseminated out, both the insights, whether they are those probabilities, predictions and recommendations and the actual processes, which are getting automated, as well. So if you think about healthcare as just a process, you can automate a whole lot of that and we can move that needle really fast and actually take that 70-year number down to a couple months. >> In the early days when we were all talking about AI and getting excited about digital, I would often ask the question, will machines be able to make better diagnoses than doctors and to your point, Ben, that's not the right question. >> Exactly. >> It isn't the right question. >> The question is, how can machines compress the time to better patient outcomes- >> Yes. >> in concert with humans and that's what we're seeing now. >> That's right, it's surfacing those insights to start a conversation. >> We've been talking a lot about artificial intelligence for the last two days. As clinician or someone with a clinical background, how do you see the clinical experience changing as machines grow more intelligent? >> I think that there's a learning curve for people to feel confident in an artificial intelligence. It makes sense. So someone spent decades, perhaps, of their life obtaining medical training, doing fellowships, doing additional training that they have trust in that deep training. There are times, however, where a technology is able to surface something that we didn't know that we didn't know and it's important, as we make use of artificial intelligence, that we clearly validate it with independent means and that we clearly also bring in additional analysis to understand what are the elements and then test that new technology in an environment before we scale it widely, so that clinicians can see, yes, this was useful. If it wasn't useful, how can we make it even better? So it goes back to what we were talking about earlier that we have to bring in human-centered design to figuring out how do we make use of AI or machine learning models and make sure that there's trust in those models and that we can clearly articulate value for the clinicians and care teams on the ground. >> Is that a natural evolution of Arches? >> This is all built around it. Arches is the technology platform, but there's no magical technology that's going to change how humans interact. And so the way we think about each project is we think about what are the technological components and what are the human factors components? And we have to think about the entire care pathway. I'll go back to that example, the chronic kidney disease. She identified that we were under identifying African Americans for chronic kidney disease. So she changed the algorithm. Not only did she change the algorithm, we also had to think about who would be informed of those changes, how would that change, who would be connected to the veteran in that point of care and build out the care pathway in the care team and that's really how you actually influence an outcome. Surfacing an insight is important, but it's one part of a much larger picture. >> So what is Arches? You said it's a technology platform built on open source. At least, there's a lot of open source in there. And it's got API connectors to all the legacy technologies that you need it to. Can you describe, paint a picture of what it actually is? >> Arches is evolving as it should. So it's designed to meet the unique needs that aren't being met by other infrastructure in the VA. So we started first by identifying the need for cloud compute, so it's in the cloud, it has open source technology so that we're not stuck with any one provider and also has the ability to use containers to be able to move insights out of Arches to an enterprise solution. We're also bringing in multi-cloud strategy, which also something had been discussed quite a bit at this conference, to make sure that we're not saying only one cloud provider can be the solution for veterans' needs. Our mission is serving veterans and so we want to have access to all the technology and not just one and so we're looking at how do we expand the scope to make sure that we have the most variety possible so we can meet the needs of veterans. >> I can add a little bit to it, as well. Think of Arches as a program. It's an incubation space under the office of innovation. So it's a place where the governance allows for trying new ideas and really pushing the envelope for VA in general. There's not a lot of organizations, if any at VA, that allow for that type of incubation and so Arches is in a unique position to create new technologies and new novel approaches to solving big problems. And then the next step to that is moving the work from Arches out into the enterprise, as you called it out. So for instance, the system of engagement where the actual clinicians interact with patients, the model needs to find its way there and we can't do that in a way that disturbs the current workflow that the clinicians have. We need to be able to bring the model to where the clinician is, have those recommendations, probabilities and predictions surfaced to the clinician in a way that is precise to their existing workflow. They need it at the time they need it. Arches itself is not delivering that part of it. It's more like the place where the innovation happens and the incubation really occurs and then it's about taking this container, really, and moving out to other systems that are already deployed out to the hospitals, the edge, and in the cloud. >> And the federated governance occurs in Arches or elsewhere? >> It happens across the continuum. It's starting in Arches. the clinical validation that happens there is wickedly important, because the clinicians need to know that what they're working with is actually legit. And so when they know that the researchers and the clinicians who are involved in that incubation period have done their work, they can feel confident with the recommendations they're getting from the machine learning models that are getting deployed to one of them. >> So many questions, so little time. What's the business impact? How would you describe that? >> For me, it's an emotional impact. People have a sense of, "I have a place to develop a solution and I can get in there quick, and I can test out an idea. I could potentially partner with an external partner or if I have the talents and skills to do it myself." It's empowering all of those innovators who have great ideas to work together to test and develop and validate solutions, and they're not waiting years to get the idea off the ground. >> Amazing. >> Go