>> From every ISV to solve the problems. You want there to be tools in place that you can use, either open source tools or whatever it is that help you build it. And slowly over time, that building will become easier and easier. So my question to you was, where do you see you playing? Do you see yourself playing to ISVs as a set of tools, which will make their life a lot easier and provide that work? >> Absolutely. >> If they don't have, so they don't have to do it. Or you're providing this for the end users? Or both? >> So it's a progression. If you go to the ISVs first, you're doomed to starved before you have time for that other option. >> Yeah. >> Right? So it's a question of phase, the phasing of it. And also if you go directly to end users, you can demonstrate the power of it and get the attention of the ISVs. I believe that the ISVs, especially those with the biggest footprints and the most, you know, coveted estates, they have already made massive investments at trying to solve decentralization of their software stack. And I believe that they have used it as a hook to try to move to a software as a service model and rope people into leasing their infrastructure. So if you look at the clouds that have been propped up by Autodesk or by Adobe, or you name the company, they are building proprietary makeshift solutions for decentralizing or hybrid clouding. Or maybe they're not even doing that at all and all they're is saying hey, if you want to get location agnosticness, then what you should just, is just move into our cloud. >> Right. >> And then they try to solve on the background how to decentralize it between different regions so they can have decent offerings in each region. But those who are more advanced have already made larger investments and will be more averse to, you know, throwing that stuff away, all of their makeshift machinery away, and using a platform that gives them high performance parallel, low level file system access, while at the same time having metadata-driven, you know, policy-based, intent-based orchestration to manage the diffusion of data across a decentralized infrastructure. They are not going to be as open because they've made such an investment and they're going to look at how do they monetize it. So what we have found with like the movie studios who are using us already, many of the app they're using, many of those software offerings, the ISVs have their own cloud that offers that software for the cloud. But what we got when I asked about this, 'cause I was dealt specifically into this question because I'm very interested to know how we're going to make that leap from end user upstream into the ISVs where I believe we need to, and they said, look, we cannot use these software ISV-specific SAS clouds for two reasons. Number one is we lose control of the data. We're giving it to them. That's security and other issues. And here you're talking about we're doing work for Disney, we're doing work for Netflix, and they're not going to let us put our data on those software clouds, on those SAS clouds. Secondly, in any reasonable pipeline, the data is shared by many different applications. We need to be agnostic as to the application. 'Cause the inputs to one application, you know, the output for one application provides the input to the next, and it's not necessarily from the same vendor. So they need to have a data platform that lets them, you know, go from one software stack, and you know, to run it on another. Because they might do the rendering with this and yet, they do the editing with that, and you know, et cetera, et cetera. So I think the further you go up the stack in the structured data and dedicated applications for specific functions in specific verticals, the further up the stack you go, the harder it is to justify a SAS offering where you're basically telling the end users you need to park all your data with us and then you can run your application in our cloud and get this. That ultimately is a dead end path versus having the data be open and available to many applications across this supercloud layer. >> Okay, so-- >> Is that making any sense? >> Yes, so if I could just ask a clarifying question. So, if I had to take Snowflake as an example, I think they're doing exactly what you're saying is a dead end, put everything into our proprietary system and then we'll figure out how to distribute it. >> Yeah. >> And and I think if you're familiar with Zhamak Dehghaniis' data mesh concept. Are you? >> A little bit, yeah. >> But in her model, Snowflake, a Snowflake warehouse is just a node on the mesh and that mesh is-- >> That's right. >> Ultimately the supercloud and you're an enabler of that is what I'm hearing. >> That's right. What they're doing up at the structured level and what they're talking about at the structured level we're doing at the underlying, unstructured level, which by the way has implications for how you implement those distributed database things. In other words, implementing a Snowflake on top of Hammerspace would have made building stuff like in the first place easier. It would allow you to easily shift and run the database engine anywhere. You still have to solve how to shard and distribute at the transaction layer above, so I'm not saying we're a substitute for what you need to do at the app layer. By the way, there is another example of that and that's Microsoft Office, right? It's one thing to share that, to have a file share where you can share all the docs. It's something else to have Word and PowerPoint, Excel know how to allow people to be simultaneously editing the same doc. That's always going to happen in the app layer. But not all applications need that level of, you know, in-app decentralization. You know, many of them, many workflows are pipelined, especially the ones that are very data intensive where you're doing drug discovery or you're doing rendering, or you're doing machine learning training. These things are human in the loop with large stages of processing across tens of thousands of cores. And I think that kind of data processing pipeline is what we're focusing on first. Not so much the Microsoft Office or the Snowflake, you know, parking a relational database because that takes a lot of application layer stuff and that's what they're good at. >> Right. >> But I think... >> Go ahead, sorry. >> Later entrance in these markets will find Hammerspace as a way to accelerate their work so they can focus more narrowly on just the stuff that's app-specific, higher level sharing in the app. >> Yes, Snowflake founders-- >> I think it might be worth mentioning also, just keep this confidential guys, but one of our customers is Blue Origin. And one of the things that we have found is kind of the point of what you're talking about with our customers. They're needing to build this and since it's not commercially available or they don't know where to look for it to be commercially available, they're all building themselves. So this layer is needed. And Blue is just one of the examples of quite a few we're now talking to. And like manufacturing, HPC, research where they're out trying to solve this problem with their own scripting tools and things like that. And I just, I don't know if there's anything you want to add, David, but you know, but there's definitely a demand here and customers are trying to figure out how to solve it beyond what Hammerspace is doing. Like the need is so great that they're just putting developers on trying to do it themselves. >> Well, and you know, Snowflake founders, they didn't have a Hammerspace to lean on. But, one of the things that's interesting about supercloud is we feel as though industry clouds will emerge, that as part of company's digital transformations, they will, you know, every company's a software company, they'll begin to build their own clouds and they will be able to use a Hammerspace to do that. >> A super pass layer. >> Yes. It's really, I don't know if David's speaking, I don't want to speak over him, but we can't hear you. May be going through a bad... >> Well, a regional, regional talks that make that possible. And so they're doing these render farms and editing farms, and it's a cloud-specific to the types of workflows in the median entertainment world. Or clouds specifically to workflows in the chip design world or in the drug and bio and life sciences exploration world. There are large organizations that are kind of a blend of end users, like the Broad, which has their own kind of cloud where they're asking collaborators to come in and work with them. So it starts to even blur who's an end user versus an ISV. >> Yes. >> Right? When you start talking about the massive data is the main gravity is to having lots of people participate. >> Yep, and that's where the value is. And that's where the value is. And this is a megatrend that we see. And so it's really important for us to get to the point of what is and what is not a supercloud and, you know, that's where we're trying to evolve. >> Let's talk about this for a second 'cause I want to, I want to challenge you on something and it's something that I got challenged on and it has led me to thinking differently than I did at first, which Molly can attest to. Okay? So, we have been looking for a way to talk about the concept of cloud of utility computing, run anything anywhere that isn't addressed in today's realization of cloud. 'Cause today's cloud is not run anything anywhere, it's quite the opposite. You park your data in AWS and that's where you run stuff. And you pretty much have to. Same with with Azure. They're using data gravity to keep you captive there, just like the old infrastructure guys did. But now it's even worse because it's coupled back with the software to some degree, as well. And you have to use their storage, networking, and compute. It's not, I mean it fell back to the mainframe era. Anyhow, so I love the concept of supercloud. By the way, I was going to suggest that a better term might be hyper cloud since hyper speaks to the multidimensionality of it and the ability to be in a, you know, be in a different dimension, a different plane of existence kind of thing like hyperspace. But super and hyper are somewhat synonyms. I mean, you have hyper cars and you have super cars and blah, blah, blah. I happen to like hyper maybe also because it ties into the whole Hammerspace notion of a hyper-dimensional, you know, reality, having your data centers connected by a wormhole that is Hammerspace. But regardless, what I got challenged on is calling it something different at all versus simply saying, this is what cloud has always meant to be. This is the true cloud, this is real cloud, this is cloud. And I think back to what happened, you'll remember, at Fusion IO we talked about IO memory and we did that because people had a conceptualization of what an SSD was. And an SSD back then was low capacity, low endurance, made to go military, aerospace where things needed to be rugged but was completely useless in the data center. And we needed people to imagine this thing as being able to displace entire SAND, with the kind of capacity density, performance density, endurance. And so we talked IO memory, we could have said enterprise SSD, and that's what the industry now refers to for that concept. What will people be saying five and 10 years from now? Will they simply say, well this is cloud as it was always meant to be where you are truly able to run anything anywhere and have not only the same APIs, but you're same data available with high performance access, all forms of access, block file and object everywhere. So yeah. And I wonder, and this is just me throwing it out there, I wonder if, well, there's trade offs, right? Giving it a new moniker, supercloud, versus simply talking about how cloud is always intended to be and what it was meant to be, you know, the real cloud or true cloud, there are trade-offs. By putting a name on it and branding it, that lets people talk about it and understand they're talking about something different. But it also is that an affront to people who thought that that's what they already had. >> What's different, what's new? Yes, and so we've given a lot of thought to this. >> Right, it's like you. >> And it's because we've been asked that why does the industry need a new term, and we've tried to address some of that. But some of the inside baseball that we haven't shared is, you remember the Web 2.0, back then? >> Yep. >> Web 2.0 was the same thing. And I remember Tim Burners Lee saying, "Why do we need Web 2.0? "This is what the Web was always supposed to be." But the truth is-- >> I know, that was another perfect-- >> But the truth is it wasn't, number one. Number two, everybody hated the Web 2.0 term. John Furrier was actually in the middle of it all. And then it created this groundswell. So one of the things we wrote about is that supercloud is an evocative term that catalyzes debate and conversation, which is what we like, of course. And maybe that's self-serving. But yeah, HyperCloud, Metacloud, super, meaning, it's funny because super came from Latin supra, above, it was never the superlative. But the superlative was a convenient byproduct that caused a lot of friction and flack, which again, in the media business is like a perfect storm brewing. >> The bad thing to have to, and I think you do need to shake people out of their, the complacency of the limitations that they're used to. And I'll tell you what, the fact that you even have the terms hybrid cloud, multi-cloud, private cloud, edge computing, those are all just referring to the different boundaries that isolate the silo that is the current limited cloud. >> Right. >> So if I heard correctly, what just, in terms of us defining what is and what isn't in supercloud, you would say traditional applications which have to run in a certain place, in a certain cloud can't run anywhere else, would be the stuff that you would not put in as being addressed by supercloud. And over time, you would want to be able to run the data where you want to and in any of those concepts. >> Or even modern apps, right? Or even modern apps that are siloed in SAS within an individual cloud, right? >> So yeah, I guess it's twofold. Number one, if you're going at the high application layers, there's lots of ways that you can give the appearance of anything running anywhere. The ISV, the SAS vendor can engineer stuff to have the ability to serve with low enough latency to different geographies, right? So if you go too high up the stack, it kind of loses its meaning because there's lots of different ways to make due and give the appearance of omni-presence of the service. Okay? As you come down more towards the platform layer, it gets harder and harder to mask the fact that supercloud is something entirely different than just a good regionally-distributed SAS service. So I don't think you, I don't think you can distinguish supercloud if you go too high up the stack because it's just SAS, it's just a good SAS service where the SAS vendor has done the hard work to give you low latency access from different geographic regions. >> Yeah, so this is one of the hardest things, David. >> Common among them. >> Yeah, this is really an important point. This is one of the things I've had the most trouble with is why is this not just SAS? >> So you dilute your message when you go up to the SAS layer. If you were to focus most of this around the super pass layer, the how can you host applications and run them anywhere and not host this, not run a service, not have a service available everywhere. So how can you take any application, even applications that are written, you know, in a traditional legacy data center fashion and be able to run them anywhere and have them have their binaries and their datasets and the runtime environment and the infrastructure to start them and stop them? You know, the jobs, the, what the Kubernetes, the job scheduler? What we're really talking about here, what I think we're really talking about here is building the operating system for a decentralized cloud. What is the operating system, the operating environment for a decentralized cloud? Where you can, and that the main two functions of an operating system or an operating environment are the process scheduler, the thing that's scheduling what is running where and when and so forth, and the file system, right? The thing that's supplying a common view and access to data. So when we talk about this, I think that the strongest argument for supercloud is made when you go down to the platform layer and talk of it, talk about it as an operating environment on which you can run all forms of applications. >> Would you exclude--? >> Not a specific application that's been engineered as a SAS. (audio distortion) >> He'll come back. >> Are you there? >> Yeah, yeah, you just cut out for a minute. >> I lost your last statement when you broke up. >> We heard you, you said that not the specific application. So would you exclude Snowflake from supercloud? >> Frankly, I would. I would. Because, well, and this is kind of hard to do because Snowflake doesn't like to, Frank doesn't like to talk about Snowflake as a SAS service. It has a negative connotation. >> But it is. >> I know, we all know it is. We all know it is and because it is, yes, I would exclude them. >> I think I actually have him on camera. >> There's nothing in common. >> I think I have him on camera or maybe Benoit as saying, "Well, we are a SAS." I think it's Slootman. I think I said to Slootman, "I know you don't like to say you're a SAS." And I think he said, "Well, we are a SAS." >> Because again, if you go to the top of the application stack, there's any number of ways you can give it location agnostic function or you know, regional, local stuff. It's like let's solve the location problem by having me be your one location. How can it be decentralized if you're centralizing on (audio distortion)? >> Well, it's more decentralized than if it's all in one cloud. So let me actually, so the spectrum. So again, in the spirit of what is and what isn't, I think it's safe to say Hammerspace is supercloud. I think there's no debate there, right? Certainly among this crowd. And I think we can all agree that Dell, Dell Storage is not supercloud. Where it gets fuzzy is this Snowflake example or even, how about a, how about a Cohesity that instantiates its stack in different cloud regions in different clouds, and synchronizes, however magic sauce it does that. Is that a supercloud? I mean, so I'm cautious about having too strict of a definition 'cause then only-- >> Fair enough, fair enough. >> But I could use your help and thoughts on that. >> So I think we're talking about two different spectrums here. One is the spectrum of platform to application-specific. As you go up the application stack and it becomes this specific thing. Or you go up to the more and more structured where it's serving a specific application function where it's more of a SAS thing. I think it's harder to call a SAS service a supercloud. And I would argue that the reason there, and what you're lacking in the definition is to talk about it as general purpose. Okay? Now, that said, a data warehouse is general purpose at the structured data level. So you could make the argument for why Snowflake is a supercloud by saying that it is a general purpose platform for doing lots of different things. It's just one at a higher level up at the structured data level. So one spectrum is the high level going from platform to, you know, unstructured data to structured data to very application-specific, right? Like a specific, you know, CAD/CAM mechanical design cloud, like an Autodesk would want to give you their cloud for running, you know, and sharing CAD/CAM designs, doing your CAD/CAM anywhere stuff. Well, the other spectrum is how well does the purported supercloud technology actually live up to allowing you to run anything anywhere with not just the same APIs but with the local presence of data with the exact same runtime environment everywhere, and to be able to correctly manage how to get that runtime environment anywhere. So a Cohesity has some means of running things in different places and some means of coordinating what's where and of serving diff, you know, things in different places. I would argue that it is a very poor approximation of what Hammerspace does in providing the exact same file system with local high performance access everywhere with metadata ability to control where the data is actually instantiated so that you don't have to wait for it to get orchestrated. But even then when you do have to wait for it, it happens automatically and so it's still only a matter of, well, how quick is it? And on the other end of the spectrum is you could look at NetApp with Flexcache and say, "Is that supercloud?" And I would argue, well kind of because it allows you to run things in different places because it's a cache. But you know, it really isn't because it presumes some central silo from which you're cacheing stuff. So, you know, is it or isn't it? Well, it's on a spectrum of exactly how fully is it decoupling a runtime environment from specific locality? And I think a cache doesn't, it stretches a specific silo and makes it have some semblance of similar access in other places. But there's still a very big difference to the central silo, right? You can't turn off that central silo, for example. >> So it comes down to how specific you make the definition. And this is where it gets kind of really interesting. It's like cloud. Does IBM have a cloud? >> Exactly. >> I would say yes. Does it have the kind of quality that you would expect from a hyper-scale cloud? No. Or see if you could say the same thing about-- >> But that's a problem with choosing a name. That's the problem with choosing a name supercloud versus talking about the concept of cloud and how true up you are to that concept. >> For sure. >> Right? Because without getting a name, you don't have to draw, yeah. >> I'd like to explore one particular or bring them together. You made a very interesting observation that from a enterprise point of view, they want to safeguard their store, their data, and they want to make sure that they can have that data running in their own workflows, as well as, as other service providers providing services to them for that data. So, and in in particular, if you go back to, you go back to Snowflake. If Snowflake could provide the ability for you to have your data where you wanted, you were in charge of that, would that make Snowflake a supercloud? >> I'll tell you, in my mind, they would be closer to my conceptualization of supercloud if you can instantiate Snowflake as software on your own infrastructure, and pump your own data to Snowflake that's instantiated on your own infrastructure. The fact that it has to be on their infrastructure or that it's on their, that it's on their account in the cloud, that you're giving them the data and they're, that fundamentally goes against it to me. If they, you know, they would be a pure, a pure plate if they were a software defined thing where you could instantiate Snowflake machinery on the infrastructure of your choice and then put your data into that machinery and get all the benefits of Snowflake. >> So did you see--? >> In other words, if they were not a SAS service, but offered all of the similar benefits of being, you know, if it were a service that you could run on your own infrastructure. >> So did you see what they announced, that--? >> I hope that's making sense. >> It does, did you see what they announced at Dell? They basically announced the ability to take non-native Snowflake data, read it in from an object store on-prem, like a Dell object store. They do the same thing with Pure, read it in, running it in the cloud, and then push it back out. And I was saying to Dell, look, that's fine. Okay, that's interesting. You're taking a materialized view or an extended table, whatever you're doing, wouldn't it be more interesting if you could actually run the query locally with your compute? That would be an extension that would actually get my attention and extend that. >> That is what I'm talking about. That's what I'm talking about. And that's why I'm saying I think Hammerspace is more progressive on that front because with our technology, anybody who can instantiate a service, can make a service. And so I, so MSPs can use Hammerspace as a way to build a super pass layer and host their clients on their infrastructure in a cloud-like fashion. And their clients can have their own private data centers and the MSP or the public clouds, and Hammerspace can be instantiated, get this, by different parties in these different pieces of infrastructure and yet linked together to make a common file system across all of it. >> But this is data mesh. If I were HPE and Dell it's exactly what I'd be doing. I'd be working with Hammerspace to create my own data. I'd work with Databricks, Snowflake, and any other-- >> Data mesh is a good way to put it. Data mesh is a good way to put it. And this is at the lowest level of, you know, the underlying file system that's mountable by the operating system, consumed as a real file system. You can't get lower level than that. That's why this is the foundation for all of the other apps and structured data systems because you need to have a data mesh that can at least mesh the binary blob. >> Okay. >> That hold the binaries and that hold the datasets that those applications are running. >> So David, in the third week of January, we're doing supercloud 2 and I'm trying to convince John Furrier to make it a data slash data mesh edition. I'm slowly getting him to the knothole. I would very much, I mean you're in the Bay Area, I'd very much like you to be one of the headlines. As Zhamak Dehghaniis going to speak, she's the creator of Data Mesh, >> Sure. >> I'd love to have you come into our studio as well, for the live session. If you can't make it, we can pre-record. But you're right there, so I'll get you the dates. >> We'd love to, yeah. No, you can count on it. No, definitely. And you know, we don't typically talk about what we do as Data Mesh. We've been, you know, using global data environment. But, you know, under the covers, that's what the thing is. And so yeah, I think we can frame the discussion like that to line up with other, you know, with the other discussions. >> Yeah, and Data Mesh, of course, is one of those evocative names, but she has come up with some very well defined principles around decentralized data, data as products, self-serve infrastructure, automated governance, and and so forth, which I think your vision plugs right into. And she's brilliant. You'll love meeting her. >> Well, you know, and I think.. Oh, go ahead. Go ahead, Peter. >> Just like to work one other interface which I think is important. How do you see yourself and the open source? You talked about having an operating system. Obviously, Linux is the operating system at one level. How are you imagining that you would interface with cost community as part of this development? >> Well, it's funny you ask 'cause my CTO is the kernel maintainer of the storage networking stack. So how the Linux operating system perceives and consumes networked data at the file system level, the network file system stack is his purview. He owns that, he wrote most of it over the last decade that he's been the maintainer, but he's the gatekeeper of what goes in. And we have leveraged his abilities to enhance Linux to be able to use this decentralized data, in particular with decoupling the control plane driven by metadata from the data access path and the many storage systems on which the data gets accessed. So this factoring, this splitting of control plane from data path, metadata from data, was absolutely necessary to create a data mesh like we're talking about. And to be able to build this supercloud concept. And the highways on which the data runs and the client which knows how to talk to it is all open source. And we have, we've driven the NFS 4.2 spec. The newest NFS spec came from my team. And it was specifically the enhancements needed to be able to build a spanning file system, a data mesh at a file system level. Now that said, our file system itself and our server, our file server, our data orchestration, our data management stuff, that's all closed source, proprietary Hammerspace tech. But the highways on which the mesh connects are actually all open source and the client that knows how to consume it. So we would, honestly, I would welcome competitors using those same highways. They would be at a major disadvantage because we kind of built them, but it would still be very validating and I think only increase the potential adoption rate by more than whatever they might take of the market. So it'd actually be good to split the market with somebody else to come in and share those now super highways for how to mesh data at the file system level, you know, in here. So yeah, hopefully that answered your question. Does that answer the question about how we embrace the open source? >> Right, and there was one other, just that my last one is how do you enable something to run in every environment? And if we take the edge, for example, as being, as an environment which is much very, very compute heavy, but having a lot less capability, how do you do a hold? >> Perfect question. Perfect question. What we do today is a software appliance. We are using a Linux RHEL 8, RHEL 8 equivalent or a CentOS 8, or it's, you know, they're all roughly equivalent. But we have bundled and a software appliance which can be instantiated on bare metal hardware on any type of VM system from VMware to all of the different hypervisors in the Linux world, to even Nutanix and such. So it can run in any virtualized environment and it can run on any cloud instance, server instance in the cloud. And we have it packaged and deployable from the marketplaces within the different clouds. So you can literally spin it up at the click of an API in the cloud on instances in the cloud. So with all of these together, you can basically instantiate a Hammerspace set of machinery that can offer up this file system mesh. like we've been using the terminology we've been using now, anywhere. So it's like being able to take and spin up Snowflake and then just be able to install and run some VMs anywhere you want and boom, now you have a Snowflake service. And by the way, it is so complete that some of our customers, I would argue many aren't even using public clouds at all, they're using this just to run their own data centers in a cloud-like fashion, you know, where they have a data service that can span it all. >> Yeah and to Molly's first point, we would consider that, you know, cloud. Let me put you on the spot. If you had to describe conceptually without a chalkboard what an architectural diagram would look like for supercloud, what would you say? >> I would say it's to have the same runtime environment within every data center and defining that runtime environment as what it takes to schedule the execution of applications, so job scheduling, runtime stuff, and here we're talking Kubernetes, Slurm, other things that