>>from around the globe. It's the Cube with digital coverage of a new era and database management brought to you by Nutanix. >>Welcome back. I'm still minimum and we're covering Nutanix Is New Era database launch Of course, we had to do instead of conversation with Monica Ambala talking about era to Dato and to dig into it a little bit further. We have some new tennis guests as well as what? One of their close partners. So going across the channel, first of all, happy to welcome to the program. Uh, the new kid UPA she is the gsc strategy and go to market with Nutanix sitting in the middle chair we have on and Akila whose product marketing leader with Nutanix and then from HCL happy to welcome to the program Tasing who is the senior vice president with HCL Technologies. I mentioned all three of you. Thank you so much for joining us. >>Glad to be here, >>right? Uh they knew What? Why don't we start with you? You handle the relationship between Nutanix and HCL. As I said, some exciting announcements database services help us understand how Ah partner like HCL takes the technology and what will help bring it to market. >>Let me start by thanking used to for this opportunity. Head Seal is a very significant partner for Nutanix and we've had this partnership for a long time now. It's one of our long standing partnership. Over the five years we've closed over 100 accounts across all three theaters. Trained professionals both on the Nutanix side on the outside, on built a 3 60 relationships so we can deliver the best experience around solutions to our partners. In the very recent announcement, we're looking to build a database as a service offering. With that CL we want Thio leverage are intelligent technology that allows us to simplify off and increase operating efficiency. Andi Couple it with head seals ability to offer world class services on it. It's a scale to reach the go to market needs needed right. We're very confident that the solution is going to drive significant incremental business for both our companies. >>Excellent taste. We would love to hear from your standpoint. What is it that excites you? We we know HCL knows the data space real well. So I think you've got some customers that air looking to take advantage of some of these new offerings. >>Yeah, So if you look at where the focus has been so far, most of the focus is on taking applications to cloud and moving them from VM two probably containers one of the most. Uh, I won't say, uh, neglected, but the space that needs to change now is the entire database space on. If you look at how customers are managing databases today, they have taken hardware on a KPIX model. They have the operating system and the database licenses on L. A model from the E. M s on. Then they have, ah, teams which are siloed depending upon the database technology that is there in the environment and managing that I think that whole model is has to change, enabling customers to transform Onda accelerate the digital transformation journey on. That is where our offering off database as a service ises very unique because it offers a full stack off services which includes right from hardware and all the way to operations on a completely utility model powered by the Nutanix era. >>Yeah, on it might make sense if you could give us a little bit of a broader context for your users. Some of the data that you have around this offering, >>yeah, you know, attend effect. All the solution, our joint solutions. Our customers, uh, they are trying to deliver the best individual experience, right? That's at the heart of it. What they're trying to do, I'll give you a couple of customer examples. For example, Arbil Bank in India. You know, they deployed their database solutions and applications, and Nutanix got 16 fasters application response. That means like they used to take 180 seconds. Uh, Thio logging into the application. And now it's, uh, 20 seconds, 36 times faster. Another example I could give. I can give many examples, but when this one is really interesting, Delaware Valley community held, you know, at the time of Kobe they went remote. They started working from home and they had medical systems applications. EMR electronic medical record applications and used to take even before they were working from home, is take like 171 seconds to log into medical systems before they could, you know, talk to their patients and look at their, you know, health results and everything and that from 171 seconds, it went to 19 seconds. So these are some of the values that customers seeing when it comes to delivering the individual experience to their customers. >>Yeah, absolutely. We've seen police stage go ahead. >>Yeah, and I just had to What men? Who said that? It's also the ability tohave self service with dynamic provisioning capability that really brings the value toe the to the I T teams and to the application teams who are consuming these services. So we have cases where customers were waiting for about a week, 10 days for the environments to be provisioned to them. And now it's a matter of seconds or minutes where they can have a full fledged environments leading to develop a productivity. And that also really adds the whole acceleration that we just spoke about. >>Yeah, we we've absolutely seen such a transformation in database for the longest time. It was, you know, a database. It didn't change too much. That's what everything run on Now there's a lot of flexibility. Open source is a big piece of what's going on there. I'd like to come back to you and you know, they know. I know you're gonna want to chime in here. You know, HCL doesn't just, you know, take this off the shelf and, you know, resell it, help us understand. You know what is unique about the offering that that HCL brings market? Uh, with with >>Nutanix. Right. So one is that we have standardized reference architectures, which really x ray the time to