ahead, bring it. >> Is Arches open source? >> Arches is a platform and it has open source component. So that the underlying infrastructure of technology is open source. >> Why was it important to you that this be built on an open source platform? >> It's important for us that we not marry ourself to any one technology and that we allow for, as much as possible, transparency and many different tools and the right tools for the right solution. So we didn't want to find ourselves connected to only one way of doing things. We want to have versatility to have the right tool for the right problem at the right time. >> I'm so sorry, we're out of time. This is so interesting and I really appreciate you here guys, coming on and sharing your insights for theCUBE audience. All right, keep it right there. This is Dave Vellante for Paul Gillin. We're in day two of Red Hat Summit 2022. You're watching theCUBE. (digital pulsing music) >> Due to the pandemic, the federal government declared a public health emergency, which created an urgency for healthcare coverage. >> One of the biggest-

>> Unleash The Power of Data. On May 4th at 11:00 AM Eastern, 8:00 AM Pacific, HPE is hosting a broadcast and we're here with Sandeep Singh, who's the Vice President of Storage Marketing at Hewlett Packard Enterprise. Sandeep, what's this event all about, who should attend and why? >> Dave, in the world of enterprise storage, there hasn't been a moment like this in decades. A point at which everything is changing for data and infrastructure and is powered by the nexus of data, cloud and AI. And the opportunity for our customers to accelerate their data-driven transformation is unfolding. HPE is excited to invite everyone to join us for a virtual event that, as Dave mentioned, Unleash the Power of Data on May 4th at 8:00 AM Pacific. And if you're an organization like most today, data is at the heart of what you do. And you're looking to accelerate data driven transformation. We hear you and we're thrilled to invite you to join us on May 4th, as we unveil a new vision for data that accelerates data driven transformation from edge to cloud. This promises to be a pivotal event and one that IT Admins, Cloud Architects, Virtualization Architects, Vice-Presidents, Directors of IT, and CIOs (indistinct) the event is hosted by a business and a tech journalist Shabani Joshi and it will feature a market in panel with a focus on the crucial data that data is playing in the transformation for customers. Antonio Neri CEO of HPE and Tom black senior vice president and general manager of HPE storage as well as industry experts, including Julia Palmer vice president at Gartner will be part of the event. We will unveil game-changing HPE innovations that will make it possible for organizations across industries to unleash the power of data. >> Sounds awesome. Okay. Go to hpe.com. Mark your calendar, and we'll see you there.

(smooth music) >> Hi, everybody. This is Dave Vellante, and with me is Sandeep Singh. He's the vice president of storage marketing at Hewlett Packard Enterprise, and we're going to riff on some of the trends in the industry, what we're seeing, and we got a little treat for you, Sandeep. Great to see you, man. >> Dave, it's a pleasure to be here. >> You and I have known each other for a long time. We've had some great discussions, some debates, (chuckles) some intriguing mind benders. What are you seeing out there in storage? So much has changed. What are the key trends you're seeing? And let's get into it. >> Yeah. Across the board, as you said, so much has changed. When you reflect back at the underlying transformation that's taking place with data, cloud, and AI across the board, first of all, for our customers, they're seeing this massive data explosion that literally now spans edge to core to cloud. They're also seeing a diversity of the application workloads across the board. The emphasis that it's placing is on the complexity that underlies overall infrastructure and data management. Across the board, we're hearing a lot from customers about just the underlying infrastructure and complexity, and the infrastructure sprawl. And then the second element of that is really extending into the complexity of data management. >> So it's interesting to talk about data management. You remember you and I were in... Well, you were in Andover. I don't know. It was probably like five years ago. And all we were talking about was media, flash this and flash that, and at the time that was kind of the hot storage topic. Well, flash came in, addressed some of the clicks that we historically talked about. Now the problem statement is really kind of, quote unquote, metaphorically moving up the stack, if you will. You mentioned management. But let's dig into that a little bit. I mean, what is management? I mean, a lot of people... That means different things to different people. You talk to a database person or a backup person. How do you look at management? What does that mean to you? >> Yeah, Dave. You mentioned that flash came in, and it actually accelerated the overall speed and latency that storage was delivering to the application workloads. But fundamentally, when you look back at storage over a couple of decades, the underlying way of how you're managing storage hasn't fundamentally changed. There's still an incredible amount of complexity for ITs. It's still a manual admin-driven experience for customers. And what that's translating to is, more often than not, IT is in the world of firefighting, and it leaves them unable to help with the more strategic projects to innovate for the business. And basically IT has that pressure point of moving beyond that, and helping bring greater levels of agility that line of business owners are asking for, and to be able to deliver on more of the strategic projects. So that's one element of it. The second element that we're hearing from customers about is as more and more data just continues to explode from edge to core to cloud, and as basically the infrastructure has grown from just being on-prem, to being at the edge, to being in the cloud, now that complexity is expanding from just being on-prem to across multiple different clouds. So when you look across the data life cycle, how do you store it? How do you secure it? How do you basically protect it, and archive it, and analyze that data? That end to end life cycle management of data, today resides on just a fragmented set of overall infrastructure, and tools, and processes, and administrative boundaries. That's creating a massive challenge for customers. And