(upbeat music) >> Okay, we're now going to try and stretch our minds a little bit and stretch Supercloud to the edge. Supercloud, as we've been discussing today and reporting through various breaking analyses, is a term we use to describe a continuous experience across clouds, or even on-prem, that adds new value on top of hyperscale infrastructure. Priya Rajagopal is the director of product management at Couchbase. She's a developer, a software architect, co-creator on a number of patents as well as being an expert on edge, IoT, and mobile computing technologies. And we're going to talk about edge requirements. Priya, you've been around software engineering and mobile and edge technologies your entire career, and now you're responsible for bringing enterprise class database technology to the edge and IoT environments, synchronizing. So, when you think about the edge, the near edge, the far edge, what are the fundamental assumptions that you have to make with regards to things like connectivity, bandwidth, security, and any other technical considerations when you think about software architecture for these environments? >> Sure, sure. First off, Dave, thanks for having me here. It's really exciting to be here again, my second time. And thank you for that kind introduction. So, quickly to get back to your question. When it comes to architecting for the edge our principle is prepare for the worst and hope for the best. Because, really, when it comes to edge computing, it's sort of the edge cases that come to bite you. You mentioned connectivity, bandwidth, security. I have a few more. Starting with connectivity, as you import on low network connectivity, think offshore oil rigs, cruise ships, or even retail settings, when you want to have business continuity, most of the time you've got an internet connection, but then when there is disruption, then you lose business continuity. Then when it comes to bandwidth, the notion or the approach we take is that bandwidth is always limited or it's at a premium. Data plans can go up through the roof, depending on the volume of data. Think medical clinics in rural areas. When it comes to security, edge poses unique challenges because you're moving away from this world garden, central cloud-based environment, and now everything is accessible over the internet. And the internet really is inherently untrustworthy. Every bit of data that is written or read by an application needs to be authenticated, needs to be authorized. The entire path needs to be secured end-to-end. It needs to be encrypted. That's confidentiality. Also the persistence of data itself. It needs to be encrypted on disk. Now, one of the advantages of edge computing or distributing data is that the impacted edge environment can be isolated away without impacting the other edge location. Looking at the classic retail architecture, if you've got retail use case, if you've got a a retail store where there's a security breach, you need to have a provision of isolating that store so that you don't bring down services for the other stores. When it comes to edge computing, you have to think about those aspects of security. Any of these locations could be breached. And if one of them is breached, how do you control that? So, that's to answer those three key topics that you brought up. But there are other considerations. One is data governance. That's a huge challenge. Because we are a database company at Couchbase, we think of database, data governance, compliance, privacy. All that is very paramount to our customers. It's not just about enforcing policies right now. We are talking about not enforcing policies in a central location, but you have to do it in a distributed fashion because one of the benefits of edge computing is, as you probably very well know, is the benefits it brings when it comes to data privacy, governance policies. You can enforce that at a granular scale because data doesn't have to ever leave the edge. But again, I talked about this in the context of security, there needs to be a way to control this data at the edge. You have to govern the data when it is at the edge remotely. Some of the other challenges when thinking about the edge is, of course, volume, scale, think IoT, mobile devices, classic far edge type scenarios. And I think the other criteria that we have to keep in mind when we are architecting a platform for this kind of computing paradigm is the heterogeneity of the edge itself. It's no longer a uniform set of compute and storage resources that are available at your disposal. You've got a variety of IoT devices. You've got mobile devices, different processing capabilities, different storage capabilities. When it comes to edge data centers, it's not uniform in terms of what services are available. Do they have a load balancer? Do they have a firewall? Can I deploy a firewall? These are all some key architectural considerations when it comes to actually architecting a solution for the edge. >> Great. Thank you for that awesome setup. Talking about stretching to the edge this idea of Supercloud that connote that single logical layer that spans across multiple clouds. It can include on on-prem, but a critical criterion is that the developer, and, of course, the user experience, is identical or substantially similar. Let's say identical. Let's say identical, irrespective of physical location. Priya, is that vision technically achievable today in the world of database. And if so, can you describe the architectural elements