consume the offering. We're not building anything from from from ground up. Three Nutanix is also part off our velocity framework, which helps customers deploy software defined infrastructure as the as a foundation element for their for their private cloud. Now, what is unique is also the ability toe not only provide operations on different databases that are there in the environment on a completely utility model, but also help customers, you know, move to cloud and adopt the database clouded of databases and then manage the whole show seamlessly using using the BP platform and that really, you know, if you look at the trend that is there, there's a short term impact on the long term impact off transformation. In the short term, there's hardly an industry which is not touched by by covert on most of our customers are either looking at cost or initiatives or are looking at ah platform, which will help them in a weight or find new business model to to sail through. In the long term, we strongly believe that the customers will be in a hybrid, multi cloud world where they will still have the heritage environments. The article and the Sequels on a lot off cloud native data business will also start coming into picture. How do you manage is also seamlessly is what will be the next challenge for for most of the customers. And that's where we come in, along with Nutanix, to solve the problem. >>Well, very simply put right, we have different categories of customers. One off them refers to buy the ingredients and make their own meal on some really large customers, and global customers prefer to buy the meal and pay for it on on as consumer basis. What that seal does is take era, which simplifies a lot of the database operations, puts it into a full stack solution and gives the customer the full stack solution. Everything from assessing that environment to deploying, to making sure that the designers I accurate and then of course, the day and through they do through and, uh, uh, environment, right. So literally the customer can Now I'll offload any off their data center, our database management and operation to hit cl from my perspective on do rest assured, run their projects toe, etc. Also, excel becomes their extended arm, the beauty off. It is also like working with dead C. Elgar now able to offer the entire solution on a pay as you go model or pay as you use model, which is very relevant to the existing times where everybody is trying to cut their Catholics costs and and optimized on the utilization. >>Well, great. Great to hear about that. You've mentioned that this partnership has been for many years, so I know you've got plenty of joint customers. Anything specifically could share about these new offerings on. And I know you've got a lot of the customer stories there. Maybe you could start would look love, freedom. The rest of you, >>Thio, I'll start what? You know, Like I talked about a couple of customers. But recently I'm really excited about. And this is something that to be a announcing today as well. Ah, study that we did with Forrester called Forrester T I study, which is what it means total economic impact study. And what they do is that they topped with customers, uh, interviewed them, four of them. And based on their experience, uh, you know what? They observe what kind of benefit they got, what challenges they had, what was cause they built an economic model. And based on that economic model, they found that customers were rolled all off them were able to get their payback within six months. So Bala talked about it earlier that, you know, like all the great experience, all the great value that we offer, but at a very, very good cost. So the six less than six months payback was used and the r y for the three years period and again, this is ah, model based on four enterprises was 2 91 100% almost like three times mawr. So whatever they invested, I think on an average day the cost was 2.3 million and the benefit was nine million or so so huge value customers have observed already. And with this new launch, I believe that it will just go to the next level. All the things about provisioning copy data saving that the stories All of that adds to the R Y that I'm talking about and our joint customers with SCL or otherwise, who are customers who are running their applications, their business critical applications on you can X Platform managed by era an era is built out off a bunch off best practices that over time that we have done. I talked about custom performance earlier, and a lot of the performance comes from fine tuning. You do that like a lot of tea tuning and to get to the right kind of performance. Uh, era comes with that, those best practices. So when your provisioning an application, you know, it gives you you don't have to do all that tuning. So that's the value customers are experiencing. And I'm really excited about the joint customers what they could experience and benefit out off the new expanded solution. >>Great Tiger. Any other customer examples that you'd like to share? >>Well, we got a lot of go ahead page, >>but it's okay. >>No, I was just saying that we've had a lot of success with Head cl across the board anywhere from data center organization Thio v. D. I. We had a very large manufacturing company in America where we partner together. They have a huge number of sub brands. We partnered together to go evaluate that environment and then also even that is a B infrastructure with databases. It's a relatively new offering we're announcing today. But we're leveraging the expertise that SCL has in the market, uh, to go to go deeper into that market with cl eso. I will leave it to page to give us the NCL examples. >>So one thing that is happening is the very definition off infrastructure and infrastructure operation itself is changing. So a couple of years ago, for many of our customers, it was about operating system