the impact of that, ultimately, is essentially comes at a cost to agility, to innovation, and ultimately business risk. >> Yeah, so we've seen obviously the cloud has addressed a lot of these problems, but the problem is the cloud is in the cloud. And much of my stuff, most of my stuff, isn't in the cloud. (chuckles) So I have all these other workloads that are either on-prem, and now you've got this emerging edge. And so I wonder if we could just talk a little vision here for a minute. I mean, what I've been envisioning is this abstraction layer that cuts across all, whether... It doesn't really matter where it is. If it's on-prem, if it's across cloud, if it's in the cloud, on the edge. We could talk about what that all means. But if customers that I talk to, they're sort of done with the complexity of that underlying infrastructure. They want technology to take care of that. They want automation. They want AI brought into that equation. And it seems like we're on the cusp of the decade where that might happen. What's your take? >> Well, yeah. Certainly, I mentioned that data cloud and AI are really the disruptive forces that are propelling the digital transformation for customers. Cloud has set the standard for agility, and AI-driven insights and intelligence are really helping to make the underlying infrastructure invisible. And yet a lot of their application workloads and data is on-prem and is increasingly growing at the edge. So they want that same experience to be able to truly bring that agility to wherever their data and apps load. And that's one of the things that we're continuing to hear from customers. >> And this problem's just going to get worse. I mean, we... For decades we marched to the cadence of Moore's law, and everybody's kind of forgets about Moore's law. And they'll say, "Ah, it's dying," or whatever. But actually, when you look at the processing power that's coming out now, it's not... It's more than doubling every two years, quadrupling every two years. So now you've got this capability in your hands, and application designers, storage companies, networking companies, they're going to have all this power to now bring in AI and do things that we've never even imagined before. So it's not about the box, and the speeds and feeds of the box. It's really more about this abstraction layer that I was talking about, the management, if you will, that you were discussing, and what we can do in terms of being able to power new workloads, machine intelligence. It's this kind of ubiquitous... Call it the cloud, but it's expanding pretty much everywhere in every part of our lives, (chuckles) even to the edge. You think about autonomous vehicles, you think about factories. It's actually quite mind boggling where we're headed. >> It is, and you touched upon AI, and certainly when you look at infrastructure, for example, there's been a ton of complexity in infrastructure management. One of the studies that was done, actually by IDC, indicated that over 90% of the challenges that arise, for example, ultimately down at the storage infrastructure layer that's powering the apps, ultimately, arises from way above the stack all the way from the server layer on down, or even the virtual machine layer. And there, for example, AI ops for infrastructure has become a game changer for customers to be able to bring the power of AI, and machine learning, and multi-variate analysis to be able to predict and prevent issues. Dave, you also touched upon edge, and across the board, what we're seeing is the enterprise edge is becoming that frontier for customer experiences, and the opportunity to reimagine customer experiences, as well as just the frontier for commerce that's happening when you look at retail, and manufacturing, and/or financial services. So across the board, with the data growth that's happening, and this edge becoming the strategic frontier for delivering the customer experiences, how you power your application workloads there, how you deliver that data, and protect that data, and be able to seamlessly manage that overall infrastructure, as you mentioned, abstracted away at a higher level, becomes incredibly important for our customers. >> It's so interesting to hear how the conversation's changing, I'd like to say. I go back to whatever it was, five years ago, we're talking about flash, storage class memory, and NVMe, and those things are still there, but your emphasis now, you're talking about machine learning, AI, math around deep learning. It's really software is really what you're focusing on these days. >> Very much so. Certainly, this notion of software and services that are delivering and unlocking a whole new experience for customers, that's really the game changer going forward for customers, and that's what we're focused on. >> Well, I said we had a little surprise for you. So you guys are having an event on May 4th. It's called Unleash the Power of Data. What's that event all about, Sandeep? >> Yeah. We are very much excited about our May 4th event. As you mentioned, it's called Unleash the Power of Data. And as most organizations today are data driven, and data is at the heart of what they're doing, we're excited to invite everyone to join this event. And through this event, we're unveiling a new vision for data that accelerates the data-driven transformation from edge to cloud. This event promises to be a pivotal event, and one that IT admins, cloud architects, virtual machine admins, vice-presidents, directors of IT, and CIOs really won't want to miss. Across the board, this event is just bringing a new way of articulating the overall problem statement, and a market-in focused the articulation of the trends that we were just discussing. It's an event that's going to be hosted by business and technology journalist, Shibani Joshi. It will feature a market-in panel with a focus on the crucial role that data is playing in customers' digital transformation. It will also include and feature Antonio Neri, CEO of HPE, and Tom Black, senior vice president and general manager of HPE storage business, and industry experts, including Julia Palmer, research vice president at Gartner. We will unveil game-changing HPE innovations that will make it possible for organizations across edge to cloud to unleash the power of data. >> Sounds like a great event. I presume I can go to hpe.com. And what? Get information. Is it a registered event? How does that all work? >> Yeah, we invite everyone to visit hpe.com, and by visiting there, you can click and save the date of May 4th at 8:00 AM Pacific. We invite everyone to join