that make it possible to perform well and have low latency and the security and other criteria that you just mentioned? What's the technical enablers? Is it just good software. Is it architecture? Help us understand that. >> Sure. You brought up two aspects. You mentioned user experience, and then you mentioned from a developer standpoint, what does it take? And I'd like to address the two separately. They are very tightly related, but I'd like to address them separately. Just focusing on the easier of the two when it comes to user experience, what are the factors that impact user experience? You're talking about reliability of service. Always on, always available applications. It doesn't matter where the data is coming from. Whether the data is coming from my device, it's sourced from an on-prem data center, or if it is from the edge of the cloud, it's from a central cloud data center, from an end-user perspective, all they care about is that their application is available. The next is, of course, responsiveness. Users are getting increasingly impatient. Do you want to reduce wait times to service? You want something which is extremely fast. They're looking for immersive applications or immersive experiences, AR, VR, mixed reality use cases. Then something which is very critical, and what you just touched upon, is this sort of seamless experience. Like this omnichannel, as we talk about in the context of retail kind of experience, Or what I like to refer to as park and pick up reference. You park, you start your application, running your application, you start a transaction on one device, you park it, pick it up on another device. Or in case of retail, you walk into a store, you pick it up from there. So, there's a park and pick up. Seamless mobility of data is extremely critical. In the context of a database, when we talk about responsiveness, two key, the KPIs are latency, bandwidth. And latency is really the round trip time from the time it takes to make a request for data, and the response comes back. The factors that impact latency are, of course, the type of the network itself, but also the proximity of the data source to the point of consumption. And so the more number of hubs that the data packets have to take to reach from the source to its destination, then you're going to incur a lot of latency. And when it comes to bandwidth, we are talking about the capacity of the network. How much data can be shot through the pipe? And, of course, when edge computing, large number of clients. I talked about scale, the volume of devices. And when you're talking about all of them concurrently connected, then you're going to have network congestion which impacts bandwidth which, in turn, impacts performance. And so when it comes to how do you architect a solution for that, if you completely remove the reliance on network to the extent possible, then you get the highest guarantees when it comes to responsiveness, availability, reliability. Because your application is always going to be on. In order to do that, if you have the database and the data processing components co-located with the application that needs it, that would give you the best experience. But, of course, you want to bring it as close. A lot of times, it's not possible to end with that data within your application itself. And that's where you have options of your an on-prem data center, the edge of the cloud, max end and so on. So the closer you bring the data, you're going to get the better experience. Now, that's all great. But then when it comes to something to achieve a vision of Supercloud, when we talked about, "Hey, one way from a developer standpoint, I have one API to set up this connection to a server, but then behind the scenes, my data could be resident anywhere." How do you achieve something like that? And so, a critical aspect of the solution is data synchronization. I talked about data storage as a database, data storage database, that's a critical aspect of what database is really where the data is persisted, data processing, the APIs to access and query the data. But another really critical aspect of distributing a database is the data synchronization technology. And so once all the islands of data, whether it is on the device, whether it's an on-prem data center, whether it's the edge of the cloud, or whether it is a regional data center, once all those databases are kept in sync, then it's a question of when connectivity to one of those data centers goes down, then there needs to be a seamless switch to another data center. And today, at least when it comes to Couchbase, a lot of our customers do employ global load balancers which can automatically detect. So, from a perspective of an application, it's just one URL end point. But then when one of those services goes down or data centers goes down, we have active failover and standby. And so the load balance automatically redirects all the traffic to the backup data center. And of course, for that to happen, those two data centers need to be in sync. And that's critical. Did that answer your question? >> Yeah, let me jump in here. Thank you again for that. I want to unpack some of those, and I want use the example of Couchbase Light, which, as the name implies, a mobile version of Couchbase. I'm interested in a number of things that you said. You talked about, in some cases, you want to get data from the most proximate location. Is there a some kind of metadata intelligence that you have access to? I'm interested in how