management, hardware management, network management and all the use. Uh, the concept that you're going back to customer is about platform operations. That means everything to do with application operations. Downward is going to be done by one integrated unit. Now, with Nutanix, we can we can really bring a lot of change, and we're bringing a lot of change in our in the operations model for for lot off a large customers where earlier you had siloed teams around Compute network storage, offering system databases both at the Level two and level three, and you had a level one, which was basically command center. Now, we're saying is that with the artificial intelligence and machine learning driven OBS, you can practically eliminate the need for command center on the level two layer because the platform enables you toe be multi skilled. You need not have siloed engineers looking after databases separately on and operating system separately. You can have the same sort of people who are cross train, multi skilled, looking at the entire state. On at level three. You may want to keep people who are deep into databases as a separate team, then from people who are managing the Nutanix platform, which is a combination off compute storage and and and and the SCN. So that's the change that we're bringing. A lot of our customers were going about infrastructure, platform modernization, Azaz, the public cloud or hybrid clubs. >>Well, I think you're really articulated well, that modernization journey we've seen so many companies going through. The thing I've been saying with Nutanix for years is modernize the platform, then you can modernize everything that runs on top of it. All the applications on, of course, did databases a major piece of this on. And that brings up a point I want to get your take on. We haven't talked about developers, you know, the DEV ops trend. Something we've seen, you know, huge growth for for a number of years. So what >>does this >>mean from developers? This something that you know, mostly the infrastructure team's gonna handle. Or how do you bridge that gap to the people that really are? You know, building and building and building the APS. >>Yeah. And in this digital world, you know the cycle time from idea to production. Everyone is trying to reduce that. What that means is that things are moving left. People are trying to develop and test early in the life cycle when it is easy to find a problem and easy to and cheaper to fix. Right. So for that, you need a your application environment, your application and database available to test and develop in, uh, you know, like in volume. And that's where databases the service era helps developers and develops professionals to provision in the whole infrastructure for testing and involvement in hundreds and thousands of them at the same time without, you know, worrying about the storage back back and how much story it is consuming. So it is. It helps developers to to really expedite their development and testing left lifecycle ultimately resulting in excellent and unique experience. >>Yeah, absolutely way no. Of just moving faster. Being able to respond to the business so critically important. Uh, they know Tasia wanna let you have the final word Talk about the partnership and what we should expect, you know, in the coming months and quarters. >>So, uh, I'll go first. And then we can come in, uh, a salon and Nutanix you to share the same values where we believe that we need to provide a very innovative platform for our customers to accelerate their digital transformation journey. No matter what it is right, we share common values and way have a 3 60 degree relationship. It started way back in 2015 and we have come a long way since then. A C also does engineering services for for Nutanix, and we have closed about 850 r plus people who has prayed and 35 on Nutanix Solutions. Providing manage services to our customers on Nutanix is also part off our software defined infrastructure portfolio on we're taking it to our customers as part of our entire infrastructure platform modernization that, I suppose talk about earlier three recent announcement off Nutanix clusters running on AWS. I think it's a significant announcement and it will provide a lot off options to our customers. And as an S, I, uh, you know, we are able to bring a lot of value to our customers. We're looking at adopting cloud the database as a service offering. I think we're very excited about it. I I think we have about 300 plus customers, and many of them are still stuck with the way they are managing databases the old way. And we can bring in a lot of value to those customers, whether it is about reducing cars or increasing agility or helping them modern ice, The platform one ended up hybrid multi club >>business critical lapse are growing, are still growing, and data is pretty much gold in these scenarios, right? It's it's doubling every two years, if not more with every transaction being remote today with zeal. We actually look forward to addressing that market and optimizing the environment for our customers. Both of our companies believe in partnership crossed and the customer first mindset. And when you have that belief, trust comes with delivering the best experience to our customers. So we're looking forward to this partnership and you're looking forward to growing our joint revenue and modernizing our customers platforms with this often? >>Well, I wanna thank all three of you for for sharing the exciting news. Absolutely. It looks like a strong partnership. Lots of potential there for the future. So thank you so much for joining us. Thank you for >>having thank you. Mhm. >>All right, when I think the audience were watching this lot with Nutanix, the new era in database management personally, a big thank you to the Nutanix community has been a pleasure being able to host these interviews with Nutanix for for many years. So I'm still minimum and thank you as always for watching the Cube