us. We couldn't be more excited to get to this event, and be able to share the vision and game-changing HPE innovations. >> Awesome. So it's... So I don't have to register, right? I don't have to give up my three children's name, and my social security number to attend your event, is that right? (chuckles) >> No registration required. Come by, click on hpe.com. Save the date on your calendar. And we very much look forward to having everyone join us for this event. >> I love it. It's pure content event. I'm not going to get a phone call afterwards saying, "Hey, buy some stuff from me." That could come other channels, so that's good. (chuckles) Thank you for that. Thanks for providing that service to the industry. I'm excited to see what you guys are going to be announcing that day. And look, Sandeep, I mean, like I said, we've known each other a while. We've seen a lot of trends, but the next 10 years, it ain't going to look like the last 10, is it? >> It's going to be very different, and we couldn't be more excited. >> Well, Sandeep, thanks so much for coming to theCUBE, and riffing with me on the industry, and giving us a preview for your event. Good luck with that, and always great to see you. >> Thanks a lot, Dave. Always great to see you as well. >> All right, and thank you, everybody. This is Dave Vellante for theCUBE, and we'll see you next time. (smooth music)

>> Unleash the Power of Data. On May 4th at 11:00 am Eastern, 8:00 am Pacific HPE is hosting a broadcast. And we're here with Sandeep Singh who's the vice president of Storage Marketing at Hewlett Packard Enterprise. Sandeep, what's this event all about? Who should attend, and why? >> Dave, in the world of enterprise storage, there hasn't been a moment like this in decades. A point at which everything is changing for data and infrastructure, and is powered by the nexus of data cloud and AI. And the opportunity for our customers to accelerate their data-driven transformation is unfolding. HPE is excited to invite everyone to join us for a virtual event, that as Dave mentioned, Unleash the Power of Data on May 4th at 8:00 am Pacific. And if you're an organization like most today, data is at the heart of what you do. And you're looking to accelerate data-driven transformation. We hear you, and we're thrilled to invite you to join us on May 4th as we unveil a new vision for data that accelerates data-driven transformation from edge to cloud. This promises to be a pivotal event, and one that IT admins, cloud architects, virtualization architects, vice presidents, directors of IT, and CIOs won't want to miss. The event is hosted by business and a tech journalist Shabani Joshi, and it will feature a market end panel with a focus on the crucial data that data is playing in the transformation for customers. Antonio Neri, CEO of HPE, and Tom Black, senior vice president and general manager of HPE Storage, as well as industry experts, including Julia Palmer, vice president at Gartner, will be part of the event. We will unveil game-changing HPE innovations that will make it possible for organizations across industries to unleash the power of data. >> Sounds awesome! Okay, go to hpe.com. Mark your calendar, and we'll see you there.

(upbeat music) >> Hi everybody, this is Dave Volante. And with me is Sandeep Singh, he is the vice president of Storage Marketing at Hewlett Packard Enterprise. And we're going to riff on some of the trends in the industry, what we're seeing. And we got a little treat for you. Sandeep, great to see you man. >> Dave, it's a pleasure to be here. >> You and I've known each other for a long time. We've had some great discussions, some debates, some intriguing mind benders. What are you seeing out there in Storage? So much has changed. What are the key trends you're seeing and let's get into it. >> Yeah, across the board, as you said, so much has changed. When you reflect back at the underlying transformation that's taken place with data, cloud and AI across the board. First of all, for our customers they're seeing this massive data explosion that literally now spans edge to core to cloud. They're also seeing a diversity of the application workloads across the board. And the emphasis that it's placing is on the complexity that underlies overall infrastructure and data management. Across the board, we're hearing a lot from customers about just the underlying infrastructure complexity and the infrastructure sprawl. And then the second element of that is really extending into the complexity of data management. >> So it's interesting you're talking about data management. You remember you and I, we were in Andover. It was probably like five years ago and all we were talking about was media. Flash this and flash that, and at the time that was kind of the hot storage topic. Well, flash came in addressing some of the mics that we historically talked about it. Now the problem statement is really kind of quote unquote metaphorically moving up the stack if you will, you mentioned management but let's dig into that a little bit. I mean, what is management? I mean, a lot of people that means different things to different people. You talk to a database person or a backup person. How do you look at management? What does that mean to you? >> Yeah, Dave, you mentioned that the flash came in and it actually accelerated the overall speed and latency that storage was delivering to the application workloads. But fundamentally when you look back at storage over a couple of decades the underlying way of how you're managing storage hasn't fundamentally changed. There's still an incredible amount of complexity for IT. It's still a manual admin driven experience for customers. And what that's translating to is more often than not IT is in the world of firefighting and it's leaves them unable to help with them more strategic projects to innovate for the business. And basically IT has that pressure point of moving beyond that and helping bring greater levels of agility that line of business owners are asking for and to be able to deliver on more of the strategic projects. So that's one element of it. The second element that we're hearing from customers about is as more and more data just continues to explode from edge to core to cloud. And as basically the infrastructure has grown from just being on-Prem to being at the Edge to being in the cloud. Now that complexity is expanding from just being on-Prem to across multiple different clouds. So when you look across the date data life cycle how do you store it? How do you secure it? How do you basically protect it and archive