you do the synchronization. How do you deal with conflict resolution and recovery if something goes wrong? You're talking about distributed database challenges. How do you approach all that? >> Wow, great question. And probably one that I could occupy the entire session for, but I'll try and keep it brief and try and answer most of the points that you touched upon. So, we talked about distributed database and data sync. But here's the other challenge. A lot of these distributed locations can actually be disconnected. So, we've just exacerbated this whole notion of data sync. And that's what we call offline first, not just we call, what is typically referred to as offline first sync. But the ability for an application to run in a completely disconnected mode, but then when there is network connectivity, the data is synced back to the backend data servers. In order for this to happen, you need a sync protocol (indistinct). Since you asked in the context of Couchbase, our sync protocol, it's a web sockets, extremely lightweight data synchronization protocol that's resilient to network disruption. So, what this means is I could have hundreds of thousands of clients that are connected to a data center, and they could be at various stages of disconnect. And you have a field application, and then you are veering in and out of pockets of network connectivity, so network is disrupted, and then network connectivity is restored. Our sync protocol has got a built-in checkpoint mechanism that allows the two replicating points to have a handshake of what is the previous sync point, and only data from that previous sync point is sent to that specific client. And in order to achieve that you mentioned Couchbase Light, which is, of course, our embedded database for mobile, desktop and any embedded platform. But the one that handles the data synchronization is our Sync Gateway. So, we got a component, Sync Gateway, that sits with our Couchbase server, and that's responsible for securely syncing the data and implementing this protocol with Couchbase Light. You talked about conflict resolution. And it's great that you mentioned that. Because when it comes to data sync, a lot of times folks think, "Oh well, how hard can that be?" I mean, you request for some data, and you pull down the a data, and that's great. And that's the happy path. When all of the clients are connected, when there is reliable network connectivity, that's great. But we are, of course, talking about unreliable network connectivity and resiliency to network disruptions. And also the fact that you have lots of concurrently connected clients, all of them potentially updating the same piece of data. That's when you have a conflict, When two or more clients are updating the same, clients or writers. You could have the writes coming in from the clients. You could have the writes coming in from the backend systems. Either way, multiple writers do the same piece of data. That's when you have conflicts. Now, when it comes to, so, a little bit to explain how conflict resolution is handled within our data sync protocol in Couchbase, it would help to understand a little bit about what kind of database we are, how is data itself stored within our database. So, Couchbase Light is a NoSql JSON document store, which means everything is stored as JSON documents. And so every time there is a write, an update to a document, let's say you start with an initial version of the document, the document is created. Every time there is a mutation to a document, you have a new revision to that document. So, as you build in more rights or more mutations to that document, you build out what's called a revision tree. And so when does a conflict happen? Conflict happens when there is a branch in the tree. So, you've got two writers, writing to the same revision, then you get a branch, and that's what is a conflict. We have a way of detecting those conflicts automatically. That's conflict detection. So, now we know there's a conflict, but we have to resolve it. And within Couchbase, you have two options. You don't have to do anything about it. The system has built-in automatic conflict resolution heuristics built in. So, it's going to check, pick a winning revision. And so we use a bunch of criteria, and we pick a winning revision. So, if two writers are updating the same revision of the document, version of the document, we pick a winner. But then that seemed to work from our experience, 80% of the use cases. But then for the remaining 20%, applications would like to have more control over how the winner of the conflict is picked. And for that, applications can implement a custom conflict resolver. So, we'll automatically detect the conflicting revisions and send these conflicting revisions over to the application via a callback, and the application has access to the entire document body of the two revisions and can use whatever criteria needs to merge >> So, that's policy based in that example? >> Yes. >> Yeah, yeah, okay. >> So you can have user policy based, or you can have the automatic heuristics. >> Okay, I got to wrap because we're out of time, but I want to run this scenario by you. One of the risks to the Supercloud Nirvana that we always talk about is this notion of a new architecture emerging at the edge, far edge really, 'cause they're highly-distributed environments. They're low power, tons of data. And this idea of AI inferencing at the edge, a lot of the AI today is done in modeling in the cloud. You think about ARM