>> We continue with cube on cloud. We here with Mai-Lan Tomsen Bukovec who's the vice president of block and object storage at AWS which comprises elastic block storage, AWS S3 and Amazon glacier. Mai-Lan Great to see you again. Thanks so much for coming on the program. >> Nice to be here. Thanks for having me, Dave. >> You're very welcome. So here we're unpacking the future of cloud and we'd love to get your perspectives on how customers should think about the future of infrastructure things like applying machine intelligence to their data but just to set the stage, when we look back at the history of storage and the cloud has obviously started with S3 and then a couple of years later AWS introduced EBS for block storage and those are the most well-known services in the portfolio but there's more of this cold storage and new capabilities that you announced recently at reinvent around, you know, super-duper block storage and in tiering is another example. But it looks like AWS is really starting to accelerate and pick up the pace of customer options in storage. So my first question is how should we think about this expanding portfolio? >> Well, I think you have to go all the way back to what customers are trying to do with their data Dave. The path to innovation is paved by data. If you don't have data, you don't have machine learning. You don't have the next generation of analytics applications that helps you chart a path forward into a world that seems to be changing every week. And so in order to have that insight in order to have that predictive forecasting that every company needs, regardless of what industry that you're in today, it all starts from data. And I think the key shift that I've seen is how customers are thinking about that data, about being instantly usable. Whereas in the past, it might've been a backup. Now it's part of a data lake. And if you can bring that data into a data lake you can have not just analytics or machine learning or auditing applications, it's really what does your application do for your business and how can it take advantage of that vast amount of shared data set in your business? >> Awesome, so thank you. So I want to make sure we're hitting on the big trends that you're seeing in the market that kind of are informing your strategy around the portfolio, and what you're seeing with customers. Instant usability, you know, you bring in machine learning into the equation. I think people have really started to understand the benefits of cloud storage as a service and the pay by the drink. and that whole model. Obviously COVID has accelerated that, you know, cloud migration is accelerated. Anything else we're missing there? What are the other big trends that you see? If any. >> Well, Dave, you did a good job of capturing a lot of the drivers. The one thing I would say that just sits underneath all of it is the massive growth of digital data year over year. IDC says digital data is growing at a rate of 40% year over year. And that has been true for a while and it's not going to stop. It's going to keep on growing because the sources of that data acquisition keeps on expanding and whether it's IOT devices whether it is a content created by users, that data is going to grow and everything you're talking about depends on the ability to not just capture it and store it. But as you say, use it. >> Well, you know, and we talk about data growth a lot and sometimes it can, it becomes bromide. But I think the interesting thing that I've observed over the last couple of decades really is that the growth is non-linear and it's really the curve is starting to shape exponentially. You guys always talk about that flywheel effect it's really hard to believe, you know people say trees don't grow to the moon. It seems like data does. >> It does and what's interesting about working in a world of AWS storage Dave is that it's counter-intuitive but our goal with a data growth is to make it cost effective. And so year over year how can we make it cheaper and cheaper? It is have customers store more and more data so they can use it. But it's also to think about the definition of usage and what kind of data is being tapped by businesses for their insights and make that easier than it's ever been before. >> Let me ask you a follow up question on that Mai-Lan. Cause I get asked this a lot, or I hear comments a lot that yes AWS continuously and rigorously reduces pricing but it's just kind of following the natural curve of Moore's law or whatever. How do you respond to that? Are there other factors involved? Obviously labor is another, you know, cost reducing factor, but what's the trend line say? >> Well, cost efficiency is in our DNA, Dave we come to work every day in AWS across all of our services and we ask ourselves, how can we lower our costs and be able to pass that along to customers. As you say, there are many different aspects to costs. There's a cost to the storage itself There's a cost to the data center. And that's really what we've seen impact a lot of customers that were slower or just getting started with a move to the cloud, is they entered 2020 and then they found out exactly how expensive that data center was to maintain because they had to put in safety equipment and they had to do all the things that you have to do in a pandemic, in a data center. And so sometimes that cost is a little bit hidden or it won't show up until you really don't need to have it land. But the costs of managing that explosive growth of data is very real. And when we're thinking about costs, we're thinking about costs in terms of how can I lower it on a per gigabyte per month basis, but we're also building into the product itself, adaptive discounts. Like we have a storage class in S3 that's called intelligent tiering. And in intelligent tiering we have built-in monitoring where if particular objects aren't frequently accessed in a given month, a customer will automatically get a discounted price for that storage or a customer can, you know, as of late last year say that they want to automatically move storage in the storage class that has been stored for example longer than 180 days and saves 95% by moving it into deep archive storage. And so it's not just, you know relentlessly going after and lowering the cost of storage. It's also building into the products these new ways where we can adaptively discount storage based on what a customer's storage is actually doing. >> Right, and I would add to already is the other thing Gatos has done is it's really forced transparency almost the same way that Amazon has done on retail. And now Mai-Lan when we talked last I mentioned that S3 was an object store. And of course that's technically correct but your comment to me was Dave, it's more than that. And you started to talk about SageMaker and AI and bringing in machine learning. And I wonder if you could talk a little bit about the future of how storage is going to be leveraged in the cloud. That's maybe different than what we've been used to in the early days of S3. And how your customers should be thinking about infrastructure, not as bespoke services, but as a suite of capabilities and maybe some of those adjacent services that you see as most leverageable for customers and why? >> Well, to tell this story, Dave, we're going to have to go a little bit back in time, all the way back to the 1990s or before then. When all you had was a set of hardware appliance vendors that sold you appliances that you put in your data center and inherently created a data silo because those hardware appliances were hardwired to your application. And so an individual application that was dealing with auditing as an example wouldn't really be able to access the storage for another application, because you know, the architecture of that legacy world is tied to a data silo and S3 came out launched in 2006 and introduced very low cost storage. That is an object. And I'll tell you, Dave, you know, over the last 10 plus years we have seen all kinds of data coming to S3. Whereas before it might've been backups or it might've been images and videos. Now a pretty substantial data set is our parquet files and work files. These files are there for business analytics for more real-time type of processing. And that has really been the trend of the future, is taking these different files putting them in a shared file layer, so any application today or in the future can tap into that data. And so this idea of the shared file layer is a major trend that has been taking off for the last I would say five or six years. And I expect that to not only keep on going but to really open up the type of services that you can then do on that shared file layer. And whether that's Sage maker or some of the machine learning introduced by our connect service, it's bringing together the data as a starting point and then the applications can evolve very rapidly on top of that. >> I want to ask your opinion about big data architectures. One of our guests Chamakh Tigani, she's amazing data architect. And she's put forth this notion of a distributed global mesh. And picking up on some of the comments, Andy Jassy made it at re-invent how essentially, "Hey we're bringing AWS to the edge. "We see the data center is just another edge node." So you're seeing this massive distributed system evolving. You guys have talked about that for a while and data by its very nature is distributed but we've had this tendency to put it into a monolithic data Lake or a data warehouse and it's sort of antithetical to that distributed nature. So how do you see that playing out? What do you see customers in the future doing in terms of their big data architectures and what does that mean for storage? >> It comes down to the nature of the data and again the usage and Dave that's where I see the biggest difference in these modern data architectures from the legacy of 20 years ago, is the idea that the data need drives the data storage. So let's take an example of the type of data that you always want to have on the edge. We have customers today that need to have storage in the field and whether the field of scientific research or oftentimes it's content creation in the film industry, or if it's for military operations there's a lot of data that needs to be captured and analyzed in the field. And for us, what that means is that, you know we have a suite of products called snow ball and whether it's snow ball or snow cone, take your pick. That whole portfolio of AWS services is targeted at customers that need to do work with storage at the edge. And so, you know, if you think about the need for multiple applications acting on the same data set that's when you keep it in an AWS region. And what we've done in AWS storage is we've recognized that depending on the need of usage where you put your data and how you interact with it may vary. But we've built a whole set of services like data transfer to help make sure that we can connect data from, for example that new snow cone into a region automatically. And so our goal Dave is to make sure that when customers are operating at the edge or they're operating in the region they have the same quality of storage service and they have easy ways to go between them. You shouldn't have to pick, you should be able to do it all. >> So in the spirit of do it all there's this sort of age old dynamic in the tech business where you've got the friction between the best of breed and the integrated suite. And my question is around what you're optimizing for customers. And can you have your cake and eat it too? In other words, why AWS storage? What makes it compelling? Is it because it's kind of a best of breed storage service or is it because it's integrated with AWS? Would you ever sub optimize one in order to get an advantage to the other? Or can you actually, you know have your cake and eat it too? >> The way that we build storage is to focus on being both the breadth of capabilities and the depth of capabilities. And so where we identify a particular need where we think that it takes a whole new service to deliver we'll go build that service. And an example for that as FTP our AWS SFTP service, which, you know, there's a lot of SFTP usage out there and there will be for a while because of the, you know, the legacy B2B type of architectures that still live in the business world today. And so we looked at that problem. We said, how are we going to build that in the best depth way, in the best focus? And we launched a separate service for that. And so our goal is to take the individual building blocks of EBS and glacier and S3 and make the best of class and the most comprehensive in the capabilities of what we can do and where we identify a very specific need. We'll go build a service for it. But Dave, you know as an example for that idea of both depth and breadth, S3 Storage Lens is a great example of that. S3 Storage Lens is a new capability that we launched late last year. And what it does is it lets you look across all your regions and all your accounts and get a summary view of all your S3 storage and whether that's buckets or the most active prefixes that you have and be able to drill down from that. And that is built in to the S3 service and available for any customer that wants to turn it on in the AWS management console. >> Right, and we saw just recently made, I called it super-duper block storage but you can make some improvements in really addressing the highest performance. I want to ask you, so we've all learned about an experience that benefits of cloud over the last several years and especially in the last 10 months during the pandemic but one of the challenges and it's particularly acute with IO is of course latency and moving data around and accessing data remotely. It's a challenge for customers, you know, due to speed of light, et cetera. So my question is how was AWS thinking about all that data that's still resides on premises? I think we heard at reinvent, that's still on 90% of the opportunity is, or the the workloads are still on prem that live inside a customer's data centers. So how do you tap into those and help customers innovate with on-prem data, particularly from a storage angle? >> Well, we always want to provide the best of class solution for those little latency workloads. And that's why we launched Block Express just late last year at reinvent. And Block Express has a new capability in preview on top of our IO to provisioned IOPS volume type. And what's really interesting about block express Dave is that the way that we're able to deliver the performance of Block Express, which is sound performance with cloud elasticity is that we went all the way down to the network layer and we customize the hardware software. And at the network layer we built Block Express on something called SRD which stands for a scalable reliable diagrams. And basically what it's letting us do is offload all of our EBS operations for Block Express on the nitrile card on hardware. And so that type of innovation where we're able to, you know, take advantage of modern cop commodity, multi-tenant data center networks, where we're sending in this new network protocol across a large number of network paths. And that type of innovation all the way down to that protocol level helps us innovate in a way that's hard. In fact, I would say impossible for other sound providers to kind of really catch up and keep up. And so we feel that the amount of innovation that we have for delivering those low latency workloads in our AWS cloud storage is unlimited really because of that ability to customize software hardware and network protocols as we go along without requiring upgrades from a customer it just gets better. And the customer benefits. Now, if you want to stay in your data center that's why we build outposts. And for outposts, we have UVS and we have S3 for outposts and our goal there is that some customers will have workloads where they want to keep them resident in the data center. And for those customers we want to give them that AWS storage opportunities as well. >> So thank you for coming back to Block Express. So you call it, you know, sand in the cloud. So is that essentially it comprises a custom built essentially storage network. Is that right? What you just described SRD? I think you called it. >> Yeah, it's a SRD is used by other AWS services as well but it is a custom network protocol that we designed to deliver the lowest latency experience and we're taking advantage of it with Block Express. >> So sticking with traditional data centers for a moment I'm interested in your thoughts on the importance of the cloud pricing approach, I.e the consumption model to pay by the drink. Obviously it's one of the most attractive features, and I asked that because we're seeing what Andy Jassy refers to as the old guard Institute, flexible pricing models two of the biggest storage companies, HP with GreenLake and Dell has this thing called apex. They've announced such models for on-prem and presumably cross cloud. How do you think this is going to impact your customers leverage of AWS cloud storage? Is it something that you have an opinion on? >> Yeah, I think it all comes down to, again that usage of the storage, and this is where I think there's an inherent advantage for our cloud storage. So there might be an attempt by the old guard to lower prices or add flexibility but at the end of the day it comes down to what the customer actually needs to tune. And if you think about gp3 which is the new EBS volume. The idea with gp3 is we're going to pass a long savings to the customer by making the storage 20% cheaper than gp2. And we're going to make the product better by giving a great, reliable baseline performance. But we're also going to let customers who want to run workloads like Cassandra on EBS tune their throughput separately, for example from their capacity. So if you're running Cassandra sometimes you don't need to change your capacity. Your storage capacity works just fine. But what happens with, for example Cassandra workload is that you may need more throughput. And if you're buying hardware appliance you just have to buy for your peak. You have to buy for the max of what you think your throughput and the max of what your storage is. And this inherent flexibility that we have for AWS storage and being able to tune throughput separate from up separate from capacity like you do for gp3 that is really where the future is for customers having control over costs and control over customer experience without compromising or trading off either one. >> Awesome, thank you for that. So in the time we have remaining Mai-Lan, I want to talk about the topic of diversity social impact, and as a woman leader, women executive, and I really want to get your perspectives on this. And I've shared with the audience previously, one of my breaking analysis segments, your boxing video which is awesome. And so, you've got a lot of unique non-traditional aspects to your life and I love it, but I want to ask you this. So it's obviously, you know, certainly politically and socially correct to talk about diversity, the importance of diversity, there's data that suggests that diversity is good both economically, not just socially, and of course it's the right thing to do. But there are those, you know, Peter teal is probably the most prominent but there are others that say, "You know what? "Forget that, just hire people, just like you'll be able "to go faster, ramp up more quickly, hit escape "velocity it's natural." And that's what you should do. Why is that not the right approach? Why is diversity both, of course, socially, you know responsible, but also, you know, good for business >> For Amazon we think about diversity as something that is essential to how we think about innovation. And so, Dave, as you know, from listening to some of the announcements at reinvent, we launch a lot of new ideas, like new concepts and new services in AWS. And just bringing that lens down to storage. Astri has been reinventing itself every year since we launched in 2006. EBS introduced the first sun on the cloud late last year, and continues to reinvent how customers think about block storage. We would not be able to look at a product in a different way and think to ourselves, not just what is the legacy system do in a data center today but how do we want to build this new distributed system in a way that helps customers achieve not just what they're doing today, but what they want to do in five and 10 years. You can't get that innovative mindset without bringing different perspectives to the table. And so we strongly believe in hiring people who are from under represented groups and whether that's gender or it's related to racial equality or if it's geographic diversity and bringing them in to have the conversation because those diverse viewpoints inform how we can innovate at all levels in AWS. >> Right, and so I really appreciate their perspectives on that. And we've had, as you probably know the cube has been, you know a very big advocate of diversity, you know, generally but women in tech specifically, we participated a lot. And I often ask this question is, you know, as a smaller company, I, and some of my other colleagues in small business, sometimes we struggle. And so my question is how do you go beyond what's your advice for going beyond, you know the good old boys network? I think it's large companies like AWS and, you know, the big players, you've got responsibility too that you can put somebody in charge and make it their full-time job. How should smaller companies that are largely white male dominated, how should they become more diverse? What should they do to increase that diversity? >> I think the place to start is voice. A lot of what we try to do is make sure that the under represented voice is heard. And so Dave, any small business owner of any industry can encourage voice for your under represented or your unheard populations. And honestly, it is as simple as being in a meeting and looking around that table or on your screen, as it were and asking yourself, who hasn't talked? Who hasn't weighed in? Particularly if the debate is contentious or even animated. And you will see, particularly if you note this over time you will see that there may be somebody and whether it's an under represented group or it's a woman who's early career, or it's not it's just a member of your team who happens to be a white male too, who's not being heard. And you can ask that person for their perspective. And that is a step that every one of us can and should do which is ask to have everyone's voice at the table to listen and to weigh in on it. So I think that is something everyone should do. I think if you are a member of an under represented group as for example, I'm Vietnamese American and I'm a female in tech, I think, it's something to think about how you can make sure that you're always taking that bold step forward. And it's one of the topics that we covered at re-invent. We had a great discussion with a group of women CEOs and a lot of it we talked about is being bold taking the challenge of being bold in tough situations. And that is an important thing, I think for anybody to keep in mind, but especially for members of under represented groups, because sometimes Dave that bold step that you kind of think of as like, "Oh I don't know if I should ask for that promotion." or "I don't know if I should volunteer for that project." It's not a big ask, but it's big in your head. And so if you can internalize as a member of some, you know, a group that maybe isn't heard as or seen as much how you can take those bold challenges and step forward and learn, maybe fail also cause that's how you learn. Then that is a way to also have people learn and develop and become leaders in whatever industry it is. >> That's great advice. It reminds me of, I think most of us can relate to that Mai-Lan, because when we started in the industry, we may be timid. You didn't want to necessarily speak up. And I think it's incumbent upon those in a position of power. And by the way power might just be running a meeting agenda to maybe call on those folks that are, maybe it's not diversity of gender or, you know, or race. Maybe it's just the under represented. Maybe that's a good way to start building muscle memory. So that's unique advice that I hadn't heard before. So thank you very much for that. I appreciate it. And Hey, listen. Thanks so much for coming on the Cube On Cloud. We're out of time and really always appreciate your perspectives and you're doing a great job. And thank you. >> Great, thank you Dave. Thanks for having me and have a great day. >> All right, and Keep it right there buddy. You're watching the Cube On Cloud. Right back. (gentle upbeat music)

>>Hi, buddy. Welcome back to the keeps coverage of AWS 2021. Re-invent you're watching the cube and I'm really excited. We're going to go outside the storage box. I like to say with my lawn Thompson Bukovac, who's the vice-president of block and object storage and Wayne Duso was a VP of storage edge and data governance guys. Great to see you again, we saw you at storage day, the 15 year anniversary of AWS, of course, the first product service ever. So awesome to be here. Isn't it. Wow. >>So much energy in the room. It's so great to see customers learning from each other, learning from AWS, learning from the things that you're observing as well. >>A lot of companies decided not to do physical events. I think you guys are on the right side of history. We're going to show you, you weren't exactly positive. How many people are going to show up. Everybody showed. I mean, it's packed house here, so >>Number 10. Yeah. >>All right. So let's get right into it. Uh, news of the week. >>So much to say, when you want to kick this off, >>We had a, we had a great set of announcements that Milan, uh, talked about yesterday, uh, in her talk and, and a couple of them in the file space, specifically a new, uh, member of the FSX family. And if you remember that the FSA, Amazon FSX is, uh, for customers who want to run fully managed versions of third party and open source file systems on AWS. And so yesterday we announced a new member it's FSX for open ZFS. >>Okay, cool. And there's more, >>Well, there's more, I mean, one of the great things about the new match file service world and CFS is it's powered by gravity. >>It is taught by Gravatar and all of the capabilities that AWS brings in terms of networking, storage, and compute, uh, to our customers. >>So this is really important. I want the audience to understand this. So I I've talked on the cube about how a large proportion let's call it. 30% of the CPU cycles are kind of wasted really on things like offloads, and we could be much more efficient, so graviton much more efficient, lower power and better price performance, lower cost. Amazon is now on a new curve, uh, cycles are faster for processors, and you can take advantage of that in storage it's storage users, compute >>That's right? In fact, you have that big launch as well for luster, with gravity. >>We did in fact, uh, so with, with, uh, Yasmin of open CFS, we also announced the next gen Lustre offering. And both of these offerings, uh, provide a five X improvement in performance. For example, now with luster, uh, customers can drive up to one terabyte per second of throughput, which is simply amazing. And with open CFS, right out of, right out of the box at GA a million IOPS at sub-millisecond latencies taking advantage of gravitas, taking advantage of our storage and networking capabilities. >>Well, I guess it's for HPC workloads, but what's the difference between these days HPC, big data, data intensive, a lot of AI stuff, >>All right. You to just, there's a lot of intersection between all of those different types of workloads they have, as you said, and you know, it all, it all depends on it all matters. And this is the reason why having the suite of capabilities that the, if you would, the members of the family is so important to our guests. >>We've talked a lot about, it's really can't think about traditional storage as a traditional storage anymore. And certainly your world's not a box. It's really a data platform, but maybe you could give us your point of view on that. >>Yeah, I think, you know, if, if we look, if we take a step back and we think about how does AWS do storage? Uh, we think along multiple dimensions, we have the dimension that Wayne's talking about, where you bring together the power of compute and storage for these managed file services that are so popular. You and I talked about, um, NetApp ONTAP. Uh, we went into some detail on that with you as well, and that's been enormously popular. And so that whole dimension of these managed file services is all about where is the customer today and how can we help them get to the cloud? But then you think about the other things that we're also imagining, and we're, re-imagining how customers want to grow those applications and scale them. And so a great example here at reinvent is let's just take the concept of archive. >>So many people, when they think about archive, they think about taking that piece of data and putting it away on tape, putting it away in a closet somewhere, never pulling it out. We don't think about archive like that archive just happens to be data that you just aren't using at the moment, but when you need it, you need it right away. And that's why we built a new storage class that we launched just yesterday, Dave, and it's called glacier instead of retrieval, it has retrieval and milliseconds, just like an Esri storage class has the same pricing of four tenths of a cent as glacier archive. >>So what's interesting at the analyst event today, Adam got a question about, and somebody was poking at him, you know, analysts can be snarky sometimes about, you know, price, declines and so forth. And he said, you know, one of the, one of the things that's not always shown up and we don't always get credit for lowering prices, but we might lower costs. And there's the archive and deep archive is an example of that. Maybe you could explain that point of view. >>Yeah. The way we look at it is that our customers, when they talk to us about the cost of storage, they talked to us about the total cost of the storage, and it's not just storing the data, it's retrieving it and using it. And so we have done an amazing amount across all the portfolio around reducing costs. We have glacier answer retrieval, which is 68% cheaper than standard infrequent access. That's a big cost reduction. We have EBS snapshots archive, which we introduced yesterday, 75% cheaper to archive a snapshot. And these are the types of that just transform the total cost. And in some cases we just eliminate costs. And so the glacier storage class, all bulk retrievals of data from the glacier storage class five to 12 hours, it's now free of charge. If you don't even have to think about, we didn't even reduce it. We just eliminated the cost of that data retrieval >>And additive to what Milan said around, uh, archiving. If you look at what we've done throughout the entire year, you know, a interesting statistic that was brought up yesterday is over the course of 2021, between our respective teams, we've launched over 105 capabilities for our customers throughout this year. And in some of them, for instance, on the file side for EFS, we launched one zone which reduced, uh, customer costs by 47%. Uh, you can now achieve on EFS, uh, cost of roughly 4.30 cents per gigabyte month on, uh, FSX, we've reduced costs up to 92%, uh, on Lustre and FSX for windows and with the introduction of ONTAP and open CFS, we continue those forward, including customers ability to compress and Dedoose against those costs. So they ended up seeing a considerable savings, even over what our standard low prices are. >>100 plus, what can I call them releases? And how can you categorize those? Are they features of eight? Do they fall into, >>Because they range for major services, like what we've launched with open ZFS to major features and really 95 of those were launched before re-invent. And so really what you have between the different teams that work in storage is you have this relentless drive to improve all the storage platforms. And we do it all across the course of the year, all across the course of the year. And in some cases, the benefit shows up at no cost at all to a customer. >>Uh, how, how did this, it seems like you're on an accelerated pace, a S3 EBS, and then like hundreds of services. I guess the question is how come it took so long and how is it accelerating now? Is it just like, there was so much focus on compute before you had to get that in place, or, but now it's just rapidly accessing, >>I I'll tell you, Dave, we took the time to count this year. And so we came to you with this number of 106, uh, that acceleration has been in place for many years. We just didn't take the time to couch. Correct. So this has been happening for years and years. Wayne and I have been with AWS for, for a long time now for 10 plus years. And really that velocity that we're talking about right now that has been happening every single year, which is where you have storage today. And I got to tell you, innovation is in our DNA and we are not going to stop now >>So 10 years. Okay. So it was really, the first five years was kind of slow. And then >>I think that's true at all. I don't think that try, you know, if you, if you look at, uh, the services that we have, we have the most complete portfolio of any cloud provider when it comes to storage and data. And so over the years, we've added to the foundation, which is S3 and the foundation, which is EBS. We've come out with a number of storage services in the, in the file space. Now you have an entire suite of persistent data stores within AWS and the teams behind those that are able to accelerate that pace. Just to give you an example, when I joined 10 years ago, AWS launched within that year, roughly a hundred and twenty, a hundred and twenty eight services or features our teams together this year have launched almost that many, just in those in, just in this space. So AWS continues to accelerate the storage teams continue to accelerate. And as my line said, we just started counting >>The thing. And if you think about those first five years, that was laying the baseline to launch us three, to launch EBS, to get that foundation in place, get lifecycle policies in place. But really, I think you're just going to see an even faster acceleration that number's going up. >>No, I that's what I'm saying. It does appear that way. And you had to build a team and put teams in place. And so that's, you know, part of the equation. But again, I come back to, it's not even, I don't even think of it as storage anymore. It's it's data. People are data lake is here to stay. You might not like the term. We always use the joke about a data ocean, but data lake is here to say 200,000 data lakes. Now we heard Adam talk about, uh, this morning. I think it was Adam. No, it was Swami. Do you want a thousand data lakes in your customer base now? And people are adding value to that data in new ways, injecting machine intelligence, you know, SageMaker is a big piece of that. Tying it in. I know a lot of customers are using glue as catalogs and which I'm like, wow, is glue a catalog or, I mean, it's just so flexible. So what are you seeing customers do with that base of data now and driving new business value? Because I've said last decade plus has been about it transformation. And now we're seeing business transformation. Maybe you could talk about that a little bit. >>Well, the base of every data lake is going to be as three yesterday has over 200 trillion objects. Now, Dave, and if you think about that, if you took every person on the planet, each of those people would have 26,000 S3 objects. It's gotten that big. And you know, if you think about the base of data with 200 trillion plus objects, really the opportunity for innovation is limitless. And you know, a great example for that is it's not just business value. It's really the new customer experiences that our customers are inventing the NFL. Uh, they, you know, they have that application called digital athlete where, you know, they started off with 10,000 labeled images or up to 20,000 labeled images now. And they're all using it to drive machine learning models that help predict and support the players on the field when they start to see things unfold that might cause injury. That is a brand new experience. And it's only possible with vast amounts of data >>Additive to when my line said, we're, we're in you talk about business transformation. We are in the age of data and we represent storage services. But what we really represent is what our customers hold one of their most valuable assets, which is their data. And that set of data is only growing. And the ability to use that data, to leverage that data for value, whether it's ML training, whether it's analytics, that's only accelerated, this is the feedback we get from our customers. This is where these features and new capabilities come from. So that's, what's really accelerating our pace >>Guys. I wish we had more time. I'd have to have you back because we're on a tight clock here, but, um, so great to see you both especially live. I hope we get to do more of this in 2022. I'm an optimist. Okay. And keep it right there, everybody. This is Dave Volante for the cube you're leader in live tech coverage, right back.