it and analyze that data. That end to end life cycle management of data today resides on just a fragmented set of overall infrastructure and tools and processes and administrative boundaries. That's creating a massive challenge for customers. And the impact of that ultimately is essentially comes at a cost to agility, to innovation and ultimately business risk. >> Yeah, so we've seen obviously the cloud has addressed a lot of these problems but the problem is the cloud is in the cloud and much of my stuff, most of my stuff, isn't in the cloud. So I have all these other workloads that are either on-Prem and now you've got this emerging Edge. And so I wonder if we could just talk a little vision here for a minute. I mean what I've been envisioning is this abstraction layer that cuts across all weather. It doesn't really matter where it is. If it's on-Prem, if it's across cloud, if it's in the cloud, on the edge, we could talk about what that all means. But if customers that I talked to they're sort of done with the complexity of that underlying infrastructure. They want technology to take care of that. They want automation they want AI brought in to that equation. And it seems like we're from the cusp of the decade where that might happen. What's your take? >> Well, yeah, certainly I mentioned that data cloud and AI are really the disruptive forces, better propelling. The digital transformation for customers. Cloud has set the standard for agility and AI driven insights and intelligence are really helping to make the underlying infrastructure invisible and customers are looking for this notion of being able to get that cloud operational agility pretty much everywhere because they're discovering that that's a game changer. And yet a lot of their application workloads and data is on-Prem and is increasingly growing at the edge. So they want same experience to be able to truly bring that agility to wherever their data in absolute. And that's one of the things that we're continuing to hear from customers. >> And this problem is just going to get worse. I mean for decades we marched to the cadence of Moore's Law and everybody's going to forgets about Moore's Law. And say, "Ah, it's dying or whatever." But actually when you look at the processing power that's coming out now, it's more than doubling every two years, quadrupling every two years. So now you've got this capability in your hands and application design minors, storage companies, networking companies. They're going to have all this power to now bring in AI and do things that we've never even imagined before. So it's not about the box and the speeds and feeds of the box. It's really more about this abstraction layer that I was talking about. The management if you will that you were discussing and what we can do in terms of being able to power new workloads in machine intelligence, it's this kind of ubiquitous, call it the cloud but it's expanding pretty much everywhere in every part of our lives even to the edge you think about autonomous vehicles, you think about factories it's actually quite mind boggling where we're headed. >> It is and you touched upon AI. And certainly when you look at infrastructure, for example there's been a ton of complexity in infrastructure management. One of the studies that was done actually by IDC indicated that over 90% of the challenges that arise, for example ultimately down at the storage infrastructure layer that's powering the apps ultimately arises from way above the stack all the way from the server layer on down where even the virtual machine layer. And there, for example, AIOps for infrastructure has become a game changer for customers to be able to bring the power of AI and machine learning and multi-variate analysis to be able to predict and prevent issues. Dave, you also touched upon Edge and across the board. What we're seeing is the Enterprise Edge is becoming that frontier for customer experiences and the opportunity to reimagine customer experiences as well as just the frontier for commerce that's happening. When you look at retail and manufacturing and or financial services. So across the board with the data growth that's happening and this Edge becoming the strategic frontier for delivering the customer experiences how you power your application workloads there and how you deliver that data and protect that data and be able to seamlessly manage that overall infrastructure. As you mentioned abstracted away at a higher level becomes incredibly important for customers. >> So interesting to hear how the conversations changed. I'd like to say, I go back to whatever it was five years ago, we're talking about flash storage class memory, NVMe and those things are still there but your emphasis now you're talking about machine learning, AI, math around deep learning. It's really software is really what you're focusing on these days. >> Very much so. Certainly this notion of software and services that are delivering and unlocking a whole new experience for customers that's really the game changer going forward for customers. And that's what we're focused on. >> Well, I said we had a little surprise for you. So you guys are having an event on May 4th. It's called Unleash The Power of Data. What's that event all about Sandeep? >> Yeah. We are very much excited about our May 4th event. As you mentioned, it's called Unleash The Power of Data. And as most organizations today are data driven and data is at the heart of what they're doing. We're excited to invite everyone to join this event. And through this event we're unveiling a new vision for data that accelerates the data driven transformation from Edge to cloud. This event promises to be a pivotal event and one that IT admins, cloud architects, virtual machine admins, vice presidents, directors of IT and CIO really won't want to mess. Across the board this event is just bringing a new way of articulating the overall problem statement and in market in focused the articulation of the trends that we were just discussing. It's an event that's going to be hosted by a Business and Technology Journalist, Shabani Joshi. It will feature a market in panel with a focus on the crucial role that data is playing in customers digital transformation. It will also include and feature Antonio Neary, CEO of HPE and Tom black, senior vice president and general manager of HPE Storage Business and industry experts including Julia Palmer, research vice president at Gartner. We will unveil game changing HPE innovations that will make it possible for organizations across Edge to cloud to unleash the power of