do job scheduling. We're talking about having a common way to, you know, instantiate compute resources. So a global compute environment, having a common compute environment where you can instantiate things that need computing. Okay? So that's the first part. And then the second is the data platform where you can have file block and object volumes, and have them available with the same APIs in each of these distributed data centers and have the exact same data omnipresent with the ability to control where the data is from one moment to the next, local, where all the data is instantiate. So my definition would be a common runtime environment that's bifurcate-- >> Oh. (attendees chuckling) We just lost them at the money slide. >> That's part of the magic makes people listen. We keep someone on pin and needles waiting. (attendees chuckling) >> That's good. >> Are you back, David? >> I'm on the edge of my seat. Common runtime environment. It was like... >> And just wait, there's more. >> But see, I'm maybe hyper-focused on the lower level of what it takes to host and run applications. And that's the stuff to schedule what resources they need to run and to get them going and to get them connected through to their persistence, you know, and their data. And to have that data available in all forms and have it be the same data everywhere. On top of that, you could then instantiate applications of different types, including relational databases, and data warehouses and such. And then you could say, now I've got, you know, now I've got these more application-level or structured data-level things. I tend to focus less on that structured data level and the application level and am more focused on what it takes to host any of them generically on that super pass layer. And I'll admit, I'm maybe hyper-focused on the pass layer and I think it's valid to include, you know, higher levels up the stack like the structured data level. But as soon as you go all the way up to like, you know, a very specific SAS service, I don't know that you would call that supercloud. >> Well, and that's the question, is there value? And Marianna Tessel from Intuit said, you know, we looked at it, we did it, and it just, it was actually negative value for us because connecting to all these separate clouds was a real pain in the neck. Didn't bring us any additional-- >> Well that's 'cause they don't have this pass layer underneath it so they can't even shop around, which actually makes it hard to stand up your own SAS service. And ultimately they end up having to build their own infrastructure. Like, you know, I think there's been examples like Netflix moving away from the cloud to their own infrastructure. Basically, if you're going to rent it for more than a few months, it makes sense to build it yourself, if it's at any kind of scale. >> Yeah, for certain components of that cloud. But if the Goldman Sachs came to you, David, and said, "Hey, we want to collaborate and we want to build "out a cloud and essentially build our SAS system "and we want to do that with Hammerspace, "and we want to tap the physical infrastructure "of not only our data centers but all the clouds," then that essentially would be a SAS, would it not? And wouldn't that be a Super SAS or a supercloud? >> Well, you know, what they may be using to build their service is a supercloud, but their service at the end of the day is just a SAS service with global reach. Right? >> Yeah. >> You know, look at, oh shoot. What's the name of the company that does? It has a cloud for doing bookkeeping and accounting. I forget their name, net something. NetSuite. >> NetSuite. NetSuite, yeah, Oracle. >> Yeah. >> Yep. >> Oracle acquired them, right? Is NetSuite a supercloud or is it just a SAS service? You know? I think under the covers you might ask are they using supercloud under the covers so that they can run their SAS service anywhere and be able to shop the venue, get elasticity, get all the benefits of cloud in the, to the benefit of their service that they're offering? But you know, folks who consume the service, they don't care because to them they're just connecting to some endpoint somewhere and they don't have to care. So the further up the stack you go, the more location-agnostic it is inherently anyway. >> And I think it's, paths is really the critical layer. We thought about IAS Plus and we thought about SAS Minus, you know, Heroku and hence, that's why we kind of got caught up and included it. But SAS, I admit, is the hardest one to crack. And so maybe we exclude that as a deployment model. >> That's right, and maybe coming down a level to saying but you can have a structured data supercloud, so you could still include, say, Snowflake. Because what Snowflake is doing is more general purpose. So it's about how general purpose it is. Is it hosting lots of other applications or is it the end application? Right? >> Yeah. >> So I would argue general purpose nature forces you to go further towards platform down-stack. And you really need that general purpose or else there is no real distinguishing. So if you want defensible turf to say supercloud is something different, I think it's important to not try to wrap your arms around SAS in the general sense. >> Yeah, and we've kind of not really gone, leaned hard into SAS, we've just included it as a deployment model, which, given the constraints that you just described for structured data would apply if it's general purpose. So David, super helpful. >> Had it sign. Define the SAS as including the hybrid model hold SAS. >> Yep. >> Okay, so with your permission, I'm going to add you to the list of contributors to the definition. I'm going to add-- >> Absolutely. >> I'm going to add this in. I'll share with Molly. >> Absolutely. >> We'll get on the calendar for the date. >> If Molly can share some specific language that we've been putting in that kind of goes to stuff we've been talking about, so. >> Oh, great. >> I think we can, we can share some written kind of concrete recommendations around this stuff, around the general purpose, nature, the common data thing and yeah. >> Okay. >> Really look forward to it and would be glad to be part of this thing. You said it's in February? >> It's in January, I'll let Molly know. >> Oh, January. >> What the date is. >> Excellent. >> Yeah, third week of January. Third week of January on a Tuesday, whatever that is. So yeah, we would welcome you in. But like I said, if it doesn't work for your schedule, we can prerecord something. But it would be awesome to have you in studio. >> I'm sure with this much notice we'll be able to get something. Let's make sure we have the dates communicated to Molly and she'll get my admin to set it up outside so that we have it. >> I'll get those today to you, Molly. Thank you. >> By the way, I am so, so pleased with being able to work with you guys on this. I think the industry needs it very bad. They need something to break them out of the box of their own mental constraints of what the cloud is versus what it's supposed to be. And obviously, the more we get people to question their reality and what is real, what are we really capable of today that then the more business that we're going to get. So we're excited to lend the hand behind this notion of supercloud and a super pass layer in whatever way we can. >> Awesome. >> Can I ask you whether your platforms include ARM as well as X86? >> So we have not done an ARM port yet. It has been entertained and won't be much of a stretch. >> Yeah, it's just a matter of time. >> Actually, entertained doing it on behalf of NVIDIA, but it will absolutely happen because ARM in the data center I think is a foregone conclusion. Well, it's already there in some cases, but not quite at volume. So definitely will be the case. And I'll tell you where this gets really interesting, discussion for another time, is back to my old friend, the SSD, and having SSDs that have enough brains on them to be part of that fabric. Directly. >> Interesting. Interesting. >> Very interesting. >> Directly attached to ethernet and able to create a data mesh global file system, that's going to be really fascinating. Got to run now. >> All right, hey, thanks you guys. Thanks David, thanks Molly. Great to catch up. Bye-bye. >> Bye >> Talk to you soon.

[Music] welcome back to aws storage day 2022 i'm dave vellante and we're pleased to have back on thecube edname the gm of aws file storage ed how you doing good to see you i'm good dave good good to see you as well you know we've been tracking aws storage for a lot of years 16 years actually we we've seen the evolution of services of course we started with s3 and object and saw that expand the block and file and and now the pace is actually accelerating and we're seeing aws make more moves again today and block an object but what about file uh it's one format in the world and the day wouldn't really be complete without talking about file storage so what are you seeing from customers in terms of let's start with data growth how are they dealing with the challenges what are those challenges if you could address you know specifically some of the issues that they're having that would be great and then later we're going to get into the role that cloud file storage plays take it away well dave i'm definitely increasingly hearing customers talk about the challenges in managing ever-growing data sets and they're especially challenged in doing that on-premises when we look at the data that's stored on premises zettabytes of data the fastest growing data sets consist of unstructured data that are stored as files and many cups have tens of petabytes or hundreds of petabytes or even exabytes of file data and this data is typically growing 20 30 percent a year and in reality on-premises models really designed to handle this amount of data in this type of growth and i'm not just talking about keeping up with hardware purchases and hardware floor space but a big part of the challenge is labor and talent to keep up with the growth they're seeing companies managing storage on-prem they really need an unprecedented number of skilled resources to manage the storage and these skill sets are in really high demand and they're in short supply and then another big part of the challenge that customers tell me all the time is that that operating at scale dealing with these ever-growing data sets at scale is really hard and it's not just hard in terms of the the people you need and the skill sets that you need but operating at scale presents net new challenges so for example it becomes increasingly hard to know what data you have and what storage media your data stored on when you have a massive amount of data that's spanning hundreds of thousands or uh thousands of applications and users and it's growing super fast each year and at scale you start seeing edge technical issues get triggered more commonly impacting your availability or your resiliency or your security and you start seeing processes that used to work when you were a much smaller scale no longer work it's just scale is hard it's really hard and then finally companies are wanting to do more with their fast growing data sets to get insights from it and they look at the machine learning and the analytics and the processing services and the compute power that they have at their fingertips on the cloud and having that data be in silos on-prem can really limit how they get the most out of their data you know i've been covering glad you brought up the skills gap i've been covering that quite extensively with my colleagues at etr you know our survey partner so that's a really important topic and we're seeing it across the board i mean really acute in cyber security but for sure just generally in i.t and frankly ceos they don't want to invest in training people to manage storage i mean it wasn't that long ago that managing loans was a was a talent and that's of course nobody does that anymore but they'd executives would much rather apply skills to get value from data so my specific question is what can be done what is aws doing to address this problem well with the growth of data that that we're seeing it it's just it's really hard for a lot of it teams to keep up with just the infrastructure management part that's needed so things like deploying capacity and provisioning resources and patching and conducting compliance reviews and that stuff is just table stakes the asks on these teams to your point are growing to be much bigger than than those pieces so we're really seeing fast uptake of our amazon fsx service because it's such an easy path for helping customers with these scaling challenges fsx enables customers to launch and to run and to scale feature rich and highly performant network attached file systems on aws and it provides fully managed file storage which means that we handle all of the infrastructure so all of that provisioning and that patching and ensuring high availability and customers simply make api calls to do things like scale up their storage or change their performance level at any point or change a backup policy and a big part of why fsx has been so feeling able to customers is it really enables them to to choose the file system technology that powers their storage so we provide four of the most popular file system technologies we provide windows file server netapp ontap open zfs and luster so that storage and application admins can use what they're familiar with so they essentially get the full capabilities and even the management clis that they're used to and that they've built workflows and applications around on-premises but they get along with that of course the benefits of fully managed elastic cloud storage that can be spin up and spun spin down and scaled on demand and performance changed on demand etc and what storage and application admins are seeing is that fsx not only helps them keep up with their scale and growth but it gives them the bandwidth to do more of what they want to do supporting strategic decision making helping their end customers figure out how they can get more value from their data identifying opportunities to reduce cost and what we realize is that for for a number of storage and application admins the cloud is is a different environment from what they're used to and we're making it a priority to help educate and train folks on cloud storage earlier today we talked about aws storage digital badges and we announced a dedicated file badge that helps storage admins and professionals to learn and demonstrate their aws skills in our aws storage badges you can think of them as credentials that represent cloud computing learning that customers can add to their repertoire add to their resume as they're embarking on this cloud journey and we'll be talking more in depth on this later today especially around the file badge which i'm very excited about so a couple things there that i wanted to comment on i mean i was there for the netapp you know your announcement we've covered that quite extensively this is just shows that it's not a zero-sum game necessarily right it's a win-win-win for customers you've got your you know specific aws services you've got partner services you know customers want want choice and then the managed service model you know to me is a no-brainer for most customers we learned this in the hadoop years i mean it just got so complicated then you saw what happened with the managed services around you know data lakes and lake houses it's just really simplified things for customers i mean there's still some customers that want to do it yourself but a managed service for the file storage sounds like a really easy decision especially for those it teams that are overburdened as we were talking about before and i also like you know the education component is nice touch too you get the badge thing so that's kind of cool so i'm hearing that if the fully managed file storage service is a catalyst for cloud adoption so the question is which workloads should people choose to move into the cloud where's the low friction low risk sweet spot ed well that's one of the first questions that customers ask when they're about to embark on their cloud journey and i wish i could give a simple or a single answer but the answer is really it varies and it varies per customer and i'll give you an example for some customers the cloud journey begins with what we call extending on-premises workloads into the cloud so an example of that is compute bursting workloads where customers have data on premises and they have some compute on premises but they want to burst their processing of that data to the cloud because they really want to take advantage of the massive amount of compute that they get on aws and that's common with workloads like visual effects ringer chip design simulation genomics analysis and so that's an example of extending to the cloud really leveraging the cloud first for your workloads another example is disaster recovery and that's a really common example customers will use a cloud for their secondary or their failover site rather than maintaining their their second on-prem location and so that's a lot of customers start with some of those workloads by extending to the cloud and then there's there's a lot of other customers where they've made the decision to migrate most or all of their workloads and they're not they're skipping the whole extending step they aren't starting there they're instead focused on going all in as fast as possible because they really want to get to the full benefits of the cloud as fast as possible and for them the migration journey is really it's a matter of sequencing sequencing which specific workloads to move and when and what's interesting is we're increasingly seeing customers prioritizing their most important and their most mission-critical applications ahead of their other workloads in terms of timing and they're they're doing that to get their workloads to benefit from the added resilience they get from running on the cloud so really it really does uh depend dave yeah thank you i mean that's pretty pretty good description of the options there and i i just come something you know bursting obviously i love those examples you gave around genomics chip design visual effects rendering the dr piece is again very common sort of cloud you know historical you know sweet spots for cloud but then the point about mission critical is interesting because i hear a lot of customers especially with the digital transformation push wanting to change their operating model i mean on the one hand not changing things put it in the cloud the lift and shift you have to change things low friction but then once they get there they're like wow we can do a lot more with the cloud so that was really helpful those those examples now last year at storage day you released a new file service and then you followed that up at re-event with another file service introduction sometimes i can admit i get lost in the array of services so help us understand when a customer comes to aws with like an nfs or an smb workload how do you steer them to the right managed service you know the right horse for the right course yeah well i'll start by saying uh you know a big part of our focus has been in providing choice to customers and what customers tell us is that the spectrum of options that we provide to them really helps them in their cloud journey because there really isn't a one-size-fits-all file system for all workloads and so having these options actually really helps them to to be able to move pretty easily to the cloud um and so my answer to your question about uh where do we steer a customer when they have a file workload is um it really depends on what the customer is trying to do and uh in many cases where they're coming from so i'll walk you through a little bit of of of how we think about this with customers so for storage and application admins who are extending existing workloads to the cloud or migrating workloads to aws the easiest path generally is to move to an fsx file system that provides the same or really similar underlying file system engine that they use on premises so for example if you're running a netapp appliance on premises or a windows file server on premises choosing that option within fsx provides the least effort for a customer to lift their application and their data set and they'll get the full safe set of capabilities that they're used to they'll get the performance profiles that they're used to but of course they'll get all the benefits of the cloud that i was talking about earlier like spin up and spin down and fully managed and elastic capacity then we also provide open source file systems within the fsx family so if you're a customer and you're used to those or if you aren't really wedded to a particular file system technology these are really good options and they're built on top of aws's latest infrastructure innovations which really allows them to provide pretty significant price and performance benefits to customers so for example the file system file servers for these offerings are powered by aws's graviton family of processors and under the hood we use storage technology that's built on top of aws's scalable reliable datagram transport protocol which really optimizes for for speed on the cloud and so for those two open source file systems we have open zfs and that provides a really powerful highly performant nfs v3 and v4 and 4.1 and 4.2 file system built on a fast and resilient open source linux file system it has a pretty rich set of capabilities it has things like point-to-time snapshots and in-place data cloning and our customers are really using it because of these capabilities and because of its performance for a pretty broad set of enterprise i.t workloads and vertically focused workloads like within the financial services space and the healthcare life sciences space and then luster is a scale-out file system that's built on the world's most popular high-performance file system which is the luster open source file system and customers are using it for compute intensive workloads where they're throwing tons of compute at massive data sets and they need to drive tens or hundreds of gigabytes per second of throughput it's really popular for things like machine learning training and high performance computing big data analytics video rendering and transcoding so really those scale out compute intensive workloads and then we have a very different type of customer very different persona and this is the individual that we call the aws builder and these are folks who are running cloud native workloads they leverage a broad spectrum of aws's compute and analytic services and they have really no history of on-prem examples are data scientists who require a file share for training sets research scientists who are performing analysis on lab data developers who are building containerized or serverless workloads and cloud practitioners who need a simple solution for storing assets for their cloud workflows and and these these folks are building and running a wide range of data focused workloads and they've grown up using services like lambda and building containerized workloads so most of these individuals generally are not storage experts and they look for storage that just works s3 and consumer file shares uh like dropbox are their reference point for how cloud storage works and they're indifferent to or unaware of bio protocols like smb or nfs and performing typical nas administrative tasks is just not it's not a natural experience for them it's not something they they do and we built amazon efs to meet the needs of that group it's fully elastic it's fully serverless spreads data across multiple availability zones by default it scales infinitely it works very much like s3 so for example you get the same durability and availability profile of s3 you get intelligent tiering of colder data just like you do on s3 so that service just clicks with cloud native practitioners it's it's intuitive and it just works there's mind-boggling the number of use cases you just went through and this is where it's so you know it's you know a lot of times people roll their eyes oh here's amazon talking about you know customer obsession again but if you don't stay close to your customers there's no way you could have predicted when you're building these services how they were going to be put to use the only way you can understand it is watch what customers do with it i loved the conversation about graviton we've written about that a lot i mean nitro we've written about that how it's you've completely rethought virtualization the security components in there the hpc luster piece and and the efs for data scientists so really helpful there thank you i'm going to change uh topics a little bit because there's been this theme that you've been banging on at storage day putting data to work and i tell you it's a bit of a passion of mine ed because frankly customers have been frustrated with the return on data initiatives it's been historically complicated very time consuming and expensive to really get value from data and often the business lines end up frustrated so let's talk more about that concept and i understand you have an announcement that fits with this scene can you tell us more about that absolutely today we're announcing a new service called amazon file cache and it's a service on aws that accelerates and simplifies hybrid workflows and specifically amazon file cache provides a high speed cache on aws that makes it easier to process file data regardless of where the data is stored and amazon file cache serves as a temporary high performance storage location and it's for data that's stored in on-premise file servers or in file systems or object stores in aws and what it does is it enables enterprises to make these dispersed data sets available to file based applications on aws with a unified view and at high speeds so think of sub millisecond latencies and and tens or hundreds of gigabytes per second of throughput and so a really common use case it supports is if you have data stored on premises and you want to burst the processing workload to the cloud you can set up this cache on aws and it allows you to have the working set for your compute workload be cached near your aws compute so what you would do as a customer when you want to use this is you spin up this cache you link it to one or more on-prem nfs file servers and then you mount this cache to your compute instances on aws and when you do this all of your on-prem data will appear up automatically as folders and files on the cache and when your aws compute instances access a file for the first time the cache downloads the data that makes up that file in real time and that data then would reside on the cache as you work with it and when it's in the cache your application has access to that data at those sub millisecond latencies and at up to hundreds of gigabytes per second of throughput and all of this data movement is done automatically and in the background completely transparent to your application that's running on the compute instances and then when you're done with your workload with your data processing job you can export the changes and all the new data back to your on-premises file servers and then tear down the cache another common use case is if you have a compute intensive file-based application and you want to process a data set that's in one or more s3 buckets you can have this cache serve as a really high speed layer that your compute instances mount as a network file system you can also place this cache in front of a mix of on-prem file servers and s3 buckets and even fsx file systems that are on aws all of the data from these locations will appear within a single name space that clients that mount the cache have access to and those clients get all the performance benefits of the cache and also get a unified view of their data sets and and to your point about listening to customers and really paying attention to customers dave we built this service because customers asked us to a lot of customers asked us to actually it's a really helpful enable enabler for a pretty wide variety of cloud bursting workloads and hybrid workflows ranging from media rendering and transcoding to engineering design simulation to big data analytics and it really aligns with that theme of extend that we were talking about earlier you know i often joke that uh aws has the best people working on solving the speed of light problem so okay but so this idea of bursting as i said has been a great cloud use case from the early days and and bringing it to file storage is very sound and approach with file cache looks really practical um when is the service available how can i get started you know bursting to aws give us the details there yeah well stay tuned we we announced it today at storage day and it will be generally available later this year and once it becomes available you can create a cache via the the aws management console or through the sdks or the cli and then within minutes of creating the cache it'll be available to your linux instances and your instances will be able to access it using standard file system mount commands and the pricing model is going to be a pretty familiar one to cloud customers customers will only pay for the cash storage and the performance they need and they can spin a cash up and use it for the duration of their compute burst workload and then tear it down so i'm really excited that amazon file cache will make it easier for customers to leverage the agility and the performance and the cost efficiency of aws for processing data no matter where the data is stored yeah cool really interested to see how that gets adopted ed always great to catch up with you as i said the pace is mind-boggling it's accelerating in the cloud overall but storage specifically so by asking us can we take a little breather here can we just relax for a bit and chill out uh not as long as customers are asking us for more things so there's there's more to come for sure all right ed thanks again great to see you i really appreciate your time thanks dave great catching up okay and thanks for watching our coverage of aws storage day 2022 keep it right there for more in-depth conversations on thecube your leader in enterprise and emerging tech coverage [Music] you