(pensive music) >> Thank you, Jenna, it's great to see you guys and thank you for watching theCUBE's continuous coverage of AWS Storage Day. We're here at The Spheres, it's amazing venue. My name is Dave Vellante. I'm here with Mai-Lan Tomsen Bukovec who's Vice President of Block and Object Storage. Mai-Lan, always a pleasure to see you. Thanks for coming on. >> Nice to see you, Dave. >> It's pretty crazy, you know, this is kind of a hybrid event. We were in Barcelona a while ago, big hybrid event. And now it's, you know, it's hard to tell. It's almost like day-to-day what's happening with COVID and some things are permanent. I think a lot of things are becoming permanent. What are you seeing out there in terms of when you talk to customers, how are they thinking about their business, building resiliency and agility into their business in the context of COVID and beyond? >> Well, Dave, I think what we've learned today is that this is a new normal. These fluctuations that companies are having and supply and demand, in all industries all over the world. That's the new normal. And that has what, is what has driven so much more adoption of cloud in the last 12 to 18 months. And we're going to continue to see that rapid migration to the cloud because companies now know that in the course of days and months, you're, the whole world of your expectations of where your business is going and where, what your customers are going to do, that can change. And that can change not just for a year, but maybe longer than that. That's the new normal. And I think companies are realizing it and our AWS customers are seeing how important it is to accelerate moving everything to the cloud, to continue to adapt to this new normal. >> So storage historically has been, I'm going to drop a box off at the loading dock and, you know, have a nice day. And then maybe the services team is involved in, in a more intimate way, but you're involved every day. So I'm curious as to what that permanence, that new normal, some people call it the new abnormal, but it's the new normal now, what does that mean for storage? >> Dave, in the course of us sitting here over the next few minutes, we're going to have dozens of deployments go out all across our AWS storage services. That means our customers that are using our file services, our transfer services, block and object services, they're all getting improvements as we sit here and talk. That is such a fundamentally different model than the one that you talked about, which is the appliance gets dropped off at the loading dock. It takes a couple months for it to get scheduled for setup and then you have to do data migration to get the data on the new appliance. Meanwhile, we're sitting here and customers storage is just improving, under the hood and in major announcements, like what we're doing today. >> So take us through the sort of, let's go back, 'cause I remember vividly when, when S3 was announced that launched this cloud era and people would, you know, they would do a lot of experimentation of, we were storing, you know, maybe gigabytes, maybe even some terabytes back then. And, and that's evolved. What are you seeing in terms of how people are using data? What are the patterns that you're seeing today? How is that different than maybe 10 years ago? >> I think what's really unique about AWS is that we are the only provider that has been operating at scale for 15 years. And what that means is that we have customers of all sizes, terabytes, petabytes, exabytes, that are running their storage on AWS and running their applications using that storage. And so we have this really unique position of being able to observe and work with customers to develop what they need for storage. And it really breaks down to three main patterns. The first one is what I call the crown jewels, the crown jewels in the cloud. And that pattern is adopted by customers who are looking at the core mission of their business and they're saying to themselves, I actually can't scale this core mission on on-premises. And they're choosing to go to the cloud on the most important thing that their business does because they must, they have to. And so, a great example of that is FINRA, the regulatory body of the US stock exchanges, where, you know, a number of years ago, they took a look at all the data silos that were popping up across their data centers. They were looking at the rate of stock transactions going up and they're saying, we just can't keep up. Not if we want to follow the mission of being the watchdog for consumers, for transactions, for stock transactions. And so they moved that crown jewel of their application to AWS. And what's really interesting Dave, is, as you know, 'cause you've talked to many different companies, it's not technology that stops people from moving to the cloud as quick as they want to, it's culture, it's people, it's processes, it's how businesses work. And when you move the crown jewels into the cloud, you are accelerating that cultural change and that's certainly what FINRA saw. Second thing we see, is where a company will pick a few cloud pilots. We'll take a couple of applications, maybe one or a several across the organization and they'll move that as sort of a reference implementation to the cloud. And then the goal is to try to get the people who did that to generalize all the learning across the company. That is actually a really slow way to change culture. Because, as many of us know, in large organizations, you know, you have, you have some resistance to other organizations changing culture. And so that cloud pilot, while it seems like it would work, it seems logical, it's actually counter-productive to a lot of companies that want to move quickly to the cloud. And the third example is what I think of as new applications or cloud first, net new. And that pattern is where a company or a startup says all new technology initiatives are on the cloud. And we see that for companies like McDonald's, which has transformed their drive up experience by dynamically looking at location orders and providing recommendations. And we see it for the Digital Athlete, which is what the NFL has put together to dynamically take data sources and build these models that help them programmatically simulate risks to player health and put in place some ways to predict and prevent that. But those are the three patterns that we see so many customers falling into depending on what their business wants. >> I like that term, Digital Athlete, my business partner, John Furrier, coined the term tech athlete, you know, years ago on theCUBE. That third pattern seems to me, because you're right, you almost have to shock the system. If you just put your toe in the water, it's going to take too long. But it seems like that third pattern really actually de-risks it in a lot of cases, it's so it's said, people, who's going to argue, oh, the new stuff should be in the cloud. And so, that seems to me to be a very sensible way to approach that, that blocker, if you will, what are your thoughts on that? >> I think you're right, Dave. I think what it does is it allows a company to be able to see the ideas and the technology and the cultural change of cloud in different parts of the organization. And so rather than having a, one group that's supposed to generalize it across an organization, you get it decentralized and adopted by different groups and the culture change just goes faster. >> So you, you bring up decentralization and there's a, there's an emerging trend referred to as a data mesh. It was, it was coined, the term coined by Zhamak Dehghani, a very thought-provoking individual. And the concept is basically the, you know, data is decentralized, and yet we have this tendency to sort of shove it all into, you know, one box or one container, or you could say one cloud, well, the cloud is expanding, it's the cloud is, is decentralizing in many ways. So how do you see data mesh fitting in to those patterns? >> We have customers today that are taking the data mesh architectures and implementing them with AWS services. And Dave, I want to go back to the start of Amazon, when Amazon first began, we grew because the Amazon technologies were built in microservices. Fundamentally, a data mesh is about separation or abstraction of what individual components do. And so if I look at data mesh, really, you're talking about two things, you're talking about separating the data storage and the characteristics of data from the data services that interact and operate on that storage. And with data mesh, it's all about making sure that the businesses, the decentralized business model can work with that data. Now our AWS customers are putting their storage in a centralized place because it's easier to track, it's easier to view compliance and it's easier to predict growth and control costs. But, we started with building blocks and we deliberately built our storage services separate from our data services. So we have data services like Lake Formation and Glue. We have a number of these data services that our customers are using to build that customized data mesh on top of that centralized storage. So really, it's about at the end of the day, speed, it's about innovation. It's about making sure that you can decentralize and separate your data services from your storage so businesses can go faster. >> But that centralized storage is logically centralized. It might not be physically centralized, I mean, we put storage all over the world, >> Mai-Lan: That's correct. >> right? But, but we, to the developer, it looks like it's in one place. >> Mai-Lan: That's right. >> Right? And so, so that's not antithetical to the concept of a data mesh. In fact, it fits in perfectly to the point you were making. I wonder if we could talk a little bit about AWS's storage strategy and it started of course, with, with S3, and that was the focus for years and now of course EBS as well. But now we're seeing, we heard from Wayne this morning, the portfolio is expanding. The innovation is, is accelerating that flywheel that we always talk about. How would you characterize and how do you think about AWS's storage strategy per se? >> We are a dynamically and constantly evolving our AWS storage services based on what the application and the customer want. That is fundamentally what we do every day. We talked a little bit about those deployments that are happening right now, Dave. That is something, that idea of constant dynamic evolution just can't be replicated by on-premises where you buy a box and it sits in your data center for three or more years. And what's unique about us among the cloud services, is again that perspective of the 15 years where we are building applications in ways that are unique because we have more customers and we have more customers doing more things. So, you know, I've said this before. It's all about speed of innovation Dave, time and change wait for no one. And if you're a business and you're trying to transform your business and base it on a set of technologies that change rapidly, you have to use AWS services. Let's, I mean, if you look at some of the launches that we talk about today, and you think about S3's multi-region access points, that's a fundamental change for customers that want to store copies of their data in any number of different regions and get a 60% performance improvement by leveraging the technology that we've built up over, over time, leveraging the, the ability for us to route, to intelligently route a request across our network. That, and FSx for NetApp ONTAP, nobody else has these capabilities today. And it's because we are at the forefront of talking to different customers and that dynamic evolution of storage, that's the core of our strategy. >> So Andy Jassy used to say, oftentimes, AWS is misunderstood and you, you comfortable with that. So help me square this circle 'cause you talked about things you couldn't do on on-prem, and yet you mentioned the relationship with NetApp. You think, look at things like Outposts and Local Zones. So you're actually moving the cloud out to the edge, including on-prem data centers. So, so how do you think about hybrid in that context? >> For us, Dave, it always comes back to what the customer's asking for. And we were talking to customers and they were talking about their edge and what they wanted to do with it. We said, how are we going to help? And so if I just take S3 for Outposts, as an example, or EBS and Outposts, you know, we have customers like Morningstar and Morningstar wants Outposts because they are using it as a step in their journey to being on the cloud. If you take a customer like First Abu Dhabi Bank, they're using Outposts because they need data residency for their compliance requirements. And then we have other customers that are using Outposts to help, like Dish, Dish Networks, as an example, to place the storage as close as account to the applications for low latency. All of those are customer driven requirements for their architecture. For us, Dave, we think in the fullness of time, every customer and all applications are going to be on the cloud, because it makes sense and those businesses need that speed of innovation. But when we build things like our announcement today of FSx for NetApp ONTAP, we build them because customers asked us to help them with their journey to the cloud, just like we built S3 and EBS for Outposts for the same reason. >> Well, when you say over time, you're, you believe that all workloads will be on the cloud, but the cloud is, it's like the universe. I mean, it's expanding. So what's not cloud in the future? When you say on the cloud, you mean wherever you meet customers with that cloud, that includes Outposts, just the programming, it's the programmability of that model, is that correct? That's it, >> That's right. that's what you're talking about? >> In fact, our S3 and EBS Outposts customers, the way that they look at how they use Outposts, it's either as part of developing applications where they'll eventually go the cloud or taking applications that are in the cloud today in AWS regions and running them locally. And so, as you say, this definition of the cloud, you know, it, it's going to evolve over time. But the one thing that we know for sure, is that AWS storage and AWS in general is going to be there one or two steps ahead of where customers are, and deliver on what they need. >> I want to talk about block storage for a moment, if I can, you know, you guys are making some moves in that space. We heard some announcements earlier today. Some of the hardest stuff to move, whether it's cultural or maybe it's just hardened tops, maybe it's, you know, governance edicts, or those really hardcore mission critical apps and workloads, whether it's SAP stuff, Oracle, Microsoft, et cetera. You're clearly seeing that as an opportunity for your customers and in storage in some respects was a blocker previously because of whatever, latency, et cetera, then there's still some, some considerations there. How do you see those workloads eventually moving to the cloud? >> Well, they can move now. With io2 Block Express, we have the performance that those high-end applications need and it's available today. We have customers using them and they're very excited about that technology. And, you know, again, it goes back to what I just said, Dave, we had customers saying, I would like to move my highest performing applications to the cloud and this is what I need from the, from the, the storage underneath them. And that's why we built io2 Block Express and that's how we'll continue to evolve io2 Block Express. It is the first SAN technology in the cloud, but it's built on those core principles that we talked about a few minutes ago, which is dynamically evolving and capabilities that we can add on the fly and customers just get the benefit of it without the cost of migration. >> I want to ask you about, about just the storage, how you think about storage in general, because typically it's been a bucket, you know, it's a container, but it seems, I always say the next 10 years aren't going to be like the last, it seems like, you're really in the data business and you're bringing in machine intelligence, you're bringing in other database technology, this rich set of other services to apply to the data. That's now, there's a lot of data in the cloud and so we can now, whether it's build data products, build data services. So how do you think about the business in that sense? It's no longer just a place to store stuff. It's actually a place to accelerate innovation and build and monetize for your customers. How do you think about that? >> Our customers use the word foundational. Every time they talk about storage, they say for us, it's foundational, and Dave, that's because every business is a data business. Every business is making decisions now on this changing landscape in a world where the new normal means you cannot predict what's going to happen in six months, in a year. And the way that they're making those smart decisions is through data. And so they're taking the data that they have in our storage services and they're using SageMaker to build models. They're, they're using all kinds of different applications like Lake Formation and Glue to build some of the services that you're talking about around authorization and data discovery, to sit on top of the data. And they're able to leverage the data in a way that they have never been able to do before, because they have to. That's what the business world demands today, and that's what we need in the new normal. We need the flexibility and the dynamic foundational storage that we provide in AWS. >> And you think about the great data companies, those were the, you know, trillions in the market cap, their data companies, they put data at their core, but that doesn't mean they shove all the data into a centralized location. It means they have the identity access capabilities, the governance capabilities to, to enable data to be used wherever it needs to be used and, and build that future. That, exciting times we're entering here, Mai-Lan. >> We're just set the start, Dave, we're just at the start. >> Really, what ending do you think we have? So, how do you think about Amazon? It was, it's not a baby anymore. It's not even an adolescent, right? You guys are obviously major player, early adulthood, day one, day zero? (chuckles) >> Dave, we don't age ourself. I think if I look at where we're going for AWS, we are just at the start. So many companies are moving to the cloud, but we're really just at the start. And what's really exciting for us who work on AWS storage, is that when we build these storage services and these data services, we are seeing customers do things that they never thought they could do before. And it's just the beginning. >> I think the potential is unlimited. You mentioned Dish before, I mean, I see what they're doing in the cloud for Telco. I mean, Telco Transformation, that's an industry, every industry, there's a transformation scenario, a disruption scenario. Healthcare has been so reluctant for years and that's happening so quickly, I mean, COVID's certainly accelerating that. Obviously financial services have been super tech savvy, but they're looking at the Fintech saying, okay, how do we play? I mean, there isn't manufacturing with EV. >> Mai-Lan: Government. >> Government, totally. >> It's everywhere, oil and gas. >> There isn't a single industry that's not a digital industry. >> That's right. >> And there's implications for everyone. And it's not just bits and atoms anymore, the old Negroponte, although Nicholas, I think was prescient because he's, he saw this coming, it really is fundamental. Data is fundamental to every business. >> And I think you want, for all of those in different industries, you want to pick the provider where innovation and invention is in our DNA. And that is true, not just for storage, but AWS, and that is driving a lot of the changes you have today, but really what's coming in the future. >> You're right. It's the common editorial factors. It's not just the, the storage of the data. It's the ability to apply other technologies that map into your business process, that map into your organizational skill sets that drive innovation in whatever industry you're in. It's great Mai-Lan, awesome to see you. Thanks so much for coming on theCUBE. >> Great seeing you Dave, take care. >> All right, you too. And keep it right there for more action. We're going to now toss it back to Jenna, Canal and Darko in the studio. Guys, over to you. (pensive music)

>>from around the globe. It's the Cube with digital coverage of AWS reinvent 2020 sponsored by Intel and AWS. Yeah, hello, everyone, and welcome back to the Cubes Walter Wall coverage of AWS reinvent 2020. We've gone virtual along with reinvent and we heard in Andy Jassy is hours long. Keynote a number of new innovations in the area of storage. And with me to talk about that is Milan Thompson Bukovec. She's the vice president of Block and Object Storage and AWS. That's everything. Elastic block storage s three Glacier, the whole portfolio Milon. Thanks for coming on. >>Great to see you. >>Great to see you too. So you heard Andy. We all heard Andy talk a lot about reinventing different parts of the platform, reinventing industries and a really kind of exciting and visionary put talk that he put forth. Let's >>talk >>about storage, though. How is storage reinventing itself? >>Well, as you know, cloud storage was essentially invented by a W s a number of years ago. And whether that's in 2000 and six, when US three was launched, or 2000 and eight when CBS was launched and we first came up with this model of pay as you go for durable, attached storage. Too easy to instances. And so we haven't stopped and we haven't slowed down. If anything, we've picked up the rate of reinvention that we've done across the portfolio for storage. I think, as Andy called out, speed matters. And it matters for how customers air thinking about how do they pivot and move to the cloud as quickly as they can, particularly this year. And it matters a lot in storage as well, because the changing access patterns of what customers air doing with their new cloud applications, you know they're they're transforming their businesses and their applications, and they need a modern storage platform underneath it. And that's what you have with AWS Storage. And he talked about some of the key releases, particularly in block storage. It's actually kind of amazing. What's what's been done with CBS is here. We launched GP three GP two was the previous generation general purpose volume type. We launched that in 2000 and 14 again thief, first type of general purpose volume that had this great combination of simplicity and price, and just about everybody uses it for a boot or often a data volume. And with GP three, which was available yesterday with Andy's announcement, we added four times peak throughput on top of GP two, and it's a 20% lower storage price per gigabyte per month. And we took the feedback. The number one feedback we got on GP to which was how can I separate buying throughput and I ops from storage capacity? And that is really important. That goes back to the promise of the cloud. And it goes back to being able to pick what aspect do you want to scale your storage on? And so, with GP three, you could buy a certain amount of capacity. And if you're good with that capacity, but you need more throughput, more eye ops, you can buy those independently. And that is that fine grained customization for those changing data patterns that I just talked about. And it's available for GP three today. >>Yeah, that was I looked at that, like my life is a knob that you could turn Okay, juice my eye ops. And don't touch my capacity. I'm happy there. I don't wanna pay for more of it. >>And thio add to that it's a knob you could turn if you need it. We have more throughput, more eye ops as a baseline capacity for your storage capacity than we did for GP to. But then you can tune it based on whatever you need, not just now, but in the future. >>So so given the pandemic, I mean, how has that affected E? Everybody is talking about going to the cloud, because where else you gonna go? But But how has that affected what customers are doing this year, and does it change your roadmap at all? Does it change your thinking? >>Well, I have to say, there's two main things that we've seen. One is it's really accelerated customers thinking about getting off of on premises and into the club. It's done that because nobody really wants to manage the data center. And if there's ever a year you don't want to manage the data center, it's 2020 and it's because, particularly with storage appliances, it takes a long time to acquire. Let's just take storage area networks or sense super expensive. You get a fixed amount of capacity you have to acquire. It takes months to come in you gotta rack and stack. Then you gotta change all your networking and maintain it. Ah, lot of customers don't want to do that. And so what it's done for us is it's really, uh, you know, accelerated our thinking and you saw yesterday and Andy's keynote as well. Of how do we build the first san in the cloud? And we launched Io two. In August of this year, we introduced the first nines of durability, again reinventing how people think about durability and their block storage. But just this week we now have a Iot to block Express with 2 56 K ai ops, four K megabytes of throughput in 64 terabytes of capacity, that sand level performance. And it's available for preview because I 02 is going to be your son in the cloud. And that is a direct correlation to what we hear from customers, which is how can I get away from these expensive on premises purchases like Sands and combine the performance with the elasticity that I need? So that's the first thing. How can we accelerate getting off of these very rigid procurement cycles that we have and having to manage a data center. It's not just for EBS, its for S. Trias. Well, the second thing we're hearing from customers is how can I have the agility? So you talk to customers as well. He talked to CEOs and C. T. O s. It's been a crazy year in 2020. It was one thing that a company has to do its pivot. It's really figure out. How are you going to adjust and adjust quickly? And so we have customers like Ontario Telehealth Network up in Canada, where they went from 8000 to 30,000 users because they're doing virtual health for Ontario. And we have other customers who, you know, that's a pivot. That's an increase. And we have other customers, like APS Flyer, where their goal is to just save money without changing their application. And they also did a pivot. They used the intelligence hearing storage class, which is the most popular storage class, as three offers for data lakes, and they were able to make that change save 18% on their storage cost, no change of their application, just using the capabilities of AWS. And so his ability to pivot helped you know really make us think and accelerate what we're building as well. And so one of the things that we launched just recently for intelligent hearing is we added two new archival tears to intelligent hearing. And those are archival tears, you know, just like intelligence hearing automatically watches every object industry storage and your data lake and gives you dynamic pricing based on if it's frequently accessed in a month or inflict infrequently accessed, you can turn on archival tear. And if your object your pork a file, for example, isn't access or your backup isn't access for 90 days, intelligence hearing will automatically move it to glacier characteristics of archival or too deep archive and give you the same price. A dollar, a terabyte per month. If your data is an access to 180 days, it's done automatically, and it means you save up to 90% 95% and cost on that storage. And so, if you if you think about those two trends, how can I get away from getting locked into those on premises Hardware cycles? How can I get away from it faster for sands and other hardware appliances and then the other trend is how can I pivot and use the innovation and the reinvention in our storage services to just save money and be more agile in these changing conditions? >>So I gotta ask you follow up question on staying in the cloud, because when you think of sand, you think of switches. You think of complexity, but I get that you're connecting to the performance of a sand. But you guys are all about simplicity. So how did you What's behind there? Can you take us under the covers? Just you guys build your own little storage network because it's cloud. It's gotta be fast and simple. >>That's right. When we're thinking about performance and cost, we go down to the metal for this stuff. We think about Unicosta a very fine grained level, and when we're building new technology that we know is gonna be the foundation for everything we're doing for that high performance, we went down to the protocol level. We're using something called Us RD. It's all rolled up under the hood for Block Express, and it's the foundation of that super super high performance. As you know, there's a lot of engineering behind the scenes in the cloud and for for what we've done this year, as part of that reinvention we've reinvented all the way down to the protocol way. >>Let me ask you that the two things that come up in our survey when you talk to CEOs, they say two priorities. Security is actually second cloud migration actually popped up to the top. So where does storage fit in that whole notion about cloud migration, >>Storage eyes, usually where a lot of people start, you know, Luckily, with a W s, you don't have to choose between security or cloud of migration. Security is job one for every AWS service. And so when customers air thinking about how do I move an application, they gotta move the data first. And so they start from the from the data. What storage do I use? What is the best fit for the storage and how do I best secure that's storage? And so the innovation that we dio on storage always comes with that. That combination of, you know, migration, the set of tools that we provide for getting data from on premises into the cloud. We have tools like aws data sync which do a great job of this on. Then we also look at things like how do we continue to take the profile of security forward? And one example of that is something we launched just this week called Bucket keys s three bucket keys. And it drops the cost of using kms for service side encryption with us three by over 90%. And the way it does it is that we've integrated those two services super closely together so that you can minimize the amount of costs that you make for very, very frequent request. Because in data lakes you have millions and billions of objects and our goal is to make security so cost effective people don't even think about it. That also goes for other parts of the platform. We have guard duty for us three now, and what that does is security anomaly detection automatically to track your access patterns across as three and flag when something is not quite what it should be. And so this idea of like how do I not only get my data into the cloud? But then how do I take advantage of the breath of the storage portfolio, but also the breath of the AWS services to really maximize that security profile as well as the access patterns that I want from my application. >>Well, my way hit the major announcements and unfortunately, out of time. But I really would love to have you back and go deeper and have you share your vision of what the cloud storage piece looks like going forward. Thanks so much for coming in. The Cube is great to have you. >>Great to be here. Thanks, Dave. CIA. >>See you later and keep it right, everybody. You're watching the cubes. Coverage of aws reinvent 2020 right back.