data. >> Sounds like great event. I presume I can go to hpe.com and get information, is it a registered event? How does that all work? Yeah, we invite everyone to visit hpe.com and by visiting there you can click and save the date of May 4th at 8:00 AM Pacific. We invite everyone to join us. We couldn't be more excited to get to this event and be able to share the vision and game-changing HPE innovations. >> Awesome. So I don't have to register, right? I don't have to give up my three children's name and my social security number to attend your event. Is that right? >> No registration required, come by, click on hpe.com. Save the date on your calendar. And we very much look forward to having everyone join us for this event. >> I love it, it's pure content event. I'm not going to get a phone call afterwards saying, "Hey, buy some stuff from me." That could come other channels but so that's good. Thank you for that. Thanks for providing that service to the industry. I'm excited to see what you guys are going to be announcing that day and look Sandeep. I mean, like I said, we've known each other a while. We've seen a lot of trends but the next 10 years it ain't going to look like the last 10 is it? >> It's going to be very different and we couldn't be more excited. >> Well, Sandeep, thanks so much for coming to theCube and riffing with me on the industry and giving us a preview for your event. Good luck with that. And always great to see you. >> Thanks a lot, Dave. Always great to see you as well. >> All right. And thank you everybody. This is Dave Volante for theCube and we'll see you next time. (upbeat music)

>>from Las Vegas. It's the Q covering a ws re invent 2019. Brought to you by Amazon Web service is and in along with its ecosystem partners. >>Hey, welcome back to the Cubes. Live coverage in Las Vegas for eight of his re invent 2019 R Seventh year out of the eight years I've had it, we've seen the rise and dominance of Amazon continued to thunder away at the competition span. Their lead printing money stew minimum in my coz right here next to me. I'm John, very extracting from noise. Our next guest, steam A lady who's the presidency of Aviatrix Cube alone was on Tuesdays part of our editorial segment. Who his company or one of his employees going to term. You take the tea at a cloud native cloud naive, which has been going viral. Welcome back to the Cube. Thank you. All right, so let's get into the aviatrix value. Probably wanna get digging more, but first explain what you guys do and what market you're targeting. >>So we do. I would say cloud native, not naive. Cloud native networking that embraces and extends the basic constructs the native constructs of the public clouds, not just a W s, but all the public clouds and builds a multi cloud architecture, networking in security architecture for enterprise customers that that delivers the simplicity and the automation that people want from cloud. That's why they want a cloud native but yet brings along the functionality, the performance and the visibility and control that they had on on Prem. So that kind of taste great less filling, not one of the other. Both. I want the simplicity and automation of anything that expect from the cloud. But I need that enterprise functionality that control the security, the performance that he used to have on Prem because I wasn't doing that for my own health. I need to bring that along. That's what we do. >>What main problem you solving for customers? What's the big pain point? So what are you enabling? >>The big pain point is the center of gravity, as Andy Joshi's talked is moving from on Prem into the cloud. So it's so it's no longer. I mean, data centers aren't going away. They're going to still be there. But the investment architecture is in the cloud and you're going to see the clouds start moving out with their their announcements. You see everything that outposts and on everything else they're doing is taking the architecture moving out. The problem we solve is A W S says to every enterprise customer. We will give you anything and everything you ever need from networking and security. You don't need anybody else. And so so what ends up happening is as enterprises. So for an SNB, that's great. If you've got a few, VP sees life is good. Use all the native stuff from AWS. What happens, though, is your Qualcomm or your USA or your new name it big 50 year old 100 year old enterprise. You have complex networking and security demands. You go to the cloud. There's so many limitations of what the native constructs of all the clouds could do. You start realizing, okay, I need Maur. And so we're very complimentary to AWS. We sit on top of that. We leverage those basic constructs. We program those contracts and then we extend that functionality to deliver the functionality that they need. >>That's awesome, stupid when I want to dig into that, but I want to first get to the hard news you guys have news here at reinvent? What's the big news story that you guys were putting out there? Two >>announcements and actually goes perfectly with the way the world's going and also with the embrace and extend of a W s. So the first is we introduced what we call aviatrix Cloud win. So they announced Transit Gateway Network manager with accelerated VPN leveraging global accelerator as just a way to bring in basically embraced branch offices into the cloud. So if you think of SD win in that market, if your if your center of gravity is on Prem in a data center on pls is horrible, you needed a better way to do branch office connectivity. SC wait is fantastic, and it's a great, optimal way to get back to that data center. Well, as the center of gravity moves into the cloud, their data centers in the cloud. I just need to get better optimal access performance in late and see into a W s because that's the center of gravity. So AWS with the global accelerator allows youto get on one of their 250 pops around the world as quickly as possible. So if you're in Singapore, get on that pop VPN in, and then you go across the global backbone of AWS all the way out to that BBC in Virginia. It's beautiful, because guess what? That is the most optimal way to get there instead of vpc to vpc across the Internet right on the AWS backbone. Well, Steve, it's fascinating stuff because if you look at the traditional network, it was I knew the knobs and how I need to get everything to work. But the big challenge for most network people is most of the network that they're responsible for. They can't touch it. That's right. They can adjust it. So are we recreating some of the