(upbeat music) >> I'm Dave Nicholson and welcome to Dell Technologies World 2022. I'm delighted to be joined by Tony Bishop. Tony is senior vice president, enterprise strategy at Digital Realty. Tony, welcome to theCUBE. >> Thank you, Dave. Happy to be here. >> So Tony, tell me about your role at Digital Realty and give us a little background on Digital Realty and what you do. >> Absolutely, so my job is to figure out how to make our product and experience relevant for enterprises and partners alike. Digital Realty is probably one of the best kept secrets in the industry. It's the largest provider of multi-tenant data center capacity in the world, over 300 data centers, 50 submetros, 26 countries, six continents. So it's a substantial provider of data center infrastructure capacity to hyperscale clouds to the largest enterprise in the world and everywhere in between. >> So what's the connection with Dell? What are you guys doing with Dell? >> I think it's going to be a marriage made in heaven in terms of the partnership. You think of Dell as the largest leading provider of critical IT infrastructure for companies around the world. They bring expertise in building the most relevant performant efficient infrastructure, combine that with the largest most relevant full spectrum capability provider of data center capacity. And together you create this integrated pre-engineered kind of experience where infrastructure can be delivered on demand, secure and compliant, performant and efficient and really unlock the opportunity that's trapped in the world around data. >> So speaking of data, you have a unique view at Digital Realty because you're seeing things in aggregate, in a way that maybe a single client wouldn't be seeing them. What are some of the trends and important things we need to be aware of as we move forward from a data center, from an IT perspective, frankly. >> Yeah, it's an excellent question. The good part of the vantage point is we see emerging trends as they start to unfold 'cause you have the most unique diverse set of customers coming together and coming together, almost organized like in a community effect because you have them connecting and attaching to each other's infrastructure sharing data. And what we've seen is in explosion in data being created, data being processed, aggregated, stored, and then being enriched. And it's really around that, what we call the data creation life cycle, where what we're seeing is that data then needs to be shared across many different devices, applications, systems, companies, users, and that ends up creating this new type of workflow driven world that's very intelligent and is going to cause a radical explosion in all our eyes of needing more infrastructure and more infrastructure faster and more infrastructure as a service. >> Yeah, when you talk about data and you talk about all of these connectivity points and communication points, talk about how some of those are explained to us. Some of these are outside of your facilities and some of them are within your facilities. In this virtualized abstracted world we live in it's easy to think that everything lives in our endpoint mobile device but talk about how that gravity associated with data affects things moving forward. >> Absolutely, glad you brought up about the mobile device because I think it's probably the easiest thing to attach to, to think about how the mobile device has radically liberated and transformed end users and in versions of mobile devices, even being sensors, not just people on a mobile phone proliferating everywhere. So that proliferation of these endpoints that are accessing and coming over different networks mobile networks, wifi networks, corporate networks, all end up generating data that then needs to be brought together and processed. And what we found is that we've found a study that we've been spending multiple years and multiple millions of dollars building into an index in a tool called the Data Gravity Index where we've been able to quantify not only this data creation life cycle, but how big and how fast and how it creates a gravitational effect because as more data gets shared with more applications, it becomes very localized. And so we've now measured and predicted for 700 mentors around the world where that data gravity effect is occurring and it's affecting every industry, every enterprise, and it's going to fundamentally change how infrastructure needs to be architected because it needs to become data centric. It used to be connectivity centric but with these mobile phones and endpoints going everywhere you have to create a meeting place. And it has to be a meeting place where the data comes together and then systems and services are brought and user traffic comes in and out of. >> So in other words, despite your prowess in this space you guys have yet to solve the speed of light issue and the cost of bandwidth moving between sites. So is it fair to say that in an ideal world you could have dozens of actually different customers, separate entities that are physically living in data center locations that are built and posted and run by Digital Realty, communicating with one another. So when these services are communicating instead of communicating over a hundred miles or a thousand miles, it's like one side of the chicken wire fence to the other, not that you use chicken wire in your data center but you get the point, is that fair. >> It is, it's like the mall analogy, right? You're building these data malls and everybody's bringing their relevant infrastructure and then using private secure connections between each other and then enabling the ability for data to be exchanged, enriched and new business be conducted. So no, physics hasn't been solved, Dave, just to add to that. And what we're finding is it's not just physics. One of the other things that we're continuing to see and hear from customers and that we continue to study as a trend is regulations, compliance and security are becoming as big a factors as physics is. So it's not just physics and cost which I agree with what you're saying but there's also these other dimensions that's in effect in placement, connectivity in the management of data and infrastructure, basically, in all major metros around the world where companies do business and providers support them, or customers come to meet them both physically and digitally. It's an interesting trend, right? I think a number of the industrians call it a digital twin where there's a virtual version and of a digital version and a physical version and that's probably the best way to think of us, is that secure meeting place where each can have their own secure infrastructure of what's being digitized but actually being placed physically. >> Yeah, that's interesting. When you look at this from the Dell, Digital Realty partnership perspective we know here at theCUBE that Dell is trying to make consumption of what they build, very, very simple for end user customers. Removing the complexity of the underlying hardware. There's a saying that the hardware doesn't matter anymore. You hear things referred to as serverless or no code, low code, those sort of abstract away from the reality of what's going on under the covers. But APEX, as an example from Dell allows things to be consumed as operational expense, dramatically simplifying the process of consuming that hardware. Now, if you go down to almost the concrete layer where Digital Realty starts up, you're looking at things like density and square footage and power consumption, right? >> Yep. >> So tell me, you mentioned infrastructure. Tell me about the kind of optimization from a hardware standpoint that you expect to see from Dell. >> Yeah, in the data center, the subset of an industry, they call it digital or mission critical infrastructure, the space, the power, the secure housing, how do you create physical isolation? How do you deal with cooling and containment? How do you deal with different physical loads? 'Cause some of the more dense computers likely working with Dell and some of the various semiconductors that Dell takes and wraps into intelligent compute and storage blocks, the specialized processing for our use cases like artificial intelligence and machine learning, they run very fast, they generate a lot of heat and they consume a lot of power. So that means you have to be very smart about the critical infrastructure and the type of server infrastructure storage coming together where the heat can be quickly removed. The power is obviously distributed to it, so it can run as constant and as fast as possible to unlock insights and processing. And then you also need to be able to deal with things like, hey, the cabling between the server and the storage has to be that when you're running parallel calculations that there's an equal distance between the cabling. Well, if I don't think about how I'm physically bringing the server storage and all of that together and then having space that can accommodate and ensure the equal cabling in the layout, oh and then handle these very heavy physical computers. So that physical load into the floor, it becomes very problematic. So it's hidden, most people don't understand that engineering but that's the partnership that why we're excited about with Dell is you're bringing all that critical expertise of supporting all those various types of use cases of infrastructure combinations and then combining the engineering understanding of how do I build for the right performance, the right density, the right TCO and also do it where physical layout of having things in proximity and in a contiguous space can then be the way to unlock processing of data and connecting to others. >> Yeah, so from an end user perspective, I don't need to care about any of what you just said. All I heard was wawawawawa (chuckles). I will consume my APEX delivered Dell by the drink, as a service, as OPEX, however I want to consume it. But I can rest assured that Digital Realty and Dell are actually taking care of those meaningful things that are happening under the hood. Maybe I'm revealing my long term knuckle dragging hardware guy credentials when I just get that little mentioning. >> (indistinct) you got it, performance secure compliant and I don't need to worry about it. The two of you're taking care of it and you're taking care of it for me. And every major mentor around the world delivered in the experience it needs to be delivered in. >> So from the Digital Realty point of view, what are the things that not necessarily keep you up at night worrying, but sort of wake you up in the morning early with a sense of renewed opportunity when it comes to the data center space, a lot of people would think, well we're in the era of cloud, no one's building any data centers except for monster cloud players. But that's definitely not the case, is it? There's a demand for what you folks are building and delivering. So first, what's the opportunity look like and then what are the constraints that are out there? Is it dirt, is it power? What are the constraints you face? >> We have probably all the above, is the shortest answer, right? So we're not wawawa, right Dave? But what we are is the opportunity is huge because it's not one platform, there's many platforms there isn't one business that exists today that doesn't use many applications, doesn't consume many different services both internally and externally, and doesn't generate a ton of data that they may not even know where it is. So that's the exciting part. And that continues to force a requirement that says I need to be able to connect to all those clouds which you can do at our platform but I also need to be able to put infrastructure or the storage of data next to it and in between it. So it's like an integration approach that says if I think physical first think physical that's within logical proximity to where I have employees, customers, partners, I have business presence. That's what drives us, and in our industry continues to grow both. And we see it in our own business. It's a double digit growth rate for both commercial oriented enterprises and service providers in the telco cloud, or content kind of space. So it's kind of like a best of both worlds. I think that's what gets us excited. If I should take a second part of the question, what ends up boring is like all of us, it is a physical world, physical world start with, do we have enough power? Is it durable, sustainable and secure? Is it available? Do we have the right connectivity options. Keeping things available is a full-time job, making it so that you can accommodate local nuances when you start going in different regions and countries and metros there's a lot of regional policy compliance or market specific needs that have to be factored in. But you're still trying to deliver that consistent physical availability and experience. So it's a good problem to have but it's a critical infrastructure problem that I would put in the same kind of bucket as power companies, energy companies, telecommunication companies, because it's a meeting place for all of that. >> So you've been in this business, not just at Digital Realty but you you've been in this part of the IT world for a while. >> Yeah. >> How has the persona of a customer for a Digital Realty changed over time? Have we seen the kind of consolidation that people would expect in this space in terms of fewer but larger customers coming in and seeking floor space? >> Well, I think it's been the opposite of what probably people predict. And I pause there intentionally being very candid and open. And it's probably why that using data as the proxy to understand, is that it's a many to many world that's only getting bigger, not smaller. As much as companies consolidate, there's more that appear. Innovation is driving new businesses and new industries or the digitization of old industries which is then creating a whole multiplier effect. So what we're seeing is we're actually seeing a rapid uptake in the enterprise side of our business which is why I'm here in driving that. That really was much more nominal five years ago for being the provider of the space and capabilities for telcos and large hyperscalers continues to go because it's not like a once and done, it's I need to do this in many places. I need to continue to bring as there's a push towards the edge, I need to be able to create meeting places for all of it. And so to us, we're seeing a constant growth in more companies becoming customers on the enterprise side more enterprises deploying in more places solving more use cases. And more service providers figuring out new ways to monetize by bringing their infrastructure and making an accessibility to be connected to on our platform. >> So if I'm here hearing you right, you're saying that people who believe that we are maybe a few years away from everything being in a single cloud are completely off base. >> Mmh hmm. >> That is not the direction that we're heading, from your view, right? >> We love our cloud customers, they're going to continue to grow. But it's not all going to one cloud. I think what you would see is, that you would see where a great way to assess that and break it down is enterprise IT, Gartner's Forecast 4.2, four and a half trillion a year in spend, less than a third of that's hitting public cloud. So there's a long tail first of all, it's not going to one cloud of people. There's like seven or eight major players and then you go, okay, well, what do I do if it's not in seven or eight major players? Well, then I need to put it next to it. Oh, that's why we'll go to a Digital Realty. >> Makes a lot of sense. Tony Bishop, Digital Realty. Thanks for joining us on theCUBE. Have a great Dell Technologies World. For me, Dave Nicholson, stay tuned more live coverage from Dell Technologies World 2022 as we resume in just a moment. (soft music)

>> We're back at AWS reinvent 2021. You're watching theCUBE. We're here live with one of the first live events, very few live events this year. It's the biggest hybrid event really of the year, of the season. Hopefully it portends a great future. We don't know it's a lot of uncertainty, but AWS said they're going to go for it. Close to 30,000 people here, Chris Wiborg is here. He's the VP of product marketing at Cohesity. Chris, great to see you face to face man. >> It's great to see you live again Dave. You understand that. >> Over the last couple of years we've had a lot of virtual meetup, hang out, and we talk every other quarter. >> Yeah. >> So it's great to see. Wow. You know, we were talking before the show. Well, we didn't really know what it was going to be like. I don't think AWS knew. >> No. >> It's like everything these days. >> You know, we did our own virtual event back in October because that was the time. And this is the first thing we've been back to live. And I was wondering, what's going to be like when I show up, but it's great to see all the folks that are here. >> Yeah. So I could see the booth. You know, you guys have had some good traffic. >> We have, yeah. >> A lot of customers here, obviously huge ecosystem. This, you know, the "flywheel keeps going". >> Yeah. You and I had a conversation recently about data management. It's something that you guys have put a stake in the ground. >> Absolutely. >> Saying, you know, we're not just backup, we're a good data management. It's fuzzy to a lot of people, we've had that conversation, but you're really starting to, through customer feedback, hone that message and the product portfolio. So let's start from the beginning. What is data management to cohesity? >> Well, so for us it's about the data lifecycle, right? And you heard a little bit about this actually during the keynote today, right? >> Right. >> When you think about the various services, you need to apply to data along the way to do basic things like protect it, be able to make sure you can recover from disasters, obviously deal with security today given the prevalence of ransomware out there, all the way down to at the end, how do you get more value out of it? And we do that in some cases with our friends from AWS using some of their AIML services. >> So your view of data may mean, it's kind of stops at the database right underneath. There's an adjacency to security that we've talked about. >> Yeah, very much. >> Data protection is now becoming an increasingly important component of a security strategy. >> It is. >> It's not a direct security play, but it's just the same way that it's not just the SecOps team has to worry about security anymore. It's kind of other parts of the organization. Talk about that a little bit. >> Yeah, well, we actually had a customer advisory board about two months or so ago now. And we talked to many of our customers there, and one of them I won't name, a large financial institution. We asked them, you know, where did we stand in your spend these days? And he's able to tell you, a while back about a year ago, having new backup and recovery is a starting point was kind of on the wishlist. And he said today it's number two. And I said, well why? He said well, because of ransomware, right? You'd be able to come back from that and ask, well, great, what's number one? He said, well, endpoint security. So there you are, number one and number two, right? Top of mind for customers these days in dealing with really the scourge that's affecting so many organizations out there. And I think where you're going, you starting to see these teams work together in a way that perhaps they hadn't before, or you've got the SecOps team, you've got the IT operations team. And while exactly your point, we don't position ourselves as just a data security company, that's part of what we do. We are part of that strategy now where if you have to think about the various stages and dealing with that, defending your backups, 'cause that's often the first point of attack now for the bad guys. Being able to detect what's going on through AI and the anomaly detection and such, and then being able to rapidly recover, right? In the recover phase, that's not something that security guys spend time on necessarily, but it's important for the business to be able to bring themselves back when they're subject to an attack, and that's where we come in in spades. >> Yeah. So the security guys are busy trying to figure out, okay, what happened? How do we stop it from happening again? >> There's another business angle which is okay, how do we get back up and running? How much data did we lose? Ideally none. How fast can we get it back up? That's that's another vector that's now becoming part of that broader security stack. >> That's right. I mean, I think if you look at the traditional NIST cybersecurity framework, right? Stage five has always been the recover piece. And so this is where we're working with some of the players in the security space. You may see an announcement we did with Cisco around secure access recently. Where, you know, we're working together, not only to unite two tribes within large organizations. Right? The SecOps and ITOps guys. But then bringing vendors together because it's through that, that really, we think we're going to solve that problem best. >> Before we get into the portfolio, and I want to talk about how you've evolved that, let's talk a little about ransomware, it's in the news. You know, I just wrote a piece recently and just covered some of the payments that have made. I mean, I think the biggest is 40 million, but many tens of millions here and there. And it was, you know, one case, I think it was the Irish health service did not pay, thus far hasn't paid, but it's costing him $600 million to recover as the estimate. So this is serious threat. And as I've said, many times on theCUBE, exactly anybody can be a ransomware as they go on the dark web. >> Ransomware is a service. >> Right, ransomware is a service. Hey, can you set up a help desk for me to help me negotiate? And I'm going to put a stick into a server and you know, I hope that individual gets arrested but you never know. Okay. So now it's top of mind, what are you guys doing? First of all, what are you seeing from customers? How are they responding? What are you guys doing to help? >> Well, I think you're right. First of all, it's just a huge problem. I think the latest stat I saw was something like every 11 seconds there's a new attack because I can go into your point with a credit card, sign up as a service and then launch an attack. And the average payment is around 4.2 million or such, but there's some that are obviously lots bigger. And I think what's challenging is beyond the costs of recovering and invent itself is there's also the issue around brand and reputation, and customer service. And all these downstream effects that I think, you know, the IT guys don't think about necessarily. We talked to one customer or a regional hospital where the gentleman there told me that what he's starting to see after the fact is now, you've actually got class action suits from patients coming after them saying like, "Hey you, you let my data get stolen. Right? Can you imagine no IT guys thinking about that. So the cost is huge. And so it's not just an issue I think that was once upon a time just for ITOps or SecOps through the CIO, even it's even past the board level now if you can imagine. It's something the general public worries about and we actually did a survey recently where we asked people on the consumer side, are you more or less likely to do business with companies if you know they've been subject to ransomware or attacks? And they said, no, we are concerned about that, we are more reticent to do business with people as consumers if they're not doing the right things to defend their business against ransomware. Fascinating. Right? It's long past the tipping point where this is an IT only issue. >> So, high-level strategy. So we talk about things like air gaps, when I talked about your service to ensure immutability, >> Yeah, yeah. >> And at 50,000 foot level, what's the strategy then I want to get into specifics on it. >> Let's talk a little bit about, so the evolution of the attack, nature of attacks, right? So once upon a time, this is in the distant past now, the bad guys that you used to come after your production data, right? And so that was pretty easy to fix with companies like us. It's just restore from backup. They got a little smarter< let's call that ransomware 2.0, right? Where now, they say, let's go after the backup first and encrypt or destroy that. And so there, to your point, you need immutability down to the file system level. So you can't destroy the backup. You got to defend the backup data itself. And increasingly we're seeing people take in isolation in a different way than they used to. So you probably recall the sort of standard three, two, one rule, right? >> Yeah, sure. >> Where the one traditionally meant, take that data offsite on magnetic tape, send it to Iron mountain for example, and then get the data back when I need it. Well, you know, if your business is at risk, trying to recover from tape, it just takes too long. That's just no reason. >> It can be weeks. >> It can be weeks and you've got to locate the tapes, you got to ship them, then you got to do the restore. And just because of the physical media nature, it takes a while. So what we're starting to see now is people figuring out how to use the cloud as a way to do that and be able to have effectively that one copy stored offsite in a different media, and use the cloud for that. And so one of the things we announced actually back in our show in October, was a new service that allows you to do just that. We're calling it for now Project Fort Knox. We're not sure if that name is going to work globally, right? But the idea is a bunker, an isolated copy of the data in the cloud that's there, that can restore quickly. Now, is it as fast as having a local replica copy? Of course not. But, it's way better than tape. And this is a way to really give you that sort of extra layer of insurance on top of what you're already doing probably to protect your data. >> And I think that's the way to think of it. It's an extra layer. It's not like, hey, do this instead of tape, you're still going to do tape, you know. >> There's some that do that for all sorts of reasons, including compliance and governance and regulatory ones. Right? >> Yeah. >> And, you know, even disaster recovery scenarios of the worst case, I hope I never have to go through it. Yeah, you could go to the cloud. >> That's right. >> So, local copy is the best. If that's not there, you've got your air gap copy in the cloud. >> Yap. >> If that's not there for some crazy reason. >> We have a whole matrix we've been sharing with our customers recently with a different options. Right? And it's actually really interesting the conversation that occurs between the IT operations folks, and the SecOps folks back to that. So, you know, some SecOps folks, if they could, they just unplug everything from the network, it's safe. Right? But they can't really do business that way. So it's always a balance of what's the return that you need to meet. And by return I mean, coming back from an attack or disaster versus the security. And so again, think of this as an extra layer that gives you that ability to sleep better at night knowing that you've got a third, a tertiary copy, stored somewhere offsite in a different media, but you can bring it back at the same time. >> How have you evolve your portfolio to deal with both the data management trends that we've talked about and the cyber threats. >> Yeah. Well, a number of things. So amongst the other announcements we made back in October is DR. So DR is not a security thing per se, you know, who gets paged when something goes wrong? It's not the info SEC guys for DR, it's the ITOps guys. And so we've always had that capability, but one of the things we announced is be able to do that to do that to the cloud now in AWS. So, instead of site to site, being able to do it site to cloud, and for some organizations, that is all about being able to maybe eliminate a secondary site, you know, smaller organizations, others that are larger enterprises, they probably have a hybrid strategy where that's a part of their strategy now. And the value there is, it's an OpEx cost, right? It's not CapEx anymore. And so again, you lower your cost of operations. So that's one thing in the data management side. On the security side, another thing we announced was yet another service that runs in AWS, we call Cohesity Data Govern. And this is a way to take a look at your data before something ever occurs. One of the key things in dealing with ransomware is hygiene is prevention, right? And so you sort of have classically security folks that are trying to protect your data, and then another set of folks, certainly a large enterprise that are more on the compliance regulatory front, wanting to know where your PII is, your private sensitive data. And we believe those things need to come together. So this data governance product actually does that. It takes a look at first classifying your data, and then being able to detect anomalies in terms of who's coming in from where to get to it, to help you proactively understand what's at threat, and first of all, you know, where your crown jewels really are and make sure that you're protecting those appropriately and maybe modifying access policies If you have set up in your existing native applications,. So it's a little bit of awareness, a little bit prevention, and then when things start to go wrong, another layer that helps you know what's wrong. >> I love that the other side of the coin, I mean, you going to get privacy as a service along with my data protection as a service, know that's a better model. Tight on time sir, but the last question. >> Sure. >> The ecosystem. >> Yeah. >> So you mentioned endpoint security, I know identity access is cloud security, and since the remote work has really escalated, we talk about the ecosystem and some of the partnerships that you're enabling, API integration. >> Yeah, totally. So, you know, we have this, what we call our threat defense model, has got four layers to it. One is the core, is all about resiliency. You need to assume failure. We have, you know, the ability to fail over, fail back down our file system. It has to be immutable to keep the bad guys out. You have to have encryption, basic things like that. The next layer, particularly in this world of zero trust. Right? Is you have to have various layers access control, obvious things like multifactor authentication, role-based access control, as well as things like quorum features. It's the two keys in the safety deposit box to unlock it. But that's not enough. The third layer is AI powered anomaly detection, and being able to do data classification and stuff and such. But then the fourth layer, and this was beyond just us, is the ability to easily integrate in that ecosystem. Right? So I'll go back to the Cisco example I gave you before. We know that despite having our own admin console, there's no SecOps person that's going to be looking at that. They're going to look at something like a SecureAX, or maybe a Palo Alto XR, and be able to pull signals from different places including endpoints, including firewall. >> You going to feed that. >> Exactly. So we'll send signals over that, they can get a better view and then because we're all API based, they can actually invoke the remedy on their side and initiate the workflow that then triggers us to do the right thing from a data protection standpoint, and recovery standpoint. >> It's great to have you here. Thanks so much for coming on. >> It's good to see you again live today. >> See you in the evolution of cohesity. Yes, absolutely. Hopefully we do this a lot in 2022, Chris. >> Absolutely, looking forward to. >> All right. Me too. All right, thank you for watching this is theCUBE's coverage, AWS reinvent. We are the leader in high tech coverage, we'll be right back.