>> Hello everyone, we're here at the Snowflake Data Cloud Summit. This is the Tech Titans panel. We're going to explore some of the trends that are shaping new data capabilities and specifically how organizations are transforming their companies, with data and insights. And with me are three amazing guest panelists. Christian Kleinerman is the senior vice president of product at Snowflake. He's joined by Mark Nelson, who's the EVP of product development at Salesforce/Tableau and Mai-Lan Thompson Bukovec, who's the vice president of Block and Object Storage at Amazon web services. Folks, thanks so much for coming on the program. Great to see you all. >> Thanks for having us. >> Nice to see you. >> Glad to be here. >> Excellent, so here in this session, you know, we have the confluence of the data cloud. We have simple and cost effective storage repositories and the visualization of data. These are three ingredients that are really critical for quickly analyzing and turning data into insights and telling stories with data. So, Christian, let me start with you. Of course, this is all enabled by the Cloud and Snowflake. You're extending that to this data cloud. One of the things that we can do today with data that we say weren't able to do maybe five years ago. >> Yeah, certainly I think there is lots of things that we can integrate specific actions but if you were to zoom out and look at the big picture, our ability to reason through data to inform our choices to date with data is bigger than ever before. There are still many companies that have to decide to sample data or to throw away older data, or they don't have the right data from external companies to put their decisions and actions in context. Now we have the technology and the platforms to bring all that data together, tear down silos and look a 360 of a customer or entire action. So I think it's reasoning through data that has increased the capability of organizations dramatically in the last few years. >> So Mai-Lan, when I was a young pup, at IDC, I started the storage program there, many, many moons ago. And so I always pay attention to what's going on in storage, back of my mind. And S3 people forget, sometimes, that was actually the very first cloud product announced by AWS, which really ushered in the cloud era. And that was 2006, it fundamentally changed the way we think about storing data. I wonder if you can explain how S3 specifically in an object storage generally, you know, with get put really transformed storage from a blocker to an enabler of some of these new workloads that we're seeing. >> Absolutely, I think it has been transformational for many companies in every industry. And the reason for that is because in S3, you can consolidate all the different data sets that today are scattered around so many companies, different data centers. And so if you about it, S3 gives the ability to put unstructured data which are video recordings and images. It puts semi structured data which is the CSV file, which every company has lots of. And that has also support for structured data types like parquet files, which drive a lot of the business decisions that every company has to make today. And so if you think about S3, which launched on Pi day in March of 2006, S3 started off as an object store, but it has evolved into so much more than that, where companies all over the world, and every industry are taking those different data sets, they're putting it in S3, they're growing their data and then they're growing the value that they capture on top of that data. And that is the separation we see that snowflake talks about and many of the pioneers across different industries talk about, which is a separation of the growth of storage and the growth of your computer applications. And what's happening is that when you have a place to put your data like S3, which is secure by default and has the availability and the durability and the operational profile you know, and can trust, then the innovation of the application developers really take over, and you know, one example of that is where we have a customer in the financial sector and they started to use S3 to put their customer care recordings. And they were just using it for storage because that obviously dataset grows very quickly. And then somebody in their fraud department got the idea of doing machine learning on top of those customer care recordings. And when they did that they found really interesting data that they could then feed into their fraud detection models. And so you get this kind of alchemy of innovation that happens when you take the datasets of today and yesterday and tomorrow you put them all in one place which is the history and the innovation of your application, developers just takes over and builds, not just what you need today but what you need in the future as well. >> Thank you for that. Mark, I want to bring you into this panel. It's great to have you here. So thank you. I mean, Tableau has been a game changer for organizations. I remember my first, Tableau conference, passionate customers and really bringing cloud-like agility and simplicity to visualization just totally changed the way people thought about data and met with massive data volumes and simplified access. And now we're seeing new workloads that are developing on top of data and Snowflake data and the cloud. Can you talk about how your customers are really telling stories and bringing to life those stories with data on top of things like S3, which Mai-Lan was just talking about? >> Yeah, for sure. Building on what Christian and Mai-Lan have already said our mission at Tableau has always been help people see and understand data. And you look at the amazing advances that are happening in storage and data processing. And now, the data that you can see and play with is so amazing, right? Like at this point in time, it's really nothing short of a new microscope or a new telescope that really lets you understand patterns. They were always there in the world, but you literally couldn't see them because of the limitations of the amount of data that you could bring into the picture, because of the amount of processing power and the amount of sharing of data that you could bring into the picture. And now like you said, these three things are coming together and this amazing ability to see and tell stories with your data combined with the fact that you've got so much more data at your fingertips, the fact that you can now process that data, look at that data share that data in ways that was never possible. Again, I'll go back to that analogy. It feels like the invention of a new microscope, a new telescope a new way to look at the world and tell stories and get to insights that were just, were never possible before. >> So thank you for that, and then Christian I want to come back to this notion of the data cloud and, you know, it's a very powerful concept and of course it's good marketing, but I wonder if you could add some additional color for the audience. I mean, what more can you tell us about the data cloud, how you're seeing it evolving and maybe building on some of the things that Mark was just talking about just in terms of, you know, bringing this vision into reality? >> Certainly, yeah. Data cloud for sure, is bigger and more concrete than just the marketing value of it. The big insight behind our vision for the data cloud is that just the technology, a capability, just a cloud data platform is not what gets organizations to be able to be a data driven, to be able to make great use of data or be highly capable in terms of data ability. The other element beyond technology is the access and availability of data to put their own data in context or enrich based on the knowledge or data from other third parties. So the data cloud, the way to think about it is, is a combination of both technology, which for Snowflake is our Cloud Data platform in all the workloads, the ability to do data warehousing and queries and speeds and feeds fit in there and data engineering, et cetera. But it's also, how do we make it easier for our customers to have access to the data that they need or they could benefit to improve the decisions for their own organizations. Think of the analogy of a set top box. I can give you a great technically set top box but if there's no content on the other side, it makes it difficult for you to get value out of it. That's how we should all be thinking about it, the data cloud, it's technology, but it's also seamless access to data. >> And Mai-Lan, can you give us a sense of the scope and what kind of scale are you seeing with Snowflake on AWS? >> Well, Snowflake has always driven as Christian as a very high transaction rate to S3. And in fact, when Christian and I were talking just yesterday, we were talking about some of the things that have really been remarkable about the long partnership that we've had over the years. And so I'll give you an example of how that evolution has really worked. So as you know, S3 has, is, you know, the first AWS services that is launched and we have customers who have petabytes, hundreds of petabytes and exabytes of storage on history. And so from the ground up S3 has been built for scale. And so when we have customers, like Snowflake that have very high transaction rates for requests, for S3 storage, we put our customer hat on and we ask customers like Snowflake, how do you think about performance? Not just what performance do you need but how do you think about performance? And you know, when Christian and his team were working through the demands of making requests to their S3 data, they were talking about some pretty high spikes over time and just a lot of volume. And so when we built improvements, into our performance over time, we put that hat on for work, you know, Snowflake was telling us what they needed. And then we built our performance model not around a bucket or an account. We built it around a request rate per prefix, because that's what Snowflake and other customers told us they needed. And so when you think about how we scale our performance, we scale it based on a prefix and not a bucket in our account, which other cloud providers do. We do it in this unique way because 90% of our customer roadmap across AWS comes from customer requests. And then that's what Snowflake and other customers were saying is that, "Hey, I think about my performance based on a prefix and of an object and not some, you know, arbitrary semantic of how I happened to organize my buckets." I think the other thing I would also throw out there for skill is, as you might imagine, S3 is a very large distributed system. And again, if I go back to how we architected for our performance improvements, we architected in such a way that a customer like Snowflake, could come in and they could take advantage of horizontally scaling. They can do parallel data retrievals and puts in gets for your data. And when they do that they can get tens of thousands of requests per second because they're taking advantage of the scale of S3. And so, you know, when we think about scale it's not just scale which is the growth of your storage, which every customer needs. IDC says that digital data is growing at 40% year over year. So every customer needs a place to put all of those storage sets that are growing. But the way we also have worked together for many years is this, how can we think about how Snowflake and other customers are driving these patterns of access on top of the data, not just the last history of the storage, but the access and then how can we architect often very uniquely as I talked about with our request rate in such a way that they can achieve what they need to do not just today, but in the future. >> I don't know, three companies here that don't often take their customer hats off. Mark, I wonder if we could come to you, you know, during the Data Cloud Summit, we've been exploring this notion that innovation in technology is really evolved from point products you know, the next generation of server or software tool to platforms that made infrastructure simpler or called functions and now it's evolving into leveraging ecosystems. You know, the power of many versus the resources of one. So my question is, you know, how are you all collaborating and creating innovations that your customers can leverage? >> Yeah, for sure, so certainly, you know Tableau and Snowflake, you know, kind of where were dropped at natural partners from the beginning, right? Like putting that visualization engine on top of Snowflake to, you know, combine that processing power and data and the ability to visualize it was obvious. As you talk about the larger ecosystem now of course, Tableau is part of Salesforce. And so there's a much more interesting story now to be told across the three companies, one in two and a half maybe as we talk about Tableau and Salesforce combined together of really having this full circle of Salesforce you know, with this amazing set of business apps that so much value for customers and getting the data that comes out of their Salesforce applications, putting it into Snowflake so that you can combine that, share that, you process it combine it with data, not just for across Salesforce, but from your other apps in a way that you want. And then put Tableau on top of it. Now you're talking about this amazing platform ecosystem of data, you know, coming from your most valuable business applications in the world with the most, you know, sales opportunity objects, marketing, service, all of that information flowing into this flexible data platform and then this amazing visualization platform on top of it. And there's really no end of the things that our customers can do with that combination >> Christian we're out of time, but I wonder if you could bring us home and I want to end with, you know let's say, you know, people, some people here maybe they don't, maybe they're still struggling with the cumbersome nature of let's say their on-prem data, warehouses. You know, the kids just unplugged them because they rely on them for certain things like reporting but let's say they to raise the bar on their data and analytics, what would you advise for a next step for them? >> Yeah I think the first part or first step to take is around embrace the cloud and the promise on the abilities of cloud technology. There's many studies where relative to peers, companies that are embracing data are coming out ahead and outperforming their peers. And with traditional technology on-prem technology, you ended up with a proliferation of silos and copies of data. And a lot of energy went into managing those on-prem systems and making copies and data governance and security and cloud technology and the type of platform that the Snowflake has brought to market enables organizations to focus on the data, the data model, the data insights, and not necessarily on managing the infrastructure. So I think that will be the first recommendation from our end. Embrace cloud, get onto a modern cloud data platform, make sure that you're spending your time on data, not managing infrastructure and seeing what the infrastructure lets you do. >> It makes a lot of sense, guys. Thanks, thanks so much. We'll have to end it there and thank you everybody for watching. Keep it right there. We'll be back, with the next segment, right after this short break.

>> Live from San Francisco, it's theCUBE covering IBM Think 2019. Brought to you by IBM. >> Welcome back, we're here at Moscone North. You're watching theCUBE, the leader in live tech coverage. This is day four of our wall to wall coverage of IBM the Think. The second annual IBM Think, first year at Moscone. Dave Vellante here with Stu Miniman. Eric Herzog is here, he's the CMO of IBM Storage and Sam Werner is the VP of Offering Management for Storage Software at IBM. Guys welcome back to theCUBE. Always good to see ya both. >> Thanks >> Thank you. >> So we were joking yesterday and today, of course multi cloud, the clouds opened, it's been raining, it's been sunny today, so multi cloud is all the rage. Evidently you guys have done some work in multi cloud. Some research that you can share with us. >> Yeah, so couple things. First of all, the storage vision in multi cloud at IBM for years. We work with all the cloud providers including IBM cloud, but we work with Amazon and we work with Azure, we work with Google cloud and in fact our Spectrum Protect, modern data protection product, has about 350 small and medium cloud providers across the world that use it for the engine for their back up as a service. So we've been doing that for a long time, but I think what you're getting is, what we found in a survey multi cloud and I actually had had a panel yesterday and all three of my panelists, including Aetna, use a minimum of five different public cloud providers. So what we're seeing is hybrid is a subset of that, right? On and off, but even if someone is saying, I'm using cloud providers, they're using between five and 10, not counting software as a service because many of the people in the survey didn't realize software as a service is theoretically a type of cloud deployment, right? >> So that's obviously not just the big three or the big five, we're talking about a lot of small guys. Some of the guys maybe you could have used in your Spectrum Protect for back up, local cloud providers, right? And then add sas to that, you could probably double or triple it, right? >> Right, well we've have been very successful with sas providers so for example, one of people on the panel, a company called Follett, they're a privately held, in the mid close to a billion dollars, they provide services to universities and school districts and they have a software package for universities for the bookstores to manage the textbooks and another software as a service for school districts across the United States. They have 1,500 and it's all software service. No on prem licensing and that's an example. That's in my mind, that's a cloud deployment, right? >> Ginni talked Tuesday about chapter two how chapter one was kind of, I call it commodity cloud, but you know, apps that are customer facing, chapter two, a lot of chapter two anyways, is going to be about hybrid and multi cloud. I feel like to date it's largely been, not necessarily a purposeful strategy to go multi cloud, it's just we're multi vendor. Do you see customers actually starting to think about a multi cloud strategy? If so, what's behind that and then more specifically, what are you guys doing from a software stand point to support that? >> Yeah, so in the storage space where we are, we find customers are now trying to come up with a data management strategy in a multi cloud model, especially as they want to bring all their data together to come up with insights. So as they start wanting to build an AI strategy and extend what they're doing with analytics and try to figure out how to get value out of the data they're building a model that's able to consolidate the data, allow them to ingest it and then actually build out AI models that can gain insights from it. So for our software portfolio, we're working with the different types of service providers. We're working closely with all the big cloud providers and getting our software out there and giving our customers flexible ways to move and manage their data between the clouds and also have clear visibility into all the data so they can bring it together. >> You know, I wonder sort of what the catalyst is there? I wrote an article that's going up on SiliconANGLE later and I talked about how the first phase was kind of tire kicking of cloud and then when the down turn hit, people went from capex to opex. It was sort of a CFO mandate and then coming out of the down turn, the lines of business were like, whoa agility, I love this. So shadow IT and then IT sort of bought in and said, "we got to clean up this mess." and that seems to be why, at least one catalyst, for companies saying, "hey, we want a single data management strategy." Are you seeing that or is there more to it? >> Well I think first of all, we're absolutely seeing it and there's a lot of drivers behind it There's absolutely IT realizing they need to get control over this again. >> Governance, compliance, security, edix >> And think about all the new regulations. GDPR's had a huge impact. All a sudden, these IT organizations need to really track the data and be able to take action on it and now you have all these new roles in organizations, like data scientists who want to get their hands on data. How do you make sure that you have governance models around that data to ensure you're not handing them things like pi? So they realized very quickly that they need to have much better control. The other thing you've seen is, the rise of the vulnerabilities. You see much more public attacks on data. You've seen C level executives lose their jobs over this. So there's a lot more stress about how we're keeping all this data safe. >> You're right. Boards are gettin' flipped and it's a big, big risk these days >> Well the other thing you're seeing is legal issues. Canada, the data has to stay in Canada. So if you're multi national and you're a Japanese company, all your Canadian offices, the data has to be some cloud of ours got an office in Canada. So if you're a Japanese headquarter company, using NTT cloud, then you got to use IBM or Amazon or Azure, 'cause you have to have a data center inside the country just to have the cloud data. You also have shier maturity in the market. I would argue, the cloud used to be called the web and before it was the web, it was called the internet and so now that you're doing that, what happens in the bigger companies, procurement is involved, just the way they've been involved in storage servers and networking for a long time. Great you're using CISCO for the network. You did get a quote from HP or using IBM storage, but make sure you get at least one other quote so as that influences aside from definitely getting the control is when procurement get involved, everything goes out for RFP or RFQ or at ten dure, as they say in Europe and you have to have multiple vendors and you sometimes may end up for purely, we need the way to club 'em on price so we need IBM cloud and Microsoft so we can keep 'em honest. So when everyone rushed the cloud, they didn't necessarily do that, but now that it's maturing >> Yeah, it's a sign of maturity. >> It's a sign of maturity that people want to control pricing. >> Alright, so one of the other big themes we've been talking a lot about this week is AI. So Eric talks about, when we roll back the clock, I think back to the storage world, we've been talking about intelligence in storage for longer than my career. So Sam, maybe you can tell us what's different about AI in storage than the intelligence we've been talking and what's the latest about how AI fits into the portfolio? >> Yeah, that's a great question and actually a lot of times we talk about AI and how storage is really important to make the data available for AI, but we're also embedding AI in our storage products. If you think about it, if you have a problem with your storage product, you don't just take down one application. You can take down an entire company, so you've got to make sure your storage is really resilient. So we're building AI in that can actually predict failures before they happen so that our storage never takes any outages or has any down time. We can also predict by looking at behavior out in the network, we can predict or identify issues that a host might be causing on the network and proactively tell a customer before they get the call that the applications are slowing down and we can point out exactly which host is causing the problem. So we're actually proactively finding problems out on the storage network before they become an issue. >> Yeah and Eric, what is it about the storage portfolio that IBM has that makes it a good solution for customers that are deploying AI as an application in use cases? >> Yeah so we look at all, so one is AI, in the box if you will, in the array and we've done a ton of work there, but the other is as the underlying foundation for AI workloads and applications so a couple things. Clearly, AI often is performance dependent and we're focused on all flash. Second thing as Sam already put it out, resilience and availability. If you're going to use AI in an automotive factory to control the supply chain and to control the actual factory floor, you can't have it go down because they could be out tens of millions, hundreds of millions of year just for that day of building Mercedes or Toyotas or whatever they're building if you have an automated factory. The other areas we've created what we call, the data pipeline and it involves three, four members of our storage software family. Our Spectrum Scale, a highly parallel file system that allows incredible performance for AI. Our Spectrum Discover which allows you to use meta data which is information about the data to more accurately plan and the AI software from any vendor can use an API and go in and see this meta data information to make the AI software more efficient that they would use. Our IBM Cloud Object Storage and our Spectrum Archive, you have to archive the data, but easily bring it back because AI is like a human. We are, smart humans are learning non-stop, whether you're five, whether you're 25, or whether you're 75, you're always learning. You read the newspaper, you see of course theCUBE and you learn new things, but you're always comparing that to what you used to know. Are the Russians our friends or our enemies? It depends on your point in time. Do we love what's going on in Germany? It depends on your point in time. In 1944, I'd say probably not. Today you'd say, what a great Democratic country, but you have to learn and so this data pipeline, this loop, our software is on our storage arrays and allows it to be used. We'll even sell the software without our storage arrays for use on any AI server platform, so that softwares really the huge differentiator for us. >> So can you, as a follow up to that, can you address the programmability of your portfolio? Whether it's through software or maybe the infrastructure as well. Infrastructure, I'm thinking infrastructure's code. You mentioned you know API's. You mentioned the ability to go into like Spectrum Discover for example, access meta data. How programmable is your infrastructure and how are you enabling that? >> I mean across our entire portfolio, we build restful API's to make our infrastructure completely extensible. We find that more and more enterprises are looking to automate the deployment of the infrastructure and so we provide API's for programming and deploying that. We're also moving towards containerizing most of our storage products so that as enterprises move towards cubernetes type clusters, we work with both Red Hat and with our own ICP and as customers move towards those deployment models and automate the deployment of their clusters, we're making all of our storage's available to be deployed within those environments. >> So do you see an evolution of the role of a storage admin, from one that's sort of provisioning luns to one that's actually becoming a coder, maybe learning Python, learning how to interact through API's, maybe even at some point developing applications for automation? Is that happening? >> I think there's absolutely a shift in the skills. I think you've got skills going in two directions. One, in the way of somebody else to administer hardware and replace parts as they fail. So you have lower skilled jobs on that side and then I believe that yes, people who are managing the infrastructure have to move up and move towards coding and automating the infrastructure. As the amount of data grows, it becomes too difficult to manage it in the old manual ways of doing it. You need automation and intelligence in the storage infrastructure that can identify problems and readjust. For example, in our storage infrastructure, we have automated data placement that puts it on the correct tier. That use to be something a storage administrator had to do manually and figure out how to place data. Now the storage can do it themselves, so now they need to move up into the automation stack. >> Yeah, so we've been talking about automation and storage also for a lot of years. Eric, how are enterprises getting over that fear that either I'm going to lose my job or you know, this is my business we're talking about here. How do I let go and trust? I love, I saw downstairs, there was a in the automation booth for IBM, it was free the humans, so we understand that we need to go there. We can't not put automation with the scale and how things are moving, but what's the reality out in the field? >> So I think that the big difference is and this is going to sound funny, but the economic down turn of seven, eight and nine, when downturn hit and certainly was all over the IT press, layoff, layoff, layoff, layoff, layoffs, so we also know that storage is growing exponentially, so for example, if I'm Fortune 500 company x and I had 100 people doing storage across the planet. If I laid off 50 of them and now I'm recovered. I'm making tons of money, my IT budget is back up. I didn't go to the CIO and say, you can hire the 50 storage people back. You can hire 50 people back, but no more than five or six can be storage people. Everything else has to be dev ops or something else. So what that means is, they are managing an un-Godly amounts of more storage every year with essentially the same people they had in 2008 or maybe a tiny bit more. So what matters is, you don't manage a peta bite or in the old days, half a peta bite. Now, one storage admin or back up admin or anyone in that space, they want you to manage 20 peta bites and if you don't have automation, that will never happen. >> Stu and I were interviewing Steven Hill from KPMG yesterday and he was talking about the macro numbers show we're not (stutters) as globally and even in the US, we're not seeing productivity gains. I'm saying yeah, you're not looking at the storage business you know, right? Because if you look at anybody who's running storage, they're doing way more with much less, to your point. >> Which is why, so for example when Sam talked about our easy tier, we can tier, not only as AI base. So in the old days, when you guys weren't even born yet, when I was doing it. >> Well I don't know about that >> What was it? It was move the data after 90, so first it was manual movement, then it was set up something, a policy. Remember policy automation was the big deal 10 years ago? Automatically move the data when its 90, 60, or 30 days old. AI based, what we have an easy tier, automatically will determine what tier it should go on, whether when the data's hot or when the data's cold and on top of that, because we can tier over 440 arrays that are not IBM logo'd, multi vendor tiering, we can tier from our box to an EMC box. So if you have a flash array, you've got an old or all hard drive that you've moved into your back up in archive tier, we can automatically tier to that. We can tier from the EMC array out to the Cloud, but it's all done automatically. The admin doesn't do anything, it just says source and target and the AI does all the work. That's how you get the productivity that you're talking about, that you need in storage and back ups even worse because you got to keep everything now, which Sam mentioned GDPR, all these new regulations and the Federal Government its like keep the data forever. >> But in that case, the machine can determine whether or not it's okay to put it in the Cloud, if it's in Canada or Germany or wherever, the machine can adjudicate and make those decisions. >> And that's what the AI, so in that case you're using AI inside of the storage system versus what we talked about with our other software that makes our storage systems a great platform for other AI workloads that are not, if you will, AI for storage. AI for everything else, cars or hospitals or resume analysis. That's what the platform can, but we put all this AI inside of the system 'cause there aren't that big, giant, global, Fortune 500 has 55 storage admins and in 2007 or eight, they had 100, but they've quintupled the amount of storage easily if not 10x'd it, so who's going to manage that? Automation. >> Guys, good discussion. Not everyday, boring, old storage. It's talking about intelligence, real intelligence this time. Eric, Sam, thanks very much for coming to theCUBE. Great to see you guys again. >> Thank you. >> Thank you. >> You're welcome. Alright, keep it right there everybody. Stu and I will be back with our next guest shortly, right after this break. John Furrier is also here. IBM Think, Day four, you're watching theCUBE. Be right back. (tech music)