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

>> Hi, I'm Peter Burris, and welcome to Wikibon's Action Item. (funky electronic music) We're broadcasting, once again, from theCUBE studios in lovely Palo Alto. And I've got the Wikibon research team assembled here with me. I want to introduce each of them. David Floyer. >> Hi. >> George Gilbert are here in the studio with me. Remote we have Jim Kobielus, Stu Miniman, and Neil Raden. Thanks everybody for joining. Now, we're going to talk about something that is increasingly overlooked, that we still think has enormous importance in the industry. And that is, does hardware matter? For 50 years, in many respects, the rate of change in industry has been strongly influenced, if not determined by the rate of change in the underlying hardware technologies. As hardware technologies improved, the result was that software developers would create software that would fill up that capacity. But we're experiencing a period where some of the traditional approaches to improving hardware performance are going down. We're also seeing that there is an enormous, obviously, move to the cloud. And the cloud is promising different ways of procuring the infrastructure capacity that businesses need. So that raises the question with potential technologies constraints on the horizon, and an increasing emphasis on utilization of the cloud, is systems integration and hardware going to continue to be a viable business option? And something that users are going to have to consider as they think about how to source their infrastructure? Now there are a couple of considerations today that are making this important right now. Jim Kobielus, what are some of those considerations that increase the likelihood that we'll see some degree of specialization that's likely to turn into different hardware options? >> Yeah Peter, hi everybody. I think one of the core considerations is that edge computing has become the new approach to architecting enterprise and consumer grade applications everywhere. And edge computing is nothing without hardware on the edge, devices as well as hubs and gateways and so forth, to offload and the handle much of the processing needed. And increasingly, it's AI, artificial intelligence. deep learning, machine learning. So going forward now, looking at how it's shaping up, hardware's critically important. Burning AI, putting AI onto chipsets, low power, low cost chips that can do deep learning, machine learning, natural language processing, fast, cheaply, in an embedded form factor, critically important for the development of edge computing as a truly end-to-end distributed fabric for the next generation of application. >> So Jim, are we likely to see greater specialization of some of those AI algorithms and data structures and what not, drive specialization and the characteristics of the chips that support it, or is it all going to be just default down to tensor flow or GPUs? >> It has been GPUs for AI. Much of AI, in terms of training and inferencing, has been in the cloud, and much of it has been based historically, heretofore, on GPUs, and video being the predominant provider. However, GPUs historically have not been optimized for AI, because they've been built for gaming and consumer applications. However, the next generation, the current generation, from Nvidia and others, are chipsets in the cloud and other form factors for AI, incorporates what's called tensor core processing, really a highly densely packed tensor core processing components to be able to handle deep learning neural networks, very fast, very efficiently for inferencing and training. So Nvidia and everybody else now is making a big bet on tensor core processing architecture. Of course Google's got one of the more famous ones, their TPU architecture, but they're not the only ones. So going forward, we're looking at, in the AI ecosystem, especially for edge computing, there increasingly will be a blend of GPUs like for cloud based core processing, TPUs or similar architecture, or device-level processing. But also, FPGAs, A6, and CPUs are not out of the running because for example, CPUs are critically important for systems on the chip, which are quite fundamentally important for unattended operation as well as attended operation in terms of edge devices to handle things like natural language processing for conversational UIs. >> So that suggests that we're going to see a lot of new architecture thinking introduced as a consequence of trying to increase the parallelism through a system by incorporating more processing at the edge. >> Jim: Right. >> That's going to have an impact on volume economics and where the industry goes from an architecture standpoint. David Floyer, does that ultimately diminish the importance of systems integration as we move from the edge back towards the core and towards cloud in whatever architectural form it takes? >> I think the opposite, it actually is, systems integration becomes more important. And the key question has been can software do everything? Do we need specialized hardware for anything? And the answer is yes, because the standard x86 systems are just not improving in speed at all. >> Why not? >> That's a long answer to that. But it's to do with the amount of heat that's produced, and the degree of density that you can achieve. Even the chip itself-- >> So the ability to control bits flying around the chip-- >> Correct. >> Is going down-- >> Right. >> As a consequence of dispersion of energy and heat into the chip. >> Right, There are a lot of other factors as well. >> Other reasons as well, sure. >> But the important thing is, how do you increase the speed? And a standard x86 cycle time with it's instruction set, that's now fixed. So what can you do? Well, you can obviously, reduce the number