>> from the Silicon Angle Media Office in Boston, Massachusetts. It's the cue now Here's your host Day Volonte. >> Hello, everyone, this is David lot. They fresh fresh off the red eye from VM World 2019. And what I wanted to do was share with you some analysis that I've done with our friends at E. T. R. Enterprise Technology Research. We've begun introducing you to some of their data. They have this awesome database 4500 panel, a panel of 4500 end users end customers, and they periodically go out and do spending surveys. They've given me access to that spending data and what I wanted to do because because you had a number of companies announced this this quarter, I wanted to do a storage drill down so pure. Announced in late July, Del just announced yesterday late August. Netapp was mid August. HP was last week again late August, and IBM was mid July. So you have all these companies, some of which are pure plays like pure netapp. Others of you know, big systems companies on DSO. But nonetheless, I wanted to squint through the data and share with you the storage spending snapshot for the second half of 2019. So let's start with the macro. >> What you heard on the conference calls was some concern about the economy. There's no question that the tariffs are on people's minds, particularly those with large exposure exposure in China. I mean, Del obviously sells a lot of PCs in China, so they're very much concerned about that. IBM does a lot of business there, pure, really. 70% appears business roughly is North America, so they're not as exposed so But the macro is probably looks like about 2% GDP growth for the quarter i. D. C. Has the overall tech market growing at two ex GDP. Interestingly, a Gartner analyst told me in May on the Cube that there is no correlation between GDP and I t spend, which surprised me. Some people disagree with that, but But that surprised me. But nonetheless, we we still look at GDP and look at that ratio. Sometimes the other macro is component costs for years. For the storage business the last several years, NAND pricing has been a headwind. Supply has been down, it's kept prices up. It has kept all flash arrays more expensive relative to some of the spinning disc spread the brethren something that we thought would attenuate sooner. It finally has. Nan pricing is now a tailwind, so prices air coming down. What that does is it opens up new workloads that we're really kind of the domain of spinning disk before big data kind of workloads is an example. Not exclusively big data, but it just opens up more workloads for storage companies, particularly Flash Cos The other big macro we're seeing is people shifting to subscription models. They want to bring that cloud like model to the data wherever two lives on Prem in ah, hybrid environment in a public cloud and company storage companies trying to be that that data management plane across clouds, whether on prime it. And that's a That's a big deal for a lot of these companies. I'll talk a little bit more about that, so you're seeing this vision of a massively parallel, scalable distributed system play out >> where >> data stays where it lives. Edge on Prem Public Cloud and storage is really a key part of that. Obviously, that's where the data lives, but you're not seeing data move across clouds so much. What you are seeing is metadata, move and compute. Move to the data so that type of architecture is being set up. It's supported by architecture's, not the least of which are all flash, and so I want to get into it. >> Now I want to share with you some data on this slide. If you wouldn't mind bringing it up. Alex on spending momentum. So the title size spending moment of pure leads, the storage packs and what this shows is the vendor on the left hand side. And it essentially looks at the breakdown of the spending survey where e t r ask the buyers of the different companies products. What percent of the spending is going to go toward replacing? They're gonna replace the vendor. Are they gonna decrease? Spend. That's the bright red is replace. The sort of pinkish is decreased, the spending. The gray is flat. The sort of evergreen forest green is increase in the lime. Green is ad, so if you take the lime green in the forest, green ad and the grow on you subtract the rest. You get the net score, so the higher the net score, the better. you can see here that pure storage has the highest net score by far 48%. I'll show you some data later. That correlates to that when we pull out some of the data from the income statements. >> So this is Ah, the >> July 2019 spending intention surveys specifically asking relative to the second half what the spending intentions are. So this looks good for pure on again. I'll show you Cem, Cem Cem Income State income statement data that really affirms this Hewlett Packard Enterprise actually was pretty strong in the spending survey. Particularly nimble is growing HP Overall, the storage business was was down a little bit, I think, three points, but nimble was up 28%. So you're seeing some spending activity there. Netapp did not have a great quarter. They were down substantially. I'll show you that in a minute. On dhe, it looks like they've got some work to do. Deli M. C. I had a flat quarter. Dell has a such a huge install base. They're everywhere on DSO. Everybody wants a piece of their pie. Del. After the merger of the acquisition of the emcee, their storage share declined. They then bounce back. They had a much, much stronger year last year, and now it's sort of a dogfight with the rest. IBM IBM is in a major cycle shift. IBM storage businesses is heavily tied to its mainframe businesses. Mainframe business was way, way down, its overall systems. Business was down, even though power was up a little bit. But the mainframe is what drives the systems business, and it drags along a lot of storage. IBM has got a new mainframe announcement that it's got to get out. It's got a new high end storage announcement that it's got to get out, and it's really relying on that. So you can see here from the E T. R data, you know, pure way out ahead of the pack continues to gain share about over 1000 respondents to this. So a lot of shared accounts by shared accounts mean the number of accounts that that actually have some combination of multiple storage vendors. And so they were able to answer this 1068 respondents pure the clear winner here. Now let's put this into context. So the next slide I want to show you some of the key performance indicators from the June quarter off the income statements. >> So again you see, I get the vendor. The revenue for the quarter of the year to year growth for that quarter relative to last year. The gross margin in the free cash flow, just some of the key performance indicators that I'd like to look at. So look at pure Let's go, Let's go to the third column Look at growth pure 28% growth. Del flat 0% for this is just for storage. There's a storage growth. NETAPP down 16% end up in a bad quarter, HP down 3%. IBM down 21% Do due to the cycle that I discussed, You see the revenue, um, pure, growing very, very fast. But you know, from a small base or at 396 million versus compared that to Dell's 4.2 billion net APs 1,000,000,000 plus H p e. Almost a billion in IBM not nearly as large. And then look at the gross margin line. Pure is the industry's leading gross margin. It's just slightly above 69%. Dell is a blended that Asterix is a blended gross margin, so it includes PCs, servers, service's of V M wear, everything and, of course, storage. So now, when dehl was a public company before it went private, it's gross. Margins were in the high teens. So Del is in gross margin heaven with with both E, M C and V M wear now as part of its portfolio NetApp high gross margins of 67%. But that gross margin is largely driven by its gross margins from software and maintenance. And so that's a screen considerable contributor. Their product gross margins air in the mid fifties, kind of where I think E. M. C. Probably is these days. And when the emcee was a public company, it's gross. Margins were in the mid sixties, but then, as it was before, went private. I think it was dipping into the high fifties as I recall you CHP again, that's a blended gross margin, just roughly around 34%. I don't have as much visibility on their their storage gross margins. I would I would say they are below, in my view, what DMC and net out well below what Netapp would be on then IBM. That's again blended gross margin includes hardware. Software service is 47.4% probably half or more of IBM businesses. Professional service is on. IBM has, of course, a large software business as well. So and then the free cash flow you can see pure crushing it from the standpoint of of gaining share, I mean way, way ahead of the other market players, but only 14 million in free cash flow. So coming from a much, much smaller base, however pure, is purely focused on storage. So there are Andy. All their R and D is going into that storage space. DEL. Free cash flow very large. 3.4 billion that again is across the entire company. Net App. You can see 278 million h p e 648 million great quarter for HP from a free cash flow standpoint, I think year to date they're probably 838 140 million. So big Big quarter. For them. An IBM A 2.4 billion again. Dell, HP, IBM. That's across the company, as is the gross margin. So the the spending data from E. T. R. Really shows us that pure, strong Aziz showed you that very high net score and the intentions look strong, so I would suspect pure is going to continue to lead in the market share game. I don't see that changing. Certainly there's no evidence in the data. I think I think everybody else is in a sort of a dogfight del holding firm, you know, 0%. You'd like to see a little bit of growth out of that, but I think Del is actually, you know, Dell's key metric is, Are we growing faster than the market? That's that's they're sort of a primary criterion in metric for Dell is to grow faster than the overall market because that means you're growing some share. I think Del is comfortable with that. Della's gross margins actually were helped this this quarter by the fact that Dell server business was down 12%. There was a higher storage mix, so it propped up the margin a little bit. But again, generally speaking, it looks like pure is the market share winner here, but much, much smaller than the other guys. HB limbo very strong, and it shows up in the survey data from E T. R. And an IBM just needs to get a new product cycle out. So we'll come back. >> We'll take a look at this in in in in January and see how you know what it looked like and will continue to fall. Obviously, the income statement and the public reporting pure accelerate is coming up next month. Justin in mid September. I have no doubt, you know, pure has been first in a lot of different areas, right? They were first really all flash Ray. The only all flash. You're a company that ever reached escape velocity. They were they in Nutanix for the first kind of new $1,000,000,000 companies that people said would never have a billion dollar company. Pure is a pure play storage company, you know? Well, over a billion. Now, you know, they were first with that evergreen model. They made a lot of play there. You know, the first with envy, Emmy and first with the Nvidia relationships with Superior likes to be first. I have no doubt and accelerate next month down in Austin, curious that they picked Austin in Dell's backyard. I have no doubt that they're gonna have some other firsts at that show. Cuba be there watching just off of the emerald, the other big player here. Of course, that I'm not showing his v. San visa is very, very strong. You know, the D. E. T. Our data shows that, and certainly the data from the income statement shows of'em were NSX, the networking products, their cell phone to find network in their self defined storage of the the the V San. Very, very strong Pat Girl singer on the Cube. We asked him last week, Thio, take us through. So if someone has big memories and one of them was sort of East san, Excuse me. One of them was V San, and the board meeting at with Joe Tucci was on the Vienna where board really put a lot of pressure on Pat's and you can't do this to me. It's funny. Emcee had the shackles on the M, where for a number of years, but the shackles are off and visa is very, very strong. So these are some of the things we're keeping an eye on. Thanks for watching everybody busy day Volante, Cuban sites. We'll see you next time

>> Live, from Barcelona, Spain, it's the theCUBE, covering KUBECON and CloudNativeCon Europe 2019. Brought to you by RedHat, the Cloud Native Computing Foundation, and the Ecosystem Partners. >> Welcome back to theCUBE. I'm Stu Miniman. My co-host, Corey Quinn. 7700 here in Barcelona, Spain, for KUBECON, CLOUDNATIVECON. Happy to welcome to the program a first-time guest, Erin Boyd, who is a senior Principal Software Engineer in the office of the CEO of RedHat. Erin, thanks so much for joining us. >> Yeah, thanks for having me. >> Alright, so just a couple of weeks ago, I know I was in Boston, you probably were too, >> Yep. >> For RedHat Summit. Digging into a lot of the pieces. You focus on multi-cloud and storage. Tell us a little bit about, you know, your role, and what you're doing here at the KUBECON show. >> Sure, I'd be happy to. So for over a year now, RedHat's really been kind of leading the pack on hybrid cloud. You know, allowing customers to have more choice, you know, with both public and private cloud offerings. And, of course, OpenShift being our platform built on Kubernetes, we believe that should be the consistent API in which we have Federation. Yeah, so Erin, I got to talk to quite a few OpenShift customers at RedHat Summit. It was really how they're using that as a lever to help them really gain agility in their application deployment. But, let's start for a second, without getting too fanatic, you say hybrid cloud. What does that mean to your customers? You know, RedHat has a long legacy of, well, lives everywhere. So, public cloud, private cloud, hosting provider, all of the environments, you, RedHat, Enterprise, Linux, can live there. So in your space, what does hybrid cloud mean? >> So, hybrid cloud, I think follows a model of real. It's everywhere. So it's having OpenShift run on top of that and being able to have the application portability that you would expect. Along with the application portability, which is my focus, is having the data agility within those applications. >> Alright, how do you wind up approaching a situation where an app is now agile enough to move between providers almost seamlessly, without having it, I guess, descend down to the lowest common denominator that all providers that it's on are going to provide? I mean, at some point, doesn't that turn into treating the cloud as a place to just run your either instances or containers, and not taking advantage of, I guess, the platform level services? >> Sure, so I think that the API should expose those choices, I don't think it's a one size fits all when we talk about, you know, if you move your application maybe your data doesn't necessarily have to move. So part of the core functionality the Federation is meant to provide, which has been renamed Kubefed since Summit, is that you have the choice within that. And, you know, defining policies around the way we do this. So, perhaps your application is agile enough to span three different clouds, but due to data privacy, you want to keep your data on prem. So, Kubefed should enable you to have that choice. >> You know, so you know, help us dig down a little bit in the storage, you know, environment here, you know. >> Sure. I go back and I worked for a very large storage company that was independent before it got bought for a very large sum of money. But, we had block and file storage. And mostly, that you know, lived in a box, or in a certain application. >> Right. You know, the future, we always talked that there's going to be this wonderful object storage and actually it's designed to be, you know, we'll shard it, we'll spread it around >> Right. And it can live in lots of places. Cloud, a lot of times has that underneath it, so you know, have we started to you know, cross that gap of you know, that mythical nirvana of where say, you know, storage should actually live up to that distributive architecture that we're all looking for. >> Right, so with Kubernetes, the history is, we started off with only file systems. Block is something very new within the last couple releases that I actually personally worked on. The next piece that we're doing at Red Hat is leading the charge to create CRDs for object storage. So it's defining those APIs so customers can dynamically provision and manage their object storage with that. In addition, we recently acquired a company called NooBaa that does exactly that. They're able to have that data mobility through object buckets across many clouds doing the sharding and replication with the ability to dedupe. And that's super important because it opens up for our customers to have image streams, photos, things like that that they typically use within an enterprise, and quickly move the data and copy it as they need to. >> Yeah, so I've actually talked to the Noovaa team. I would joke with them that, didn't they deduplicate, couldn't they deduplicate their name 'cause it's like Noovaa. >> (laughs) yeah. >> So you know, plenty of vowels there. But, right, storage built for the cloud world is, you know, what we're talking about there. >> Right. >> How's that different from some of the previous storage solutions that we've been dealing with? >> So I think before, we were trying to maybe make fit what didn't work. That's not to say that file and block aren't important. I mean, having local storage for a high performance application is absolutely critical. So I think we're meeting the market where it is. It's dependent on the behavior of the application. and we should be able to provide that. And applications that primarily run in the cloud and need that flexibility, we should be offering object as a first-class citizen, and that's why our work with those CRDs is really critical. >> What is the customer need that drives this? Historically, with my own work with object stores, I tend to view that as almost exclusively accessed via HTTP end points. And at that point, it almost doesn't matter where that lives, as long as the networking and security and latency requirements are being met. What is it that's driving this as making it a first-class citizen built in to Kubernetes itself, the Rook? >> So it allows us to create the personas that we need. So it allows an administrator to administrate storage, just like they would normally with your persistent volume, persistent volume claims and quotas. And then it abstracts the details of, for instance, including that URL in your application. We use a config map within the app so the user doesn't have access necessarily to your keys in the cloud. It also creates a user so you're able to manage users like you would normal objects, which is a little bit different than the PV PVC, and that's why we feel like you know, it's important to have a CRD that defines object in that sense because it is a little bit different. >> All right, so Erin, is this Rook we're talking about then, is, you know, Rook, did I understand, I think got to 1.0, just got released. >> Yeah. >> You know, give us the update on what Rook is, you know, how that fits with this conversation we've been having. >> Right. You know, where we are with the maturity of it. And Rook, as was on the keynote this morning, you know, is a great CNCF project with a really healthy community behind it. One of the provisioners we've created as part of those object CRDs is a Rook provisioner for CEF block, or excuse me, CEF object. We also have an s3 provisioner. So, you know, we hope to have, just like we had external provisioners in Kubernetes, use, you know, allow for the same contribution from the community for those. >> Okay, yeah, there, I remember a couple of years ago at the show, this fixing storage for containers in Kubernetes was something that was a little bit contention in there, and there were a few different projects out there. >> Right. >> For that, you know, where are we with that? We understand that it's never, you know, one solution for every single use case. You know, you already talked about, you know, block file and object. >> Right. >> And how there's going to be a you know, a spectrum of options. >> Sure and so I think there's lots of things to fix. >> Yeah. >> When you talk about that. One of the key things that Rook offered was the ability to ease the deployment of the storage and administration of it, and, as you know, Rook you know, has a plethora of different storage systems that it provides. And, you know, what we're really pushing at RedHat, which I think is important, is having, you know, operators. Like the operator hub that was released with OpenShift 4.0. Rook will be an operator in there. So what that allows is for more automation and true scaling. 'Cause that's where we want to get to with hybrid cloud. If you're managing 10,000 clusters, you cannot do that manually. So having Rook, having operators, and automating the storage piece underneath is really critical to make it now-scale happen. >> Forgive my ignorance. When you say that Rook winds up exposing, for example, now an object store underneath. Is that it's own pile of disks on a system somewhere that it's running? Is it wrapping around object store provided by other cloud providers? Is it something else entirely? What is the, where do the actual drives that hold my data, when I'm using Rook's object store, live? So with Rook today, the object storage that it uses is CEF object. So it exposes the ability to create, you know the CEF components underneath, which Rook can lay down and then expose the object piece of that. So that's the first provisioner in there, yep. >> Wonderful. >> Alright, so I guess when I think about object storage, for years it's been, well, I've got s3 compatibility. And that's kind of the big thing. >> Yep. Is Rook s3 compatible then? Is it, you know, giving more flexibility to users to make this the standard in a cloud native environment? Help us, you know, put a fine as to what this is and isn't. >> Yeah, that's a great question, actually, and we get asked it often. So one of the first provisioners we did is just a proof the concept was an s3, a generic s3 provisioner. And of course, CEF is s3 compliant, so it also does that, but you know, there isn't a standard for object. So most providers of object are s3 compatible. We found it very easy to take off the s3 provisioner we created to create the CEF one. There wasn't much differentiation, which means it's a great pattern for anyone to want to onboard. >> Yeah. Do you find that as s3 itself, and of course, it's competitors of other cloud providers, become more capable, you're starting to see differentiation. Now easy example would be with some of the object storage tiers, where there's increased latency on retrievals. In some cases, as little as five minutes, or as much as 12 hours. Other providers, like Google Cloud, for example, or Azure, have consistent retrieval times on their archive storage. As an easy example, is that something that you're going to start seeing divergence on as object storage becomes smarter by, I guess, all of the providers as they race each other to improve their products. >> Absolutely. I think tiering is one of the facets of object that's really critical. And you know, of course, as we spoke earlier, it's physics, you know, and having data consistency at that very low threshold is important. So, you know, using the storage for what it's worth. Using the best tools, and pulling object into the ecosystem is part of that. >> Yeah, Erin, is there anything that differentiates kind of Kubernetes storage from, you know, what people are familiar with in the past? >> I think Kubernetes storage continues to evolve. The more we learn about how people use Kubernetes, and their needs, I think we listen closely to the community and we develop against that. >> Okay, I guess the other thing is, you know, what kind of feedback are you getting from customers? Where are we along this maturation journey. You know, my history is you know, I worked when we had to fix networking and storage in virtualized environment, and it took about a decade. We're five years into Kubernetes. It feels like we've, you know, accelerated that based on what we've done in the past, but you know, definitely, you know, when it first started, it was you know, let's put stateless stuff in containers and you know, storage will be an afterthought. >> Right. >> Or something that was kind of a side car over here where you had your repository. >> Right. And I think that's the beauty of Kubefed, is that in order to have true hybrid cloud, and have Federation, we have to come together in consensus with both network compute and storage. So it really brings the story full circle. >> Perfect. What do you think right now customers are having their biggest challenges with, as they start wrapping their minds around this new way of thinking? I mean, again, it's easy for a tiny start-up, it's Twitter for Pets, or something like that, to spin off in a pure cloud native way, but larger companies with this legacy concept known as a business model that might involve turning a profit, generally predate cloud, and have done an awful lot of stuff on the data center. What are they seeing as currently being limiting factors on their digital transformation? >> So with Kubernetes just being five years old, as we celebrate the birthday today, I think customers are also maturing. You know, they're entering the landscape, learning about Kubernetes, learning how to containerize, you know, lift and ship their applications, and then they're running up, to costs, right? And lock-ins and things they want to avoid. And that's really where we in the community want to provide a platform and a runway for them to have that choice. >> Alright. Erin, any customer successes that you can share with us, either about the operator or about work specifically? >> Certainly not with Federation. We haven't released it. It will come out in OpenShift 4.2, so we don't have any customer success stories yet, but I would say definitely it's a request, and you know, we're asking customers about it, and if they're interested. And you will find many times maybe they're not familiar with the word Federation, but they're definitely interested in that use case. >> Okay, how's the general feel. You know, what kind of feedback are you getting from customers so far, things that you're excited about that are happening here at the show? >> I'm just excited that Kubernetes is kind of growing up. And it's you know, becoming a true enterprise-level project that customers rely on, and build their business on. >> Well, Erin Boyd, really appreciate you joining us, sharing all the updates. Look forward to the upcoming release, and definitely get to follow up with you soon, to hear about those customers as they start rolling it out. >> Alright, great. Thank you. >> Alright. For Corey Quinn, I'm Stu Miniman, here at KUBECON, CLOUDNATIVECON 2019, Barcelona, Spain. Thanks for watching theCUBE (techno music)