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Now you may think you know Choice, but we don't own hotels. We're a technology company. And Nutanix is helping us innovate the way we operate to support our franchisees. This is my first visit to New Orleans and my first .Next. >> Well Stacy, you're in for a treat. New Orleans is known for its fabulous food and its marvelous music, but most importantly the free spirit. >> Well I can't wait, and speaking of free, it's my pleasure to introduce the Nutanix Freedom video, enjoy. ♪ I lose everything, so I can sing ♪ ♪ Hallelujah I'm free ♪ ♪ Ah, ah, ♪ ♪ Ah, ah, ♪ ♪ I lose everything, so I can sing ♪ ♪ Hallelujah I'm free ♪ ♪ I lose everything, so I can sing ♪ ♪ Hallelujah I'm free ♪ ♪ I'm free, I'm free, I'm free, I'm free ♪ ♪ Gritting your teeth, you hold onto me ♪ ♪ It's never enough, I'm never complete ♪ ♪ Tell me to prove, expect me to lose ♪ ♪ I push it away, I'm trying to move ♪ ♪ I'm desperate to run, I'm desperate to leave ♪ ♪ If I lose it all, at least I'll be free ♪ ♪ Ah, ah ♪ ♪ Ah, ah ♪ ♪ Hallelujah, I'm free ♪ >> PA Announcer: Ladies and gentlemen, please welcome chief marketing officer Ben Gibson ♪ Ah, ah ♪ ♪ Ah, ah ♪ ♪ Hallelujah, I'm free ♪ >> Welcome, good morning. >> Audience: Good morning. >> And welcome to .Next 2018. There's no better way to open up a .Next conference than by hearing from two of our great customers. And Matthew, thank you for welcoming us to this beautiful, your beautiful state and city. And Stacy, this is your first .Next, and I know she's not alone because guess what It's my first .Next too. And I come properly attired. In the front row, you can see my Nutanix socks, and I think my Nutanix blue suit. And I know I'm not alone. I think over 5,000 people in attendance here today are also first timers at .Next. And if you are here for the first time, it's in the morning, let's get moving. I want you to stand up, so we can officially welcome you into the fold. Everyone stand up, first time. All right, welcome. (audience clapping) So you are all joining not just a conference here. This is truly a community. This is a community of the best and brightest in our industry I will humbly say that are coming together to share best ideas, to learn what's happening next, and in particular it's about forwarding not only your projects and your priorities but your careers. There's so much change happening in this industry. It's an opportunity to learn what's coming down the road and learn how you can best position yourself for this whole new world that's happening around cloud computing and modernizing data center environments. And this is not just a community, this is a movement. And it's a movement that started quite awhile ago, but the first .Next conference was in the quiet little town of Miami, and there was about 800 of you in attendance or so. So who in this hall here were at that first .Next conference in Miami? Let me hear from you. (audience members cheering) Yep, well to all of you grizzled veterans of the .Next experience, welcome back. You have started a movement that has grown and this year across many different .Next conferences all over the world, over 20,000 of your community members have come together. And we like to do it in distributed architecture fashion just like here in Nutanix. And so we've spread this movement all over the world with .Next conferences. And this is surging. We're also seeing just today the current count 61,000 certifications and climbing. Our Next community, close to 70,000 active members of our online community because .Next is about this big moment, and it's about every other day and every other week of the year, how we come together and explore. And my favorite stat of all. Here today in this hall amongst the record 5,500 registrations to .Next 2018 representing 71 countries in whole. So it's a global movement. Everyone, welcome. And you know when I got in Sunday night, I was looking at the tweets and the excitement was starting to build and started to see people like Adile coming from Casablanca. Adile wherever you are, welcome buddy. That's a long trip. Thank you so much for coming and being here with us today. I saw other folks coming from Geneva, from Denmark, from Japan, all over the world coming together for this moment. And we are accomplishing phenomenal things together. Because of your trust in us, and because of some early risk candidly that we have all taken together, we've created a movement in the market around modernizing data center environments, radically simplifying how we operate in the services we deliver to our businesses everyday. And this is a movement that we don't just know about this, but the industry is really taking notice. I love this chart. This is Gartner's inaugural hyperconvergence infrastructure magic quadrant chart. And I think if you see where Nutanix is positioned on there, I think you can agree that's a rout, that's a homerun, that's a mic drop so to speak. What do you guys think? (audience clapping) But here's the thing. It says Nutanix up there. We can honestly say this is a win for this hall here. Because, again, without your trust in us and what we've accomplished together and your partnership with us, we're not there. But we are there, and it is thanks to everyone in this hall. Together we have created, expanded, and truly made this market. Congratulations. And you know what, I think we're just getting started. The same innovation, the same catalyst that we drove into the market to converge storage network compute, the next horizon is around multi-cloud. The next horizon is around whether by accident or on purpose the strong move with different workloads moving into public cloud, some into private cloud moving back and forth, the promise of application mobility, the right workload on the right cloud platform with the right economics. Economics is key here. If any of you have a teenager out there, and they have a hold of your credit card, and they're doing something online or the like. You get some surprises at the end of the month. And that surprise comes in the form of spiraling public cloud costs. And this isn't to say we're not going to see a lot of workloads born and running in public cloud, but the opportunity is for us to take a path that regains control over infrastructure, regain control over workloads and where they're run. And the way I look at it for everyone in this hall, it's a journey we're on. It starts with modernizing those data center environments, continues with embracing the full cloud stack and the compelling opportunity to deliver that consumer experience to rapidly offer up enterprise compute services to your internal clients, lines of businesses and then out into the market. It's then about how you standardize across an enterprise cloud environment, that you're not just the infrastructure but the management, the automation, the control, and running any tier one application. I hear this everyday, and I've heard this a lot already this week about customers who are all in with this approach and running those tier one applications on Nutanix. And then it's the promise of not only hyperconverging infrastructure but hyperconverging multiple clouds. And if we do that, this journey the way we see it what we are doing is building your enterprise cloud. And your enterprise cloud is about the private cloud. It's about expanding and managing and taking back control of how you determine what workload to run where, and to make sure there's strong governance and control. And you're radically simplifying what could be an awfully complicated scenario if you don't reclaim and put your arms around that opportunity. Now how do we do this different than anyone else? And this is going to be a big theme that you're going to see from my good friend Sunil and his good friends on the product team. What are we doing together? We're taking all of that legacy complexity, that friction, that inability to be able to move fast because you're chained to old legacy environments. I'm talking to folks that have applications that are 40 years old, and they are concerned to touch them because they're not sure if they can react if their infrastructure can meet the demands of a new, modernized workload. We're making all that complexity invisible. And if all of that is invisible, it allows you to focus on what's next. And that indeed is the spirit of this conference. So if the what is enterprise cloud, and the how we do it different is by making infrastructure invisible, data centers, clouds, then why are we all here today? What is the binding principle that spiritually, that emotionally brings us all together? And we think it's a very simple, powerful word, and that word is freedom. And when we think about freedom, we think about as we work together the freedom to build the data center that you've always wanted to build. It's about freedom to run the applications where you choose based on the information and the context that wasn't available before. It's about the freedom of choice to choose the right cloud platform for the right application, and again to avoid a lot of these spiraling costs in unanticipated surprises whether it be around security, whether it be around economics or governance that come to the forefront. It's about the freedom to invent. It's why we got into this industry in the first place. We want to create. We want to build things not keep the lights on, not be chained to mundane tasks day by day. And it's about the freedom to play. And I hear this time and time again. My favorite tweet from a Nutanix customer to this day is just updated a lot of nodes at 38,000 feed on United Wifi, on my way to spend vacation with my family. Freedom to play. This to me is emotionally what brings us all together and what you saw with the Freedom video earlier, and what you see here is this new story because we want to go out and spread the word and not only talk about the enterprise cloud, not only talk about how we do it better, but talk about why it's so compelling to be a part of this hall here today. Now just one note of housekeeping for everyone out there in case I don't want anyone to take a wrong turn as they come to this beautiful convention center here today. A lot of freedom going on in this convention center. As luck may have it, there's another conference going on a little bit down that way based on another high growth, disruptive industry. Now MJBizCon Next, and by coincidence it's also called next. And I have to admire the creativity. I have to admire that we do share a, hey, high growth business model here. And in case you're not quite sure what this conference is about. I'm the head of marketing here. I have to show the tagline of this. And I read the tagline from license to launch and beyond, the future of the, now if I can replace that blank with our industry, I don't know, to me it sounds like a new, cool Sunil product launch. Maybe launching a new subscription service or the like. Stay tuned, you never know. I think they're going to have a good time over there. I know we're going to have a wonderful week here both to learn as well as have a lot of fun particularly in our customer appreciation event tonight. I want to spend a very few important moments on .Heart. .Heart is Nutanix's initiative to promote diversity in the technology arena. In particular, we have a focus on advancing the careers of women and young girls that we want to encourage to move into STEM and high tech careers. You have the opportunity to engage this week with this important initiative. Please role the video, and let's learn more about how you can do so. >> Video Plays (electronic music) >> So all of you have received these .Heart tokens. You have the freedom to go and choose which of the four deserving charities can receive donations to really advance our cause. So I thank you for your engagement there. And this community is behind .Heart. And it's a very important one. So thank you for that. .Next is not the community, the moment it is without our wonderful partners. These are our amazing sponsors. Yes, it's about sponsorship. It's also about how we integrate together, how we innovate together, and we're about an open community. And so I want to thank all of these names up here for your wonderful sponsorship of this event. I encourage everyone here in this room to spend time, get acquainted, get reacquainted, learn how we can make wonderful music happen together, wonderful music here in New Orleans happen together. .Next isn't .Next with a few cool surprises. Surprise number one, we have a contest. This is a still shot from the Freedom video you saw right before I came on. We have strategically placed a lucky seven Nutanix Easter eggs in this video. And if you go to Nutanix.com/freedom, watch the video. You may have to use the little scrubbing feature to slow down 'cause some of these happen quickly. You're going to find some fun, clever Easter eggs. List all seven, tweet that out, or as many as you can, tweet that out with hashtag nextconf, C, O, N, F, and we'll have a random drawing for an all expenses paid free trip to .Next 2019. And just to make sure everyone understands Easter egg concept. There's an eighth one here that's actually someone that's quite famous in our circles. If you see on this still shot, there's someone in the back there with a red jacket on. That's not just anyone. We're targeting in here. That is our very own Julie O'Brien, our senior vice president of corporate marketing. And you're going to hear from Julie later on here at .Next. But Julie and her team are the engine and the creativity behind not only our new Freedom campaign but more importantly everything that you experience here this week. Julie and her team are amazing, and we can't wait for you to experience what they've pulled together for you. Another surprise, if you go and visit our Freedom booths and share your stories. So they're like video booths, you share your success stories, your partnerships, your journey that I talked about, you will be entered to win a beautiful Nutanix brand compliant, look at those beautiful colors, bicycle. And it's not just any bicycle. It's a beautiful bicycle made by our beautiful customer Trek. I actually have a Trek bike. I love cycling. Unfortunately, I'm not eligible, but all of you are. So please share your stories in the Freedom Nutanix's booths and put yourself in the running, or in the cycling to get this prize. One more thing I wanted to share here. Yesterday we had a great time. We had our inaugural Nutanix hackathon. This hackathon brought together folks that were in devops practices, many of you that are in this room. We sold out. We thought maybe we'd get four or five teams. We had to shutdown at 14 teams that were paired together with a Nutanix mentor, and you coded. You used our REST APIs. You built new apps that integrated in with Prism and Clam. And it was wonderful to see this. Everyone I talked to had a great time on this. We had three winners. In third place, we had team Copper or team bronze, but team Copper. Silver, Not That Special, they're very humble kind of like one of our key mission statements. And the grand prize winner was We Did It All for the Cookies. And you saw them coming in on our Mardi Gras float here. We Did It All for Cookies, they did this very creative job. They leveraged an Apple Watch. They were lighting up VMs at a moments notice utilizing a lot of their coding skills. Congratulations to all three, first, second, and third all receive $2,500. And then each of them, then were able to choose a charity to deliver another $2,500 including Ronald McDonald House for the winner, we did it all for the McDonald Land cookies, I suppose, to move forward. So look for us to do more of these kinds of events because we want to bring together infrastructure and application development, and this is a great, I think, start for us in this community to be able to do so. With that, who's ready to hear form Dheeraj? You ready to hear from Dheeraj? (audience clapping) I'm ready to hear from Dheeraj, and not just 'cause I work for him. It is my distinct pleasure to welcome on the stage our CEO, cofounder and chairman Dheeraj Pandey. ("Free" by Broods) ♪ Hallelujah, I'm free ♪ >> Thank you Ben and good morning everyone. >> Audience: Good morning. >> Thank you so much for being here. It's just such an elation when I'm thinking about the Mardi Gras crowd that came here, the partners, the customers, the NTCs. I mean there's some great NTCs up there I could relate to because they're on Slack as well. How many of you are in Slack Nutanix internal Slack channel? Probably 5%, would love to actually see this community grow from here 'cause this is not the only even we would love to meet you. We would love to actually do this in a real time bite size communication on our own internal Slack channel itself. Now today, we're going to talk about a lot of things, but a lot of hard things, a lot of things that take time to build and have evolved as the industry itself has evolved. And one of the hard things that I want to talk about is multi-cloud. Multi-cloud is a really hard problem 'cause it's full of paradoxes. It's really about doing things that you believe are opposites of each other. It's about frictionless, but it's also about governance. It's about being simple, and it's also about being secure at the same time. It's about delight, it's about reducing waste, it's about owning, and renting, and finally it's also about core and edge. How do you really make this big at a core data center whether it's public or private? Or how do you really shrink it down to one or two nodes at the edge because that's where your machines are, that's where your people are? So this is a really hard problem. And as you hear from Sunil and the gang there, you'll realize how we've actually evolved our solutions to really cater to some of these. One of the approaches that we have used to really solve some of these hard problems is to have machines do more, and I said a lot of things in those four words, have machines do more. Because if you double-click on that sentence, it really means we're letting design be at the core of this. And how do you really design data centers, how do you really design products for the data center that hush all the escalations, the details, the complexities, use machine-learning and AI and you know figure our anomaly detection and correlations and patter matching? There's a ton of things that you need to do to really have machines do more. But along the way, the important lesson is to make machines invisible because when machines become invisible, it actually makes something else visible. It makes you visible. It makes governance visible. It makes applications visible, and it makes services visible. A lot of things, it makes teams visible, careers visible. So while we're really talking about invisibility of machines, we're talking about visibility of people. And that's how we really brought all of you together in this conference as well because it makes all of us shine including our products, and your careers, and your teams as well. And I try to define the word customer success. You know it's one of the favorite words that I'm actually using. We've just hired a great leader in customer success recently who's really going to focus on this relatively hard problem, yet another hard problem of customer success. We think that customer success, true customer success is possible when we have machines tend towards invisibility. But along the way when we do that, make humans tend towards freedom. So that's the real connection, the yin-yang of machines and humans that Nutanix is really all about. And that's why design is at the core of this company. And when I say design, I mean reducing friction. And it's really about reducing friction. And everything we do, the most mundane of things which could be about migrating applications, spinning up VMs, self-service portals, automatic upgrades, and automatic scale out, and all the things we do is about reducing friction which really makes machines become invisible and humans gain freedom. Now one of the other convictions we have is how all of us are really tied at the hip. You know our success is tied to your success. If we make you successful, and when I say you, I really mean Main Street. Main Street being customers, and partners, and employees. If we make all of you successful, then we automatically become successful. And very coincidentally, Main Street and Wall Street are also tied in that very same relation as well. If we do a great job at Main Street, I think the Wall Street customer, i.e. the investor, will take care of itself. You'll have you know taken care of their success if we took care of Main Street success itself. And that's the narrative that our CFO Dustin Williams actually went and painted to our Wall Street investors two months ago at our investor day conference. We talked about a $3 billion number. We said look as a company, as a software company, we can go and achieve $3 billion in billings three years from now. And it was a telling moment for the company. It was really about talking about where we could be three years from now. But it was not based on a hunch. It was based on what we thought was customer success. Now realize that $3 billion in pure software. There's only 10 to 15 companies in the world that actually have that kind of software billings number itself. But at the core of this confidence was customer success, was the fact that we were doing a really good job of not over promising and under delivering but under promising starting with small systems and growing the trust of the customers over time. And this is one of the statistics we actually talk about is repeat business. The first dollar that a Global 2000 customer spends in Nutanix, and if we go and increase their trust 15 times by year six, and we hope to actually get 17 1/2 and 19 times more trust in the years seven and eight. It's very similar numbers for non Global 2000 as well. Again, we go and really hustle for customer success, start small, have you not worry about paying millions of dollars upfront. You know start with systems that pay as they grow, you pay as they grow, and that's the way we gain trust. We have the same non Global 2000 pay $6 1/2 for the first dollar they've actually spent on us. And with this, I think the most telling moment was when Dustin concluded. And this is key to this audience here as well. Is how the current cohorts which is this audience here and many of them were not here will actually carry the weight of $3 billion, more than 50% of it if we did a great job of customer success. If we were humble and honest and we really figured out what it meant to take care of you, and if we really understood what starting small was and having to gain the trust with you over time, we think that more than 50% of that billings will actually come from this audience here without even looking at new logos outside. So that's the trust of customer success for us, and it takes care of pretty much every customer not just the Main Street customer. It takes care of Wall Street customer. It takes care of employees. It takes care of partners as well. Now before I talk about technology and products, I want to take a step back 'cause many of you are new in this audience. And I think that it behooves us to really talk about the history of this company. Like we've done a lot of things that started out as science projects. In fact, I see some tweets out there and people actually laugh at Nutanix cloud. And this is where we were in 2012. So if you take a step back and think about where the company was almost seven, eight years ago, we were up against giants. There was a $30 billion industry around network attached storage, and storage area networks and blade servers, and hypervisors, and systems management software and so on. So what did we start out with? Very simple premise that we will collapse the architecture of the data center because three tier is wasteful and three tier is not delightful. It was a very simple hunch, we said we'll take rack mount servers, we'll put a layer of software on top of it, and that layer of software back then only did storage. It didn't do networks and security, and it ran on top of a well known hypervisor from VMware. And we said there's one non negotiable thing. The fact that the design must change. The control plane for this data center cannot be the old control plane. It has to be rethought through, and that's why Prism came about. Now we went and hustled hard to add more things to it. We said we need to make this diverse because it can't just be for one application. We need to make it CPU heavy, and memory heavy, and storage heavy, and flash heavy and so on. And we built a highly configurable HCI. Now all of them are actually configurable as you know of today. And this was not just innovation in technologies, it was innovation in business and sizing, capacity planning, quote to cash business processes. A lot of stuff that we had to do to make this highly configurable, so you can really scale capacity and performance independent of each other. Then in 2014, we did something that was very counterintuitive, but we've done this on, and on, and on again. People said why are you disrupting yourself? You know you've been doing a good job of shipping appliances, but we also had the conviction that HCI was not about hardware. It was about a form factor, but it was really about an operating system. And we started to compete with ourselves when we said you know what we'll do arm's length distribution, we'll do arm's length delivery of products when we give our software to our Dell partner, to Dell as a partner, a loyal partner. But at the same time, it was actually seen with a lot of skepticism. You know these guys are wondering how to really make themselves vanish because they're competing with themselves. But we also knew that if we didn't compete with ourselves someone else will. Now one of the most controversial decisions was really going and doing yet another hypervisor. In the year 2015, it was really preposterous to build yet another hypervisor. It was a very mature market. This was coming probably 15 years too late to the market, or at least 10 years too late to market. And most people said it shouldn't be done because hypervisor is a commodity. And that's the word we latched on to. That this commodity should not have to be paid for. It shouldn't have a team of people managing it. It should actually be part of your overall stack, but it should be invisible. Just like storage needs to be invisible, virtualization needs to be invisible. But it was a bold step, and I think you know at least when we look at our current numbers, 1/3rd of our customers are actually using AHV. At least every quarter that we look at it, our new deployments, at least 35% of it is actually being used on AHV itself. And again, a very preposterous thing to have said five years ago, four years ago to where we've actually come. Thank you so much for all of you who've believed in the fact that virtualization software must be invisible and therefore we should actually try out something that is called AHV today. Now we went and added Lenovo to our OEM mix, started to become even more of a software company in the year 2016. Went and added HP and Cisco in some of very large deals that we talk about in earnings call, our HP deals and Cisco deals. And some very large customers who have procured ELAs from us, enterprise license agreements from us where they want to mix and match hardware. They want to mix Dell hardware with HP hardware but have common standard Nutanix entitlements. And finally, I think this was another one of those moments where we say why should HCI be only limited to X86. You know this operating systems deserves to run on a non X86 architecture as well. And that gave birth to this idea of HCI and Power Systems from IBM. And we've done a great job of really innovating with them in the last three, four quarters. Some amazing innovation that has come out where you can now run AIX 7.x on Nutanix. And for the first time in the history of data center, you can actually have a single software not just a data plane but a control plane where you can manage an IBM farm, an Power farm, and open Power farm and an X86 farm from the same control plane and have you know the IBM farm feed storage to an Intel compute farm and vice versa. So really good things that we've actually done. Now along the way, something else was going on while we were really busy building the private cloud, we knew there was a new consumption model on computing itself. People were renting computing using credit cards. This is the era of the millennials. They were like really want to bypass people because at the end of the day, you know why can't computing be consumed the way like eCommerce is? And that devops movement made us realize that we need to add to our stack. That stack will now have other computing clouds that is AWS and Azure and GCP now. So similar to the way we did Prism. You know Prism was really about going and making hypervisors invisible. You know we went ahead and said we'll add Calm to our portfolio because Calm is now going to be what Prism was to us back when we were really dealing with multi hypervisor world. Now it's going to be multi-cloud world. You know it's one of those things we had a gut around, and we really come to expect a lot of feedback and real innovation. I mean yesterday when we had the hackathon. The center, the epicenter of the discussion was Calm, was how do you automate on multiple clouds without having to write a single line of code? So we've come a long way since the acquisition of Calm two years ago. I think it's going to be a strong pillar in our overall product portfolio itself. Now the word multi-cloud is going to be used and over used. In fact, it's going to be blurring its lines with the idea of hyperconvergence of clouds, you know what does it mean. We just hope that hyperconvergence, the way it's called today will morph to become hyperconverged clouds not just hyperconverged boxes which is a software defined infrastructure definition itself. But let's focus on the why of multi-cloud. Why do we think it can't all go into a public cloud itself? The one big reason is just laws of the land. There's data sovereignty and computing sovereignty, regulations and compliance because of which you need to be in where the government with the regulations where the compliance rules want you to be. And by the way, that's just one reason why the cloud will have to disperse itself. It can't just be 10, 20 large data centers around the world itself because you have 200 plus countries and half of computing actually gets done outside the US itself. So it's a really important, very relevant point about the why of multi-cloud. The second one is just simple laws of physics. You know if there're machines at the edge, and they're producing so much data, you can't bring all the data to the compute. You have to take the compute which is stateless, it's an app. You take the app to where the data is because the network is the enemy. The network has always been the enemy. And when we thought we've made fatter networks, you've just produced more data as well. So this just goes without saying that you take something that's stateless that's without gravity, that's lightweight which is compute and the application and push it close to where the data itself is. And the third one which is related is just latency reasons you know? And it's not just about machine latency and electrons transferring over the speed light, and you can't defy the speed of light. It's also about human latency. It's also about multiple teams saying we need to federate and delegate, and we need to push things down to where the teams are as opposed to having to expect everybody to come to a very large computing power itself. So all the ways, the way they are, there will be at least three different ways of looking at multi-cloud itself. There's a centralized core cloud. We all go and relate to this because we've seen large data centers and so on. And that's the back office workhorse. It will crunch numbers. It will do processing. It will do a ton of things that will go and produce results for you know how we run our businesses, but there's also the dispersal of the cloud, so ROBO cloud. And this is the front office server that's really serving. It's a cloud that's going to serve people. It's going to be closer to people, and that's what a ROBO cloud is. We have a ton of customers out here who actually use Nutanix and the ROBO environments themselves as one node, two node, three node, five node servers, and it just collapses the entire server closet room in these ROBOs into something really, really small and minuscule. And finally, there's going to be another dispersed edge cloud because that's where the machines are, that's where the data is. And there's going to be an IOT machine fog because we need to miniaturize computing to something even smaller, maybe something that can really land in the palm in a mini server which is a PC like server, but you need to run everything that's enterprise grade. You should be able to go and upgrade them and monitor them and analyze them. You know do enough computing up there, maybe event-based processing that can actually happen. In fact, there's some great innovation that we've done at the edge with IOTs that I'd love for all of you to actually attend some sessions around as well. So with that being said, we have a hole in the stack. And that hole is probably one of the hardest problems that we've been trying to solve for the last two years. And Sunil will talk a lot about that. This idea of hybrid. The hybrid of multi-cloud is one of the hardest problems. Why? Because we're talking about really blurring the lines with owning and renting where you have a single-tenant environment which is your data center, and a multi-tenant environment which is the service providers data center, and the two must look like the same. And the two must look like the same is that hard a problem not just for burst out capacity, not just for security, not just for identity but also for networks. Like how do you blur the lines between networks? How do you blur the lines for storage? How do you really blur the lines for a single pane of glass where you can think of availability zones that look highly symmetric even though they're not because one of 'em is owned by you, and it's single-tenant. The other one is not owned by you, that's multi-tenant itself. So there's some really hard problems in hybrid that you'll hear Sunil talk about and the team. And some great strides that we've actually made in the last 12 months of really working on Xi itself. And that completes the picture now in terms of how we believe the state of computing will be going forward. So what are the must haves of a multi-cloud operating system? We talked about marketplace which is catalogs and automation. There's a ton of orchestration that needs to be done for multi-cloud to come together because now you have a self-service portal which is providing an eCommerce view. It's really about you know getting to do a lot of requests and workflows without having people come in the way, without even having tickets. There's no need for tickets if you can really start to think like a self-service portal as if you're just transacting eCommerce with machines and portals themselves. Obviously the next one is networking security. You need to blur the lines between on-prem and off-prem itself. These two play a huge role. And there's going to be a ton of details that you'll see Sunil talk about. But finally, what I want to focus on the rest of the talk itself here is what governance and compliance. This is a hard problem, and it's a hard problem because things have evolved. So I'm going to take a step back. Last 30 years of computing, how have consumption models changed? So think about it. 30 years ago, we were making decisions for 10 plus years, you know? Mainframe, at least 10 years, probably 20 plus years worth of decisions. These were decisions that were extremely waterfall-ish. Make 10s of millions of dollars worth of investment for a device that we'd buy for at least 10 to 20 years. Now as we moved to client-server, that thing actually shrunk. Now you're talking about five years worth of decisions, and these things were smaller. So there's a little bit more velocity in our decisions. We were not making as waterfall-ish decision as we used to with mainframes. But still five years, talk about virtualized, three tier, maybe three to five year decisions. You know they're still relatively big decisions that we were making with computer and storage and SAN fabrics and virtualization software and systems management software and so on. And here comes Nutanix, and we said no, no. We need to make it smaller. It has to become smaller because you know we need to make more agile decisions. We need to add machines every week, every month as opposed to adding you know machines every three to five years. And we need to be able to upgrade them, you know any point in time. You can do the upgrades every month if you had to, every week if you had to and so on. So really about more agility. And yet, we were not complete because there's another evolution going on, off-prem in the public cloud where people are going and doing reserved instances. But more than that, they were doing on demand stuff which no the decision was days to weeks. Some of these things that unitive compute was being rented for days to weeks, not years. And if you needed something more, you'd shift a little to the left and use reserved instances. And then spot pricing, you could do spot pricing for hours and finally lambda functions. Now you could to function as a service where things could actually be running only for minutes not even hours. So as you can see, there's a wide spectrum where when you move to the right, you get more elasticity, and when you move to the left, you're talking about predictable decision making. And in fact, it goes from minutes on one side to 10s of years on the other itself. And we hope to actually go and blur the lines between where NTNX is today where you see Nutanix right now to where we really want to be with reserved instances and on demand. And that's the real ask of Nutanix. How do you take care of this discontinuity? Because when you're owning things, you actually end up here, and when you're renting things, you end up here. What does it mean to really blur the lines between these two because people do want to make decisions that are better than reserved instance in the public cloud. We'll talk about why reserved instances which looks like a proxy for Nutanix it's still very, very wasteful even though you might think it's delightful, it's very, very wasteful. So what does it mean for on-prem and off-prem? You know you talk about cost governance, there's security compliance. These high velocity decisions we're actually making you know where sometimes you could be right with cost but wrong on security, but sometimes you could be right in security but wrong on cost. We need to really figure out how machines make some of these decisions for us, how software helps us decide do we have the right balance between cost, governance, and security compliance itself? And to get it right, we have introduced our first SAS service called Beam. And to talk more about Beam, I want to introduce Vijay Rayapati who's the general manager of Beam engineering to come up on stage and talk about Beam itself. Thank you Vijay. (rock music) So you've been here a couple of months now? >> Yes. >> At the same time, you spent the last seven, eight years really handling AWS. Tell us more about it. >> Yeah so we spent a lot of time trying to understand the last five years at Minjar you know how customers are really consuming in this new world for their workloads. So essentially what we tried to do is understand the consumption models, workload patterns, and also build algorithms and apply intelligence to say how can we lower this cost and you know improve compliance of their workloads.? And now with Nutanix what we're trying to do is how can we converge this consumption, right? Because what happens here is most customers start with on demand kind of consumption thinking it's really easy, but the total cost of ownership is so high as the workload elasticity increases, people go towards spot or a scaling, but then you need a lot more automation that something like Calm can help them. But predictability of the workload increases, then you need to move towards reserved instances, right to lower costs. >> And those are some of the things that you go and advise with some of the software that you folks have actually written. >> But there's a lot of waste even in the reserved instances because what happens it while customers make these commitments for a year or three years, what we see across, like we track a billion dollars in public cloud consumption you know as a Beam, and customers use 20%, 25% of utilization of their commitments, right? So how can you really apply, take the data of consumption you know apply intelligence to essentially reduce their you know overall cost of ownership. >> You said something that's very telling. You said reserved instances even though they're supposed to save are still only 20%, 25% utilized. >> Yes, because the workloads are very dynamic. And the next thing is you can't do hot add CPU or hot add memory because you're buying them