environment? Or how? Because clubs supposed to be simple? Well, that's easy, but it needs to meet the enterprise requirements. Help that network administrator there there, sometimes going away to the cloud administrator. You still networkings tough and therefore, how do we make that? That's part of what we do is that's the other thing that we solve is people think they go to the cloud and they think, Oh, go build. I don't want to build anything. I want to consume. It's still difficult. We come in and abstract away a lot of the details for them such that we deliver that service on the cloud win. The other thing that we do again, back to embracing and extending. What do you What? What router is out in that branch office 87% of the time. Sisko, right? I mean, course it is. So the S D wearing guys will go in and say, We'll rip that box out and put in another little box like a 20,000 branches. I'm not ripping out anything, right? That's very painful. So with our cloud win, we can orchestrate and reconfigure the Sisko. All of our engineers came from Cisco. So any Cisco IOS router out there, we can orchestrate and reconfigure to set up the VPN automatically through our orchestrator so that when you don't rip and replace out that Roger that's existing there. So now AWS loves it because that's the last piece of friction. They want no friction, and it's always in that physical to cloud transition There. All the complexity is, and by enabling their network manager and an accelerated VPN and global accelerated to use the existing Cisco. Roger, that's out there. No one else does that Cisco doesn't do that. We're the only ones. So when you embrace a native construct, what's the native construct in the branch office? B, G P. And Cisco IOS. We embrace it and then and then enhance it and make it better. >>Are you only on Cisco about June 1st? >>Wait. Now it's just go. Francisco's 87% >>of every bridge your software abstraction software across. And you you basically change the game with SD. Win a little bit, you modernize >>It s t win is great for the old way of doing networking. When you look for the next five years, you're still gonna need SD went. It's a bubble market. It's like when optimization us riverbed. If when optimization is a great market, it was for a while, just like SC win. But that's kind of the old way. But Maur Maura, what you're gonna find is what Where my branches need to connect to is in the cloud. And if you do that, you don't need esti win. You just need better connectivity. Tate of us provide. >>I gotta ask you the question about the cloud naive because there's a lot of old school I t people who still think there's food in the data center. Still, action there on box makers are all in the vendor side supplying boxes. They're still want to supply boxes, right? So as those old guys and gals do their thing, they're stuck in their ways, right? That's friction. Total gas. He talks about the transformation as new leadership. What has to change in that old world? What should those C I ose and CEOs tell their their staff? And what should the staff do themselves? >>I actually think the customers air there. I think the vendors are that the vendors are the one that aren't They're the ones who are cloud naive. They actually don't even know what they don't know. The customers are the ones they say, Oh, no, And this is the whole shift that Josh was talking about business transformation. They understand. And they are bringing along all their people and they have some people that are probably further along and experts in AWS. But they absolutely number one requirement for them is we've got to bring along the people they don't want to leave them behind and say, You get to work on the old data center and these guys are gonna work in Cloud. They're bringing them all in. >>Talk about your customers who's buying from you? What's it look like? What kind of scope do you have? A customer base? >>It's funny. It's It's It's all the old networking guys. It is not. It's not developers signing that. It's it's It's old. I t. Now they don't want to do it the old way. They want to do it the new cloud way. But these guys understand BDP. They understand networking, and they're in charge now. And so it's like because it's gotten so serious for enterprises. This the networking team, the security team it is. It is I t that is running this, so that's a big company. Small companies, we get him. All right, Steve, I want to make sure I understand this because when I hear cod Native, I really think a lot about that application. Mind shift. Yeah, Micro Service is our protector, and that's on it for sure. Networking. Unfortunately, for the most part, it's nothing. Bites are going through the pipes, and I haven't really thought about that. So you know, it's not just because it's cloud but cloud native and therefore things like your container and doctors Dr. Rise thing. This is what this world is built for that your solution is solving for yes. So I'll give you a perfect example. So So we help. We actually helped a dhobi us come out with T g. W. Last year, Cheri, I found, was on stage with Day Brown and the networking keynote launching T g w whenever Great. Of course, before that, you were just doing bpc the vpc peering It was a horrible mess. So you need a transit architecture. So they came out with T g w Fantastic. So we embrace and extend T g w. So the problem is, they come out with T g. W. But guess what a Doris doesn't do. Don't propagate routes to spoke VP sees. Okay, so how did the routes get propagated? Well, you have a person. They need manual. If there's an update on the on from you manually update the routes. Well, that might work. If you've got three. VP sees again. You're an SNB. But I'm an enterprise. I got 3000 vpc That is not gonna work. So cloud native we are We are not just sitting on top of AWS. We are in the matrix we are in. We understand natively. So our central control, it will actually like we're not. There's no b g p running at that layer, but our central control it will push routes an update, routing tables everywhere. It needs to be learned. The routes from Amman Prem push it where it needs to be, and then everything automatically works. Yeah, it reminds me, you know, we had more than a decade ago. We went from all the north south traffic to the East west, propagated by VM. Yes, is an order of magnitude 8 12 and know that this cloud environment people can't do it. There's not enough people. I don't have enough man hours because the machine learning So here's devices need to be here. Another thing that's happened in guys is there is there is 