>> Live, from Bellevue, Washington, it's theCUBE. Covering Smartsheet Engage 18. Brought to you by Smartsheet. >> Welcome back to theCUBE's continuing coverage of Smartsheet Engage 2018, I am Lisa Martin with Jeff Frick in Bellevue, Washington, our first time here. Second annual Smartsheet Engage and we're very please to be joined, welcoming back to theCUBE, Mark Mader, the CEO of Smartsheet. Mark, it's great to have you on the program. >> Thank you, good to be with you. >> Great job on the keynote. >> Thank you, appreciate it. >> So, you can see the buzz behind us, we just got out of the keynote, where, you guys kicked it up, there was a coupla things Jeff and I were talking about that were unique, that I haven't seen very much of at all, in all the keynotes that we go to. One, you started off with an explorer who had a very empowering, enlightening message, all about communication. And then, something that you did that I thought was really cool, that I don't think I've ever seen, is you actually, during your keynote, went into the audience, where you have about 2000 customers here, representing 1100 companies, across 20 countries, and just ad-libbed, hey guys, tell me about your company, how is Smartsheet empowering you, and as you said, that was all natural. >> I think part of it making it real for somebody, is giving you somebody that's relatable. So, we started off the conversation, as you said, with Ed Viesturs, arguably the most famous accomplished climber in the world, today, and he talked about the importance of communication and preparation, and teamwork, and clear decision making, in a context that was spectacularly visual, right, this mountain and those climbing shots, so, people relate to that, and then when you introduces those conducts in the business setting, it's like, oh, yeah, this applies to me, it applies to all of us. So, the notion of getting into the crowd, in a non-rehearsed way, is to really get people comfortable with, hey, I can share something, I can share an experience, and there's no one right answer, it's my experience. >> And that's why you're here, as you said in your keynote, and we know this as well, if companies aren't designing technology for the users, what's the point? >> Yeah, you're right and, one of the things I tried to highlight was, when you say for the user, it's not just for the user, the end user, like developed by a few people, spread to everybody, but it's empowering each and every person to say, hey I want to do something more transformational. I want to manage, automate, scale it, I don't want to be given that solution by someone, I want to do it. And there are hundreds of millions of people, who have the appetite and the interest, and the need for it. So, that's what we're trying to sell into. >> You know, Mark, we got to, so many shows, right, and everyone's chasing innovation. How do we get more innovative? Especially big companies, right? And you did show two really interesting messages, one, was your kind of core message, empowering everyone to improve, how they work, so, like you said, not just the top level decision makers, not down in the developer weave, but everybody up and down this stack. And then you shared a statement covey quote, really talking about how do people, keep 'em engaged and the way people are engaged is that they feel they're empowered to do something for their clients and their customers. So it's such an importannt piece and I think it's easy to talk about, harder to execute, but what is the answer to innovation? Giving more people the data, the tools and the power to take all that and do something for their customers, and thereby unlock all this tremendous value that you already have in your four doors. >> Absolutely, and I think the point of unlocking, so we have, you have 100% of your workforce. If you empower only 4.3% of them, for instance, the developers in your group, you're leaving so much opportunity on the table. And again, you don't get that unlock or that innovative spirit by just using something. You have to live with it, you have to work with it, you have to wrestle with it, And through that, innovation occurs. Ideas get generated. So, if you can get that ideation happening at the midpoint of your company, not the top 5%, huge opportunity. >> I think you were even quoted in the press release, maybe around the IPO that happened a few months ago, congratulations, >> Thank you. >> In saying that, maybe naysayers in the beginning, when you were a company of six, as you were talking about in your keynote, people thought, you're going to build this on a spreadsheet construct? And you said, but four hundred to five hundred million people know that construct. >> Right, right So you're going into an audience if knowledge workers, of which there's a massive percentage, designing something for lines of business, IT, finance, marketing, sales, who actually need to work with that, we're not talking about API's and developer and code speak, you're building this for a very large percentage of the population. >> We are, and I think when we talk about serving a large population, it's tempting to say, well, they can't handle much, let's go with the most common denominator. Let's give them something super, super simple. The problem is, with simple, you don't always get value. So how do you combine relevance and comfort and understanding, with capability. And the product's changed a lot since the early days, it's no longer just a grid, we have dashboards, we have forms, we have card view, we have all these elements that are now being brought forward, but one thing that we've always respected from the beginning is, don't throw away what somebody understands, and is comfortable with. That doesn't necessarily mean that it's the best, but they know it. And people are very nervous about just jettisoning the things they know, so like, embrace it. And then, what we had talked about earlier, was, how do you really listen to that customer's signal, and say okay, I'm comfortable, I like this, but I want more. And that ability to respond to that request, I think has really helped define who Smartsheet is today. You know, 12 years later. >> The other piece you talked on is kind of sideways off of that, is people have systems already in place, they have tools that they use every day. Right, there's this competition for the top layer of the desktop, but the reality is that we have many, many applications that we have to interact with every day. You guys are really taking a coopation approach with all these existing, >> Absolutely >> where it fits, where it's working, to your point, they're already using it and make it work. Integrate with. Don't try to rip and replace all these other systems that're in there. >> Yeah, and I think, you know you come across so many people in life, who want everything. I need total, complete, presence. And you're really discounting what people appreciate. And I think when you take the view of, I'm going to listen to my client, I'm going to listen to what they love and understand, and I'm going to let them articulate how they want it to work, we are in a very diverse, multi-app world today. If you actually march in somewhere and say, yeah all those decisions you made, those were the wrong decisions, you should trust me on everything, you'll be walked out of the building in about 4.2 seconds. So, we're really living that philosophy, and I think in great partnerships with Google, Microsoft and Slack, and Tableau, and others, we're actually able to demonstrate that. >> Yeah, and then to take it from the concept to reality, a great demo, I'm sure you didn't have this planned a couple of weeks ago, was, you talked about the state of North Carolina, and the preparation and the response to Hurricane Florence, and that they were very quickly able to build a super informative dashboard, to let everybody know who needed to know, what they needed to know. >> Correct. >> And how long did that take to put together? Amazing. >> That was under 24 hours. >> 24 hours? >> And the difference here is the difference between building or developing something, and configuring something. So, the difference there is when you actually build something from scratch, we have bare dirt, we need to put a foundation, we need to build a house, we need to shingle it, we need to insulate, that takes you a long time. So how about, we go to a house that exists, let's change the colors of the blinds, let's put in a certain sofa, let's furnish it. And the configuration element, versus construction, that gives people velocity. Now, what they also want is, they want to actually put their own texture to it, they want to make it their own, so the Department of Transportation dashboard that they produced for FEMA and the Coast Guard and the state governor's office, it didn't look like anybody else's dashboard. It was tailored, but it was so quick to build. And the great thing there was, so many people who accessed that site for information on on runway status and power and fuel, they could focus on the citizens as opposed to what the heck is going on, on the ground. >> Right. >> That provides a lot of purpose to our team, when we see our product used that way. >> You talked about speed just a minute ago, and speed, obviously, every enterprise of whatever size, needs to move and quite a bit quickly, to gain competitive advantage, to increase revenues, et cetera, you guys have some really very eye-catching statistics. That you're enabling customers to achieve. I read, enabling an average business leader to save 300 hours a year, 60,000 hours a year saved across on average organization. That's a big impact. How is speed a factor there? >> Yeah, I think speed I look at in a couple dimensions, One is, is it time saved, but there's also an element which is speed of experimentation So we go into an initiative, we say we have this amazing idea and we're going to have all these returns, we think. (chuckling) Well, not all the bets you place actually makes it. Or actually yields, so if you can empower a team to more quickly experiment, configure, try things, see what works and then double down behind those, if you can run five times as many plays as your competitor, you have five times as many chances to find that next winner. And so when we talk about speed, it's again, velocity of decision making, saving time, but also, organizationally, how can you unlock those possibilities? >> Part of that also is enabling cultural change. Which is not easy, it's essential for digital transformation, we talk about that at every event, and it's true, but how do you put that in action? You and I were chatting off camera about one of your customers that is an 125 year old oil and gas company. How do you enable them to kind of absorb and digest a culture of experimentation so that they can really move their business forward as quickly as they need to? >> Well, I think there's a great quote that one of my mentors early gave me. And it was, "All hat, no cattle." And the "All hat, no cattle" refers to the person who talks about how big their ranch is and how big their... Where's your herd? So you can talk a lot, but you have to demonstrate it. So when they go in, and there was another gentleman who talked about this idea of transforming their implementations across 300 project managers, and the quote was, we're going to get you up and running in two to three weeks, and he goes, "Never. No chance." Now, he ended up working with us, and we proved it to him and when you get a win like that, and you can demonstrate speed and impact, those things carry a lot of weight in organizations, but you have to show evidence. And when you talk about why we're landing and expanding in some of the world's largest brands, it's not because we're just talkin' a big game, it's because you're able to demonstrate those wins, and those lead to further growth. >> Right. And then you topped it off with a bit about the catalysts. But even more, I liked the concept of the point guard. Good point guards make everybody else on the team better. They do a little bit on their own, they hit a couple key shots, but they make everybody else better. And you're seeing that in terms of the expansion, and just in the way your go to market is, you don't come in usually as a big enterprise sale, I don't think, you come in small, you come in a group level, and then let the catalyst let those point guards, built successful in their own team, and then branch it out to a broader audience. >> Yeah, and I'm a big believer, and I don't think people can be classified into catalysts and non-catalysts. That's a very sort of blunt force approach. I view it as, you've catalysts, you've catalysts that haven't been unlocked, and then you have people that aren't catalysts. But very often that point guard, is going to activate the power forward, the center and holy smokes, where did that come from? And what we see is, when we see this growth happening in companies, those players around that point guard, get lit, get sparked, and once they're sparked, it's on. And then we see that growth happen for a long, long time. >> We saw some of that quotes, quotes >> We did. (all speaking at once) >> Queen of the world? >> Queen of the world. That's a big statement. >> That's empowerment, right there. >> It is empowerment. >> And the one where, I tweeted this, one of the quotes, I won't share this product name, but it can actually seem smart, she can help reduce work place anxiety. >> Anxiety! >> Which everybody needs. So, it's been six months since the IPO, you have doubled your attendance in your second year only, at Engage, up here in Bellevue, Washington, What are some of the exciting things that you anounced this morning, that have been fueled by the momentum of the IPO has as I imagine, ignited? >> Yeah, couple big things, is we, at every tech conference, you're going to hear about new capabilities. Here are the new bells and whistles and features and capabilities we have. But what we're hearing from customers, they also want us to frame those capabilites and things that are consumable. So, not everybody wants to configure or build as we talked about earlier today, they say I have a need, it's specific to this area, and do you have something for me. More turnkey, like that gentleman I said, two to three weeks to turn and sold him my implementation team. So those are being referred to as accelerators. So we announced a few new accelerators today in the sales realm, in terms of being able to better manage engagement plans with prospects and clients and on sophisticated deals it's a very common thing. And the other piece that I think is really important is, not just talking about business users, which is a huge focus for us, but also how do we better support IT and their needs to regulate, control, have visibility and to how Smartsheet is used. So, those were a couple of highlights, and then the ability to give people more controls over how they share their data. There've been some issues in the news recently, where people have shared too broadly, they've said that's the issue, so we're hearing from our customers, give us some more fine gated controls and confidence over how our corporate information is shared with others. Well, Mark Mader, I wish we had more time, but we thank you so much for stopping by theCUBE, and chatting with Jeff and me. >> Great to see you. >> Great momentum, we look forward to a number of your execs and customers and analysts on the program tonight. >> Great, thank you. >> Thank you, good to see you. >> Thanks, Mark, good to see you again. >> We just want to thank you for watching theCUBE, I'm Lisa Martin with Jeff Frick live from Smartsheet Engage 2018. Stick around, Jeff and I will be right back with our next guest. (techno music)

>> Live from Las Vegas, it's theCUBE! Covering VMworld 2018. Brought to you by VMware and its Ecosystem partners. >> Welcome back to theCUBE, I'm Lisa Martin with David Floyer, and Dave and I are here, day three, David, of our VMworld 2018 coverage, if you can believe it. We're excited to welcome to theCUBE, for the first time, a couple of gentlemen from Micron. We have Eric Caward, business development manager, and Greg Kincaid, ecosystem enablement program manager. Welcome guys. >> Thank you, good to be here. >> Thank you very much. >> So day three, you still have voices, that's impressive, your feet are doing okay? >> Yes, yeah. >> Pretty good, pretty good. >> Good, so Greg, tell us a little bit about your role and specifically what some of the new exciting announcements from Micron with respect to flash. >> So my role is to find deployments where SSDs can improve the performance significantly. Also, any case where you can have simplicity for the system administrator. So, with the new version of VMware 6.7, we've got, we've implemented, using NVMe as our cache layer, and set as our capacity layer to get tremendous performance across the spectrum of reads and writes. >> So can you give us some examples of how good that performance is? What sort of impact have you had? >> So, take for instance using NVMe as the cache layer and as data and a capacity layer, you can get small block random reads of 500,000 for a new cluster. >> That's very impressive. >> Yeah. >> Yeah. So can you make some savings in terms of the improvements in the VM density and things like that that you can achieve-- >> Absolutely, so almost all of these, well, all of the SSDs are in a two and a half form factor, and so you can get much better density per U with those kinds of SSDs, as opposed to a hard drive where you have to go to a three-inch to get that kind of density. >> So performance density, tons of data, what are some of the things in your opinion, Greg, that differentiate Micron Solution here, versus all those other guys out there? >> Well, we don't just put together a solution. We actually do considerable amount of testing, both in benchmarking, we also do a quite a bit of application testing as well. And we publish a very thorough reference architecture that's available on our website to act as a pragmatic blueprint for those who want to implement those kinds of solutions. >> Excellent, excellent. So, Eric, you're a part of the NVDIMM brigades. >> Yes. >> Tell us what is NVDIMM. Why is it important? >> Well, NVDIMM is very exciting. It's basically a memory that doesn't forget. So it's on the memory bus, it's comprised of DRAM, a controller, and NAND, and when the power is catastrophically lost, all your data is retained. >> So you go up to, what is it, 32 gigabytes on the DIMM? >> Actually, yes we're releasing our 32 gig NVDIMM in production next month, which is right around the corner. >> Wow, and and how many DIMMs can you have in a? >> You can have up to, typically in a 24 socket system, you can have up to 22 of those can be NVDIMM should you wish to. >> That's a lot of memory. >> It is a lot, and it's very, very fast. >> Very, very fast OK, so, tell us some of the changes that need to be made in order to exploit this. This is this is different, isn't it? So, can you give some examples of how you're working with the ISVs, for example? >> Certainly, certainly. From the operating system standpoint, Microsoft Windows Server 2016 supports, natively supports persistent memory. So does the Linux kernel version 4.2 and newer. Along with that, not only that, but you also have applications that are written from the ground up to support to be persistent memory aware. You have Exchange Server, you have SQL Server 2016, and with those applications they can actually access the persistent memory in byte mode, which is much faster than block mode, but you also can more legacy applications can get benefit from block mode, also. >> Wasn't, sorry Dave, I was just going to say let's dig into a customer example. I always love to hear how are these technologies, one, being co-developed as in collaboration with the end-users, right? And two, how are you seeing them in the, in the field actually helping customers transform their businesses from the inside out? >> Well, so one example that comes to mind, actually VMware just did a study with Oracle licensing, and they took a 12 core solution, and they put the redo log onto traditional storage, and they were able to get a certain amount of performance. Let's just call it a hundred units of performance. They did the same thing with the same workload, but they only used nine cores. So, that's actually a reduction in 25% course, but because the redo log was actually put on persistent memory, which again you're accessing that storage at DRAM like speeds, it kept the CPU much, much more busy, much more active, and they actually saw about a 2% increase in performance, but because the licensing costs are tied to your core count; actually, you could potentially save on licensing cost, even though you purchased a NVDIMM to have faster persistent storage. >> What about other benefits like to a data center in terms of energy efficiency? One of the things that Pat Gelsinger said on Monday was that VMware and their Green Charter, if you will, has saved 540 million, I think, tons of CO2 emissions. What I'm hearing Eric, what you're saying, are customers seeing pretty significant like power savings, and that were like roll into cost savings with the performance in this speed that you're able to deliver? >> Yes, if you look at it one of the other use cases for the NVDIMM, persistent memory, is that they used to NAND storage to write these logs, but because of the endurance, it ends up that they would have to replace the SSDs on a three month cadence. Because of the NVDIMM, the endurance it has just natively comes with DRAM, they were able to replace the SSDs with the NVDIMM, and then continue to use that for many, many quarters. >> It's a big cost savings. >> Definitely. >> So, can I go back to the what we were talking about before in terms of implementation of this? >> Yes. >> So, what's necessary? You need the software, the ISV software. You obviously need the Micron and the DIMM. >> That is correct. >> Anything else that you need? >> Yes, the actual, the hardware that you have to have, you have to have, not necessarily a specific CPU, but if you have to have the BIOS that basically goes in and is aware of NVDIMM. >> Right. >> And, one of the reasons why is when a system boots up, that supports NVDIMM, it goes out and looks and sees, is there a valid image set to true? If so, it will load that image from the NAND, through the controller, into the DRAM. Then when it's completed, it will go on to booting up the OS. The OS is none the wiser that that data wasn't sitting in DRAM the entire time, but as you can see if your, if your bios support isn't there from the start with that, that process would never happen. >> But, you can have that BIOS is available on most, most system. >> On multiple, multiple OEM systems. Yes, that is supported. >> Great. So, that there's no requirement for anything special with other than that? >> Other than that, correct. >> That's amazing. So, you've got a pretty, are you going through other ISVs as well? Are you. >> Yes, there are multiple ISVs that we're working with to enable that, basically the performance benefit and the endurance and the low latency of NVDIMMs. >> And people like SAP, for example? >> Yes. >> Perfect. Okay, that's very excited, very, very exciting indeed. Are you doing the same thing with your, class? >> Yes, we actually work with many partners. We work with not just Vmware, but all of the enterprise partners. We do case studies, and we do cost analysis as well. So, for instance we found that if you statistically, strategically add an SSD to a 200 node cluster for Hadoop, you can get the same performance there that if you had added 80 additional nodes for the entire cluster. So, that's quite a bit of a savings of 80 nodes versus an additional 200 NVMe SSDs. >> Yeah, that's great. >> What's some of the feedback on these new advancements that you're hearing from some of the people that are coming by to visit the Micron booth here at VMworld? >> Well, I think people are a little surprised that we are so focused on systems, and making sure that they work on the performance with SSDs. I think people, sometimes they think of Micron in the early days when we were just simply a commodities broker with DRAM, but we're much, much more than that. >> So, customers are reacting to what sounds like an evolution of Micron? >> Absolutely, absolutely. >> Eric, what are some of your-- >> And to be honest, my favorite is when people come by, and they look at the numbers, and they're just like oh my gosh. (laughing) The performance is really outstanding when you look at an NVDIMM, and it's just, it's simply because it is DRAM acting as a storage device. It's sitting on the memory bus. It's sitting on the memory channel, right next to the CPU. The latency is absolutely fantastic. There are certain workloads that are really, really gain a lot of benefit by low latency for quality of service. Then you have just the raw bandwidth, and this is only with two NVDIMMs in this particular demo system. We could have, excuse me, we could have gone up to six in a CPU. So, we could have tripled our performance just with one CPU on one node. So, it's pretty exciting when when the people that are coming in the booth, they get excited too. It makes, it makes this show really fun. >> I think people also don't understand that there's more than one kind of SSD, and we just announced that QLC, a NAND based SSD, that for write once read many could actually supplant many of the hard drives that are used in secondary storage or archives. >> So, it also must be kind of fun to educate people on, hey guess what? There's not just different flavors, but look what Micron is doing. >> Right. >> Evolving our technologies and enabling them to you know, learn about things that they didn't know about. I imagine that must also be a pretty cool. >> I'm working with a software developers as well, so closely, so this is exciting. >> I mean the applications are just innumerable. I mean we're working with artificial intelligence. We're working on machine learning. Applications are other than just the standard database that most people think of accelerating with SSDs. >> Excellent. >> And, to be honest, I'm very passionate about technology, just, I love to geek out, if you will. >> I can tell. >> And, I love seeing the light bulbs come on in people that I'm talking about. It's just very rewarding. >> So we're gone, more than halfway through 2018, scary. September 1st is Saturday. (laughing) So, going towards the end of the of the calendar year, this excitement that I'm getting from both of you, what are you excited about Micron, you know going into early part of 2019, being able to surprise and delight your customers with? >> All right. >> Well, we're going to continue to, to do all of the performance testings that were done. We're going to, as we bring new SSDs to the market, we're going to continue to add tuning advice, and detailed deployment instructions for our customers. We're going continue to partner with the major players to make sure that our SSDs, their performance and their applications. >> And I think with the fact that we're releasing our 32 gig NVDIMM, actually in September. The ecosystem, as it solidifies, it becomes more robust. There's just going to be use cases that our engineers and our team haven't thought of yet. And, so it's going to be really exciting to see what new use cases are out there for super, very fast NVDIMMs. >> Well guys, thanks so much for stopping by and talking with David and me about-- >> Thanks for having us. >> The evolution of Micron, and the excitement that you get from from hearing that validation in the field, and we look forward to hearing what's coming out shortly. So, we'll have to have you back on. >> Sounds great, thanks Lisa, thanks David. >> Love to be back. >> Excellent. Greg, Eric, thanks for your time. For David Floyer my co-host, I'm Lisa Martin, you're watching theCUBE, live from Vmworld 2018. Stick around, we'll be right back with our next guests. (electronic music)