for peak capacity. There is no convergence of scaling that apart from the scaling as another node. >> So you actually sized it for peak, but then using 20%, 30%, you're still paying for the peak. >> That's right. >> Dheeraj: That can actually add up. >> That's what we're trying to say. How can we deliver visibility across clouds? You know how can we deliver optimization across clouds and consumption models and bring the control while retaining that agility and demand elasticity? >> That's great. So you want to show us something? >> Yeah absolutely. So this is Beam as just Dheeraj outlined, our first SAS service. And this is my first .Next. And you know glad to be here. So what you see here is a global consumption you know for a business across different clouds. Whether that's in a public cloud like Amazon, or Azure, or Nutanix. We kind of bring the consumption together for the month, the recent month across your accounts and services and apply intelligence to say you know what is your spent efficiency across these clouds? Essentially there's a lot of intelligence that goes in to detect your workloads and consumption model to say if you're spending $100, how efficiently are you spending? How can you increase that? >> So you have a centralized view where you're looking at multiple clouds, and you know you talk about maybe you can take an example of an account and start looking at it? >> Yes, let's go into a cloud provider like you know for this business, let's go and take a loot at what's happening inside an Amazon cloud. Here we get into the deeper details of what's happening with the consumption of a specific services as well as the utilization of both on demand and RI. You know what can you do to lower your cost and detect your spend efficiency of a dollar to see you know are there resources that are provisioned by teams for applications that are not being used, or are there resources that we should go and rightsize because you know we have all this monitoring data, configuration data that we crunch through to basically detect this? >> You think there's billions of events that you look at everyday. You're already looking at a billon dollars worth of AWS spend. >> Right, right. >> So billions of events, billing, metering events every year to really figure out and optimize for them. >> So what we have here is a very popular international government organization. >> Dheeraj: Wow, so it looks like Russians are everywhere, the cloud is everywhere actually. >> Yes, it's quite popular. So when you bring your master account into Beam, we kind of detect all the linked accounts you know under that. Then you can go and take a look at not just at the organization level within it an account level. >> So these are child objects, you know. >> That's right. >> You can think of them as ephemeral accounts that you create because you don't want to be on the record when you're doing spams on Facebook for example. >> Right, let's go and take a look at what's happening inside a Facebook ad spend account. So we have you know consumption of the services. Let's go deeper into compute consumption, and you kind of see a trendline. You can do a lot of computing. As you see, looks like one campaign has ended. They started another campaign. >> Dheeraj: It looks like they're not stopping yet, man. There's a lot of money being made in Facebook right now. (Vijay laughing) >> So not only just get visibility at you know compute as a service inside a cloud provider, you can go deeper inside compute and say you know what is a service that I'm really consuming inside compute along with the CPUs n'stuff, right? What is my data transfer? You know what is my network? What is my load blancers? So essentially you get a very deeper visibility you know as a service right. Because we have three goals for Beam. How can we deliver visibility across clouds? How can we deliver visibility across services? And how can we deliver, then optimization? >> Well I think one thing that I just want to point out is how this SAS application was an extremely teachable moment for me to learn about the different resources that people could use about the public cloud. So all of you who actually have not gone deep enough into the idea of public cloud. This could be a great app for you to learn about things, the resources, you know things that you could do to save and security and things of that nature. >> Yeah. And we really believe in creating the single pane view you know to mange your optimization of a public cloud. You know as Ben spoke about as a business, you need to have freedom to use any cloud. And that's what Beam delivers. How can you make the right decision for the right workload to use any of the cloud of your choice? >> Dheeraj: How 'about databases? You talked about compute as well but are there other things we could look at? >> Vijay: Yes, let's go and take a look at database consumption. What you see here is they're using inside Facebook ad spending, they're using all databases except Oracle. >> Dheeraj: Wow, looks like Oracle sales folks have been active in Russia as well. (Vijay laughing) >> So what we're seeing here is a global view of you know what is your spend efficiency and which is kind of a scorecard for your business for the dollars that you're spending. And the great thing is Beam kind of brings together you know through its intelligence and algorithms to detect you know how can you rightsize resources and how can you eliminate things that you're not using? And we deliver and one click fix, right? Let's go and take a look at resources that are maybe provisioned for storage and not being used. We deliver the seamless one-click philosophy that Nutanix has to eliminate it. >> So one click, you can actually just pick some of these wasteful things that might be looking delightful because using public cloud, using credit cards, you can go in and just say click fix, and it takes care of things. >> Yeah, and not only remove the resources that are unused, but it can go and rightsize resources across your compute databases, load balancers, even past services, right? And this is where the power of it kind of comes for a business whether you're using on-prem and off-prem. You know how can you really converge that consumption across both? >> Dheeraj: So do you have something for Nutanix too? >> Vijay: Yes, so we have basically been working on Nutanix with something that we're going to deliver you know later this year. As you can see here, we're bringing together the consumption for the Nutanix, you know the services that you're using, the licensing and capacity that is available. And how can you also go and optimize within Nutanix environments >> That's great. >> for the next workload. Now let me quickly show you what we have on the compliance side. This is an extremely powerful thing that we've been working on for many years. What we deliver here just like in cost governance, a global view of your compliance across cloud providers. And the most powerful thing is you can go into a cloud provider, get the next level of visibility across cloud regimes for hundreds of policies. Not just policies but those policies across different regulatory compliances like HIPA, PCI, CAS. And that's very powerful because-- >> So you're saying a lot of what you folks have done is codified these compliance checks in software to make sure that people can sleep better at night knowing that it's PCI, and HIPA, and all that compliance actually comes together? >> And you can build this not just by cloud accounts, you can build them across cloud accounts which is what we call security centers. Essentially you can go and take a deeper look at you know the things. We do a whole full body scan for your cloud infrastructure whether it's AWS Amazon or Azure, and you can go and now, again, click to fix things. You know that had been probably provisioned that are violating the security compliance rules that should be there. Again, we have the same one-click philosophy to say how can you really remove things. >> So again, similar to save, you're saying you can go and fix some of these security issues by just doing one click. >> Absolutely. So the idea is how can we give our people the freedom to get visibility and use the right cloud and take the decisions instantly through one click. That's what Beam delivers you know today. And you know get really excited, and it's available at beam.nutanix.com. >> Our first SAS service, ladies and gentleman. Thank you so much for doing this, Vijay. It looks like there's going to be a talk here at 10:30. You'll talk more about the midterm elections there probably? >> Yes, so you can go and write your own security compliances as well. You know within Beam, and a lot of powerful things you can do. >> Awesome, thank you so much, Vijay. I really appreciate it. (audience clapping) So as you see, there's a lot of work that we're doing to really make multi-cloud which is a hard problem. You know think about working the whole body of it and what about cost governance? What about security compliance? Obviously what about hybrid networks, and security, and storage, you know compute, many of the things that you've actually heard from us, but we're taking it to a level where the business users can now understand the implications. A CFO's office can understand the implications of waste and delight. So what does customer success mean to us? You know again, my favorite word in a long, long time is really go and figure out how do you make you, the customer, become operationally efficient. You know there's a lot of stuff that we deliver through software that's completely uncovered. It's so latent, you don't even know you have it, but you've paid for it. So you've got to figure out what does it mean for you to really become operationally efficient, organizationally proficient. And it's really important for training, education, stuff that you know you're people might think it's so awkward to do in Nutanix, but it could've been way simpler if you just told you a place where you can go and read about it. Of course, I can just use one click here as opposed to doing things the old way. But most importantly to make it financially accountable. So the end in all this is, again, one of the things that I think about all the time in building this company because obviously there's a lot of stuff that we want to do to create orphans, you know things above the line and top line and everything else. There's also a bottom line. Delight and waste are two sides of the same coin. You know when we're talking about developers who seek delight with public cloud at the same time you're looking at IT folks who're trying to figure out governance. They're like look you know the CFOs office, the CIOs office, they're trying to figure out how to curb waste. These two things have to go hand in hand in this era of multi-cloud where we're talking about frictionless consumption but also governance that looks invisible. So I think, at the end of the day, this company will do a lot of stuff around one-click delight but also go and figure out how do you reduce waste because there's so much waste including folks there who actually own Nutanix. There's so much software entitlement. There's so much waste in the public cloud itself that if we don't go and put our arms around, it will not lead to customer success. So to talk more about this, the idea of delight and the idea of waste, I'd like to bring on board a person who I think you know many of you actually have talked about it have delightful hair but probably wasted jokes. But I think has wasted hair and delightful jokes. So ladies and gentlemen, you make the call. You're the jury. Sunil R.M.J. Potti. ("Free" by Broods) >> So that was the first time I came out from the bottom of a screen on a stage. I actually now know what it feels to be like a gopher. Who's that laughing loudly at the back? Okay, do we have the... Let's see. Okay, great. We're about 15 minutes late, so that means we're running right on time. That's normally how we roll at this conference. And we have about three customers and four demos. Like I think there's about three plus six, about nine folks coming onstage. So we'll have our own version of the parade as well on the main stage for the next 70 minutes. So let's just jump right into it. I think we've been pretty consistent in terms of our longterm plans since we started the company. And it's become a lot more clearer over the last few years about our plans to essentially make computing invisible as Dheeraj mentioned. We're doing this across multiple acts. We started with HCI. We call it making infrastructure invisible. We extended that to making data centers invisible. And then now we're in this mode of essentially extending it to converging clouds so that you can actually converge your consumption models. And so today's conference and essentially the theme that you're going to be seeing throughout the breakout sessions is about a journey towards invisible clouds, but make sure that you internalize the fact that we're investing heavily in each of the three phases. It's just not about the hybrid cloud with Nutanix, it's about actually finishing the job about making infrastructure invisible, expanding that to kind of go after the full data center, and then of course embark on some real meaningful things around invisible clouds, okay? And to start the session, I think you know the part that I wanted to make sure that we are all on the same page because most of us in the room are still probably in this phase of the journey which is about invisible infrastructure. And there the three key products and especially two of them that most of you guys know are Acropolis and Prism. And they're sort of like the bedrock of our company. You know especially Acropolis which is about the web scale architecture. Prism is about consumer grade design. And with Acropolis now being really mature. It's in the seventh year of innovation. We still have more than half of our company in terms of R and D spend still on Acropolis and Prism. So our core product is still sort of where we think we have a significant differentiation on. We're not going to let our foot off the peddle there. You know every time somebody comes to me and says look there's a new HCI render popping out or an existing HCI render out there, I ask a simple question to our customers saying show me 100 customers with 100 node deployments, and it will be very hard to find any other render out there that does the same thing. And that's the power of Acropolis the code platform. And then it's you know the fact that the velocity associated with Acropolis continues to be on a fast pace. We came out with various new capabilities in 5.5 and 5.6, and one of the most complicated things to get right was the fact to shrink our three node cluster to a one node, two node deployment. Most of you actually had requirements on remote office, branch office, or the edge that actually allowed us to kind of give us you know sort of like the impetus to kind of go design some new capabilities into our core OS to get this out. And associated with Acropolis and expanding into Prism, as you will see, the first couple of years of Prism was all about refactoring the user interface, doing a good job with automation. But more and more of the investments around Prism is going to be based on machine learning. And you've seen some variants of that over the last 12 months, and I can tell you that in the next 12 to 24 months, most of our investments around infrastructure operations are going to be driven by AI techniques starting with most of our R and D spend also going into machine-learning algorithms. So when you talk about all the enhancements that have come on with Prism whether it be formed by you know the management console changing to become much more automated, whether now we give you automatic rightsizing, anomaly detection, or a series of functionality that have gone into it, the real core sort of capabilities that we're putting into Prism and Acropolis are probably best served by looking at the quality of the product. You probably have seen this slide before. We started showing the number of nodes shipped by Nutanix two years ago at this conference. It was about 35,000 plus nodes at that time. And since then, obviously we've you know continued to grow. And we would draw this line which was about enterprise class quality. That for the number of bugs found as a percentage of nodes shipped, there's a certain line that's drawn. World class companies do about probably 2% to 3%, number of CFDs per node shipped. And we were just broken that number two years ago. And to give you guys an idea of how that curve has shown up, it's now currently at .95%. And so along with velocity, you know this focus on being true to our roots of reliability and stability continues to be, you know it's an internal challenge, but it's also some of the things that we keep a real focus on. And so between Acropolis and Prism, that's sort of like our core focus areas to sort of give us the confidence that look we have this really high bar that we're sort of keeping ourselves accountable to which is about being the most advanced enterprise cloud OS on the planet. And we will keep it this way for the next 10 years. And to complement that, over a period of time of course, we've added a series of services. So these are services not just for VMs but also for files, blocks, containers, but all being delivered in that single one-click operations fashion. And to really talk more about it, and actually probably to show you the real deal there it's my great pleasure to call our own version of Moses inside the company, most of you guys know him as Steve Poitras. Come on up, Steve. (audience clapping) (rock music) >> Thanks Sunil. >> You barely fit in that door, man. Okay, so what are we going to talk about today, Steve? >> Absolutely. So when we think about when Nutanix first got started, it was really focused around VDI deployments, smaller workloads. However over time as we've evolved the product, added additional capabilities and features, that's grown from VDI to business critical applications as well as cloud native apps. So let's go ahead and take a look. >> Sunil: And we'll start with like Oracle? >> Yeah, that's one of the key ones. So here we can see our Prism central user interface, and we can see our Thor cluster obviously speaking to the Avengers theme here. We can see this is doing right around 400,000 IOPs at around 360 microseconds latency. Now obviously Prism central allows you to mange all of your Nutanix deployments, but this is just running on one single Nutanix cluster. So if we hop over here to our explore tab, we can see we have a few categories. We have some Kubernetes, some AFS, some Xen desktop as well as Oracle RAC. Now if we hope over to Oracle RAC, we're running a SLOB workload here. So obviously with Oracle enterprise applications performance, consistency, and extremely low latency are very critical. So with this SLOB workload, we're running right around 300 microseconds of latency. >> Sunil: So this is what, how many node Oracle RAC cluster is this? >> Steve: This is a six node Oracle RAC deployment. >> Sunil: Got it. And so what has gone into the product in recent releases to kind of make this happen? >> Yeah so obviously on the hardware front, there's been a lot of evolutions in storage mediums. So with the introduction of NVME, persistent memory technologies like 3D XPoint, that's meant storage media has become a lot faster. Now to allow you to full take advantage of that, that's where we've had to do a lot of optimizations within the storage stack. So with AHV, we have what we call AHV turbo mode which allows you to full take advantage of those faster storage mediums at that much lower latency. And then obviously on the networking front, technologies such as RDMA can be leveraged to optimize that network stack. >> Got it. So that was Oracle RAC running on a you know Nutanix cluster. It used to be a big deal a couple of years ago. Now we've got many customers doing that. On the same environment though, we're going to show you is the advent of actually putting file services in the same scale out environment. And you know many of you in the audience probably know about AFS. We released it about 12 to 14 months ago. It's been one of our most popular new products of all time within Nutanix's history. And we had SMB support was for user file shares, VDI deployments, and it took awhile to bake, to get to scale and reliability. And then in the last release, in the recent release that we just shipped, we now added NFS for support so that we can no go after the full scale file server consolidation. So let's take a look at some of that stuff. >> Yep, let's do it. So hopping back over to Prism, we can see our four cluster here. Overall cluster-wide latency right around 360 microseconds. Now we'll hop down to our file server section. So here we can see we have our Next A File Server hosting right about 16.2 million files. Now if you look at our shares and exports, we can see we have a mix of different shares. So one of the shares that you see there is home directories. This is an SMB share which is actually mapped and being leveraged by our VDI desktops for home folders, user profiles, things of that nature. We can also see this Oracle backup share here which is exposed to our rack host via NFS. So RMAN is actually leveraging this to provide native database backups. >> Got it. So Oracle VMs, backup using files, or for any other file share requirements with AFS. Do we have the cluster also showing, I know, so I saw some Kubernetes as well on it. Let's talk about what we're thinking of doing there. >> Yep, let's do it. So if we think about cloud, cloud's obviously a big buzz word, so is containers in Kubernetes. So with ACS 1.0 what we did is we introduced native support for Docker integration. >> And pause there. And we screwed up. (laughing) So just like the market took a left turn on Kubernetes, obviously we realized that, and now we're working on ACS 2.0 which is what we're going to talk about, right? >> Exactly. So with ACS 2.0, we've introduced native Kubernetes support. Now when I think about Kubernetes, there's really two core areas that come to mind. The first one is around native integration. So with that, we have our Kubernetes volume integration, we're obviously doing a lot of work on the networking front, and we'll continue to push there from an integration point of view. Now the other piece is around the actual deployment of Kubernetes. When we think about a lot of Nutanix administrators or IT admins, they may have never deployed Kubernetes before, so this could be a very daunting task. And true to the Nutanix nature, we not only want to make our platform simple and intuitive, we also want to do this for any ecosystem products. So with ACS 2.0, we've simplified the full Kubernetes deployment and switching over to our ACS two interface, we can see this create cluster button. Now this actually pops up a full wizard. This wizard will actually walk you through the full deployment process, gather the necessary inputs for you, and in a matter of a few clicks and a few minutes, we have a full Kubernetes deployment fully provisioned, the masters, the workers, all the networking fully done for you, very simple and intuitive. Now if we hop back over to Prism, we can see we have this ACS2 Kubernetes category. Clicking on that, we can see we have eight instances of virtual machines. And here are Kubernetes virtual machines which have actually been deployed as part of this ACS2 installer. Now one of the nice things is it makes the IT administrator's job very simple and easy to do. The deployment straightforward monitoring and management very straightforward and simple. Now for the developer, the application architect, or engineers, they interface and interact with Kubernetes just like they would traditionally on any platform. >> Got it. So the goal of ACS is to ensure that the developer ecosystem still uses whatever tools that they are you know preferring while at that same time allowing this consolidation of containers along with VMs all on that same, single runtime, right? So that's ACS. And then if you think about where the OS is going, there's still some open space at the end. And open space has always been look if you just look at a public cloud, you look at blocks, files, containers, the most obvious sort of storage function that's left is objects. And that's the last horizon for us in completing the storage stack. And we're going to show you for the first time a preview of an upcoming product called the Acropolis Object Storage Services Stack. So let's talk a little bit about it and then maybe show the demo. >> Yeah, so just like we provided file services with AFS, block services with ABS, with OSS or Object Storage Services, we provide native object storage, compatibility and capability within the Nutanix platform. Now this provides a very simply common S3 API. So any integrations you've done with S3 especially Kubernetes, you can actually leverage that out of the box when you've deployed this. Now if we hop back over to Prism, I'll go here to my object stores menu. And here we can see we have two existing object storage instances which are running. So you can deploy however many of these as you wanted to. Now just like the Kubernetes deployment, deploying a new object instance is very simple and easy to do. So here I'll actually name this instance Thor's Hammer. >> You do know he loses it, right? He hasn't seen the movies yet. >> Yeah, I don't want any spoilers yet. So once we specified the name, we can choose our capacity. So here we'll just specify a large instance or type. Obviously this could be any amount or storage. So if you have a 200 node Nutanix cluster with petabytes worth of data, you could do that as well. Once we've selected that, we'll select our expected performance. And this is going to be the number of concurrent gets and puts. So essentially how many operations per second we want this instance to be able to facilitate. Once we've done that, the platform will actually automatically determine how many virtual machines it needs to deploy as well as the resources and specs for those. And once we've done that, we'll go ahead and click save. Now here we can see it's actually going through doing the deployment of the virtual machines, applying any necessary configuration, and in the matter of a few clicks and a few seconds, we actually have this Thor's Hammer object storage instance which is up and running. Now if we hop over to one of our existing object storage instances, we can see this has three buckets. So one for Kafka-queue, I'm actually using this for my Kafka cluster where I have right around 62 million objects all storing ProtoBus. The second one there is Spark. So I actually have a Spark cluster running on our Kubernetes deployed instance via ACS 2.0. Now this is doing analytics on top of this data using S3 as a storage backend. Now for these objects, we support native versioning, native object encryption as well as worm compliancy. So if you want to have expiry periods, retention intervals, that sort of thing, we can do all that. >> Got it. So essentially what we've just shown you is with upcoming objects as well that the same OS can now support VMs, files, objects, containers, all on the same one click operational fabric. And so that's in some way the real power of Nutanix is to still keep that consistency, scalability in place as we're covering each and every workload inside the enterprise. So before Steve gets off stage though, I wanted to talk to you guys a little bit about something that you know how many of you been to our Nutanix headquarters in San Jose, California? A few. I know there's like, I don't know, 4,000 or 5,000 people here. If you do come to the office, you know when you land in San Jose Airport on the way to longterm parking, you'll pass our office. It's that close. And if you come to the fourth floor, you know one of the cubes that's where I sit. In the cube beside me is Steve. Steve sits in the cube beside me. And when I first joined the company, three or four years ago, and Steve's if you go to his cube, it no longer looks like this, but it used to have a lot of this stuff. It was like big containers of this. I remember the first time. Since I started joking about it, he started reducing it. And then Steve eventually got married much to our surprise. (audience laughing) Much to his wife's surprise. And then he also had a baby as a bigger surprise. And if you come over to our office, and we welcome you, and you come to the fourth floor, find my cube or you'll find Steve's Cube, it now looks like this. Okay, so thanks a lot, my man. >> Cool, thank you. >> Thanks so much. (audience clapping) >> So single OS, any workload. And like Steve who's been with us for awhile, it's my great pleasure to invite one of our favorite customers, CSC Karen who's also been with us for three to four years. And I'll share some fond memories about how she's been with the company for awhile, how as partners we've really done a lot together. So without any further ado, let me bring up Karen. Come on up, Karen. (rock music) >> Thank you for having me. >> Yeah, thank you. So I remember, so how many of you guys were with Nutanix first .Next in Miami? I know there was a question like that asked last time. Not too many. You missed it. We wished we could go back to that. We wouldn't fit 3/4s of this crowd. But Karen was our first customer in the keynote in 2015. And we had just talked about that story at that time where you're just become a customer. Do you want to give us some recap of that? >> Sure. So when we made the decision to move to hyperconverged infrastructure and chose Nutanix as our partner, we rapidly started to deploy. And what I mean by that is Sunil and some of the Nutanix executives had come out to visit with us and talk about their product on a Tuesday. And on a Wednesday after making the decision, I picked up the phone and said you know what I've got to deploy for my VDI cluster. So four nodes showed up on Thursday. And from the time it was plugged in to moving over 300 VDIs and 50 terabytes of storage and turning it over for the business for use was less than three days. So it was really excellent testament to how simple it is to start, and deploy, and utilize the Nutanix infrastructure. Now part of that was the delight that we experienced from our customers after that deployment. So we got phone calls where people were saying this report it used to take so long that I'd got out and get a cup of coffee and come back, and read an article, and do some email, and then finally it would finish. Those reports are running in milliseconds now. It's one click. It's very, very simple, and we've delighted our customers. Now across that journey, we have gone from the simple workloads like VDIs to the much more complex workloads around Splunk and Hadoop. And what's really interesting about our Splunk deployment is we're handling over a billion events being logged everyday. And the deployment is smaller than what we had with a three tiered infrastructure. So when you hear people talk about waste and getting that out and getting to an invisible environment where you're just able to run it, that's what we were able to achieve both with everything that we're running from our public facing websites to the back office operations that we're using which include Splunk and even most recently our Cloudera and Hadoop infrastructure. What it does is it's got 30 crawlers that go out on the internet and start bringing data back. So it comes back with over two terabytes of data everyday. And then that environment, ingests that data, does work against it, and responds to the business. And that again is something that's smaller than what we had on traditional infrastructure, and it's faster and more stable. >> Got it. And it covers a lot of use cases as well. You want to speak a few words on that? >> So the use cases, we're 90%, 95% deployed on Nutanix, and we're covering all of our use cases. So whether that's a customer facing app or a back office application. And what are business is doing is it's handling large portfolios of data for fortune 500 companies and law firms. And these applications are all running with improved stability, reliability, and performance on the Nutanix infrastructure. >> And the plan going forward? >> So the plan going forward, you actually asked me that in Miami, and it's go global. So when we started in Miami and that first deployment, we had four nodes. We now have 283 nodes around the world, and we started with about 50 terabytes of data. We've now got 3.8 petabytes of data. And we're deployed across four data centers and six remote offices. And people ask me often what is the value that we achieved? So simplification. It's all just easier, and it's all less expensive. Being able to scale with the business. So our Cloudera environment ended up with one day where it spiked to 1,000 times more load, 1,000 times, and it just responded. We had rally cries around improved productivity by six times. So 600% improved productivity, and we were able to actually achieve that. The numbers you just saw on the slide that was very, very fast was we calculated a 40% reduction in total cost of ownership. We've exceeded that. And when we talk about waste, that other number on the board there is when I saved the company one hour of maintenance activity or unplanned downtime in a month which we're now able to do the majority of our maintenance activities without disrupting any of our business solutions, I'm saving $750,000 each time I save that one hour. >> Wow. All right, Karen from CSE. Thank you so much. That was great. Thank you. I mean you know some of these data points frankly as I started talking to Karen as well as some other customers are pretty amazing in terms of the genuine value beyond financial value. Kind of like the emotional sort of benefits that good products deliver to some of our customers. And I think that's one of the core things that we take back into engineering is to keep ourselves honest on either velocity or quality even hiring people and so forth. Is to actually the more we touch customers lives, the more we touch our partner's lives, the more it allows us to ensure that we can put ourselves in their shoes to kind of make sure that we're doing the right thing in terms of the product. So that was the first part, invisible infrastructure. And our goal, as we've always talked about, our true North is to make sure that this single OS can be an exact replica, a truly modern, thoughtful but original design that brings the power of public cloud this AWS or GCP like architectures into your mainstream enterprises. And so when we take that to the next level which is about expanding the scope to go beyond invisible infrastructure to invisible data centers, it starts with a few things. Obviously, it starts with virtualization and a level of intelligent management, extends to automation, and then as we'll talk about, we have to embark on encompassing the network. And that's what we'll talk about with Flow. But to start this, let me again go back to one of our core products which is the bedrock of our you know opinionated design inside this company which is Prism and Acropolis. And Prism provides, I mentioned, comes with a ton of machine-learning based intelligence built into the product in 5.6 we've done a ton of work. In fact, a lot of features are coming out now because now that PC, Prism Central that you know has been decoupled from our mainstream release strain and will continue to release on its own cadence. And the same thing when you actually flip it to AHV on its own train. Now AHV, two years ago it was all about can I use AHV for VDI? Can I use AHV for ROBO? Now I'm pretty clear about where you cannot use AHV. If you need memory overcome it, stay with VMware or something. If you need, you know Metro, stay with another technology, else it's game on, right? And if you really look at the adoption of AHV in the mainstream enterprise, the customers now speak for themselves. These are all examples of large global enterprises with multimillion dollar ELAs in play that have now been switched over. Like I'll give you a simple example here, and there's lots of these that I'm sure many of you who are in the audience that are in this camp, but when you look at the breakout sessions in the pods, you'll get a sense of this. But I'll give you one simple example. If you look at the online payment company. I'm pretty sure everybody's used this at one time or the other. They had the world's largest private cloud on open stack, 21,000 nodes. And they were actually public about it three or four years ago. And in the last year and a half, they put us through a rigorous VOC testing scale, hardening, and it's a full blown AHV only stack. And they've started cutting over. Obviously they're not there yet completely, but they're now literally in hundreds of nodes of deployment of Nutanix with AHV as their primary operating system. So it is primetime from a deployment perspective. And with that as the base, no cloud is complete without actually having self-service