of instructions and then parallelize those instructions within that same space. And that's going to give you a very significant improvement. And that's the basis of GPUs and FPGAs. So GPUs for example, you could have floating point arithmetic, or standard numbers or extended floating point arithmetic. All of those help in calculations, large scale calculations. The FPGAs are much more flexible. They can be programmed in very good ways, so they're useful for smaller volume things. A6 are important, but what we're seeing is a movement to specialized hardware to process AI in particular. And one area is very interesting to me is, to take the devices at the edge, what we call the level one systems. Those devices need to be programmed very, very intently for what is happening there. They are bringing all the data in, they're making that first line reduction of data, they're making the inferences, they're taking the decisions based on that information coming in and then sending much less data up to the level twos above it. So what are examples of this type of system that exist now? Because in hardware, volume matters. The amount of stuff you produce, the costs go down dramatically. >> And software too, in the computing industry, volume matters. >> Absolutely, absolutely. >> I think it's pretty safe to say that. >> Yeah, absolutely. So volume matters, so it's interesting to look at one of the first real volume AI applications, which is in the iPhone X. And Apple have introduced the latest chipset. It has neural networks within it. It has GPUs built in, and it's being used for simple things like face recognition and other areas of AI. And the interesting thing is the cost of this. The cost of that whole set, the chip itself, is $27. The total cost with all the senors and everything, to do that sort of AI work is $100. And that's a very low bar, and very, very difficult to introduce in other ways. So this level of integration for the consumer business in my opinion, is going to have a very significant effect on the choices that are made by manufacturers of devices going into industry and other things. They're going to take advantage of this in a big way. >> So Neil Raden, we've heard, or we've been down the FPGA road for example, in the past, data warehousing introduced, or it was thought that data warehouse workloads which did not necessarily lend themselves to a lot of the prevailing architectures in the early 90s, could get this enormous acceleration by giving users greater programmable control over the hardware. How'd that work out? >> Well, for Intersil for example, what actually worked out pretty well for awhile. But what they did is they used that PGA to handle the low-level data stuff and maybe reducing the complexity of the query before it was passed on to the CPUs where things ran in parallel. But that was before Intel introduced multi-core chips. And it kind of killed the effectiveness. And the other thing was, it was highly proprietary which made it impossible to take up to the cloud. And there was no programming. I always laugh when people say FPGA because it should have been called FGA. Because there was no end user computing of an FPGA. >> So that means that, although we still think we're going to see some benefit from this. But it kind of brings us back to the cloud, because if hardware economics are improved to scale, then that says that there are a few companies that are likely to drive a lot of the integration issues. If things like FPGAs don't get broadly diffused and programmed by large numbers of people, but we can see how they could, in fact, dramatically improve the performance, and quality of workloads, then it suggests that some of these hyperscalers are going to have an enormous impact ultimately on defining what constitutes systems integration. Stu, take us through some of the challenges that we've heard recently on the cloud, or on theCUBE at reinvent and other places, about how we start seeing some of the hyperscalers make commitments about specialized hardware, the role that systems integration's going to play, and then we'll talk about whether that could be replicated across more on-premise types of systems. >> Sure Peter, and to go back to your opening remarks for this segment, does hardware matter? When we first saw cloud computing roll out, many people thought that this was just undifferentiated commodity equipment. But if you really dig in and understand what the hyperscalers, the public cloud companies are doing, they really do what I've called hyperoptimize the solution. So when James Hamilton and AWS talks about their infrastructure, they don't just take components and throw a bunch of stuff from off the shelf out there. They build for every application, a configuration, and they just scale that to tens of thousands of nodes. So like what we had done in the enterprise before, which was build a stack for an application, now the public cloud does that for services and for applications that they're building up the stack. So hardware absolutely matters. And if we look not only at the public cloud, but you mentioned on the enterprise side, it's where do I need to think about hardware? Where do I need to put time and effort? What David Floyer's talked about is that integration is still critically important. But the enterprise should not be worrying about taking all of the pieces and putting them together. They should be able to buy solutions, leverage platforms that take care of that environment. Very timely discussion about all of the Intel issues that are happening. If I'm using a public cloud, well I don't have to necessarily worry about, I need to worry about that there was an issue, but I need to go to my supplier (chuckles) and make sure that they are handling that. And if I'm using serverless technology, obviously I'm a little bit detached from what that, whether or not I have that issue, and how