(dramatic music) >> Welcome to this special Cube Conversation here in Palo Alto, California. I'm John Furrier here with Shay Mowlem, Senior Vice President of Product and Strategy at Rubrik here in theCUBE Talk. Thanks for coming in today, great to see you. >> Pleasure, John, pleasure to see you. >> So the thing is, you joined Rubrik, Senior Vice President, last time we spoke to you in theCUBE, you were at Splunk. And then you did a stint at Mulesoft, famous public company, sold to Salesforce for massive amounts of money. Now you're here at Rubrik, thanks for comin' on. What's the story, what happened? >> Well, you know, Bipul, our founder and I, met a few years ago, we were introduced. I guess it was about two and a half years ago. I was running product manager and product marketing at Splunk at the time. And he just impressed me with his vision of what he was trying to do through Rubrik. The company was significantly smaller than it is today. And talked about his vision to really disrupt this 30 billion dollar market. And do it in a way that was very cloud-based, revolutionary. Allow companies to extract much bigger value out of this secondary storage arena. I thought, wow, sounds exciting. But at the time, I was just about to take a bigger role at Splunk, my timing was off. So it didn't work out, but we kept in touch. And we touched base again earlier this year. And I was just so impressed by what he had accomplished with Rubrik. In less than four years from zero to 300 million run rate. The executives that he assembled around the company, the progress that the business had made, the customers, the expansion into cloud arena, the innovation. It was just one of those opportunities you can't walk away from, and so I jumped on it. >> It's a classic Silicon Valley enterprise story. If you look at, he's been on theCUBE, so the folks watching, check out theCUBE video on Youtube or thecube.net. Bitpul, CEO, Founder of Rubrik, great interview. But it's interesting, I mean there's a lot of money thrown at Rubrik. They're growing like crazy. It's the classic rocket fuel going after the story. But there's a unique product angle that I think's interesting. And you're in charge of products and technology for the company. But you've also had a journey in the enterprise. Splunk was a very successful company. Mulesoft, very successful. Saas company sold to Salesforce. Huge tower in San Francisco. There's a new, kind of, generation shift happening with cloud computing that's forcing enterprises to change their infrastructure. And this is beyond just backup and other things. >> Yeah. >> This is a generation, once in a generation shift. >> Yes. >> In BTB, how has it changed things? And you've seen a lot of the enterprise action over the past decade or so and more. But right now, it's more than ever. What's the big shift? And I'll say, cloud force is a lot of change. What's the impact to the customer? >> You know, I think there are two phases to that. There's one that we are serving a market, this backup recovery market, represents a massive area of investment for companies. I've seen stats that suggests that there's 6x as much spend on storage infrastructure for the secondary arena than there is for production grade systems. But yet, this market just hasn't seen innovation since data domain. >> So tons of money, but nothing happening. >> Nothing happening. So we came in initially with a whole new, very customer centric approach, that delivered all of the complexity that this market had seen before, shrink wrapped into a modern era software platform running on commodity hardware. Our customers can be up and running in less than an hour. They can archive and leverage the cloud. And so it's driving both TCO benefits, agility of the business, and allowing them access to move workloads to the cloud, manage the cloud in ways that they'd never seen before. And so I think, certainly that has been one big part of the success of Rubrik. But I think, more broadly on the cloud, we're seeing many companies are really in a hybrid mode. They are moving from on-prem, they're leveraging MSPs, they're starting to build certain businesses in the cloud, and the ability to manage all that centrally, and in a way that is governed properly and allows them to extract real value from it, is something that's really resonating for our customers. >> What was the reason why you joined Rubrik. I mean, everyone has a reason. >> Obviously, you met Bitpul, you guys keeping in touch. Was it the team, was it the technology? What was the one thing that you were attracted to, that put you over the top? >> One thing, I've got so many. >> The most important thing. >> You know, I think I'm going to force you with three answers on that one. >> I'm supposed to ask you to rank them by one two and three. >> Alright, sounds good, I'll end with the last one on the product. >> Alright. >> I fell in love with Bitpul, quite honestly. I mean, remarkable guy, quite humble. Such tenacity, such a focus on customers. They team that he's assembled, to me, was just so paramount. I wanted to be part of this organization. And honestly, I'm humbled to be sitting around the table with folks like Murray Demo, who's our CFO, and Mark Smith, our head of sales from Arista, you know, Kara Wilson, our CMO. And we just keep bringing these incredible individuals to the company and the org. I'm truly humbled to be sitting around the table with them. So that excitement by the way, goes all the way down. The folks that have been hired into the organization are quite remarkable. But the thing that really, from a product perspective, that really is exciting to me, is that, not only are we disrupting this 40 billion dollar market in a way that's really connecting for our customers, we're doing it in a way that is thinking ahead. We're not treating this backup arena as some blob that's going to sit on tape somewhere. We're building it as part of an integrated management platform that then allows our customers to extract higher value services and insight from that in a way that they've never seen before. So radar is, we've had some incredible innovation over the last four months that I've been with the company. With the release of Rubrik Alta 4.2, the new product Radar for Ransomware protection. We've talked about our AWS competency and advancements there. But Radar is an example of a service that we're building on top of this data management platform that delivers higher value for our customers. And I am so excited about the exponential growth in value that we're going to deliver to our customers as we continue to deliver more of these services. >> Yeah, get the technology, got the great team. Yeah, the code of market is going to be interesting. With cloud, you've got marketplaces, you've got consumption by the users, the customers if you will, on your end, is changing, I think Saas is being a big part of it. How has the product road map shifted from classic old school product to now? Because it has to be a service. This service is out there, still commodity hardware. Software's driving the value. That's where the hardware gets sold. That's where the cloud gets sold on. It used to be the other way around. Your hardware drove what you can do with software. So that's a been flipped. >> Yes. >> How are you guys working that in the equation? Software first, cloud first? I mean, how do you explain that to customers? >> Well we're always a software company. And we built Rubrik as a very modern era expandable platform that runs on commodity hardware. And can archive and move workloads to the cloud at its core. I mean, our founders came from companies like Google and Facebook, and had really come from this world. And so, our customers were able to get that value quickly. And I think that was a big part of what attracted them to Rubrik. But if you really fast forward into the future, our vision is to have a ubiquitous centralized data management platform from which our customers can govern, manage, and establish rules that govern all of their applications that they protect across cloud boundaries, across private clouds, traditional infrastructure, cloud workloads, and we really think that's connecting for our customers. >> So about the product road map. Obviously, you're in charge of product and strategy, so you have a great market entry, the success has been documented. You guys have been one of the fastest growing companies in Silicon Valley the past couple of years. I've seen the success. You always have a big party at VM world. Your big show there, lookin' forward to this year. >> Going to happen again this year. (laughing) >> I heard there's a big performer there coming. Last year, it was great to see the Warriors there. So, but product is interesting. 'Cause at your start up, you want to have a beachhead, secure a core positioning, and then look at, kind of holistically, what the customers might want. >> Yeah. >> Can you share some insight into what that product roadmap is? And how are you guys fortifying your core and what are you adding onto the roadmap? >> Yeah, you know, the first thing that we did when we came out, was to provide this capability to protect your data and make it really easy to use, archive to the cloud, and we focused on the VMware and hypervisors, and it was very well received. And over the years, we've expanded to support other areas, other data, other applications. And so our strategy, certainly is going to continue to do that with the vision of protecting all of our customer's applications and data, regardless of where they reside. Whether they are traditional infrastructure applications running on PRIM, in private clouds, or new modern architectures that are running in the cloud. The ability to manage all of that. And that's certainly going to continue to be one of the directions of the roadmap strategy. The other is, as I mentioned, we're not really looking at these protected images as black boxes or tape images. We're going to enable our customers to extract value out of them in a way that they haven't seen before by introspecting this data and revealing insights from it. >> What's the current situation? So why can't they get that today? >> Well I think, typically, these images are stored in a proprietary blob form. And you can't really see much in there. >> You can't unlock it at all. >> You can't unlock it. And you can't really know much about what's even in the black box. And so, from the beginning, we started capturing meta-data that allows customers to classify this data and get insight into, well what applications are actually running in this particular snapshot. And so we continue to extract that level of value that is really connecting for our customers in allowing them to resurrect, move workloads, introspect for compliance reasons or otherwise in ways that, I think, are just really important. >> Yeah, things like GDPR for instance, alone. It gives it as a great use case. >> Absolutely. >> Alright, so what's the big picture? If you had to go talk to your friends and say, hey I joined Rubrik. And they say, I've never heard of Rubrik, what do they do? You don't say backup company, you say data company. How do you describe the company? >> I talk about a company that's providing data management for non-production systems. And allowing customers to extract value in ways that they haven't seen before. And I think, candidly, John, I have been very fortunate to work with some great companies. I have never seen an opportunity as exciting and as big as what Rubrik represents. It's just so important to our customers. Everybody has to protect their apps. And we're able to do it an a way that's going to allow them to extract so much more value. >> And what was your official start date? You started a couple months ago? >> April first. >> April first, four months roughly, yeah. >> Exactly, thrilled. >> And your impression, as you walk in. What's the DNA, what's the vibe of the company? If you had to describe the DNA of the company. >> You know, I'm really thrilled. I am really thrilled to be part of this organization. There's a deep sense of culture. One of the things that attracted me early on was there was an article written about Bipul talking about radical transparency. Open board room meetings, I'd never seen that before. And you know what it's about? It's about employee empowerment, he is so committed to that. To making sure that we are able to set everybody up to deliver their best in the organization. And I think it's spot on. It's why we're innovating so quickly. It's why we're attracting such top talent at all levels of the organization. And it's why I'm so confident about the future of this company. >> That's great. And you know, one of the things too that I want to get your thoughts on. Because you see in cloud disrupt a lot of things, and a great opportunity for you guys. You know, we're seeing it out there, and we talked to end-user enterprises. That the common answer is, you know clouds, that we got to go there. But the one thing that's interesting, is they all say, no matter what we do, when we talk about cloud for them, it makes them change their infrastructure. >> Yes. >> On premises, and what they do in the cloud. So it's a rethinking of things. So that's one. So that's opening up new markets. So question for you we have is, as you guys look at new markets, things like public sector for instance. We're seeing, I wrote a story today, it's looking like Oracle is challenging Amazon for the Department of Defense Deal. So public sector and global public sector. Not just in United States is a very interesting market. How are you guys doing in say that market? I know you're strong in the enterprise, but what's the sector angle? You guys competing there, you winning, what's the story? >> We are, and I would say there are multiple motions in addition to the public sector example. We're seeing a lot of Global 2000 organizations moving to manage service providers. And so that's an example of a private cloud model that really works for a lot of folks in federal organizations as well. Really looking to have a tenant, well-secured service model for their various agencies. And that is very aligned with what we're doing. In fact, in our Alta 4.2 release, we talked about Envoy that really advances how service providers can, and manage service providers even within organizations, can actually enable more self servicing capability in that regard. We see these varying segments. >> So you see public sector as an opportunity for you guys? >> No doubt. In fact, if you look at the rubric customer base today, it really spans the gamut of markets across the board, including public sector and state local agencies as well. >> Well we know you got a great relationship with Amazon Web Services, AWS. You're a competency partner with them, which is the highest award or level you can get. What is your relation with the other clouds, Google, Microsoft, Alibaba, and others? How do you guys relate to those other clouds? >> Our customers run on all platforms. And Rubrik does have a relationship with Microsoft, certainly. In fact, we have a co-sale agreement with them. We support Ajar at a relatively deep level. Same thing with Google Cloud. We enable our customers to. >> You're agnostic on cloud, basically. >> We are agnostic, and the point is, I think every one of these cloud platforms has their own unique angle and value, and we want to enable our customers to really leverage the platform of their choice. >> So a lot young people are lookin' at career choices. And some of the jobs are out there that haven't even been invented yet. At school starts to figure out curriculum, starting to see computer science. Women in tech is booming. You're seeing a lot of different, new kinds of jobs around data science, for instance. What do you advise young people, who are either in high school or college, who are thinking about careers? You don't have the classic, I'm going to be a software engineer. You could be a software developer, software artist, there's different jobs in management, marketing. All kinds of different scopes. What's the current track that you would recommend people to explore if they're interested in getting in tech? >> You know, I think it's remarkable to me to see how the internship programs have evolved. And how active they are. I was initially recruited into Oracle directly out of college. It was a very regimented process of recruiting from college. Well now you've got these internships. And I tell you, some of the interns that have worked with companies that I have been a part of just impress the hell out of me. So that's a great way to get in, to see what's about, and to have an opportunity to add value. And every single time one of those interns does something remarkable, and it happens all the time, there is an offer on the table for them to come back, too. So I think that's a very good way with many of these organizations to get in. >> I mean, it's so interesting. We do a lot of interviews. And there's no classic cookie cutter job anymore. I think you're starting to see interdisciplinary opportunities that are coming up. Some computer science, little bit of sociology, or business mixed, it's very interesting. Almost an alchemy of different projects out there that people can get involved in. >> Absolutely. >> Open source certainly is a big one. >> And it's fun because when we get new college grads, we just give them the opportunity to do a lot of different things in rotations. And that helps them also sort of get a sense of where their passion lies and what they want to do. And it's exactly the right thing to demand as you're coming into the workforce. >> It's interesting, at Google Cloud, I was talking with some folks over there. And you know, the women in tech conversation, and opportunity recognition and to level up. So many new opportunities that anyone of any gender or race can come in and quickly level up. >> Yes. >> 'Cause it's so new, the technology with Cloud. It's kind of interesting. >> Yes, I mean, I think it all comes down to your personal ability and commitment and work ethic and drive. And there's no end in sight to what's possible. >> That's right, well thanks for coming on theCUBE. Great to see you, and congratulations on your new role at Rubrik. Great company, right down the street here in Palo Alto. Rubrik, new Senior Vice President of Product and Strategy here inside theCUBE. For Cube conversation, I'm John Furrier here in Palo Alto in our studios. Thanks for watching. (dramatic music)

>> Announcer: Live from Las Vegas, it's theCUBE. Covering ServiceNow Knowledge 2018. Brought to you by ServiceNow. >> Welcome back to theCUBE's live coverage of ServiceNow Knowledge18, #Know18 we are theCUBE, the leader in live tech coverage. I'm your host Rebecca Knight along with my co-host, Dave Vellante. We are joined by Dan Rogers. He is the CMO of ServiceNow. Thanks so much for coming on theCUBE Dan. >> Thanks for inviting me. I always have a great conversation with you guys. >> Yeah, you're, you're back, you're back. So, this conference is amazing. There's so much buzz happening. 18,000 people. It gets bigger and better every year. >> How ironic, 18,000, K18. >> You got it. >> Oh my gosh. >> Well done. >> I didn't even, you did it you must've done it that's marketing genius, genius Dan. >> We might bend the curve next year though. We might bend the curve a little bit more. >> So, so what it, what in your opinion is the most sort of knew exciting things happening? >> Well you know we start the planning process as you can imagine, about six months prior. And we're really super focused this year on customer success. So, one of our principles was it's all about our customers, it's all for our customers. You probably know, unlike any other conference, most of the sessions are delivered by customers. So we have 85% of our breakouts are delivered by customers. So this is really our customers' event. And in the background here, you know we've created this customer success zone, which is where I've taken all the best practices from our customers and we're sharing that and you'll see we've got Genius Lounge, customer success clinics, customer theaters, and the whole vibe is supposed to be helping our customers be more successful. In some ways it's the anti-marketing conference. This isn't buy more stuff, this is we want to help you be successful. And so we wanted to keep the authenticity throughout. The keynotes were celebrating people, celebrating our users how users can use our products. The experiences that they can have. So I think that was the principle. Hopefully we pulled it off. >> So I wonder if you could talk about some of the challenges you have from a marketing standpoint. So let me just set it up. So, in the keynote this morning, if you didn't see it ServiceNow had kind of a fun little play on words where they had cave people in the cave trying to light a fire. We all know that, right? Light a fire under somebody's butt. And then fast forward to today's world and there's this thing called the saber tooth virus coming and so that was kind of really fun. And it explained things, you know, it resonated, I think, with a lot of people. But as you enter this new world beyond IT, I mean 2013, 5% of your business was outside of IT. You know, today it's you know, a third of your business. So you're reaching a new audience now. How do you handle sort of the marketing and messaging of that hybrid approach? That must've been a challenge for you. >> Well, you know I'm a story teller I love kind of starting with the stories. And, talking with our product leaders, the story that we're most deeply connected to really for our product road map is around experiences. So we knew this needed to be a conference about experiences. And we wanted to put a marker down that says this is the era of great experiences. You deserve great experiences at work. It really is the case that certainly when millennials come in to work they have expectations of what the work experience looks like and they arrive and it's like, wah, wah, wah, wah, No you can't, just swipe your finger, No, you have to stand in line. No you, yes, we really use telephones still, you know. And, chat experience isn't really what it ought to be. So we kind of said we're putting a marker down at this conference to say, Welcome to the Era of Great Experiences. You deserve great experiences, and we're going to create that. And if you look at our entire product roadmap, we're trying to create great experiences at work. CJ talked about the Now platform. He said there are three layers to the Now platform. The Now platform has user experiences. That's really how people want to interact with our, our products, how they want to interact with the world. Great service experiences that's all the stuff that's happening in the background. Customers, employees, they just want to touch their phone, the 20 things that happened behind, they need to be obstercated. And then, service intelligence. This idea of prediction. Now these things are not new in the consumer world, but they're very new in the enterprise world. Take the consumer world. You think about Uber, you think about OpenTable, they spend a lot of time on the user experience. Think about the service experience of something like Amazon. Amazon, you touch, you swipe, you click and they're orchestrating hundreds of processes on the, behind the scenes. And then service intelligence. Netflix is a great example. Stuff's predicting for you stuff's being recommended for you. Where are the recommendations at work? Where's the predictions at work? Where's the prioritization that's happening at work? And we've sort of said, that's what our Now platform is all about. It's about delivering those three great things that we think go into making great experiences at work. And that's what the show's about. And therefore, you see the people's centricity of the show. CJ celebrated four personas. He talked about the personas and their life. The IT topic, you know it's happening in a couple hours. We're going to talk about people. Real people and their lives, and how it's making it better. And that all rolls back to the central idea that we believe that technology should be in the service of people. Making work, work better for you. >> So that's the main spring. Love it, go ahead. >> No, I was just going to ask you, you were describing the millennial, or the post-millennial entering the workforce and this, wah, wah, wah, feeling of no it's not like that here, you got to, there's a lot of, onerous administrative tasks that you've got to do. So is that what's driving this, this change, this moment that you're saying that we're at this, this point in time where employees are demanding better and demanding more from their workplace. I mean, is that what's driving the change in your opinion? >> I think we have just this confluence of technologies around AI, around machine learning and a lot of the services being delivered by Cloud platforms. And then we have this contrast between people's work life and their home life. I have a nine-year-old son. I'll share a little experience with him. So he uses things like Khan Academy. Khan Academy, he uses his finger to write the answers and that gets converted into text. Well now when he tries to interact with any application, he's trying to use his finger and he's wondering, why you guys all using keyboards? What is this keyboard thing? And you know, and then when he interacts with any application, TV screen, he's trying to swipe on the TV screen. He can't understand why he can't swipe on the TV screen to get to the next show to the next channel. I look at that, and I'm like, it's so obvious this is where we're going, this is, this next generation, they want to interact with their applications in a very different way. And we need to get to that in the Enterprise. And we want to be first to get there in Enterprise. The acquisitions that we've made five acquisitions that we've made in the last nine months or year. I was actually just walking with some of the guys that, you know from Boas, from SkyGiraffe. SkyGiraffe, DxContinuum, Parlor, Parlo. And these are just kind of adding to our ability to create the experiences that we deserve, opposite all of those technologies, so you can just get your work done, get your work done. Get to the actions that you need. John I thought did an amazing job of explaining what it takes to create great experiences. And he had this, what I call the UX iceberg. This idea that, appearances are on the top, Anyone can make an app, mobile app that has great appearances. Just put nice skin on there, nice colors on it. But the hard work happens below the water line, which is where you think about the behaviors. How do people actually want to work? And we've filmed people, we've watched people, in their daily lives how they want to work. Go down a layer, the relationships who do they need to work with? Who do they interact with? And then, the work flows, what are the systems they need to interact with. And when we think about their entire paradigm of UX experience and then design from that paradigm, we end up not just with a pretty skin, we end up with actually something that fundamentally changes the way you get your work done, and that's what we're going after. >> So I've kind of resigned myself to the fact that I'm not going to be a ServiceNow customer anytime soon. When Jeff and I first saw it in like 2013, we were like, we want this. It's not designed for 50 person companies like ours. Okay, I can live with that. You guys aspire to be the next great Enterprise software company. As a marketing executive, you got to kind of be in Heaven, right now, because now, you and I have talked about this, I don't have the marketing gene, I find marketing very challenging, but for someone who has that marketing gene, if I compare you to, the great software companies in the Enterprise, it's Oracle, it's SAP, it's Sales Force. Our HR system, our provider, it's Oracle, it's clunky. We use Sales Force, it's Oracle, right? I don't use SAP. I don't want to use SAP. Okay, so laying down the gauntlet on experience is I think brilliant because you're living in a sea of mediocrity when it comes to experience. Now, you have to stay ahead of the game. Acquisitions are one way to do that. But how does that all play in to your marketing. >> You know, it actually starts with purpose. So we, about nine months ago began a journey to, I'd say get to the essence of our purpose. We talked to all of our employees, went on road shows around the world, Talked to our customers around the world. And we kind of said, both what do we actually do for you, what do you want us to do for you, and we grounded ourself in this central idea we make the world of work work better for people. It turns out, that is a rallying cry a firing signal for everything we do as a company. So when I think of marketing, marketing is about bringing that promise through our brand expression to life. We make the world of work, work better for people. That's a bar, a standard. This conference needs to feel like it's making work, work better for people. This conference needs to exude humanity and their experiences. This isn't a technology conference. You see the thing behind you, very deliberately. We're celebrating people, people's lives, people's work lives, so I think of the connection between our purpose and marketing. It's the standard, it's the bar for us. My website, which we refreshed in time for Knowledge, is no longer a taxonomy of products. It's talking about people, their lives, how we make their experiences better. So I think of it as this show, our keynotes, very deliberately focusing on those personas. I think of it as a watermark that kind of says make everything true to your purpose. It's also a watermark for our products. It's a litmus test for our products. Is this product ready to ship yet? Does it make the world of work work better for people? Yes, no? Yes, let's ship it. No, let's not. It's the litmus test for our sales engagements. Are you talking about how you're making experiences better for people? Or are you talking about some other abstract concept? You talking just about cost savings, you're talking about, if you're not talking about experiences, you're not living our purpose. So, it's going to exude through everything that we do. I think it's a really foundational idea for us. >> It's powerful when a brand can align its sales, its marketing, and its product and its delivery, you know to the customer. >> And the timing too just because we were really at low unemployment, we have this war for talent, particularly in technology but in other industries as well where employees are saying what can I do to attract and retain the best people. Make, make their work lives easier, more fun, more intuitive, simpler. >> I always joke that, you know, there's something that's written on a job description. And if you read the job description, You're like, yeah, I want to do that. I get to lead this thing, drive this thing, duh de tuh. The job description doesn't say, oh and by the way, you're going to spend 2.4% of your time filling in forms and you're going to spend 1.8% of your time handling manual IT requests. 4.2% of your time, you're going to, if it did, you wouldn't take the job. So we actually deserve the jobs just on our job description. And that's kind of what I think is that, you know, where we need to get to with work. >> Right, right, exactly. >> So what have we got goin' the rest of, of K18 here? You got a big show, I think Thursday night, you got the customer appreciation. What else is going on here that we should know about? >> Well the way we structure the event is we have these general session keynotes. And you can kind of think of it as John is explaining a lot about why we're doing what we're doing. CJ's explaining a lot about what are we doing. What have we been doing? What's our innovation road map look like? And then Pat Casey's going to pick up on how. How can you build those experiences that CJ's previewed, that fell into the reason why we're doing the things that CJ previewed. So there's kind of a method to the madness to the, to the three days as it were. And then below that, we have these things called topic keynotes, and as you remember we have these five Cloud services now. Of course HR, customer service, security operations, IT, and then really intelligent apps allowing me to build those up. So you have topic keynotes across each of those five Cloud services. And then beyond that, it's really the customer, customer breakouts. Interspersed amongst that is your ability to go along and have a session or success clinic in this customer success area. Or go into the Genius Lounge. Drop by the pavilion, have demos of our products. So those are some of the really, kind of exciting structural things we have around the conference. And then on Thursday night, you know, we wanted to go bigger and better than ever before, and we call it Vegas Nights. So Thursday night, instead of having, you know, the band, you know, of yesteryear, which many conferences, kind of love to do, we decided to have this kind of experiential thing. You can go and see Cirque De Soleil. You can go to the Tower Night Club. You can go to Topgolf. So there's a little menu you can choose from. We've actually reserved the Cirque De Soleil for the whole night so they're running multiple performances just for ServiceNow customers, which is pretty fun. >> So tailored to the individual. Whatever you want to do. Whatever will make your life better. >> That's the idea. Just drop it in, put it in your agenda and you're good to go. >> I love it. Well Dan, thanks so much for coming on the show. It was great to have you. >> Thank you, I enjoyed the discussion. >> Good to see ya again. >> Good to see you. >> I'm Rebecca Knight for Dave Vellante. We will have more from theCUBE's live coverage of ServiceNow Knowledge18 coming up in just a little bit. (upbeat music)