provisioning that truly drives one-click automation, and can you do that in this consumer grade design? And Calm was acquired, as you guys know, in 2016. We had a choice of taking Calm. It was reasonably feature complete. It supported multiple clouds. It supported ESX, it supported Brownfield, It supported AHV. I mean they'd already done the integration with Nutanix even before the acquisition. And we had a choice. The choice was go down the path of dynamic ops or some other products where you took it for revenue or for acceleration, you plopped it into the ecosystem and sold it at this power sucking alien on top of our stack, right? Or we took a step back, re-engineered the product, kept some of the core essence like the workflow engine which was good, the automation, the object model and all, but refactored it to make it look like a natural extension of our operating system. And that's what we did with Calm. And we just launched it in December, and it's been one of our most popular new products now that's flying off the shelves. If you saw the number of registrants, I got a notification of this for the breakout sessions, the number one session that has been preregistered with over 500 people, the first two sessions are around Calm. And justifiably so because it just as it lives up to its promise, and it'll take its time to kind of get to all the bells and whistles, all the capabilities that have come through with AHV or Acropolis in the past. But the feature functionality, the product market fit associated with Calm is dead on from what the feedback that we can receive. And so Calm itself is on its own rapid cadence. We had AWS and AHV in the first release. Three or four months later, we now added ESX support. We added GCP support and a whole bunch of other capabilities, and I think the essence of Calm is if you can combine Calm and along with private cloud automation but also extend it to multi-cloud automation, it really sets Nutanix on its first genuine path towards multi-cloud. But then, as I said, if you really fixate on a software defined data center message, we're not complete as a full blown AWS or GCP like IA stack until we do the last horizon of networking. And you probably heard me say this before. You heard Dheeraj and others talk about it before is our problem in networking isn't the same in storage. Because the data plane in networking works. Good L2 switches from Cisco, Arista, and so forth, but the real problem networking is in the control plane. When something goes wrong at a VM level in Nutanix, you're able to identify whether it's a storage problem or a compute problem, but we don't know whether it's a VLAN that's mis-configured, or there've been some packets dropped at the top of the rack. Well that all ends now with Flow. And with Flow, essentially what we've now done is take the work that we've been working on to create built-in visibility, put some network automation so that you can actually provision VLANs when you provision VMs. And then augment it with micro segmentation policies all built in this easy to use, consume fashion. But we didn't stop there because we've been talking about Flow, at least the capabilities, over the last year. We spent significant resources building it. But we realized that we needed an additional thing to augment its value because the world of applications especially discovering application topologies is a heady problem. And if we didn't address that, we wouldn't be fulfilling on this ambition of providing one-click network segmentation. And so that's where Netsil comes in. Netsil might seem on the surface yet another next generation application performance management tool. But the innovations that came from Netsil started off at the research project at the University of Pennsylvania. And in fact, most of the team right now that's at Nutanix is from the U Penn research group. And they took a really original, fresh look at how do you sit in a network in a scale out fashion but still reverse engineer the packets, the flow through you, and then recreate this application topology. And recreate this not just on Nutanix, but do it seamlessly across multiple clouds. And to talk about the power of Flow augmented with Netsil, let's bring Rajiv back on stage, Rajiv. >> How you doing? >> Okay so we're going to start with some Netsil stuff, right? >> Yeah, let's talk about Netsil and some of the amazing capabilities this acquisition's bringing to Nutanix. First of all as you mentioned, Netsil's completely non invasive. So it installs on the network, it does all its magic from there. There're no host agents, non of the complexity and compatibility issues that entails. It's also monitoring the network at layer seven. So it's actually doing a deep packet inspection on all your application data, and can give you insights into services and APIs which is very important for modern applications and the way they behave. To do all this of course performance is key. So Netsil's built around a completely distributed architecture scaled to really large workloads. Very exciting technology. We're going to use it in many different ways at Nutanix. And to give you a flavor of that, let me show you how we're thinking of integrating Flow and Nestil together, so micro segmentation and Netsil. So to do that, we install Netsil in one of our Google accounts. And that's what's up here now. It went out there. It discovered all the VMs we're running on that account. It created a map essentially of all their interactions, and you can see it's like a Google Maps view. I can zoom into it. I can look at various things running. I can see lots of HTTP servers over here, some databases. >> Sunil: And it also has stats, right? You can go, it actually-- >> It does. We can take a look at that for a second. There are some stats you can look at right away here. Things like transactions per second and latencies and so on. But if I wanted to micro segment this application, it's not really clear how to do so. There's no real pattern over here. Taking the Google Maps analogy a little further, this kind of looks like the backstreets of Cairo or something. So let's do this step by step. Let me first filter down to one application. Right now I'm looking at about three or four different applications. And Netsil integrates with the metadata. So this is that the clouds provide. So I can search all the tags that I have. So by doing that, I can zoom in on just the financial application. And when I do this, the view gets a little bit simpler, but there's still no real pattern. It's not clear how to micro segment this, right? And this is where the power of Netsil comes in. This is a fairly naive view. This is what tool operating at layer four just looking at ports and TCP traffic would give you. But by doing deep packet inspection, Netsil can get into the services layer. So instead of grouping these interactions by hostname, let's group them by service. So you go service tier. And now you can see this is a much simpler picture. Now I have some patterns. I have a couple of load balancers, an HA proxy and an Nginx. I have a web application front end. I have some application servers running authentication services, search services, et cetera, a database, and a database replica. I could go ahead and micro segment at this point. It's quite possible to do it at this point. But this is almost too granular a view. We actually don't usually want to micro segment at individual service level. You think more in terms of application tiers, the tiers that different services belong to. So let me go ahead and group this differently. Let me group this by app tier. And when I do that, a really simple picture emerges. I have a load balancing tier talking to a web application front end tier, an API tier, and a database tier. Four tiers in my application. And this is something I can work with. This is something that I can micro segment fairly easily. So let's switch over to-- >> Before we dot that though, do you guys see how he gave himself the pseudonym called Dom Toretto? >> Focus Sunil, focus. >> Yeah, for those guys, you know that's not the Avengers theme, man, that's the Fast and Furious theme. >> Rajiv: I think a year ahead. This is next years theme. >> Got it, okay. So before we cut over from Netsil to Flow, do we want to talk a few words about the power of Flow, and what's available in 5.6? >> Sure so Flow's been around since the 5.6 release. Actually some of the functionality came in before that. So it's got invisibility into the network. It helps you debug problems with WLANs and so on. We had a lot of orchestration with other third party vendors with load balancers, with switches to make publishing much simpler. And then of course with our most recent release, we GA'ed our micro segmentation capabilities. And that of course is the most important feature we have in Flow right now. And if you look at how Flow policy is set up, it looks very similar to what we just saw with Netsil. So we have load blancer talking to a web app, API, database. It's almost identical to what we saw just a moment ago. So while this policy was created manually, it is something that we can automate. And it is something that we will do in future releases. Right now, it's of course not been integrated at that level yet. So this was created manually. So one thing you'll notice over here is that the database tier doesn't get any direct traffic from the internet. All internet traffic goes to the load balancer, only specific services then talk to the database. So this policy right now is in monitoring mode. It's not actually being enforced. So let's see what happens if I try to attack the database, I start a hack against the database. And I have my trusty brute force password script over here. It's trying the most common passwords against the database. And if I happen to choose a dictionary word or left the default passwords on, eventually it will log into the database. And when I go back over here in Flow what happens is it actually detects there's now an ongoing a flow, a flow that's outside of policy that's shown up. And it shows this in yellow. So right alongside the policy, I can visualize all the noncompliant flows. This makes it really easy for me now to make decisions, does this flow should it be part of the policy, should it not? In this particular case, obviously it should not be part of the policy. So let me just switch from monitoring mode to enforcement mode. I'll apply the policy, give it a second to propagate. The flow goes away. And if I go back to my script, you can see now the socket's timing out. I can no longer connect to the database. >> Sunil: Got it. So that's like one click segmentation and play right now? >> Absolutely. It's really, really simple. You can compare it to other products in the space. You can't get simpler than this. >> Got it. Why don't we got back and talk a little bit more about, so that's Flow. It's shipping now in 5.6 obviously. It'll come integrated with Netsil functionality as well as a variety of other enhancements in that next few releases. But Netsil does more than just simple topology discovery, right? >> Absolutely. So Netsil's actually gathering a lot of metrics from your network, from your host, all this goes through a data pipeline. It gets processed over there and then gets captured in a time series database. And then we can slice and dice that in various different ways. It can be used for all kinds of insights. So let's see how our application's behaving. So let me say I want to go into the API layer over here. And I instantly get a variety of metrics on how the application's behaving. I get the most requested endpoints. I get the average latency. It looks reasonably good. I get the average latency of the slowest endpoints. If I was having a performance problem, I would know exactly where to go focus on. Right now, things look very good, so we won't focus on that. But scrolling back up, I notice that we have a fairly high error rate happening. We have like 11.35% of our HTTP requests are generating errors, and that deserves some attention. And if I scroll down again, and I see the top five status codes I'm getting, almost 10% of my requests are generating 500 errors, HTTP 500 errors which are internal server errors. So there's something going on that's wrong with this application. So let's dig a little bit deeper into that. Let me go into my analytics workbench over here. And what I've plotted over here is how my HTTP requests are behaving over time. Let me filter down to just the 500 ones. That will make it easier. And I want the 500s. And I'll also group this by the service tier so that I can see which services are causing the problem. And the better view for this would be a bar graph. Yes, so once I do this, you can see that all the errors, all the 500 errors that we're seeing have been caused by the authentication service. So something's obviously wrong with that part of my application. I can go look at whether Active Directory is misbehaving and so on. So very quickly from a broad problem that I was getting a high HTTP error rate. In fact, usually you will discover there's this customer complaining about a lot of errors happening in your application. You can quickly narrow down to exactly what the cause was. >> Got it. This is what we mean by hyperconvergence of the network which is if you can truly isolate network related problems and associate them with the rest of the hyperconvergence infrastructure, then we've essentially started making real progress towards the next level of hyperconvergence. Anyway, thanks a lot, man. Great job. >> Thanks, man. (audience clapping) >> So to talk about this evolution from invisible infrastructure to invisible data centers is another customer of ours that has embarked on this journey. And you know it's not just using Nutanix but a variety of other tools to actually fulfill sort of like the ambition of a full blown cloud stack within a financial organization. And to talk more about that, let me call Vijay onstage. Come on up, Vijay. (rock music) >> Hey. >> Thank you, sir. So Vijay looks way better in real life than in a picture by the way. >> Except a little bit of gray. >> Unlike me. So tell me a little bit about this cloud initiative. >> Yeah. So we've won the best cloud initiative twice now hosted by Incisive media a large magazine. It's basically they host a bunch of you know various buy side, sell side, and you can submit projects in various categories. So we've won the best cloud twice now, 2015 and 2017. The 2017 award is when you know as part of our private cloud journey we were laying the foundation for our private cloud which is 100% based on hyperconverged infrastructure. So that was that award. And then 2017, we've kind of built on that foundation and built more developer-centric next gen app services like PAS, CAS, SDN, SDS, CICD, et cetera. So we've built a lot of those services on, and the second award was really related to that. >> Got it. And a lot of this was obviously based on an infrastructure strategy with some guiding principles that you guys had about three or four years ago if I remember. >> Yeah, this is a great slide. I use it very often. At the core of our infrastructure strategy is how do we run IT as a business? I talk about this with my teams, they were very familiar with this. That's the mindset that I instill within the teams. The mission, the challenge is the same which is how do we scale infrastructure while reducing total cost of ownership, improving time to market, improving client experience and while we're doing that not lose sight of reliability, stability, and security? That's the mission. Those are some of our guiding principles. Whenever we take on some large technology investments, we take 'em through those lenses. Obviously Nutanix went through those lenses when we invested in you guys many, many years ago. And you guys checked all the boxes. And you know initiatives change year on year, the mission remains the same. And more recently, the last few years, we've been focused on converged platforms, converged teams. We've actually reorganized our teams and aligned them closer to the platforms moving closer to an SRE like concept. >> And then you've built out a full stack now across computer storage, networking, all the way with various use cases in play? >> Yeah, and we're aggressively moving towards PAS, CAS as our method of either developing brand new cloud native applications or even containerizing existing applications. So the stack you know obviously built on Nutanix, SDS for software fine storage, compute and networking we've got SDN turned on. We've got, again, PAS and CAS built on this platform. And then finally, we've hooked our CICD tooling onto this. And again, the big picture was always frictionless infrastructure which we're very close to now. You know 100% of our code deployments into this environment are automated. >> Got it. And so what's the net, net in terms of obviously the business takeaway here? >> Yeah so at Northern we don't do tech for tech. It has to be some business benefits, client benefits. There has to be some outcomes that we measure ourselves against, and these are some great metrics or great ways to look at if we're getting the outcomes from the investments we're making. So for example, infrastructure scale while reducing total cost of ownership. We're very focused on total cost of ownership. We, for example, there was a build team that was very focus on building servers, deploying applications. That team's gone down from I think 40, 45 people to about 15 people as one example, one metric. Another metric for reducing TCO is we've been able to absorb additional capacity without increasing operating expenses. So you're actually building capacity in scale within your operating model. So that's another example. Another example, right here you see on the screen. Faster time to market. We've got various types of applications at any given point that we're deploying. There's a next gen cloud native which go directly on PAS. But then a majority of the applications still need the traditional IS components. The time to market to deploy a complex multi environment, multi data center application, we've taken that down by 60%. So we can deliver server same day, but we can deliver entire environments, you know add it to backup, add it to DNS, and fully compliant within a couple of weeks which is you know something we measure very closely. >> Great job, man. I mean that's a compelling I think results. And in the journey obviously you got promoted a few times. >> Yep. >> All right, congratulations again. >> Thank you. >> Thanks Vijay. >> Hey Vijay, come back here. Actually we forgot our joke. So razzled by his data points there. So you're supposed to wear some shoes, right? >> I know my inner glitch. I was going to wear those sneakers, but I forgot them at the office maybe for the right reasons. But the story behind those florescent sneakers, I see they're focused on my shoes. But I picked those up two years ago at a Next event, and not my style. I took 'em to my office. They've been sitting in my office for the last couple years. >> Who's received shoes like these by the way? I'm sure you guys have received shoes like these. There's some real fans there. >> So again, I'm sure many of you liked them. I had 'em in my office. I've offered it to so many of my engineers. Are you size 11? Do you want these? And they're unclaimed? >> So that's the only feature of Nutanix that you-- >> That's the only thing that hasn't worked, other than that things are going extremely well. >> Good job, man. Thanks a lot. >> Thanks. >> Thanks Vijay. So as we get to the final phase which is obviously as we embark on this multi-cloud journey and the complexity that comes with it which Dheeraj hinted towards in his session. You know we have to take a cautious, thoughtful approach here because we don't want to over set expectations because this will take us five, 10 years to really do a good job like we've done in the first act. And the good news is that the market is also really, really early here. It's just a fact. And so we've taken a tiered approach to it as we'll start the discussion with multi-cloud operations, and we've talked about the stack in the prior session which is about look across new clouds. So it's no longer Nutanix, Dell, Lenova, HP, Cisco as the new quote, unquote platforms. It's Nutanix, Xi, GCP, AWS, Azure as the new platforms. That's how we're designing the fabric going forward. On top of that, you obviously have the hybrid OS both on the data plane side and control plane side. Then what you're seeing with the advent of Calm doing a marketplace and automation as well as Beam doing governance and compliance is the fact that you'll see more and more such capabilities of multi-cloud operations burnt into the platform. And example of that is Calm with the new 5.7 release that they had. Launch supports multiple clouds both inside and outside, but the fundamental premise of Calm in the multi-cloud use case is to enable you to choose the right cloud for the right workload. That's the automation part. On the governance part, and this we kind of went through in the last half an hour with Dheeraj and Vijay on stage is something that's even more, if I can call it, you know first order because you get the provisioning and operations second. The first order is to say look whatever my developers have consumed off public cloud, I just need to first get our arm around to make sure that you know what am I spending, am I secure, and then when I get comfortable, then I am able to actually expand on it. And that's the power of Beam. And both Beam and Calm will be the yin and yang for us in our multi-cloud portfolio. And we'll have new products to complement that down the road, right? But along the way, that's the whole private cloud, public cloud. They're the two ends of the barbell, and over time, and we've been working on Xi for awhile, is this conviction that we've built talking to many customers that there needs to be another type of cloud. And this type of a cloud has to feel like a public cloud. It has to be architected like a public cloud, be consumed like a public cloud, but it needs to be an extension of my data center. It should not require any changes to my tooling. It should not require and changes to my operational infrastructure, and it should not require lift and shift, and that's a super hard problem. And this problem is something that a chunk of our R and D team has been burning the midnight wick on for the last year and a half. Because look this is not about taking our current OS which does a good job of scaling and plopping it into a Equinix or a third party data center and calling it a hybrid cloud. This is about rebuilding things in the OS so that we can deliver a true hybrid cloud, but at the same time, give those functionality back on premises so that even if you don't have a hybrid cloud, if you just have your own data centers, you'll still need new services like DR. And if you think about it, what are we doing? We're building a full blown multi-tenant virtual network designed in a modern way. Think about this SDN 2.0 because we have 10 years worth of looking backwards on how GCP has done it, or how Amazon has done it, and now sort of embodying some of that so that we can actually give it as part of this cloud, but do it in a way that's a seamless extension of the data center, and then at the same time, provide new services that have never been delivered before. Everyone obviously does failover and failback in DR it just takes months to do it. Our goal is to do it in hours or minutes. But even things such as test. Imagine doing a DR test on demand for you business needs in the middle of the day. And that's the real bar that we've set for Xi that we are working towards in early access later this summer with GA later in the year. And to talk more about this, let me invite some of our core architects working on it, Melina and Rajiv. (rock music) Good to see you guys. >> You're messing up the names again. >> Oh Rajiv, Vinny, same thing, man. >> You need to back up your memory from Xi. >> Yeah, we should. Okay, so what are we going to talk about, Vinny? >> Yeah, exactly. So today we're going to talk about how Xi is pushing the envelope and beyond the state of the art as you were saying in the industry. As part of that, there's a whole bunch of things that we have done starting with taking a private cloud, seamlessly extending it to the public cloud, and then creating a hybrid cloud experience with one-click delight. We're going to show that. We've done a whole bunch of engineering work on making sure the operations and the tooling is identical on both sides. When you graduate from a private cloud to a hybrid cloud environment, you don't want the environments to be different. So we've copied the environment for you with zero manual intervention. And finally, building on top of that, we are delivering DR as a service with unprecedented simplicity with one-click failover, one-click failback. We're going to show you one click test today. So Melina, why don't we start with showing how you go from a private cloud, seamlessly extend it to consume Xi. >> Sounds good, thanks Vinny. Right now, you're looking at my Prism interface for my on premises cluster. In one-click, I'm going to be able to extend that to my Xi cloud services account. I'm doing this using my my Nutanix credential and a password manager. >> Vinny: So here as you notice all the Nutanix customers we have today, we have created an account for them in Xi by default. So you don't have to log in somewhere and create an account. It's there by default. >> Melina: And just like that we've gone ahead and extended my data center. But let's go take a look at the Xi side and log in again with my my Nutanix credentials. We'll see what we have over here. We're going to be able to see two availability zones, one for on premises and one for Xi right here. >> Vinny: Yeah as you see, using a log in account that you already knew mynutanix.com and 30 seconds in, you can see that you have a hybrid cloud view already. You have a private cloud availability zone that's your own Prism central data center view, and then a Xi availability zone. >> Sunil: Got it. >> Melina: Exactly. But of course we want to extend my network connection from on premises to my Xi networks as well. So let's take a look at our options there. We have two ways of doing this. Both are one-click experience. With direct connect, you can create a dedicated network connection between both environments, or VPN you can use a public internet and a VPN service. Let's go ahead and enable VPN in this environment. Here we have two options for how we want to enable our VPN. We can bring our own VPN and connect it, or we will deploy a VPN for you on premises. We'll do the option where we deploy the VPN in one-click. >> And this is another small sign or feature that we're building net new as part of Xi, but will be burned into our core Acropolis OS so that we can also be delivering this as a stand alone product for on premises deployment as well, right? So that's one of the other things to note as you guys look at the Xi functionality. The goal is to keep the OS capabilities the same on both sides. So even if I'm building a quote, unquote multi data center cloud, but it's just a private cloud, you'll still get all the benefits of Xi but in house. >> Exactly. And on this second step of the wizard, there's a few inputs around how you want the gateway configured, your VLAN information and routing and protocol configuration details. Let's go ahead and save it. >> Vinny: So right now, you know what's happening is we're taking the private network that our customers have on premises and extending it to a multi-tenant public cloud such that our customers can use their IP addresses, the subnets, and bring their own IP. And that is another step towards making sure the operation and tooling is kept consistent on both sides. >> Melina: Exactly. And just while you guys were talking, the VPN was successfully created on premises. And we can see the details right here. You can track details like the status of the connection, the gateway, as well as bandwidth information right in the same UI. >> Vinny: And networking is just tip of the iceberg of what we've had to work on to make sure that you get a consistent experience on both sides. So Melina, why don't we show some of the other things we've done? >> Melina: Sure, to talk about how we preserve entities from my on-premises to Xi, it's better to use my production environment. And first thing you might notice is the log in screen's a little bit different. But that's because I'm logging in using my ADFS credentials. The first thing we preserved was our users. In production, I'm running AD obviously on-prem. And now we can log in here with the same set of credentials. Let me just refresh this. >> And this is the Active Directory credential that our customers would have. They use it on-premises. And we allow the setting to be set on the Xi cloud services as well, so it's the same set of users that can access both sides. >> Got it. There's always going to be some networking problem onstage. It's meant to happen. >> There you go. >> Just launching it again here. I think it maybe timed out. This is a good sign that we're running on time with this presentation. >> Yeah, yeah, we're running ahead of time. >> Move the demos quicker, then we'll time out. So essentially when you log into Xi, you'll be able to see what are the environment capabilities that we have copied to the Xi environment. So for example, you just saw that the same user is being used to log in. But after the use logs in, you'll be able to see their images, for example, copied to the Xi side. You'll be able to see their policies and categories. You know when you define these policies on premises, you spend a lot of effort and create them. And now when you're extending to the public cloud, you don't want to do it again, right? So we've done a whole lot of syncing mechanisms making sure that the two sides are consistent. >> Got it. And on top of these policies, the next step is to also show capabilities to actually do failover and failback, but also do integrated testing as part of this compatibility. >> So one is you know just the basic job of making the environments consistent on two sides, but then it's also now talking about the data part, and that's what DR is about. So if you have a workload running on premises, we can take the data and replicate it using your policies that we've already synced. Once the data is available on the Xi side, at that point, you have to define a run book. And the run book essentially it's a recovery plan. And that says okay I already have the backups of my VMs in case of disaster. I can take my recovery plan and hit you know either failover or maybe a test. And then my application comes up. First of all, you'll talk about the boot order for your VMs to come up. You'll talk about networking mapping. Like when I'm running on-prem, you're using a particular subnet. You have an option of using the same subnet on the Xi side. >> Melina: There you go. >> What happened? >> Sunil: It's finally working.? >> Melina: Yeah. >> Vinny, you can stop talking. (audience clapping) By the way, this is logging into a live Xi data center. We have two regions West Coat, two data centers East Coast, two data centers. So everything that you're seeing is essentially coming off the mainstream Xi profile. >> Vinny: Melina, why don't we show the recovery plan. That's the most interesting piece here. >> Sure. The recovery plan is set up to help you specify how you want to recover your applications in the event of a failover or a test failover. And it specifies all sorts of details like the boot sequence for the VMs as well as network mappings. Some of the network mappings are things like the production network I have running on premises and how it maps to my production network on Xi or the test network to the test network. What's really cool here though is we're actually automatically creating your subnets on Xi from your on premises subnets. All that's part of the recovery plan. While we're on the screen, take a note of the .100 IP address. That's a floating IP address that I have set up to ensure that I'm going to be able to access my three tier web app that I have protected with this plan after a failover. So I'll be able to access it from the public internet really easily from my phone or check that it's all running. >> Right, so given how we make the environment consistent on both sides, now we're able to create a very simple DR experience including failover in one-click, failback. But we're going to show you test now. So Melina, let's talk about test because that's one of the most common operations you would do. Like some of our customers do it every month. But usually it's very hard. So let's see how the experience looks like in what we built. >> Sure. Test and failover are both one-click experiences as you know and come to expect from Nutanix. You can see it's failing over from my primary location to my recovery location. Now what we're doing right now is we're running a series of validation checks because we want to make sure that you have your network configured properly, and there's other configuration details in place for the test to be successful. Looks like the failover was initiated successfully. Now while that failover's happening though, let's make sure that I'm going to be able to access my three tier web app once it fails over. We'll do that by looking at my network policies that I've configured on my test network. Because I want to access the application from the public internet but only port 80. And if we look here under our policies, you can see I have port 80 open to permit. So that's good. And if I needed to create a new one, I could in one click. But it looks like we're good to go. Let's go back and check the status of my recovery plan. We click in, and what's really cool here is you can actually see the individual tasks as they're being completed from that initial validation test to individual VMs being powered on as part of the recovery plan. >> And to give you guys an idea behind the scenes, the entire recovery plan is actually a set of workflows that are built on Calm's automation engine. So this is an example of where we're taking some of power of workflow and automation that Clam has come to be really strong at and burning that into how we actually operationalize many of these workflows for Xi. >> And so great, while you were explaining that, my three tier web app has restarted here on Xi right in front of you. And you can see here there's a floating IP that I mentioned early that .100 IP address. But let's go ahead and launch the console and make sure the application started up correctly. >> Vinny: Yeah, so that .100 IP address is a floating IP that's a publicly visible IP. So it's listed here, 206.80.146.100. And that's essentially anybody in the audience here can go use your laptop or your cell phone and hit that and start to work. >> Yeah so by the way, just to give you guys an idea while you guys maybe use the IP to kind of hit it, is a real set of VMs that we've just failed over from Nutanix's corporate data center into our West region. >> And this is running live on the Xi cloud. >> Yeah, you guys should all go and vote. I'm a little biased towards Xi, so vote for Xi. But all of them are really good features. >> Scroll up a little bit. Let's see where Xi is. >> Oh Xi's here. I'll scroll down a little bit, but keep the... >> Vinny: Yes. >> Sunil: You guys written a block or something? >> Melina: Oh good, it looks like Xi's winning. >> Sunil: Okay, great job, Melina. Thank you so much. >> Thank you, Melina. >> Melina: Thanks. >> Thank you, great job. Cool and calm under pressure. That's good. So that was Xi. What's something that you know we've been doing around you know in addition to taking say our own extended enterprise public cloud with Xi. You know we do recognize that there are a ton of workloads that are going to be residing on AWS, GCP, Azure. And to sort of really assist in the try and call it transformation of enterprises to choose the right cloud for the right workload. If you guys remember, we actually invested in a tool over last year which became actually quite like one of those products that took off based on you know groundswell movement. Most of you guys started using it. It's essentially extract for VMs. And it was this product that's obviously free. It's a tool. But it enables customers to really save tons of time to actually migrate from legacy environments to Nutanix. So we took that same framework, obviously re-platformed it for the multi-cloud world to kind of solve the problem of migrating from AWS or GCP to Nutanix or vice versa. >> Right, so you know, Sunil as you said, moving from a private cloud to the public cloud is a lift and shift, and it's a hard you know operation. But moving back is not only expensive, it's a very hard problem. None of the cloud vendors provide change block tracking capability. And what that means is when you have to move back from the cloud, you have an extended period of