100% of people in there in the universe that that that no cloud, that number's growing, but there's a fixed set. Everybody's going after all those people. You've got the big clock. They're all hiring like crazy. The vendors are probably hiring. You've got customers they're stealing from each other. It's very difficult to keep a staff. And so they look and they say I probably could figure this out, but there's no way I'm going to be able to operationalize it. There's just zero chance I could do that And there's just so much change. And honestly, they say it's a full time job just keeping up with what Amazon is announcing their get implementing. And so that's where they look and they come to Austin. They say there's zero chance that I can deploy networking architecturally without aviatrix >>on the network and guys because you and I always say the neighboring guys have the keys to the kingdom. They always have. I mean, people have tried to move the center of power away from the networking guys, But now, as the cloud gets the center of gravity, some of the power networking guys got to step up their game. But they don't want to rip and replace anything is as you went out earlier. It's complex, even pull one or two out. So the concern that I might have put the question to you is Steve. Great, great energy. But I'm really nervous that these routes are not gonna be. There's gonna be some coherency issues around updating routes because that's my number one concern. How do you guys solve that? >>Well, the one thing I've always seen, who's the worst? When? When? When most things happen, Who's the culprit? Human, right? It's always a human. Does something wrong. And so I would much rather trust some sort of automated software because at least if you program it correctly, it's going to do the right thing so way have not had. I mean, it's so >>you know what I'm sure is no issue there. >>Yeah, no, there's no issue, I mean, and what we do see, sometimes our people say, because there's a lot of people that are that are very smart, they get into the cloud and they are do it yourselfers and they love to go build, and they love the complexity, and they want all that they feel they feel like this job security and what we sometimes have to do is say you. But think about day to think about handing off the operations. You might get hit by a bus, and then your company is screwed, and you gotta almost get them enlightened to realize that they should be working on higher level things other than low level things. I'd say that's something that we kind of educate. People, >>houses Amazon there, one cloud of many 34 maybe one or two jazz. He said to me. You know, mostly primaries will be picked, probably Amazon. But in some cases, as you will be a primary less than that eight arrests. So multi cloud is the word that it was Something about an Amazon sees me loosening up a bit what it is, so they recognize it. What is multi cloud? I mean, what is really going on? I think >>I think if you're a small company, absolutely pick one cloud like for sure, right, like that doesn't make sense to go multiple clouds in your small medium business. If you're not that, if your needs are not that complex, pick one cloud right? And if it's a Toby asses the later stay with them. If it just happens to be, well, I got a bunch of credits and azure. Okay, maybe do them. I think. To date most people are picking eight of us There, there, there, there, The killer here. But when you talk to the enterprise, the real enterprise right that are just now moving into the cloud, they're all multi cloud Just had one today. Super large chip company down L a San Diego area. Guess what. Use it. All three clouds. I asked him why. Well, because we started in AWS. We got some things there we've got. We've got a bunch of stuff that runs and an azure with offers 3 65 other things that they dio and Google for ml and that kind of stuff. It runs better their enterprises. They're gonna pick where the workload run best, and they're big. And so they're gonna look and they're gonna They're gonna They're gonna elevate up building architecture that works across all of them. I don't think multi cloud means I'm gonna move this workload from here to here. That's never gonna happen. Maybe in 20 years. But I doubt it. It's just that the workloads heir destined, they run better on that and they're gonna focus on >>different park loads for the cloud that picked the right guy for the right workload. >>Yeah, and I'm so big and I require different companies and I get acquired. And and and And you got to think of the on Prem data centers eyes another cloud that's a multi. And then I go into Europe, and I have GDP are and I need another cloud. I mean, they're gonna have 45 clouds, and I don't think it's gonna be 20% across all >>that could be a power lot. They'll be more than 13 closets. Be specialty clouds a riff on this all the time. Well, Steve, I want to thank you for coming on the Q. Appreciate it. Give a quick blood for the company. How many employees you're gonna hire, some of your objectives >>growing fast. We've got over 400 customers and you ask one of our customers we've got customers spending millions of dollars a year with us all the way down the customer spending $5 a month. Why? Because of the wonderful thing of cloud they can consume. We've got 400 customers all over the world and even know who probably 300 of them are right. Why they go on the market place they go like this, they download. Maybe they come on drift. Ask one question. They launch and they spent $5 a month. I don't even know what they're doing. And eventually we watched their Amar are it just grows and grows and grows and grows. And eventually like, Whoa, Now you're spending 50 grand a year. We should talk. So it's kind of like how some companies used open source that ends up being our funnel a low friction zero friction High velocity Landon expand model. And then we have the traditional enterprises that you'd imagine every so everything in between >>your hiring, >>we're hiring like crazy, hiring a whole bunch of sales organization around the world. We just raised $40 million Siri see a month ago and we're going for >>fresh financing. Aviatrix see Mulaney, CEO of aviatrix here on the Cuba Reinvent 2019 Stay with us for more coverage. Day three of our three days of World War coverage Two sets here, thanks to Intel for the being our headline sponsor without their supporting our mission, which is bringing you the best confident possible. We want to thank Intel on. All of our sponsors were right back with more coverage after this short break