>> Narrator: Live from New York, it's the Cube, covering big data New York City 2016. Brought to you by headline sponsors, CISCO, IBM, NVIDIA, and our ecosystem sponsors, now here's your host, Dave Valante. >> Welcome back to New York City, everybody, this is the Cube, the worldwide leader in live tech coverage, and this is a cube first, we've got a nine person, actually eight person panel of experts, data scientists, all alike. I'm here with my co-host, James Cubelis, who has helped organize this panel of experts. James, welcome. >> Thank you very much, Dave, it's great to be here, and we have some really excellent brain power up there, so I'm going to let them talk. >> Okay, well thank you again-- >> And I'll interject my thoughts now and then, but I want to hear them. >> Okay, great, we know you well, Jim, we know you'll do that, so thank you for that, and appreciate you organizing this. Okay, so what I'm going to do to our panelists is ask you to introduce yourself. I'll introduce you, but tell us a little bit about yourself, and talk a little bit about what data science means to you. A number of you started in the field a long time ago, perhaps data warehouse experts before the term data science was coined. Some of you started probably after Hal Varian said it was the sexiest job in the world. (laughs) So think about how data science has changed and or what it means to you. We're going to start with Greg Piateski, who's from Boston. A Ph.D., KDnuggets, Greg, tell us about yourself and what data science means to you. >> Okay, well thank you Dave and thank you Jim for the invitation. Data science in a sense is the second oldest profession. I think people have this built-in need to find patterns and whatever we find we want to organize the data, but we do it well on a small scale, but we don't do it well on a large scale, so really, data science takes our need and helps us organize what we find, the patterns that we find that are really valid and useful and not just random, I think this is a big challenge of data science. I've actually started in this field before the term Data Science existed. I started as a researcher and organized the first few workshops on data mining and knowledge discovery, and the term data mining became less fashionable, became predictive analytics, now it's data science and it will be something else in a few years. >> Okay, thank you, Eves Mulkearns, Eves, I of course know you from Twitter. A lot of people know you as well. Tell us about your experiences and what data scientist means to you. >> Well, data science to me is if you take the two words, the data and the science, the science it holds a lot of expertise and skills there, it's statistics, it's mathematics, it's understanding the business and putting that together with the digitization of what we have. It's not only the structured data or the unstructured data what you store in the database try to get out and try to understand what is in there, but even video what is coming on and then trying to find, like George already said, the patterns in there and bringing value to the business but looking from a technical perspective, but still linking that to the business insights and you can do that on a technical level, but then you don't know yet what you need to find, or what you're looking for. >> Okay great, thank you. Craig Brown, Cube alum. How many people have been on the Cube actually before? >> I have. >> Okay, good. I always like to ask that question. So Craig, tell us a little bit about your background and, you know, data science, how has it changed, what's it all mean to you? >> Sure, so I'm Craig Brown, I've been in IT for almost 28 years, and that was obviously before the term data science, but I've evolved from, I started out as a developer. And evolved through the data ranks, as I called it, working with data structures, working with data systems, data technologies, and now we're working with data pure and simple. Data science to me is an individual or team of individuals that dissect the data, understand the data, help folks look at the data differently than just the information that, you know, we usually use in reports, and get more insights on, how to utilize it and better leverage it as an asset within an organization. >> Great, thank you Craig, okay, Jennifer Shin? Math is obviously part of being a data scientist. You're good at math I understand. Tell us about yourself. >> Yeah, so I'm a senior principle data scientist at the Nielsen Company. I'm also the founder of 8 Path Solutions, which is a data science, analytics, and technology company, and I'm also on the faculty in the Master of Information and Data Science program at UC Berkeley. So math is part of the IT statistics for data science actually this semester, and I think for me, I consider myself a scientist primarily, and data science is a nice day job to have, right? Something where there's industry need for people with my skill set in the sciences, and data gives us a great way of being able to communicate sort of what we know in science in a way that can be used out there in the real world. I think the best benefit for me is that now that I'm a data scientist, people know what my job is, whereas before, maybe five ten years ago, no one understood what I did. Now, people don't necessarily understand what I do now, but at least they understand kind of what I do, so it's still an improvement. >> Excellent. Thank you Jennifer. Joe Caserta, you're somebody who started in the data warehouse business, and saw that snake swallow a basketball and grow into what we now know as big data, so tell us about yourself. >> So I've been doing data for 30 years now, and I wrote the Data Warehouse ETL Toolkit with Ralph Timbal, which is the best selling book in the industry on preparing data for analytics, and with the big paradigm shift that's happened, you know for me the past seven years has been, instead of preparing data for people to analyze data to make decisions, now we're preparing data for machines to make the decisions, and I think that's the big shift from data analysis to data analytics and data science. >> Great, thank you. Miriam, Miriam Fridell, welcome. >> Thank you. I'm Miriam Fridell, I work for Elder Research, we are a data science consultancy, and I came to data science, sort of through a very circuitous route. I started off as a physicist, went to work as a consultant and software engineer, then became a research analyst, and finally came to data science. And I think one of the most interesting things to me about data science is that it's not simply about building an interesting model and doing some interesting mathematics, or maybe wrangling the data, all of which I love to do, but it's really the entire analytics lifecycle, and a value that you can actually extract from data at the end, and that's one of the things that I enjoy most is seeing a client's eyes light up or a wow, I didn't really know we could look at data that way, that's really interesting. I can actually do something with that, so I think that, to me, is one of the most interesting things about it. >> Great, thank you. Justin Sadeen, welcome. >> Absolutely, than you, thank you. So my name is Justin Sadeen, I work for Morph EDU, an artificial intelligence company in Atlanta, Georgia, and we develop learning platforms for non-profit and private educational institutions. So I'm a Marine Corp veteran turned data enthusiast, and so what I think about data science is the intersection of information, intelligence, and analysis, and I'm really excited about the transition from big data into smart data, and that's what I see data science as. >> Great, and last but not least, Dez Blanchfield, welcome mate. >> Good day. Yeah, I'm the one with the funny accent. So data science for me is probably the funniest job I've ever to describe to my mom. I've had quite a few different jobs, and she's never understood any of them, and this one she understands the least. I think a fun way to describe what we're trying to do in the world of data science and analytics now is it's the equivalent of high altitude mountain climbing. It's like the extreme sport version of the computer science world, because we have to be this magical unicorn of a human that can understand plain english problems from C-suite down and then translate it into code, either as soles or as teams of developers. And so there's this black art that we're expected to be able to transmogrify from something that we just in plain english say I would like to know X, and we have to go and figure it out, so there's this neat extreme sport view I have of rushing down the side of a mountain on a mountain bike and just dodging rocks and trees and things occasionally, because invariably, we do have things that go wrong, and they don't quite give us the answers we want. But I think we're at an interesting point in time now with the explosion in the types of technology that are at our fingertips, and the scale at which we can do things now, once upon a time we would sit at a terminal and write code and just look at data and watch it in columns, and then we ended up with spreadsheet technologies at our fingertips. Nowadays it's quite normal to instantiate a small high performance distributed cluster of computers, effectively a super computer in a public cloud, and throw some data at it and see what comes back. And we can do that on a credit card. So I think we're at a really interesting tipping point now where this coinage of data science needs to be slightly better defined, so that we can help organizations who have weird and strange questions that they want to ask, tell them solutions to those questions, and deliver on them in, I guess, a commodity deliverable. I want to know xyz and I want to know it in this time frame and I want to spend this much amount of money to do it, and I don't really care how you're going to do it. And there's so many tools we can choose from and there's so many platforms we can choose from, it's this little black art of computing, if you'd like, we're effectively making it up as we go in many ways, so I think it's one of the most exciting challenges that I've had, and I think I'm pretty sure I speak for most of us in that we're lucky that we get paid to do this amazing job. That we get make up on a daily basis in some cases. >> Excellent, well okay. So we'll just get right into it. I'm going to go off script-- >> Do they have unicorns down under? I think they have some strange species right? >> Well we put the pointy bit on the back. You guys have in on the front. >> So I was at an IBM event on Friday. It was a chief data officer summit, and I attended what was called the Data Divas' breakfast. It was a women in tech thing, and one of the CDOs, she said that 25% of chief data officers are women, which is much higher than you would normally see in the profile of IT. We happen to have 25% of our panelists are women. Is that common? Miriam and Jennifer, is that common for the data science field? Or is this a higher percentage than you would normally see-- >> James: Or a lower percentage? >> I think certainly for us, we have hired a number of additional women in the last year, and they are phenomenal data scientists. I don't know that I would say, I mean I think it's certainly typical that this is still a male-dominated field, but I think like many male-dominated fields, physics, mathematics, computer science, I think that that is slowly changing and evolving, and I think certainly, that's something that we've noticed in our firm over the years at our consultancy, as we're hiring new people. So I don't know if I would say 25% is the right number, but hopefully we can get it closer to 50. Jennifer, I don't know if you have... >> Yeah, so I know at Nielsen we have actually more than 25% of our team is women, at least the team I work with, so there seems to be a lot of women who are going into the field. Which isn't too surprising, because with a lot of the issues that come up in STEM, one of the reasons why a lot of women drop out is because they want real world jobs and they feel like they want to be in the workforce, and so I think this is a great opportunity with data science being so popular for these women to actually have a job where they can still maintain that engineering and science view background that they learned in school. >> Great, well Hillary Mason, I think, was the first data scientist that I ever interviewed, and I asked her what are the sort of skills required and the first question that we wanted to ask, I just threw other women in tech in there, 'cause we love women in tech, is about this notion of the unicorn data scientist, right? It's been put forth that there's the skill sets required to be a date scientist are so numerous that it's virtually impossible to have a data scientist with all those skills. >> And I love Dez's extreme sports analogy, because that plays into the whole notion of data science, we like to talk about the theme now of data science as a team sport. Must it be an extreme sport is what I'm wondering, you know. The unicorns of the world seem to be... Is that realistic now in this new era? >> I mean when automobiles first came out, they were concerned that there wouldn't be enough chauffeurs to drive all the people around. Is there an analogy with data, to be a data-driven company. Do I need a data scientist, and does that data scientist, you know, need to have these unbelievable mixture of skills? Or are we doomed to always have a skill shortage? Open it up. >> I'd like to have a crack at that, so it's interesting, when automobiles were a thing, when they first bought cars out, and before they, sort of, were modernized by the likes of Ford's Model T, when we got away from the horse and carriage, they actually had human beings walking down the street with a flag warning the public that the horseless carriage was coming, and I think data scientists are very much like that. That we're kind of expected to go ahead of the organization and try and take the challenges we're faced with today and see what's going to come around the corner. And so we're like the little flag-bearers, if you'd like, in many ways of this is where we're at today, tell me where I'm going to be tomorrow, and try and predict the day after as well. It is very much becoming a team sport though. But I think the concept of data science being a unicorn has come about because the coinage hasn't been very well defined, you know, if you were to ask 10 people what a data scientist were, you'd get 11 answers, and I think this is a really challenging issue for hiring managers and C-suites when the generants say I was data science, I want big data, I want an analyst. They don't actually really know what they're asking for. Generally, if you ask for a database administrator, it's a well-described job spec, and you can just advertise it and some 20 people will turn up and you interview to decide whether you like the look and feel and smell of 'em. When you ask for a data scientist, there's 20 different definitions of what that one data science role could be. So we don't initially know what the job is, we don't know what the deliverable is, and we're still trying to figure that out, so yeah. >> Craig what about you? >> So from my experience, when we talk about data science, we're really talking about a collection of experiences with multiple people I've yet to find, at least from my experience, a data science effort with a lone wolf. So you're talking about a combination of skills, and so you don't have, no one individual needs to have all that makes a data scientist a data scientist, but you definitely have to have the right combination of skills amongst a team in order to accomplish the goals of data science team. So from my experiences and from the clients that I've worked with, we refer to the data science effort as a data science team. And I believe that's very appropriate to the team sport analogy. >> For us, we look at a data scientist as a full stack web developer, a jack of all trades, I mean they need to have a multitude of background coming from a programmer from an analyst. You can't find one subject matter expert, it's very difficult. And if you're able to find a subject matter expert, you know, through the lifecycle of product development, you're going to require that individual to interact with a number of other members from your team who are analysts and then you just end up well training this person to be, again, a jack of all trades, so it comes full circle. >> I own a business that does nothing but data solutions, and we've been in business 15 years, and it's been, the transition over time has been going from being a conventional wisdom run company with a bunch of experts at the top to becoming more of a data-driven company using data warehousing and BI, but now the trend is absolutely analytics driven. So if you're not becoming an analytics-driven company, you are going to be behind the curve very very soon, and it's interesting that IBM is now coining the phrase of a cognitive business. I think that is absolutely the future. If you're not a cognitive business from a technology perspective, and an analytics-driven perspective, you're going to be left behind, that's for sure. So in order to stay competitive, you know, you need to really think about data science think about how you're using your data, and I also see that what's considered the data expert has evolved over time too where it used to be just someone really good at writing SQL, or someone really good at writing queries in any language, but now it's becoming more of a interdisciplinary action where you need soft skills and you also need the hard skills, and that's why I think there's more females in the industry now than ever. Because you really need to have a really broad width of experiences that really wasn't required in the past. >> Greg Piateski, you have a comment? >> So there are not too many unicorns in nature or as data scientists, so I think organizations that want to hire data scientists have to look for teams, and there are a few unicorns like Hillary Mason or maybe Osama Faiat, but they generally tend to start companies and very hard to retain them as data scientists. What I see is in other evolution, automation, and you know, steps like IBM, Watson, the first platform is eventually a great advance for data scientists in the short term, but probably what's likely to happen in the longer term kind of more and more of those skills becoming subsumed by machine unique layer within the software. How long will it take, I don't know, but I have a feeling that the paradise for data scientists may not be very long lived. >> Greg, I have a follow up question to what I just heard you say. When a data scientist, let's say a unicorn data scientist starts a company, as you've phrased it, and the company's product is built on data science, do they give up becoming a data scientist in the process? It would seem that they become a data scientist of a higher order if they've built a product based on that knowledge. What is your thoughts on that? >> Well, I know a few people like that, so I think maybe they remain data scientists at heart, but they don't really have the time to do the analysis and they really have to focus more on strategic things. For example, today actually is the birthday of Google, 18 years ago, so Larry Page and Sergey Brin wrote a very influential paper back in the '90s About page rank. Have they remained data scientist, perhaps a very very small part, but that's not really what they do, so I think those unicorn data scientists could quickly evolve to have to look for really teams to capture those skills. >> Clearly they come to a point in their career where they build a company based on teams of data scientists and data engineers and so forth, which relates to the topic of team data science. What is the right division of roles and responsibilities for team data science? >> Before we go, Jennifer, did you have a comment on that? >> Yeah, so I guess I would say for me, when data science came out and there was, you know, the Venn Diagram that came out about all the skills you were supposed to have? I took a very different approach than all of the people who I knew who were going into data science. Most people started interviewing immediately, they were like this is great, I'm going to get a job. I went and learned how to develop applications, and learned computer science, 'cause I had never taken a computer science course in college, and made sure I trued up that one part where I didn't know these things or had the skills from school, so I went headfirst and just learned it, and then now I have actually a lot of technology patents as a result of that. So to answer Jim's question, actually. I started my company about five years ago. And originally started out as a consulting firm slash data science company, then it evolved, and one of the reasons I went back in the industry and now I'm at Nielsen is because you really can't do the same sort of data science work when you're actually doing product development. It's a very very different sort of world. You know, when you're developing a product you're developing a core feature or functionality that you're going to offer clients and customers, so I think definitely you really don't get to have that wide range of sort of looking at 8 million models and testing things out. That flexibility really isn't there as your product starts getting developed. >> Before we go into the team sport, the hard skills that you have, are you all good at math? Are you all computer science types? How about math? Are you all math? >> What were your GPAs? (laughs) >> David: Anybody not math oriented? Anybody not love math? You don't love math? >> I love math, I think it's required. >> David: So math yes, check. >> You dream in equations, right? You dream. >> Computer science? Do I have to have computer science skills? At least the basic knowledge? >> I don't know that you need to have formal classes in any of these things, but I think certainly as Jennifer was saying, if you have no skills in programming whatsoever and you have no interest in learning how to write SQL queries or RR Python, you're probably going to struggle a little bit. >> James: It would be a challenge. >> So I think yes, I have a Ph.D. in physics, I did a lot of math, it's my love language, but I think you don't necessarily need to have formal training in all of these things, but I think you need to have a curiosity and a love of learning, and so if you don't have that, you still want to learn and however you gain that knowledge I think, but yeah, if you have no technical interests whatsoever, and don't want to write a line of code, maybe data science is not the field for you. Even if you don't do it everyday. >> And statistics as well? You would put that in that same general category? How about data hacking? You got to love data hacking, is that fair? Eaves, you have a comment? >> Yeah, I think so, while we've been discussing that for me, the most important part is that you have a logical mind and you have the capability to absorb new things and the curiosity you need to dive into that. While I don't have an education in IT or whatever, I have a background in chemistry and those things that I learned there, I apply to information technology as well, and from a part that you say, okay, I'm a tech-savvy guy, I'm interested in the tech part of it, you need to speak that business language and if you can do that crossover and understand what other skill sets or parts of the roles are telling you I think the communication in that aspect is very important. >> I'd like throw just something really quickly, and I think there's an interesting thing that happens in IT, particularly around technology. We tend to forget that we've actually solved a lot of these problems in the past. If we look in history, if we look around the second World War, and Bletchley Park in the UK, where you had a very similar experience as humans that we're having currently around the whole issue of data science, so there was an interesting challenge with the enigma in the shark code, right? And there was a bunch of men put in a room and told, you're mathematicians and you come from universities, and you can crack codes, but they couldn't. And so what they ended up doing was running these ads, and putting challenges, they actually put, I think it was crossword puzzles in the newspaper, and this deluge of women came out of all kinds of different roles without math degrees, without science degrees, but could solve problems, and they were thrown at the challenge of cracking codes, and invariably, they did the heavy lifting. On a daily basis for converting messages from one format to another, so that this very small team at the end could actually get in play with the sexy piece of it. And I think we're going through a similar shift now with what we're refer to as data science in the technology and business world. Where the people who are doing the heavy lifting aren't necessarily what we'd think of as the traditional data scientists, and so, there have been some unicorns and we've championed them, and they're great. But I think the shift's going to be to accountants, actuaries, and statisticians who understand the business, and come from an MBA star background that can learn the relevant pieces of math and models that we need to to apply to get the data science outcome. I think we've already been here, we've solved this problem, we've just got to learn not to try and reinvent the wheel, 'cause the media hypes this whole thing of data science is exciting and new, but we've been here a couple times before, and there's a lot to be learned from that, my view. >> I think we had Joe next. >> Yeah, so I was going to say that, data science is a funny thing. To use the word science is kind of a misnomer, because there is definitely a level of art to it, and I like to use the analogy, when Michelangelo would look at a block of marble, everyone else looked at the block of marble to see a block of marble. He looks at a block of marble and he sees a finished sculpture, and then he figures out what tools do I need to actually make my vision? And I think data science is a lot like that. We hear a problem, we see the solution, and then we just need the right tools to do it, and I think part of consulting and data science in particular. It's not so much what we know out of the gate, but it's how quickly we learn. And I think everyone here, what makes them brilliant, is how quickly they could learn any tool that they need to see their vision get accomplished. >> David: Justin? >> Yeah, I think you make a really great point, for me, I'm a Marine Corp veteran, and the reason I mentioned that is 'cause I work with two veterans who are problem solvers. And I think that's what data scientists really are, in the long run are problem solvers, and you mentioned a great point that, yeah, I think just problem solving is the key. You don't have to be a subject matter expert, just be able to take the tools and intelligently use them. >> Now when you look at the whole notion of team data science, what is the right mix of roles, like role definitions within a high-quality or a high-preforming data science teams now IBM, with, of course, our announcement of project, data works and so forth. We're splitting the role division, in terms of data scientist versus data engineers versus application developer versus business analyst, is that the right breakdown of roles? Or what would the panelists recommend in terms of understanding what kind of roles make sense within, like I said, a high performing team that's looking for trying to develop applications that depend on data, machine learning, and so forth? Anybody want to? >> I'll tackle that. So the teams that I have created over the years made up these data science teams that I brought into customer sites have a combination of developer capabilities and some of them are IT developers, but some of them were developers of things other than applications. They designed buildings, they did other things with their technical expertise besides building technology. The other piece besides the developer is the analytics, and analytics can be taught as long as they understand how algorithms work and the code behind the analytics, in other words, how are we analyzing things, and from a data science perspective, we are leveraging technology to do the analyzing through the tool sets, so ultimately as long as they understand how tool sets work, then we can train them on the tools. Having that analytic background is an important piece. >> Craig, is it easier to, I'll go to you in a