downtime because there's now way of figuring out what's changing while you're moving. So you have to keep it down. So what we've done with our app mobility product is we have made sure that, one, it's extremely simple to move back. Two, that the downtime that you'll have is as small as possible. So let me show you what we've done. >> Got it. >> So here is our app mobility capability. As you can see, on the left hand side we have a source environment and target environment. So I'm calling my AWS environment Asgard. And I can add more environments. It's very simple. I can select AWS and then put in my credentials for AWS. It essentially goes and discovers all the VMs that are running and all the regions that they're running. Target environment, this is my Nutanix environment. I call it Earth. And I can add target environment similarly, IP address and credentials, and we do the rest. Right, okay. Now migration plans. I have Bifrost one as my migration plan, and this is how migration works. First you create a plan and then say start seeding. And what it does is takes a snapshot of what's running in the cloud and starts migrating it to on-prem. Once it is an on-prem and the difference between the two sides is minimal, it says I'm ready to cutover. At that time, you move it. But let me show you how you'd create a new migration plan. So let me name it, Bifrost 2. Okay so what I have to do is select a region, so US West 1, and target Earth as my cluster. This is my storage container there. And very quickly you can see these are the VMs that are running in US West 1 in AWS. I can select SQL server one and two, go to next. Right now it's looking at the target Nutanix environment and seeing it had enough space or not. Once that's good, it gives me an option. And this is the step where it enables the Nutanix service of change block tracking overlaid on top of the cloud. There are two options one is automatic where you'll give us the credentials for your VMs, and we'll inject our capability there. Or manually you could do. You could copy the command either in a windows VM or Linux VM and run it once on the VM. And change block tracking since then in enabled. Everything is seamless after that. Hit next. >> And while Vinny's setting it up, he said a few things there. I don't know if you guys caught it. One of the hardest problems in enabling seamless migration from public cloud to on-prem which makes it harder than the other way around is the fact that public cloud doesn't have things like change block tracking. You can't get delta copies. So one of the core innovations being built in this app mobility product is to provide that overlay capability across multiple clouds. >> Yeah, and the last step here was to select the target network where the VMs will come up on the Nutanix environment, and this is a summary of the migration plan. You can start it or just save it. I'm saving it because it takes time to do the seeding. I have the other plan which I'll actually show the cutover with. Okay so now this is Bifrost 1. It's ready to cutover. We started it four hours ago. And here you can see there's a SQL server 003. Okay, now I would like to show the AWS environment. As you can see, SQL server 003. This VM is actually running in AWS right now. And if you go to the Prism environment, and if my login works, right? So we can go into the virtual machine view, tables, and you see the VM is not there. Okay, so we go back to this, and we can hit cutover. So this is essentially telling our system, okay now it the time. Quiesce the VM running in AWS, take the last bit of changes that you have to the database, ship it to on-prem, and in on-prem now start you know configure the target VM and start bringing it up. So let's go and look at AWS and refresh that screen. And you should see, okay so the SQL server is now stopping. So that means it has quiesced and stopping the VM there. If you go back and look at the migration plan that we had, it says it's completed. So it has actually migrated all the data to the on-prem side. Go here on-prem, you see the production SQL server is running already. I can click launch console, and let's see. The Windows VM is already booting up. >> So essentially what Vinny just showed was a live cutover of an AWS VM to Nutanix on-premises. >> Yeah, and what we have done. (audience clapping) So essentially, this is about making two things possible, making it simple to migrate from cloud to on-prem, and making it painless so that the downtime you have is very minimal. >> Got it, great job, Vinny. I won't forget your name again. So last step. So to really talk about this, one of our favorite partners and customers has been in the cloud environment for a long time. And you know Jason who's the CTO of Cyxtera. And he'll introduce who Cyxtera is. Most of you guys are probably either using their assets or not without knowing their you know the new name. But is someone that was in the cloud before it was called cloud as one of the original founders and technologists behind Terremark, and then later as one of the chief architects of VMware's cloud. And then they started this new company about a year or so ago which I'll let Jason talk about. This journey that he's going to talk about is how a partner, slash customer is working with us to deliver net new transformations around the traditional industry of colo. Okay, to talk more about it, Jason, why don't you come up on stage, man? (rock music) Thank you, sir. All right so Cyxtera obviously a lot of people don't know the name. Maybe just give a 10 second summary of why you're so big already. >> Sure, so Cyxtera was formed, as you said, about a year ago through the acquisition of the CenturyLink data centers. >> Sunil: Which includes Savvis and a whole bunch of other assets. >> Yeah, there's a long history of those data centers, but we have all of them now as well as the software companies owned by Medina capital. So we're like the world's biggest startup now. So we have over 50 data centers around the world, about 3,500 customers, and a portfolio of security and analytics software. >> Sunil: Got it, and so you have this strategy of what we're calling revolutionizing colo deliver a cloud based-- >> Yeah so, colo hasn't really changed a lot in the last 20 years. And to be fair, a lot of what happens in data centers has to have a person physically go and do it. But there are some things that we can simplify and automate. So we want to make things more software driven, so that's what we're doing with the Cyxtera extensible data center or CXD. And to do that, we're deploying software defined networks in our facilities and developing automations so customers can go and provision data center services and the network connectivity through a portal or through REST APIs. >> Got it, and what's different now? I know there's a whole bunch of benefits with the integrated platform that one would not get in the traditional kind of on demand data center environment. >> Sure. So one of the first services we're launching on CXD is compute on demand, and it's powered by Nutanix. And we had to pick an HCI partner to launch with. And we looked at players in the space. And as you mentioned, there's actually a lot of them, more than I thought. And we had a lot of conversations, did a lot of testing in the lab, and Nutanix really stood out as the best choice. You know Nutanix has a lot of focus on things like ease of deployment. So it's very simple for us to automate deploying compute for customers. So we can use foundation APIs to go configure the servers, and then we turn those over to the customer which they can then manage through Prism. And something important to keep in mind here is that you know this isn't a manged service. This isn't infrastructure as a service. The customer has complete control over the Nutanix platform. So we're turning that over to them. It's connected to their network. They're using their IP addresses, you know their tools and processes to operate this. So it was really important for the platform we picked to have a really good self-service story for things like you know lifecycle management. So with one-click upgrade, customers have total control over patches and upgrades. They don't have to call us to do it. You know they can drive that themselves. >> Got it. Any other final words around like what do you see of the partnership going forward? >> Well you know I think this would be a great platform for Xi, so I think we should probably talk about that. >> Yeah, yeah, we should talk about that separately. Thanks a lot, Jason. >> Thanks. >> All right, man. (audience clapping) So as we look at the full journey now between obviously from invisible infrastructure to invisible clouds, you know there is one thing though to take away beyond many updates that we've had so far. And the fact is that everything that I've talked about so far is about completing a full blown true IA stack from all the way from compute to storage, to vitualization, containers to network services, and so forth. But every public cloud, a true cloud in that sense, has a full blown layer of services that's set on top either for traditional workloads or for new workloads, whether it be machine-learning, whether it be big data, you know name it, right? And in the enterprise, if you think about it, many of these services are being provisioned or provided through a bunch of our partners. Like we have partnerships with Cloudera for big data and so forth. But then based on some customer feedback and a lot of attention from what we've seen in the industry go out, just like AWS, and GCP, and Azure, it's time for Nutanix to have an opinionated view of the past stack. It's time for us to kind of move up the stack with our own offering that obviously adds value but provides some of our core competencies in data and takes it to the next level. And it's in that sense that we're actually launching Nutanix Era to simplify one of the hardest problems in enterprise IT and short of saving you from true Oracle licensing, it solves various other Oracle problems which is about truly simplifying databases much like what RDS did on AWS, imagine enterprise RDS on demand where you can provision, lifecycle manage your database with one-click. And to talk about this powerful new functionality, let me invite Bala and John on stage to give you one final demo. (rock music) Good to see you guys. >> Yep, thank you. >> All right, so we've got lots of folks here. They're all anxious to get to the next level. So this demo, really rock it. So what are we going to talk about? We're going to start with say maybe some database provisioning? Do you want to set it up? >> We have one dream, Sunil, one single dream to pass you off, that is what Nutanix is today for IT apps, we want to recreate that magic for devops and get back those weekends and freedom to DBAs. >> Got it. Let's start with, what, provisioning? >> Bala: Yep, John. >> Yeah, we're going to get in provisioning. So provisioning databases inside the enterprise is a significant undertaking that usually involves a myriad of resources and could take days. It doesn't get any easier after that for the longterm maintence with things like upgrades and environment refreshes and so on. Bala and team have been working on this challenge for quite awhile now. So we've architected Nutanix Era to cater to these enterprise use cases and make it one-click like you said. And Bala and I are so excited to finally show this to the world. We think it's actually Nutanix's best kept secrets. >> Got it, all right man, let's take a look at it. >> So we're going to be provisioning a sales database today. It's a four-step workflow. The first part is choosing our database engine. And since it's our sales database, we want it to be highly available. So we'll do a two node rack configuration. From there, it asks us where we want to land this service. We can either land it on an existing service that's already been provisioned, or if we're starting net new or for whatever reason, we can create a new service for it. The key thing here is we're not asking anybody how to do the work, we're asking what work you want done. And the other key thing here is we've architected this concept called profiles. So you tell us how much resources you need as well as what network type you want and what software revision you want. This is actually controlled by the DBAs. So DBAs, and compute administrators, and network administrators, so they can set their standards without having a DBA. >> Sunil: Got it, okay, let's take a look. >> John: So if we go to the next piece here, it's going to personalize their database. The key thing here, again, is that we're not asking you how many data files you want or anything in that regard. So we're going to be provisioning this to Nutanix's best practices. And the key thing there is just like these past services you don't have to read dozens of pages of best practice guides, it just does what's best for the platform. >> Sunil: Got it. And so these are a multitude of provisioning steps that normally one would take I guess hours if not days to provision and Oracle RAC data. >> John: Yeah, across multiple teams too. So if you think about the lifecycle especially if you have onshore and offshore resources, I mean this might even be longer than days. >> Sunil: Got it. And then there are a few steps here, and we'll lead into potentially the Time Machine construct too? >> John: Yeah, so since this is a critical database, we want data protection. So we're going to be delivering that through a feature called Time Machines. We'll leave this at the defaults for now, but the key thing to not here is we've got SLAs that deliver both continuous data protection as well as telescoping checkpoints for historical recovery. >> Sunil: Got it. So that's provisioning. We've kicked off Oracle, what, two node database and so forth? >> John: Yep, two node database. So we've got a handful of tasks that this is going to automate. We'll check back in in a few minutes. >> Got it. Why don't we talk about the other aspects then, Bala, maybe around, one of the things that, you know and I know many of you guys have seen this, is the fact that if you look at database especially Oracle but in general even SQL and so forth is the fact that look if you really simplified it to a developer, it should be as simple as I copy my production database, and I paste it to create my own dev instance. And whenever I need it, I need to obviously do it the opposite way, right? So that was the goal that we set ahead for us to actually deliver this new past service around Era for our customers. So you want to talk a little bit more about it? >> Sure Sunil. If you look at most of the data management functionality, they're pretty much like flavors of copy paste operations on database entities. But the trouble is the seemingly simple, innocuous operations of our daily lives becomes the most dreaded, complex, long running, error prone operations in data center. So we actually planned to tame this complexity and bring consumer grade simplicity to these operations, also make these clones extremely efficient without compromising the quality of service. And the best part is, the customers can enjoy these services not only for databases running on Nutanix, but also for databases running on third party systems. >> Got it. So let's take a look at this functionality of I guess snapshoting, clone and recovery that you've now built into the product. >> Right. So now if you see the core feature of this whole product is something we call Time Machine. Time Machine lets the database administrators actually capture the database tape to the granularity of seconds and also lets them create clones, refresh them to any point in time, and also recover the databases if the databases are running on the same Nutanix platform. Let's take a look at the demo with the Time Machine. So here is our customer relationship database management database which is about 2.3 terabytes. If you see, the Time Machine has been active about four months, and SLA has been set for continuously code revision of 30 days and then slowly tapers off 30 days of daily backup and weekly backups and so on, so forth. On the right hand side, you will see different colors. The green color is pretty much your continuously code revision, what we call them. That lets you to go back to any point in time to the granularity of seconds within those 30 days. And then the discreet code revision lets you go back to any snapshot of the backup that is maintained there kind of stuff. In a way, you see this Time Machine is pretty much like your modern day car with self driving ability. All you need to do is set the goals, and the Time Machine will do whatever is needed to reach up to the goal kind of stuff. >> Sunil: So why don't we quickly do a snapshot? >> Bala: Yeah, some of these times you need to create a snapshot for backup purposes, Time Machine has manual controls. All you need to do is give it a snapshot name. And then you have the ability to actually persist this snapshot data into a third party or object store so that your durability and that global data access requirements are met kind of stuff. So we kick off a snapshot operation. Let's look at what it is doing. If you see what is the snapshot operation that this is going through, there is a step called quiescing the databases. Basically, we're using application-centric APIs, and here it's actually RMAN of Oracle. We are using the RMan of Oracle to quiesce the database and performing application consistent storage snapshots with Nutanix technology. Basically we are fusing application-centric and then Nutanix platform and quiescing it. Just for a data point, if you have to use traditional technology and create a backup for this kind of size, it takes over four to six hours, whereas on Nutanix it's going to be a matter of seconds. So it almost looks like snapshot is done. This is full sensitive backup. You can pretty much use it for database restore kind of stuff. Maybe we'll do a clone demo and see how it goes. >> John: Yeah, let's go check it out. >> Bala: So for clone, again through the simplicity of command Z command, all you need to do is pick the time of your choice maybe around three o'clock in the morning today. >> John: Yeah, let's go with 3:02. >> Bala: 3:02, okay. >> John: Yeah, why not? >> Bala: You select the time, all you need to do is click on the clone. And most of the inputs that are needed for the clone process will be defaulted intelligently by us, right? And you have to make two choices that is where do you want this clone to be created with a brand new VM database server, or do you want to place that in your existing server? So we'll go with a brand new server, and then all you need to do is just give the password for you new clone database, and then clone it kind of stuff. >> Sunil: And this is an example of personalizing the database so a developer can do that. >> Bala: Right. So here is the clone kicking in. And what this is trying to do is actually it's creating a database VM and then registering the database, restoring the snapshot, and then recoding the logs up to three o'clock in the morning like what we just saw that, and then actually giving back the database to the requester kind of stuff. >> Maybe one finally thing, John. Do you want to show us the provision database that we kicked off? >> Yeah, it looks like it just finished a few seconds ago. So you can see all the tasks that we were talking about here before from creating the virtual infrastructure, and provisioning the database infrastructure, and configuring data protection. So I can go access this database now. >> Again, just to highlight this, guys. What we just showed you is an Oracle two node instance provisioned live in a few minutes on Nutanix. And this is something that even in a public cloud when you go to RDS on AWS or anything like that, you still can't provision Oracle RAC by the way, right? But that's what you've seen now, and that's what the power of Nutanix Era is. Okay, all right? >> Thank you. >> Thanks. (audience clapping) >> And one final thing around, obviously when we're building this, it's built as a past service. It's not meant just for operational benefits. And so one of the core design principles has been around being API first. You want to show that a little bit? >> Absolutely, Sunil, this whole product is built on API fist architecture. Pretty much what we have seen today and all the functionality that we've been able to show today, everything is built on Rest APIs, and you can pretty much integrate with service now architecture and give you your devops experience for your customers. We do have a plan for full fledged self-service portal eventually, and then make it as a proper service. >> Got it, great job, Bala. >> Thank you. >> Thanks, John. Good stuff, man. >> Thanks. >> All right. (audience clapping) So with Nutanix Era being this one-click provisioning, lifecycle management powered by APIs, I think what we're going to see is the fact that a lot of the products that we've talked about so far while you know I've talked about things like Calm, Flow, AHV functionality that have all been released in 5.5, 5.6, a bunch of the other stuff are also coming shortly. So I would strongly encourage you guys to kind of space 'em, you know most of these products that we've talked about, in fact, all of the products that we've talked about are going to be in the breakout sessions. We're going to go deep into them in the demos as well as in the pods. So spend some quality time not just on the stuff that's been shipping but also stuff that's coming out. And so one thing to keep in mind to sort of takeaway is that we're doing this all obviously with freedom as the goal. But from the products side, it has to be driven by choice whether the choice is based on platforms, it's based on hypervisors, whether it's based on consumption models and eventually even though we're starting with the management plane, eventually we'll go with the data plane of how do I actually provide a multi-cloud choice as well. And so when we wrap things up, and we look at the five freedoms that Ben talked about. Don't forget the sixth freedom especially after six to seven p.m. where the whole goal as a Nutanix family and extended family make sure we mix it up. Okay, thank you so much, and we'll see you around. (audience clapping) >> PA Announcer: Ladies and gentlemen, this concludes our morning keynote session. Breakouts will begin in 15 minutes. ♪ To do what I want ♪

>> Announcer: Live from San Jose. It's theCUBE, presenting Big Data Silicon Valley. Brought to you by SiliconANGLE Media, and it's ecosystem partners. (techno music) >> Welcome back to theCUBE, our continuing coverage on day one of our event, Big Data SV. I'm Lisa Martin with George Gilbert. We are down the street from the Strata Data Conference. We've got a great, a lot of cool stuff going on. You can see the cool set behind me. We are at Forager Tasting Room & Eatery. Come down and join us, be in our audience today. We have a cocktail event tonight, who doesn't want to join that? And we have a nice presentation tomorrow morning of our Wikibon's 2018 Big Data Forecast and Review. Joining us next is Matthew Baird the co-founder of AtScale. Matthew, welcome to theCUBE. >> Thanks for having me. Fantastic venue, by the way. >> Isn't it cool? >> This is very cool. >> Yeah, it is. So, talking about Big Data, you know, Gardner says, "85% of Big Data projects have failed." I often say failure is not a bad F word, because it can spawn the genesis of a lot of great business opportunities. Data lakes were big a few years ago, turned into swamps. AtScale has this vision of Data Lake 2.0, what is that? >> So, you're right. There have been a lot of failures, there's no doubt about it. And you're also right that is how we evolve, and we're a Silicon Valley based company. We don't give up when faced with these things. It's just another way to not do something. So, what we've seen and what we've learned through our customers is they need to have a solution that is integrated with all the technologies that they've adopted in the enterprise. And it's really about, if you're going to make a data lake, you're going to have data on there that is the crown jewels of your business. How are you going to get that in the hands of your constituents, so that they can analyze it, and they can use it to make decisions? And how can we, furthermore, do that in a way that supplies governance and auditability on top of it, so that we aren't just sending data out into the ether and not knowing where it goes? We have a lot of customers in the insurance, health insurance space, and with financial customers that the data absolutely must be managed. I think one of the biggest changes is around that integration with the current technologies. There's a lot of movement into the Cloud. The new data lake is kind of focused more on these large data stores, where it was HDFS with Hadoop. Now it's S3, Google's object storage, and Azure ADLS. Those are the sorts of things that are backing the new data lake I believe. >> So if we take these, where the Data Lake Store didn't have to be something that's a open source HDFS implementation, it could even be through just through a HDSF API. >> Matthew: Yeah, absolutely. >> What are some of the, how should we think about the data sources and feeds, for this repository, and then what is it on top that we need to put to make the data more consumable? >> Yeah, that's a good point. S3, Google Object Storage, and Azure, they all have a characteristic of, they are large stores. You can store as much as you want. They generally on the Clouds, and in the open source on-prem software for landing the data exists, for streaming the data and landing it, but the important thing there is it's cost-effective. S3 is a cost-effective storage system. HDFS is a mostly cost-effective storage system. You have to manage it, so it has a slightly higher cost, but the advice has been, get it to the place you're going to store it. Store it in a unified format. You get a halo effect when you have a unified format, and I think the industry is coalescing around... I'd probably say ParK's in the lead right now, but once ParK can be read by, let's take Amazon for instance, can be read by Athena, can be read by Redshift Spectrum, it can be read by their EMR, now you have this halo effect where your data's always there, always available to be consumed by a tool or a technology that can then deliver it to your end users. >> So when we talk about ParK, we're talking about columnar serialization format, >> Matthew: Yes. but there's more on top of that that needs to be layered, so that you can, as we were talking about earlier, combine the experience of a data warehouse, and the curated >> Absolutely data access where there's guard rails, >> Matthew: Yes >> and it's simple, versus sort of the wild west, but where I capture everything in a data lake. How do you bring those two together? >> Well, specifically for AtScale, we allow you to integrate multiple data access tools in AtScale, and then we use the appropriate tool to access the data for the use case. So let me give you an example, in the Amazon case, Redshift is wonderful for accessing interactive data, which BI users want, right? They want fast queries, sub-second queries. They don't want to pay to have all the raw data necessarily stored in Redshift 'cause that's pretty expensive. So they have this Redshift spectrum, it's sitting in S3, that's cost effective. So when we go and we read raw data to build these summary tables, to deliver the data fast, we can read from Spectrum, we can put it all together, drop it into Redshift, a much smaller volume of data, so it has faster characteristics for being accessed. And it delivers it to the user that way. We do that in Hadoop when we access via Hive for building aggregate tables, but Spark or Impala, is a much faster interactive engine, so we use those. As I step back and look at this, I think the Data Lake 2.0, from a technical perspective is about abstraction, and abstraction's sort of what separates us from the animals, right? It's a concept where we can pack a lot of sophistication and complexity behind an interface that allows people to just do what they want to do. You don't know how, or maybe you do know how a car engine works, I don't really, kind of, a little bit, but I do know how to press the gas pedal and steer. >> Right. >> I don't need to know these things, and I think the Data Lake 2.0 is about, well I don't need to know how Century, or Ranger, or Atlas, or any of these technologies work. I need to know that they're there, and when I access data, they're going to be applied to that data, and they're going to deliver me the stuff that I have access to and that I can see. >> So a couple things, it sounded like I was hearing abstraction, and you said really that's kind of the key, that sounds like a differentiator for AtScale, is giving customers that abstraction they need. But I'm also curious from a data value perspective, you talked about in Redshift from an expense perspective. Do you also help customers gain abstraction by helping them evaluate value of data and where they ought to keep it, and then you give them access to it? Or is that something that they need to do, kind of bring to the table? >> We don't really care, necessarily, about the source of the data, as long as it can be expressed in a way that can be accessed by whatever engine it is. Lift and shift is an example. There's a big move to move from Teradata or from Netezza into a Cloud-based offering. People want to lift it and shift it. It's the easiest way to do this. Same table definitions, but that's not optimized necessarily for the underlying data store. Take BigQuery for example, BigQuery's an amazing piece of technology. I think there's nothing like it out there in the market today, but if you really want BigQuery to be cost-effective, and perform and scale up to concurrency of... one of our customers is going to roll out about 8,000 users on this. You have to do things in BigQuery that are BigQuery-friendly. The data structures, the way that you store the data, repeated values, those sorts of things need to be taken into consideration when you build your schema out for consumption. With AtScale they don't need to think about that, they don't need to worry about it, we do it for them. They drop the schema in the same way that it exists on their current technology, and then behind the scenes, what we're doing is we're looking at signals, we're looking at queries, we're looking at all the different ways that people access the data naturally, and then we restructure those summary tables using algorithms and statistics, and I think people would broadly call it ML type approaches, to build out something that answers those questions, and adapts over time to new questions, and new use cases. So it's really about, imagine you had the best data engineering team in the world, in a box, they're never tired, they never stop, and they're always interacting with what the customers really want, which is "Now I want to look at the data this way". >> It's sounds actually like what your talking about is you have a whole set of sources, and targets, and you understand how they operate, but why I say you, I mean your software. And so that you can take data from wherever it's coming in, and then you apply, if it's machine learning or whatever other capabilities to learn from the access methods, how to optimize that data for that engine. >> Matthew: Exactly. >> And then the end users have an optimal experience and it's almost like the data migration service that Amazon has, it's like, you give us your Postgres or Oracle database, and we'll migrate it to the cloud. It sounds like you add a lot of intelligence to that process for decision support workloads. >> Yes. >> And figure out, so now you're going to... It's not Postgres to Postgres, but it might be Teradata to Redshift, or S3, that's going to be accessed by Athena or Redshift, and then let's put that in the right format. >> I think you sort of hit something that we've noticed is very powerful, which is if you can set up, and we've done this with a number of customers, if you can set up at the abstraction layer that is AtScale, on your on-prem data, literally in, say hours, you can move it into the Cloud, obviously you have to write the detail to move it into the Cloud, but once it's in the Cloud you take the same AtScale instance, you re-point it at that new data source, and it works. We've done that with multiple customers, and it's fast and effective, and it let's you actually try out things that you may not have the agility to do before because there's differences in how the SQL dialects work, there's differences in, potentially, how the schema might be built. >> So a couple things I'm interested in, I'm hearing two A-words, that abstraction that we've talked about a number of times, you also mention adaptability. So when you're talking with customers, what are some of the key business outcomes they need to drive, where adaptability and abstraction are concerned, in terms of like cost reduction, revenue generation. What are some of those see-swee business objectives that AtScale can help companies achieve? >> So looking at, say, a customer, a large retailer on the East Coast, everybody knows the stores, they're everywhere, they sell hardware. they have a 20-terabyte cube that they use for day-to-day revenue analytics. So they do period over period analysis. When they're looking at stores, they're looking at things like, we just tried out a new marketing approach... I was talking to somebody there last week about how they have these special stores where they completely redo one area and just see how that works. They have to be able to look at those analytics, and they run those for a short amount of time. So if you're window for getting data, refreshing data, building cubes, which in the old world could take a week, you know my co-founder at Yahoo, he had a week and a half build time. That data is now two weeks old, maybe three weeks old. There might be bugs in it-- >> And the relevance might be, pshh... >> And the relevance goes down, or you can't react as fast. I've been at companies where... Speed is so important these days, and the new companies that are grasping data aggressively, putting it somewhere where they can make decisions on it on a day-to-day basis, they're winning. And they're spending... I was at a company that was spending three million dollars on pay-per-click data, a month. If you can't get data everyday, you're on the wrong campaigns, and everything goes off the rails, and you only learn about it a week later, that's 25% of your spend, right there, gone. >> So the biggest thing, sorry George, it really sounds to me like what AtScale can facilitate for probably customers in any industry is the ability to truly make data-driven business decisions that can really directly affect revenue and profit. >> Yes, and in an agile format. So, you can build-- >> That's the third A; agile, adaptability, abstraction. >> There ya go, the three A's. (Lisa laughs) We had the three V's, now we have the three A's. >> Yes. >> The fact that you're building a curated model, so in retail the calendars are complex. I'm sure everybody that uses Tableau is good at analyzing data, but they might not know what your rules are around your financial calendar, or around the hierarchies of your product. There's a lot of things that happen where you want an enterprise group of data modelers to build it, bless it, and roll it out, but then you're a user, and you say, wait, you forgot x, y, and z, I don't want to wait a week, I don't want to wait two weeks, three weeks, a month, maybe more. I want that data to be available in the model an hour later 'cause that's what I get with Tableau today. And that's where we've taken the two approaches of enterprise analytics and self-service, and tried to create a scenario where you get the best of both worlds. >> So, we know that an implication of what you're telling us is that insights are perishable, and latency is becoming more and more critical. How do you plan to work with streaming data where you've got a historical archive, but you've got fresh data coming in? But fresh could mean a variety of things. Tell us what some of those scenarios look like. >> Absolutely, I think there's two approaches to this problem, and I'm seeing both used in practice, and I'm not exactly sure, although I have some theories on which one's going to win. In one case, you are streaming everything into, sort of a... like I talked about, this data lake, S3, and you're putting it in a format like ParK, and then people are accessing it. The other way is access the data where it is. Maybe it's already in, this is a common BI scenario, you have a big data store, and then you have a dimensional data store, like Oracle has your customers, Hadoop has machine data about those customers accessing on their mobile devices or something. If there was some way to access those data without having to move the Oracle stuff into the big data store, that's a Federation story that I think we've talked about in the Bay Area for a long time, or around the world for a long time. I think we're getting closer to understanding how we can do that in practice, and have it be tenable. You don't move the big data around, you move the small data around. For data coming in from outside sources it's probably a little bit more difficult, but it is kind of a degenerate version of the same story. I would say that streaming is gaining a lot of momentum, and with what we do, we're always mapping, because of the governance piece that we've built into the product, we're always mapping where did the data come from, where did it land, and how did we use it to build summary tables. So if we build five summary tables, 'cause we're answering different types of questions, we still need to know that it goes back to this piece of data, which has these security constraints, and these audit requirements, and we always track it back to that, and we always apply those to our derived data. So when you're accessing this automatically ETLed summary tables, it just works the way it is. So I think that there are two ways that this is going to expand and I'm excited about Federation because I think the time has come. I'm also excited about streaming. I think they can serve two different use cases, and I don't actually know what the answer will be, because I've seen both in customers, it's some of the biggest customers we have. >> Well Matthew thank you so much for stopping by, and four A's, AtScale can facilitate abstraction, adaptability, and agility. >> Yes. Hashtag four A's. >> There we go. I don't even want credit for that. (laughs) >> Oh wow, I'm going to get five more followers, I know it! (George laughs) >> There ya go! >> We want to thank you for watching theCUBE, I am Lisa Martin, we are live in San Jose, at our event Big Data SV, I'm with George Gilbert. Stick around, we'll be back with our next guest after a short break. (techno music)