that gets resolved. So absolutely, hardware is important. It's just, who manages that hardware, what pieces I need to think about, and where that happens. And the fascinating stuff happening in the AI pieces that Jim's been talking about, where you're really seeing some of the differentiation and innovation happening at the hardware level, to make sure that it can react for those applications that need it. >> So we've got this tension in the model right now. We've got this tension in the marketplace, where a lot of the new design decisions are going to be driven by what's happening at the edge. As we try to put more software out to where more human activity or system activity's actually taking place. And at the same time, a lot of the new design and architecture decisions being, first identified and encountered by some of the hyperscalers. The workloads are at the edge, the new design decisions are at the hyperscaler, latency is going to ensure that there is a fair amount of, a lot of workload that remains at the edge, as well as cost. So what does that mean for that central class of system? Are we going to see, as we talk about, TPC, true private cloud, becoming a focal point for new classes of designs, new classes of engineering? Are we going to see a Dell-EMC box that says, "designed in Texas," or "designed in Hopkinton," and is that going to matter to users? David Floyer, what do we think? >> So it's really important from the consumer point, from the customer's point of view, that they can deal with a total system. So if they want a system at the very edge, the level one we want, to do something in the manufacturing, they may go to Dell, but they may also go to Sony or they may go to Honeywell or NCL-- >> Rahway, or who knows. >> Rahway, yes, Alibaba. There are a whole number of probably new people that are going to be in that space. When you're talking about systems on site for the high level systems, level two and above, then they are going to be very, it will be very important to them that the service level that comes from the manufacturer, the integration of all the different components, both software and hardware, come from that manufacturer. He is organizing it from a service perspective. All of those things become actually more important in this environment. It's more complex, there are more components. There are more FPGAs and GPUs and all sorts of other things, connected together, it'll be their responsibility as the deliverer of a solution, to put that together and to make sure it works, and that it can be serviced. >> And very importantly to make sure, as you said, that it works and it can be serviced. >> Yeah. >> So that's going to be there. So the differentiation will be, does the design and engineering lead to simpler configuration, simpler change. >> Absolutely. >> Accommodate the programming requirements, accommodate the application requirements, all that are-- >> All in there, yes. >> Approximate to the realities of where data needs to be. George, you had a comment? >> Yeah, I got to say, having gone to IBM's IOT event a year ago in Munich, it was pretty clear that, when you're selling these new types of systems that we're alluding to here, it's like a turnkey appliance. It's not just bringing the Intel chip down. It's as David and Jim pointed out, it's a system on a chip that's got transistor real estate for specialized functions. And because it's not running the same scalable clustered software that you'd find in the cloud, you have small footprint software that's highly verticalized or specialized. So we're looking at lower volume, specialized turnkey appliances, that don't really share the architectural and compatibility traits of the enterprise and true private cloud cousins. And we're selling it, for the most part, to new customers, the operations technology folks, not IT, and often, you're selling it in conjunction with the supply chain master. In other words, auto OEM might go to their suppliers in conjunction with another vendor and sell these edge devices or edge gateways. >> And so that raises another very important question. Stu, I'm going to ask this of you. We're not going to be able to answer this question today. It's a topic for another conversation. But one of the things that the industry's not spending enough time talking about is that we are in the midst of a pretty consequential shift from a product orientation in business models to a service orientation in business models. We talk about APIs, we talk about renting, we talk about pay-as-you-go. And there is still an open question about how well those models are going to are going to end up on premise in a lot of circumstances. But Stu, when we think about this notion of the cloud experience, providing a common way of thinking about a cloud operating model, clearly the design decisions that are going to have to be made by the traditional providers of integrated systems are going to have to start factoring that question of how do we move from a product to a service orientation along with their business models, their way of financing, et cetera. What do you think is happening? Where's the state of the art in that today? >> Yeah, and Peter, it actually goes back to when we at Wikibon launched the true private cloud research a little bit over two years ago. It was not just saying, "How do we do something "better than virtualization?" It was really looking at, as you said, that cloud operating model. And what we're hearing very loud from customers today is, it's not that they have a public cloud strategy and an private cloud strategy. They have a cloud strategy (chuckles). And one of the challenges that they're really having is, how do they get their arms around that? Because today their private cloud and their public cloud a lot of times it's different suppliers, it's different operating environments as you said. We could spend a whole nother