moment Joe, is it easier to cross train a data scientist to be an app developer, than to cross train an app developer to be a data scientist or does it not matter? >> Yes. (laughs) And not the other way around. It depends on the-- >> It's easier to cross train a data scientist to be an app developer than-- >> Yes. >> The other way around. Why is that? >> Developing code can be as difficult as the tool set one uses to develop code. Today's tool sets are very user friendly. where developing code is very difficult to teach a person to think along the lines of developing code when they don't have any idea of the aspects of code, of building something. >> I think it was Joe, or you next, or Jennifer, who was it? >> I would say that one of the reasons for that is data scientists will probably know if the answer's right after you process data, whereas data engineer might be able to manipulate the data but may not know if the answer's correct. So I think that is one of the reasons why having a data scientist learn the application development skills might be a easier time than the other way around. >> I think Miriam, had a comment? Sorry. >> I think that what we're advising our clients to do is to not think, before data science and before analytics became so required by companies to stay competitive, it was more of a waterfall, you have a data engineer build a solution, you know, then you throw it over the fence and the business analyst would have at it, where now, it must be agile, and you must have a scrum team where you have the data scientist and the data engineer and the project manager and the product owner and someone from the chief data office all at the table at the same time and all accomplishing the same goal. Because all of these skills are required, collectively in order to solve this problem, and it can't be done daisy chained anymore it has to be a collaboration. And that's why I think spark is so awesome, because you know, spark is a single interface that a data engineer can use, a data analyst can use, and a data scientist can use. And now with what we've learned today, having a data catalog on top so that the chief data office can actually manage it, I think is really going to take spark to the next level. >> James: Miriam? >> I wanted to comment on your question to Craig about is it harder to teach a data scientist to build an application or vice versa, and one of the things that we have worked on a lot in our data science team is incorporating a lot of best practices from software development, agile, scrum, that sort of thing, and I think particularly with a focus on deploying models that we don't just want to build an interesting data science model, we want to deploy it, and get some value. You need to really incorporate these processes from someone who might know how to build applications and that, I think for some data scientists can be a challenge, because one of the fun things about data science is you get to get into the data, and you get your hands dirty, and you build a model, and you get to try all these cool things, but then when the time comes for you to actually deploy something, you need deployment-grade code in order to make sure it can go into production at your client side and be useful for instance, so I think that there's an interesting challenge on both ends, but one of the things I've definitely noticed with some of our data scientists is it's very hard to get them to think in that mindset, which is why you have a team of people, because everyone has different skills and you can mitigate that. >> Dev-ops for data science? >> Yeah, exactly. We call it insight ops, but yeah, I hear what you're saying. Data science is becoming increasingly an operational function as opposed to strictly exploratory or developmental. Did some one else have a, Dez? >> One of the things I was going to mention, one of the things I like to do when someone gives me a new problem is take all the laptops and phones away. And we just end up in a room with a whiteboard. And developers find that challenging sometimes, so I had this one line where I said to them don't write the first line of code until you actually understand the problem you're trying to solve right? And I think where the data science focus has changed the game for organizations who are trying to get some systematic repeatable process that they can throw data at and just keep getting answers and things, no matter what the industry might be is that developers will come with a particular mindset on how they're going to codify something without necessarily getting the full spectrum and understanding the problem first place. What I'm finding is the people that come at data science tend to have more of a hacker ethic. They want to hack the problem, they want to understand the challenge, and they want to be able to get it down to plain English simple phrases, and then apply some algorithms and then build models, and then codify it, and so most of the time we sit in a room with whiteboard markers just trying to build a model in a graphical sense and make sure it's going to work and that it's going to flow, and once we can do that, we can codify it. I think when you come at it from the other angle from the developer ethic, and you're like I'm just going to codify this from day one, I'm going to write code. I'm going to hack this thing out and it's just going to run and compile. Often, you don't truly understand what he's trying to get to at the end point, and you can just spend days writing code and I think someone made the comment that sometimes you don't actually know whether the output is actually accurate in the first place. So I think there's a lot of value being provided from the data science practice. Over understanding the problem in plain english at a team level, so what am I trying to do from the business consulting point of view? What are the requirements? How do I build this model? How do I test the model? How do I run a sample set through it? Train the thing and then make sure what I'm going to codify actually makes sense in the first place, because otherwise, what are you trying to solve in the first place? >> Wasn't that Einstein who said if I had an hour to solve a problem, I'd spend 55 minutes understanding the problem and five minutes on the solution, right? It's exactly what you're talking about. >> Well I think, I will say, getting back to the question, the thing with building these teams, I think a lot of times people don't talk about is that engineers are actually very very important for data science projects and data science problems. For instance, if you were just trying to prototype something or just come up with a model, then data science teams are great, however, if you need to actually put that into production, that code that the data scientist has written may not be optimal, so as we scale out, it may be actually very inefficient. At that point, you kind of want an engineer to step in and actually optimize that code, so I think it depends on what you're building and that kind of dictates what kind of division you want among your teammates, but I do think that a lot of times, the engineering component is really undervalued out there. >> Jennifer, it seems that the data engineering function, data discovery and preparation and so forth is becoming automated to a greater degree, but if I'm listening to you, I don't hear that data engineering as a discipline is becoming extinct in terms of a role that people can be hired into. You're saying that there's a strong ongoing need for data engineers to optimize the entire pipeline to deliver the fruits of data science in production applications, is that correct? So they play that very much operational role as the backbone for... >> So I think a lot of times businesses will go to data scientist to build a better model to build a predictive model, but that model may not be something that you really want to implement out there when there's like a million users coming to your website, 'cause it may not be efficient, it may take a very long time, so I think in that sense, it is important to have good engineers, and your whole product may fail, you may build the best model it may have the best output, but if you can't actually implement it, then really what good is it? >> What about calibrating these models? How do you go about doing that and sort of testing that in the real world? Has that changed overtime? Or is it... >> So one of the things that I think can happen, and we found with one of our clients is when you build a model, you do it with the data that you have, and you try to use a very robust cross-validation process to make sure that it's robust and it's sturdy, but one thing that can sometimes happen is after you put your model into production, there can be external factors that, societal or whatever, things that have nothing to do with the data that you have or the quality of the data or the quality of the model, which can actually erode the model's performance over time. So as an example, we think about cell phone contracts right? Those have changed a lot over the years, so maybe five years ago, the type of data plan you had might not be the same that it is today, because a totally different type of plan is offered, so if you're building a model on that to say predict who's going to leave and go to a different cell phone carrier, the validity of your model overtime is going to completely degrade based on nothing that you have, that you put into the model or the data that was available, so I think you need to have this sort of model management and monitoring process to take this factors into account and then know when it's time to do a refresh. >> Cross-validation, even at one point in time, for example, there was an article in the New York Times recently that they gave the same data set to five different data scientists, this is survey data for the presidential election that's upcoming, and five different data scientists came to five different predictions. They were all high quality data scientists, the cross-validation showed a wide variation about who was on top, whether it was Hillary or whether it was Trump so that shows you that even at any point in time, cross-validation is essential to understand how robust the predictions might be. Does somebody else have a comment? Joe? >> I just want to say that this even drives home the fact that having the scrum team for each project and having the engineer and the data scientist, data engineer and data scientist working side by side because it is important that whatever we're building we assume will eventually go into production, and we used to have in the data warehousing world, you'd get the data out of the systems, out of your applications, you do analysis on your data, and the nirvana was maybe that data would go back to the system, but typically it didn't. Nowadays, the applications are dependent on the insight coming from the data science team. With the behavior of the application and the personalization and individual experience for a customer is highly dependent, so it has to be, you said is data science part of the dev-ops team, absolutely now, it has to be. >> Whose job is it to figure out the way in which the data is presented to the business? Where's the sort of presentation, the visualization plan, is that the data scientist role? Does that depend on whether or not you have that gene? Do you need a UI person on your team? Where does that fit? >> Wow, good question. >> Well usually that's the output, I mean, once you get to the point where you're visualizing the data, you've created an algorithm or some sort of code that produces that to be visualized, so at the end of the day that the customers can see what all the fuss is about from a data science perspective. But it's usually post the data science component. >> So do you run into situations where you can see it and it's blatantly obvious, but it doesn't necessarily translate to the business? >> Well there's an interesting challenge with data, and we throw the word data around a lot, and I've got this fun line I like throwing out there. If you torture data long enough, it will talk. So the challenge then is to figure out when to stop torturing it, right? And it's the same with models, and so I think in many other parts of organizations, we'll take something, if someone's doing a financial report on performance of the organization and they're doing it in a spreadsheet, they'll get two or three peers to review it, and validate that they've come up with a working model and the answer actually makes sense. And I think we're rushing so quickly at doing analysis on data that comes to us in various formats and high velocity that I think it's very important for us to actually stop and do peer reviews, of the models and the data and the output as well, because otherwise we start making decisions very quickly about things that may or may not be true. It's very easy to get the data to paint any picture you want, and you gave the example of the five different attempts at that thing, and I had this shoot out thing as well where I'll take in a team, I'll get two different people to do exactly the same thing in completely different rooms, and come back and challenge each other, and it's quite amazing to see the looks on their faces when they're like, oh, I didn't see that, and then go back and do it again until, and then just keep iterating until we get to the point where they both get the same outcome, in fact there's a really interesting anecdote about when the UNIX operation system was being written, and a couple of the authors went away and wrote the same program without realizing that each other were doing it, and when they came back, they actually had line for line, the same piece of C code, 'cause they'd actually gotten to a truth. A perfect version of that program, and I think we need to often look at, when we're building models and playing with data, if we can't come at it from different angles, and get the same answer, then maybe the answer isn't quite true yet, so there's a lot of risk in that. And it's the same with presentation, you know, you can paint any picture you want with the dashboard, but who's actually validating when the dashboard's painting the correct picture? >> James: Go ahead, please. >> There is a science actually, behind data visualization, you know if you're doing trending, it's a line graph, if you're doing comparative analysis, it's bar graph, if you're doing percentages, it's a pie chart, like there is a certain science to it, it's not that much of a mystery as the novice thinks there is, but what makes it challenging is that you also, just like any presentation, you have to consider your audience. And your audience, whenever we're delivering a solution, either insight, or just data in a grid, we really have to consider who is the consumer of this data, and actually cater the visual to that person or to that particular audience. And that is part of the art, and that is what makes a great data scientist. >> The consumer may in fact be the source of the data itself, like in a mobile app, so you're tuning their visualization and then their behavior is changing as a result, and then the data on their changed behavior comes back, so it can be a circular process. >> So Jim, at a recent conference, you were tweeting about the citizen data scientist, and you got emasculated by-- >> I spoke there too. >> Okay. >> TWI on that same topic, I got-- >> Kirk Borne I hear came after you. >> Kirk meant-- >> Called foul, flag on the play. >> Kirk meant well. I love Claudia Emahoff too, but yeah, it's a controversial topic. >> So I wonder what our panel thinks of that notion, citizen data scientist. >> Can I respond about citizen data scientists? >> David: Yeah, please. >> I think this term was introduced by Gartner analyst in 2015, and I think it's a very dangerous and misleading term. I think definitely we want to democratize the data and have access to more people, not just data scientists, but managers, BI analysts, but when there is already a term for such people, we can call the business analysts, because it implies some training, some understanding of the data. If you use the term citizen data scientist, it implies that without any training you take some data and then you find something there, and they think as Dev's mentioned, we've seen many examples, very easy to find completely spurious random correlations in data. So we don't want citizen dentists to treat our teeth or citizen pilots to fly planes, and if data's important, having citizen data scientists is equally dangerous, so I'm hoping that, I think actually Gartner did not use the term citizen data scientist in their 2016 hype course, so hopefully they will put this term to rest. >> So Gregory, you apparently are defining citizen to mean incompetent as opposed to simply self-starting. >> Well self-starting is very different, but that's not what I think what was the intention. I think what we see in terms of data democratization, there is a big trend over automation. There are many tools, for example there are many companies like Data Robot, probably IBM, has interesting machine learning capability towards automation, so I think I recently started a page on KDnuggets for automated data science solutions, and there are already 20 different forums that provide different levels of automation. So one can deliver in full automation maybe some expertise, but it's very dangerous to have part of an automated tool and at some point then ask citizen data scientists to try to take the wheels. >> I want to chime in on that. >> David: Yeah, pile on. >> I totally agree with all of that. I think the comment I just want to quickly put out there is that the space we're in is a very young, and rapidly changing world, and so what we haven't had yet is this time to stop and take a deep breath and actually define ourselves, so if you look at computer science in general, a lot of the traditional roles have sort of had 10 or 20 years of history, and so thorough the hiring process, and the development of those spaces, we've actually had time to breath and define what those jobs are, so we know what a systems programmer is, and we know what a database administrator is, but we haven't yet had a chance as a community to stop and breath and say, well what do we think these roles are, and so to fill that void, the media creates coinages, and I think this is the risk we've got now that the concept of a data scientist was just a term that was coined to fill a void, because no one quite knew what to call somebody who didn't come from a data science background if they were tinkering around data science, and I think that's something that we need to sort of sit up and pay attention to, because if we don't own that and drive it ourselves, then somebody else is going to fill the void and they'll create these very frustrating concepts like data scientist, which drives us all crazy. >> James: Miriam's next. >> So I wanted to comment, I agree with both of the previous comments, but in terms of a citizen data scientist, and I think whether or not you're citizen data scientist or an actual data scientist whatever that means, I think one of the most important things you can have is a sense of skepticism, right? Because you can get spurious correlations and it's like wow, my predictive model is so excellent, you know? And being aware of things like leaks from the future, right? This actually isn't predictive at all, it's a result of the thing I'm trying to predict, and so I think one thing I know that we try and do is if something really looks too good, we need to go back in and make sure, did we not look at the data correctly? Is something missing? Did we have a problem with the ETL? And so I think that a healthy sense of skepticism is important to make sure that you're not taking a spurious correlation and trying to derive some significant meaning from it. >> I think there's a Dilbert cartoon that I saw that described that very well. Joe, did you have a comment? >> I think that in order for citizen data scientists to really exist, I think we do need to have more maturity in the tools that they would use. My vision is that the BI tools of today are all going to be replaced with natural language processing and searching, you know, just be able to open up a search bar and say give me sales by region, and to take that one step into the future even further, it should actually say what are my sales going to be next year? And it should trigger a simple linear regression or be able to say which features of the televisions are actually affecting sales and do a clustering algorithm, you know I think hopefully that will be the future, but I don't see anything of that today, and I think in order to have a true citizen data scientist, you would need to have that, and that is pretty sophisticated stuff. >> I think for me, the idea of citizen data scientist I can relate to that, for instance, when I was in graduate school, I started doing some research on FDA data. It was an open source data set about 4.2 million data points. Technically when I graduated, the paper was still not published, and so in some sense, you could think of me as a citizen data scientist, right? I wasn't getting funding, I wasn't doing it for school, but I was still continuing my research, so I'd like to hope that with all the new data sources out there that there might be scientists or people who are maybe kept out of a field people who wanted to be in STEM and for whatever life circumstance couldn't be in it. That they might be encouraged to actually go and look into the data and maybe build better models or validate information that's out there. >> So Justin, I'm sorry you had one comment? >> It seems data science was termed before academia adopted formalized training for data science. But yeah, you can make, like Dez said, you can make data work for whatever problem you're trying to solve, whatever answer you see, you want data to work around it, you can make it happen. And I kind of consider that like in project management, like data creep, so you're so hyper focused on a solution you're trying to find the answer that you create an answer that works for that solution, but it may not be the correct answer, and I think the crossover discussion works well for that case. >> So but the term comes up 'cause there's a frustration I guess, right? That data science skills are not plentiful, and it's potentially a bottleneck in an organization. Supposedly 80% of your time is spent on cleaning data, is that right? Is that fair? So there's a problem. How much of that can be automated and when? >> I'll have a shot at that. So I think there's a shift that's going to come about where we're going to move from centralized data sets to data at the edge of the network, and this is something that's happening very quickly now where we can't just hold everything back to a central spot. When the internet of things actually wakes up. Things like the Boeing Dreamliner 787, that things got 6,000 sensors in it, produces half a terabyte of data per flight. There are 87,400 flights per day in domestic airspace in the U.S. That's 43.5 petabytes of raw data, now that's about three years worth of disk manufacturing in total, right? We're never going to copy that across one place, we can't process, so I think the challenge we've got ahead of us is looking at how we're going to move the intelligence and the analytics to the edge of the network and pre-cook the data in different tiers, so have a look at the raw material we get, and boil it down to a slightly smaller data set, bring a meta data version of that back, and eventually get to the point where we've only got the very minimum data set and data points we need to make key decisions. Without that, we're already at the point where we have too much data, and we can't munch it fast enough, and we can't spin off enough tin even if we witch the cloud on, and that's just this never ending deluge of noise, right? And you've got that signal versus noise problem so then we're now seeing a shift where people looking at how do we move the intelligence back to the edge of network which we actually solved some time ago in the securities space. You know, spam filtering, if an emails hits Google on the west coast of the U.S. and they create a check some for that spam email, it immediately goes into a database, and nothing gets on the opposite side of the coast, because they already know it's spam. They recognize that email coming in, that's evil, stop it. So we've already fixed its insecurity with intrusion detection, we've fixed it in spam, so we now need to take that learning, and bring it into business analytics, if you like, and see where we're finding patterns and behavior, and brew that out to the edge of the network, so if I'm seeing a demand over here for tickets on a new sale of a show, I need to be able to see where else I'm going to see that demand and start responding to that before the demand comes about. I think that's a shift that we're going to see quickly, because we'll never keep up with the data munching challenge and the volume's just going to explode. >> David: We just have a couple minutes. >> That does sound like a great topic for a future Cube panel which is data science on the edge of the fog. >> I got a hundred questions around that. So we're wrapping up here. Just got a couple minutes. Final thoughts on this conversation or any other pieces that you want to punctuate. >> I think one thing that's been really interesting for me being on this panel is hearing all of my co-panelists talking about common themes and things that we are also experiencing which isn't a surprise, but it's interesting to hear about how ubiquitous some of the challenges are, and also at the announcement earlier today, some of the things that they're talking about and thinking about, we're also talking about and thinking about. So I think it's great to hear we're all in different countries and different places, but we're experiencing a lot of the same challenges, and I think that's been really interesting for me to hear about. >> David: Great, anybody else, final thoughts? >> To echo Dez's thoughts, it's about we're never going to catch up with the amount of data that's produced, so it's about transforming big data into smart data. >> I could just say that with the shift from normal data, small data, to big data, the answer is automate, automate, automate, and we've been talking about advanced algorithms and machine learning for the science for changing the business, but there also needs to be machine learning and advanced algorithms for the backroom where we're actually getting smarter about how we ingestate and how we fix data as it comes in. Because we can actually train the machines to understand data anomalies and what we want to do with them over time. And I think the further upstream we get of data correction, the less work there will be downstream. And I also think that the concept of being able to fix data at the source is gone, that's behind us. Right now the data that we're using to analyze to change the business, typically we have no control over. Like Dez said, they're coming from censors and machines and internet of things and if it's wrong, it's always going to be wrong, so we have to figure out how to do that in our laboratory. >> Eaves, final thoughts? >> I think it's a mind shift being a data scientist if you look back at the time why did you start developing or writing code? Because you like to code, whatever, just for the sake of building a nice algorithm or a piece of software, or whatever, and now I think with the spirit of a data scientist, you're looking at a problem and say this is where I want to go, so you have more the top down approach than the bottom up approach. And have the big picture and that is what you really need as a data scientist, just look across technologies, look across departments, look across everything, and then on top of that, try to apply as much skills as you have available, and that's kind of unicorn that they're trying to look for, because it's pretty hard to find people with that wide vision on everything that is happening within the company, so you need to be aware of technology, you need to be aware of how a business is run, and how it fits within a cultural environment, you have to work with people and all those things together to my belief to make it very difficult to find those good data scientists. >> Jim? Your final thought? >> My final thoughts is this is an awesome panel, and I'm so glad that you've come to New York, and I'm hoping that you all stay, of course, for the the IBM Data First launch event that will take place this evening about a block over at Hudson Mercantile, so that's pretty much it. Thank you, I really learned a lot. >> I want to second Jim's thanks, really, great panel. Awesome expertise, really appreciate you taking the time, and thanks to the folks at IBM for putting this together. >> And I'm big fans of most of you, all of you, on this session here, so it's great just to meet you in person, thank you. >> Okay, and I want to thank Jeff Frick for being a human curtain there with the sun setting here in New York City. Well thanks very much for watching, we are going to be across the street at the IBM announcement, we're going to be on the ground. We open up again tomorrow at 9:30 at Big Data NYC, Big Data Week, Strata plus the Hadoop World, thanks for watching everybody, that's a wrap from here. This is the Cube, we're out. (techno music)