(electronic music) >> Announcer: Live from Las Vegas, it's theCUBE, covering Interconnect 2017. Brought to you by IBM. >> Welcome back to Interconnect 2017 everybody, this is theCUBE, the leader in live tech coverage. Russ Kennedy is here. He's the Vice President of Product Strategy and Customer Success at IBM. Russ, good to see you again. >> Good to see you, Dave. >> So Russ, of course, you and I have known each other for years. >> Yes. >> From the Cleversafe. You guys came in from the Cleversafe acquisition-- >> Right. >> A phenomenal move for you guys. Great exit, awesome move for IBM. >> Yep. >> So we're now well over a year in. >> Umm-hmm. >> So the integration, you've been long past Blue Washing (laughing) you're now in, and you're integrating with other services. >> Right. >> You're embedded in the cloud, still selling on prem-- >> Right. >> Hybrid messaging, so give us the update. What's happening at Interconnect? >> Sure, well, thanks for having me on. >> Dave: You're welcome! >> It's great to see you again. And you're absolutely right. Things have been moving very rapidly since the acquisition. It's about 15 months since we've been part of IBM now. And we still have a very robust on prem business that was our heritage in the Cleversafe days, but now that we're part of IBM we're well entrenched in the cloud. We've got cloud services, object storage services in the cloud, and a variety of different flavors there. We announced a couple of new things this week that I think are very exciting for clients. I'm sure we'll get into that as we go through this discussion. And we have a hybrid combination, so if clients want to have some of their data on prem, some of their data in the cloud, we offer that hybridity as well. And I think that's very exciting for enterprises that are looking to figure out where their workloads run best, and be able to have that flexibility to move things back and forth if they need to. >> We were talking off-camera, I remember I was saying to you, Cleversafe was one of Wicky-Bon's first clients-- >> Umm-hmm. >> Back when we were tiny-- >> Umm-hmm. >> And you guys were just getting started and-- >> Right. >> I remember we were working with you guys, and sort of talking about some positioning and things like that, and I remember saying, Look, it's going to cloud! >> Russ: Right, right, right. >> It's all going there. And at the time, it was like, you guys were saying, Yeah, we think so, too, but it's just not here yet (laughing). >> Right. >> (laughing) And we're a small startup you got-- >> Yeah. >> And so, you have the conviction of belief that it's going to happen, but at the same time you have to survive-- >> Sure, sure! >> And you got investors and it's... >> Yep. >> But the growth of unstructured data and then all of a sudden the combination of that, plus cloud happened. And then boom that was a huge tailwind. >> Right. >> Talk about that. >> Right, right, no, you're exactly right. In the early days it was very, very difficult to get people to understand the value of object storage and understand the value of cloud. And we were out there pioneering discussions around this concept, but we knew that the wave was going to happen. The growth of unstructured data was already obvious. You had music services, you had video services, everything going online. People wanting to distribute information and share information, and so you knew that the wave was coming. It took a little bit longer than I think everybody thought. I think certainly success in other public cloud services like Amazon and Microsoft kind of helped drove that as well. But we were certainly there with leading technology, and as soon as people started to realize the benefits of object storage for storing large, unstructured data objects, it just took off. >> Well, you know, too, the cloud progression was really interesting. >> Umm-hmm. >> You're right. Amazon sort of popularized it. >> Yep. >> And then the downturn in 2007, 2008, caused a lot of CFOs to say, Hey, let's try this cloud thing. >> Exactly. >> And then they came out of it-- >> Russ: Exactly, yep. >> And said, Hey, this cloud thing's actually really cool. >> Russ: Umm-hmm, umm-hmm. >> Now, let's operationalize it (laughing). >> Right. >> And go mainstream. And so, and now you've got this big discussion going on around data value, right. >> Russ: Of course. >> Everybody's talking about the value of data and what it means-- >> Russ: Sure, sure. >> And moving conversations up the stack away from sort of bit slicing and-- >> Right, right (laughing). >> Object stores-- >> Yeah, exactly. >> And ups the data value. >> You're exactly right. >> What are you seeing here? >> I think that's another new interesting area that we're getting into. It's the value of information, and I think what's driven that is the tools and the technologies that are now available to analyze data in variety of formats, right. The whole analysis and analytics capability that exist in the marketplace today is giving organizations a reason to take a look at their data, and to leverage their data, and to use their data, to drive business outcomes, to be more competitive, to be more agile, to be more flexible. So they're using the information. They have tools now that can give them insight into all kinds of things, their own data, external sources of data, new data that's being generated through applications and those kinds of things. All that can come together and analysis can go on top of it, to give people really quick insights into how to drive their business. And I think that's the really exciting part about being part of IBM's cloud because IBM has all those tools. >> We've been having conversations now for... It's well over several months and going into years-- >> Umm-hmm. >> Where the CIO's not so much thinking about storage, and certainly not worried about the media. >> Right. >> But definitely talking about what services can I tap to enhance the value of my data? >> Sure. >> How do I monetize, not necessarily data itself, but how does data contribute to the monetization of my company? >> Umm-hmm. >> And you guys fit into that. >> Sure. >> So maybe talk about that a little bit-- >> Sure, well, we talked to clients all the time about the value of the data, regardless of what industry you're in, financial services, healthcare, manufacturing, all of those types of organizations have information and it's information that can help them be more productive. It can help them be more agile. It can help them win in the marketplace. All they need to do is open it up and use it, leverage it, analyze it, look at it, look at it from a variety of different sources, and it can help them do a lot of things more efficiently, so we talked to clients all the time about the value of data. Storage is certainly something that makes that value realizable, and it's the interfaces between applications and tools that make the data usable. And we open that up to clients with our storage system very easily, whether it's on prem or it's in the cloud, and that's what they like. Now, we heard David Kenny on stage the other day-- >> Umm-hmm. >> He announced IBM Cloud Object Storage Flex-- >> Yes. >> And he said, We do have a marketing department, and yes, they did come up with that name. (laughing) A funny tongue-in-cheek moment. >> Yes, yes. >> But talk about Flex. What is it? And why is it relevant? >> So a lot of clients that we've engaged with recently have talked about... They love the cloud model. They certainly love the simplicity and the ease of growth and those kinds of things that cloud gives them. But they're a little confused about the pricing and they're worrying about whether they're paying too much for the workload that they have in the cloud. So we designed Flex as a way to look at storing data. First of all, it's a very low cost entry point for storing the data. And then it's designed for data where the workload may be unpredictable. It may be cold for some period of time, and then it may become very active for a period of time, and then go back to being cold again. What Flex does is it ensures that you don't overpay when you actually utilize that data, when it's very active, very hot, maybe you're running some sort of analytics against that data. Maybe it's some sort of cognitive recognition analytics process that you're running against the data. It makes it very usable, but yet, you're not paying too much to access that data. So Flex is designed for those kinds of uneven, varied workloads, or workloads where it's very cold for some period of time and very hot. Traditional tiers are designed for hot workloads, mid-level workloads, and very cold workloads. Flex actually covers the whole gamut, and it ensures that you're not paying too much for storing and using your data. >> So that's a problem that people have because-- >> Umm-hmm. >> They don't really understand how to optimize cost-- >> Right, right. >> If they don't understand their workloads. >> Right. >> They get the cloud bill at the end of the month. They go, Whoa-- >> Yep, exactly. >> What just happened? >> Exactly. >> It's complicated for people, there's a lot of times it's different APIs for different services. >> Russ: Sure, sure. >> So talk a little bit more about how customers... How you see customers deploying that and what it's going to mean to... >> Sure. >> What's the business impact? >> Yeah, no it's a great question. So Flex, first of all, you only have to remember four numbers. There's a number to store the data, a cost to store the data, a cost to retrieve the data, a cost for what we call Class A Operations, which are write operations and then Class B, which are read operations. Four numbers you have to remember. You know that you're not going to pay over a certain amount, regardless of how often you use the data, so it's very simple for people to understand. It's one set of numbers. It doesn't matter what the workload is. You know you're not going to be overcharged for that workload. >> You set a threshold. >> Exactly, you set a cap, you set a threshold. >> Yeah. >> And you're not going to pay over that amount, so it's very simple for them to utilize. Then, so they start to use it, and let's say that over a six-month period of time they start to understand their workload, and they know it's a very active workload. They can then change that data into maybe our standard tier, and actually even save more money because it's consistent, it's predictable when it's active, they'll actually lower their cost. And we're very open with clients about that because we want to take away that complexity of using the storage, and certainly the complexity of billing, like you talked about. And give clients a very easy transition into the cloud, and make sure that they can use it and leverage it the way they need to be more productive. >> So the key to that is transparency. >> Russ: Yes, absolutely. >> And control. And that's an elastic sort of dial-up, dial-down-- >> Absolutely. >> As you need it. >> Russ: Very, very much so. Yes, definitely. >> I wanted to ask you, so we've been obviously watching... IBM made the SoftLayer acquisition, it was like, Okay, we're going to buy this bare metal hosting company. >> Umm-hmm. >> And then they bring in Bluemix, and then they start bringing in applications. >> Yes, yes. >> And then all of a sudden it's like, IBM does what IBM does (laughing), and boom! Now, you've got this machine going. >> Yes. >> And so, several acquisitions that are relevant here, Aspera. >> Yes. >> Clearleap. >> Yes. >> UStream fits there because we know Ustream because we broadcast on UStream-- >> Russ: Yes, yes, uh-huh. >> And, of course, Cleversafe. >> Umm-hmm. >> Are you beginning to leverage those acquisitions and potentially others through Bluemix-- >> Yes. >> To create services and new value for clients? >> Yeah, so we're fully integrated with all those technologies, right, the object storage system through our APIs. Every single one of those technologies can leverage and utilize the storage system underneath. I'll give you an example, Aspera, as you mentioned, a very, prominent product in the marketplace. I think just about every company in media and entertainment and certainly any company that's dealing with unstructured data objects knows and uses Aspera. They have a service now in the cloud where you can actually move data very rapidly over their protocol, into the cloud, and then store it in the object storage system. That's easy, that's simple. That makes it easy to start to leverage cloud. UStream the same way, Clearleap the same way. All of this comes together in Bluemix. Bluemix is the glue, so to speak, so if you're developing new applications you have all of the Bluemix tools that you can use, and then you got all these technologies that are integrated, including the object storage system, which is the foundation, everything's going to... All the data's going to reside in an object storage system. That makes it all usable for clients, very simple, very easy. They have a whole portfolio of things that they can do. And it's all tied together through APIs. It's very, very nice-- >> And has that opened up when you're small startup... (laughing) You don't have all these resources-- >> Right. >> How has it opened up new opportunities for you guys? >> So we see a lot of new startups coming on board, and taking advantage of the storage system-- >> Right. >> And all the different services that sit on top. Many companies today are born on the cloud, or they're new applications that are being born on the cloud, and so, they have access to, not only infrastructure, like you said within Bluemix, they also have access to other services, video services, high-speed data transfer services, object storage services. So they're able to take advantage of all those different services, build applications very quickly. Another thing that's interesting about IBM, they have this concept, you may have heard of it, this Bluemix Garage concept-- >> Dave: Yeah, I have. >> Which is a rapid deployment, rapid application development, using design thinking and agile methodologies, to quickly develop a minimum viable product that now uses object storage as part of the services, right. So as a new client, you can come in, sit in the Bluemix Garage, work on the application, and have some really rapid prototyping going on, and leverage the storage system underneath. And that gets you started, gets you going. I can see a lot of new applications coming to market through that same-- >> So they're like seven garages, is that right around the world? >> Russ: Yes, yes. Yeah, they're around the world. And so, I didn't realize... So Cleversafe's a fundamental part of that, in the object storage. >> It is now. And we just announced it this week at Interconnect, but it is now. >> So what does that mean? So I go in and I can... It's basically a set of... Sets of best practices-- >> Correct, correct. >> And accelerance and-- >> Right. And obviously in the cloud world, you need a place to place your data, right. So the integration with Cloud Object Storage, Cleversafe now called Cloud Object Storage is now all part of that, so it's integrated into the app dev that's going on in those garages. And we're excited about that because I think we'll see a lot of new technologies coming through that methodology, and certainly ones that leverage our storage technology, for sure. >> What's it been like to go from relatively small Illinois-based startup. (laughing) And now you're in IBM. >> Right. >> What was the integration like (laughing)? Are you on the rocket ship now? You were kind of on it before, but now it's like, steep part of the S-curve-- >> Sure. >> With all these global resources. Describe that. >> Well, I think the biggest part that's happened to us as an organization is exposure to a number of different accounts that we as a small company may not have had access to, certainly in certain industries, IBM's in every part of the world, in every industry, and that exposure from IBM's go to market has been very, very exciting for us. And certainly, global now, right. As Cleversafe, we were only in North America and Europe, for example, and now we're all over the world, or had the chance to be all over the world, so that's been really exciting. And then on top of that the whole integration into the cloud, right, because IBM's cloud business unity is the one that drove the acquisition of Cleversafe because they wanted the technology in the cloud. And now that we're there, we can offer storage services, object storage services as a foundation to anyone all over the world. And I think that's really exciting, and it's the exposure to all kinds of different businesses that's been exciting since we've been part of-- >> Yeah, and the speed at which you can get to that object store as a service as opposed to-- >> Absolutely. >> As opposed to saying, Okay, knocking on-- >> Yes. >> All the cloud doors, (laughing) And, hey, do you want to buy my cloud? And like, Well, you know we got our own, or whatever it is. >> Right, right. >> And now it's just boom global-- >> It's shortened that sale cycle tremendously, right. People are up and running in a few days now, or even a few hours, whereas before it may take months or, even quarters, to get started. You can get started now just by going to the portal, signing up for object storage services, starting to write data into the cloud, starting to leverage these other services that we walked about. It's very simple-- >> And the commentorial effects of what we were talking about before with, like Aspera and UStream, and so fourth-- >> Russ: Umm-hmm, umm-hmm. >> Give you the ability to add even new services. IBM 's always been very good at-- >> Yes. >> Acquisitions. >> Yes. >> We forget that sometimes IBM... (laughing) >> Acquisitions are always hard-- >> Yeah. >> But we've been fortunate we've had a lot of support and a lot help in getting integrated into the various businesses, And I think it's been a good journey. >> So what should we look for? What kind of milestones? Can you show a little leg on futures (laughing)? What should we be paying attention to? >> Well, we're going to continue to do what clients are asking us to do. We're going to develop features and functions, both on prem and in the cloud. We're going to integrate with a lot of different technologies, both IBM technologies and other company technologies. You may have seen our announcements with NetApp and VERITAS this week. >> Yeah. >> So we're going to continue to expand our integration with other technologies that exist in the marketplace because that's what clients want. They want solutions. They want end-to-end solutions, both on on prem and in the cloud. So we're focused on that. We're going to continue to do that. We'll certainly integrate with other IBM services as they come to market in the cloud. That's a really exciting thing, so we're going to continue to focus on driving success for our clients. And that's exciting. >> Oh! Russ, belated congratulations on the acquisition, and going through the integration. I'm really happy for you guys, and excited for your future. Thanks for coming on theCUBE. >> Thank you. >> You're welcome. >> Thank you, Dave. >> Alright, keep right there everybody. We'll be back with our next guest. This is theCUBE, we're live from Interconnect 2017. Be right back! (electronic music)

>> Commentator: Live from Las Vegas. It's theCUBE. Covering InterConnect 2017. Brought to you by IBM. >> Hello everyone. Welcome to theCUBE special broadcast here at the Mandalay Bay in Las Vegas for IBM InterConnect 2017. This is IBM's big Cloud show. I'm John Furrier. My co-host, David Vellante for the next three days will be wall-to-wall coverage of IBM's Cloud Watson. All the goodness from IBM. The keynote server finishing up now but this morning was the kickoff of what seems to be IBM's Cloud strategy here with Dave Vellante. Dave, you're listed in the keynote, we are hearing the presentation. We had the General Manager/Vice President of Data from Twitter on there, Chris Moody, talkin' about everything from the Trump presidential election being the avid tweeter that he is and got a lot of laughs on that. To the SVP of Cloud talking about DevOps and this is really IBM is investing 10 million dollars plus into more developer stuff in the field. This is IBM just continuing to pound the ball down the field on cloud. Your take? >> Well IBM's fundamental business premise is that cognitive, which includes analytics, John plus cloud plus specific industry solutions are the best way to solve business problems and IBM's trying to differentiate from the other cloud guys who David Kenny was on stage today saying, you know, they started with a retail business or the other guys started with search, we started with business problems, we started with data. And that's fundamental to what IBM is doing. The other point, I think is-- the other premise that IBM is putting forth is that the AI debate is over. The Artificial Intelligence, you know, wave of excitement in the 70s and 80s and then, you know, nothing is now back in full swing. An AI on the Cloud is a key differentiator from IBM. In typical IBM fashion for the last several Big Shows, IBM brought out not an IBMer but a customer or and or a partner. And today it brought out Chris Moody from Twitter talking about their relationship with IBM but more specifically the fact that Twitter's 11 years old. Some of the things you're doing with Twitter obviously connected into March Madness and then Arvind Krishna who has taken over for Robert LeBlanc as the head of the Cloud group, talked about IBM, AI, IBM's Cloud, blocked chain, trusted transactions, IoT, DevOPs, all the buzz words merged into IBM's Cloud Strategy. And of course, we reported several years ago at this event about Bluemix as the underpinning of IBM's developer strategy. And as well it showcased several partners. Indiegogo was a crowdfunding site and others. Some of those guys are going to be in theCUBE. So. You know as they say, this AI debate is over. It's real and IBM's intent is to the platform for business. >> Dave, the thing I want to get your thoughts on is IBM's on a 19 consecutive quarters of revenue problems with the business on general but they've been on a steady course and they kind of haven't wavered. So it's as if they know they got to shrink to grow approach but we just came off the heels of Google Next which is their Cloud Show. How the Amazon is on re-invent as the large public cloud but the number one question on the table that's going to power IoT, that's going to power AI, is the collision between cloud computing and IoT, cloud computing in big data I should say is colliding with IoT at the center which is going to fuel AI and so, it brings up the question of enterprise readiness. Okay? So this is the number one conversation in the hallways here at Las Vegas and every single Cloud Show in the enterprise is, can I move to the cloud? Obviously it's a hybrid world, multi-cloud world. IBM's cloud play. They had a Cloud. They're in the top four as we put them in there. Has to be enterprise ready but yet it as to spawn the development side. So again, your take on enterprise readiness and then really fueling the IoT because IoT is a real conversation at an architectural level that is shifting the-- tipping the scales if you will for where the action will be. >> Well John, you and I have talked in theCUBE for years now. Going on probably five years that IBM had to shrink to grow. They've got the shrink part down. They've divested some of its business like the x86 business and the microelectronics business. They have not solved the grow problem. Let's just say 19 straight quarters of declining revenue. But here's the question. Is IBM stronger today than it was a year ago? And I would argue yes and why is that? One is its focus. Its got a much clearer focus on its strategy around cognitive, around data and marrying that to Cloud. I think the other is as an 80 billion dollar company even though it's shrinking, its free cash flow is still 11.6 billion. So it's throwing off a lot of cash. Now of course, IBM made those numbers, made its earnings numbers by with through expense control, its got lower tax right. Some of the new ones of the financial engineering. Its got some good IP revenue. But nonetheless, I would still argue that IBM is stronger this year than it was a year ago. Having said that, IBM's service as business is still 60% of the company. The software business is still only about 30% into it but 10% is hardware. So IBM-- people say IBM has exited the hardware business. It hasn't exited completely the hardware business but it's only focusing on those high value areas like mainframe and they're trying to sort of retool power. Its got a new leader with Bob Picciano but it's still 60% of the company's business is still services and it's shifting to a (mumbles) model. An (mumbles) model. And that is sometimes painful financially. But again John, I would argue that it is stronger. It is better positioned. And now its got some growth potential in place with AI and with, as you say, IoT. We're going to have Harriet Green on. We're going to have Deon Newman on. Focusing on the IoT opportunity. The weather company acquisition as a foundation for IoT. So the key for IBM is that it's strategic imperatives are now over 40% of its business. IBM promised that it would be a 40 billion dollar business by 2018 and it's on track to do that. I think the question John is, is that business as profitable as its old business? And can it begin growing to offset the decline in things like storage, which has been seeing double digit declines and its traditional hardware business. >> So Dave, this is to my take on IBM. IBM has been retooling for multiple years. At least a five year journey that they have to do because let's just go down the enterprise cloud readiness matrix that I'm putting together and let's just go through the components and then think about what was old IBM and what's new. Global infrastructure. Compute networking, storage and content delivery, databases, developer tools, security and identity, management tools, analytics, artificial intelligence, Internet of Things, mobile services, enterprise applications, support, hybrid integration, migration, governance and security. Not necessarily in that order. That is IBM, right? So this is a company that has essentially (mumbles) together core competencies across the company and to me, this is the story that no one's talking about at IBM is that it's really hard to take those components and decouple them in a fashion that's cloud enabled. This is where, I think, you're going to start to see the bloom on the rose come out of IBM and this is what I'm looking at because IBM had a little bit here, they had a little bit here, then a little stove pipe over here. Now bringing that together and make it scalable, it's elastic infrastructure. It's going to be really the key to success. >> Well, I think, if again if you breakdown those businesses into growth businesses, the analytics business is almost 20 billion. The cloud business is about 14 billion. Now what IBM does is that they talk about as a service runway of you know, 78 billion so they give you a little dimensions on you know, their financials but that cloud business is growing at 35% a year. The as a service component, let's call it true cloud, is growing over 60% a year. Mobile growing, 35%. Security, 14%. Social, surprisingly is down actually year on year. You would thought that would be a growth theory for them but nonetheless, this strategic initiatives, this goal of being 40 billion by 2018 is fundamental to IBM's future. >> Yeah and the thing too about the enterprise rate is in the numbers, it speaks to them where the action is. So right now the hottest conversations in IT are SLA's. I need SLA's. I have a database strategy that has to be multi-database. So (mumbles) too. Database is a service. This is going to be very very important. They're going to have to come in and support multiple databases and identity and role-based stuff has to happen because now apps, if you go DevOps and you go Watson Data Analytics, you're going to have native data within the stack. So to me, I think, one of the things that IBM can bring to the table is around the enterprise knowledge. The SLA's are actually more important than price and we heard that at Google Next where Google tried it out on their technologies and so, look at all the technology, buy us 'cause we're Google. Not really. It's not so much the price. It's the SLA and where Google is lacking as an example is their SLA's. Amazon has really been suring up the SLA's on the enterprise side but IBM's been here. This is their business. So to me, I think that's going to be something I'm going to look for. As well as the customer testimonials, looking at who's got the hybrid and where the developer actually is. 'Cause I think IoT is the tell sign in the cloud game and I think a lot of people are talking about infrastructures of service but the actual B-platform as a service and the developer action. And to me, that's where I'm looking. >> Well comparing and contrasting, you know, those two companies. Google and Amazon with IBM, I think completely different animals. As you say, you know, Google kind of geeky doesn't really have the enterprise readiness yet although they're trying to talk that game. Diane Green hiring a lot of new people. AWS arguebly has, you know, a bigger lead on the enterprise readiness. Not necessarily relative to IBM but relative to where they were five years ago. But AWS doesn't have the software business that IBM has yet. We'll see. Okay so that's IBM's ace in the hole is the software business. Now having said that, David Kenny got on stage today. So he came out and he's doing his best Jeremy Burton impression. Came out in sort of a James Bond, you know, motif and guys with sunglasses and he announced the IBM Cloud Object Storage Flex. And he said, yes we have a marketing department and they came up with that name. You know, this to me is their clever safe objects tour to compete with S3, you know several years late. After Amazon has announced S3. So they're still showing up some of that core infrastructure but IBM's-- the (mumbles) of IBM strategy is the ability to layer cognitive and their SAS Portfolio on top of Cloud and superglue those things together. Along, of course, with its analytics packages. That's where IBM gets the margin. Not in volume infrastructure as a service. >> I want to get your take on squinting through the marketing messages of IBM and get down to the meat and the bone which is where is the hybrid cloud? Because if you look at what's going on in the cloud, we hear the new terms, lift and shift. Which to me is rip and replace. That's one strategy that Google has to take is if you run (mumbles) and Google, you're kind of cloud native. But IBM is dealing a lot at pre-existing enterprise legacy stuff. Data center and whatnot so the lift and shift is an interesting strategy so the question is, for you is, what does it take for them to be successful? With the data platform, with Watson, with IoT, as enterprise extend from the data center with hybrid. >> Well I think that, you know, again IBM's (mumbles) is the data and the cognitive platform. And what IBM is messaging to your question is that you own your data. We are not going to basically take your data and form our models and then resell your IP. That's what IBM's telling people. Now why don't we dig into that a little bit? 'Cause I don't understand sort of how you separate the data from the models but David Kenny on stage today was explicit. That the other guys, he didn't mention Google and Amazon, but that's who he was talkin' about, are essentially going to be taking your data into their cloud and then informing their models and then essentially training those models and seeping your IP out to your competitors. Now he didn't say that as explicitly as I just did but that's something as a customer that you have to be really careful of. Yes, it's your data. But if data trains the models, who owns the model? You own the data but who owns the model? And how do you protect your IP and keep it out of the hands of the competitors? And IBM is messaging that they are going to help you with the compliance and the governance and the (mumbles) of your organization to protect your IP. That's a big differentiator if in fact there's meat in the bone there. >> Well you mentioned data, that's a key thing. I think whether doing it really quickly is getting the hybrid equation nailed so I think that's going to like just pedal as fast as you can. Get that going. But data first enterprise is really speaks to the IoT opportunity and also the new application developers. So to me, I think, for IBM to be successful, they have to continue to nail this data as value concept. If they can do that, they're going to drive (mumbles) and I think that's their differentiation. You look at, you know, Oracle, Azure, Microsoft Azure and IBM, they're all playing their cards to highlight their differentiation. So. Table stakes infrastructures of service, get some platform as a service, cloud native, open source, all the goodness involved in all the microservices, the containers, Cooper Netties, You're seeing that marker just develop as it's developing. But for IBM to get out front, they have to have a data layer, they have to have a data first strategy and if they do that well, that's going to be consistent with what I think (mumbles). And so, you know, to me I'm going to be poking at that. I'm going to be asking all the guests. What do you think of the data strategy? That's going to be powering the AI, you're seeing artificial intelligence, and things like autonomous vehicles. You're seeing sensors, wearables. Edge of the network is being redefined so I'm going to ask the quests really kind of how that plays out in hybrid? What's your analysis going to be for the guests this week? >> Well, I think the other thing too is the degree to-- to me, a key for IBM success and their ability to grow and dominate in this new world is the degree to which they can take their deep industry expertise in health care, in financial services and certain government sectors and utilities, et cetera. Which comes from their business process, you know, the BPO organization and they're consulting and the PWC acquisition years ago. The extent to which they can take that codifier, put it in the software, marry it with their data analytics and cognitive platforms and then grow that at scale. That would be a huge differentiator for IBM and give them a really massive advantage from a business model standpoint but as I said, 60% of the IBM's business remains services so we got a ways to go. >> Alright. We're going to be drilling into it again. There's a collision between cloud and big data markets coming together that's forming the IoT. You can see machine learning. You can see artificial intelligence. And I'm really a forcing function in cloud acceleration with data analytics being the key thing. This is theCUBE. We'll be getting the data for you for the next three days. I'm John Furrier. With Dave Vellante. We'll be back with more coverage. Kicking off day one of IBM InterConnect 2017 after the short break.