call on just discussing some of the nuance and pieces here. But the real trend we've been seeing, and kind of the second half of last year, and big thing we'll see, I'm sure, through this year, is what are the solutions? And how can customers manage this much simpler? And what are the technology pieces? And operational paradigms that are going to help them through this environment? And yeah, it's a little bit detached from some of the hardware discussion we're having here. Because of course, at the end of the day, it shouldn't matter what hardware or what locale I'm in, it's how I manage the entire environment. >> But it does (laughs). >> Yeah. >> It shouldn't matter, but the reality is, I think we're concluding that it does. >> Right, we think back to, oh back in the early days, "Oh, virtualization, great. "I can take any x86. "Oh wait, but I had a BIOS problem, "and that broke things." So when containers rolled out, we had the same kind of discussion, this, "Oh wait." There was something down at the storage or networking layer that broke. So it's always, where is the proper layer? How do we manage that? >> Right, I for one just continue to hope that we're going to see the Harry Potter computing model show up at some point in time. But until then, magic is not going to run software. It's going to have to run on hardware, and that has physical and other realities. All right, thanks guys. Let's wrap this one up. Let me give some, what the action item is. So this week, we've talked about the importance of hardware in the marketplace going forward. And partly, it's catalyzed by an event that occurred this week. A security firm discovered a couple of flaws in some of the predominant, common, standard volume CPUs, including Intel's, that have long term ramifications. And while one of the fixes is not going to be easy, the other one can be fixed by software. But the suggestion is that the fix, that software fix would take out 30% of the computing power of the chip. And we were thinking to ourselves, what would happen if the world suddenly lost 30% of their computing power overnight? And the reality is, a lot of bad things would happen. And it's very clear that hardware still matters. And we have this tension between what's happening at the edge, where we're starting to see a need for greater distribution of function that's performing increasingly specialized workloads, utilizing increasingly new technology, that's not, that the prevailing stack is not necessarily built for. So the edge is driving new opportunities for design that's going to turn into new requirements for hardware that will only be possible if there's new volume markets capable of supporting it, and new suppliers bringing it to market. That doesn't however mean that the whole concept of systems integration goes away. On the contrary, even though we're going to see this enormous amount of change at the edge, there's an enormous net new invention in what does it mean to do systems integration? We're seeing a lot of that happen in the hyperscalers first, in companies like Amazon, and Google, and elsewhere. But don't be fooled. The HPE's the IBM's, the Dell-EMC's are all very cognizant of these approaches and these changes, and these challenges. And in many respects, a lot of the original work, a lot of the original invention is still being performed in their labs. So the expectation is the new design model is being driven by the edge. Plus the new engineering model's being driven by the hyperscalers, will not mean that it all ends up in two tiers. But we will see a need for modern systems integration happening in the true private cloud, on the premise, where a lot of the data and a lot of the workloads and a lot of the intellectual property is still going to reside. That however, does not mean that the model going forward is the same. Some of the new engineering dynamics, or some of the new design dynamics will have to start factoring in how the hardware simplifies configuration. For example, FPGAs have been around for a long time. But end users don't program FPGAs. So what good does it do to reflect the FPGA capability inside a box, inside a true private cloud box, if the user doesn't have any simple, straightforward, meaningful way to make use of it? So a lot of new emphasis on improve manageability, AI for ITOM, ways of providing application developers access to accelerated devices. This is where the new systems and design issues are going to manifest themselves in the marketplace. Underneath this, when we talk about unigrid, we're talking about some pretty consequential changes ultimately in how design and engineering of some of these big systems works. So our conclusion is, lots that the hardware still matters, but that the industry continued to move and drive in a direction that reduces the complexity of the underlying hardware. But that doesn't mean that users aren't going to have to, aren't going to encounter serious, serious decisions and serious issues regarding which supplier they should work with. So the action item is this. As we move from a product to a service orientation in the marketplace, hardware is still going to matter. That creates a significant challenge for a lot of users, because now we're talking about how that hardware is rendered as platforms that will have long-term consequences inside a business. So CIOs, start thinking about 2018 as the year in which you start to consider the new classes of platforms that you're going to move to. Because those platforms will be the basis for simplifying a lot of underlying decisions regarding where is the best design and engineering of infrastructure going forward. Once again, I want to thank my Wikibon teammates. George Gilbert, David Floyer, Stu Miniman, Neil Raden, Jim Kobielus, for a great Action Item. From theCUBE studios in Palo Alto, this has been Action Item. Talk to you soon. (funky electronic music)