processors in these new low-cost devices and massive processing power eventually overwhelming the economics. And then that's seeping back into the enterprise and disrupting it. Now, you still get the problem of federated governance and security, and that's probably going to be more centralized slash federated. But, in one minute, do you see that AI inferencing real-time taking off at the edge? Where is that on the S-curve? >> Oh, absolutely right. When it comes to IoT applications, it's all about massive volumes of data generated at the edge. You talked about the economics doesn't add up. Now you need to actually, the data needs to be actioned at some point. And if you have to transfer all of that over the internet for analysis, the responsiveness, you're going to lose that. You're not going to get that real-time responsiveness and availability. The edge is the perfect location. And a lot of this data is temporal in nature. So, you don't want that to be sent back to the cloud for long-term persistence, but instead you want that to be actioned close as possible to the source itself. And when you talk about, there are, of course, the really small microcontrollers and so on. Even there, you can actually have some local processing done, like tiny ML models, but then mobile devices, when you talk about those, as you're very well aware, these are extremely capable. They're capable of running neural, they have neural network processors. And so they can do a lot of processing locally itself. But then when you want to have an aggregated view within the edge, you want to process that data in an IoT gateway and only send the aggregated data back to the cloud for long-term analytics and persistence. >> Yeah, this is something we're watching, and I think could be highly disruptive, and it's hard to predict. Priya, I got to go. Thanks so much for coming on the "theCube." Really appreciate your time. >> Yeah, thank you. >> All right, you're watching "Supercloud 22." We'll be right back right after this short break. (upbeat music)

>> Welcome to the Cubes coverage of Couchbase connect online 2021. I'm Lisa Martin. I have a first timer here on the cube Priya Rajgopal, the director of product management from Couchbase joins me next. Priya, welcome to the program. >> Thank you, Lisa. Thanks for having me here and glad to be here. First timer. So really excited. >> Yeah. Well, we'll make sure that you're going to have fun. We're going to talk about edge computing and what I'd love to get is your perspectives on what's going on and the evolution in the last 18 months. I'm sure so much has changed, but talk to me about edge computing what's going on >> Sure. >> Sure. there's 6 lot of literature on there and different there's a lot of literature on there and different interpretations and the way we see it at Couchbase, it's a distributed computing paradigm, that brings compute and storage to the edge. And what is the edge? The edge is the location where data is generated or consumed. And so the edge, again, the taxonomy is varied, but it's really a continuum. So it's not a thing, right? So it's a location. So it could be a single device or it could actually be a data center. And so it's getting a lot of traction with the proliferation of a lot of applications around AR, VR, IOT, and mobile devices and mobile applications. Because it delivers on the promise of ultra low latency access to data because you know, the edges where the data is generated and consumed, data privacy, governance, residency to network disruptions, low bandwidth usage. So to your question on how does mobile fit into the space of edge computing? In my view, mobile application, mobile devices are a classic example of edge computing because think about mobile devices, right, they're generating data, they're processing data, applications are processing data right there on the devices. You can store data in offline mode on those devices. So it is a classic edge device. And of course, the data doesn't have to be generated on the device itself. There's mobile applications could sort of be gateways to other external like variables for instance, and other IOT devices, which can connect to these mobile applications. And these mobile applications could process that data. >> Got it. So thank you for sharing Couchbase's definition. And it's a good point to do that as so many times, there's so many different terms and solutions and technologies that can be interpreted and explained many different ways. Let's now go through Couchbase's role in edge computing. Help the audience understand where you fit into that. >> Sure. So if you recap the definition, right? edge computing is all about storage and compute to the edge. So clearly a database has a key role to play in this model, Right? Or in this paradigm, because when you think about it, a classic application architecture, you've got three tiers, you've got an application tier, it includes your business logic, some of the UI elements, that's optional. You've got your database tier, which drives the application, Does the obligation needs data? It's driven by the database tier. And then you've got the infrastructure tier, that includes your network storage compute. Now, when you're talking about an edge computing architecture, you're talking about distributing all these three tiers. Your application tier, a database tier, as well as your infrastructure tier and a Couchbase is a fully distributed, no sequel database solution. So it fits in right into this paradigm of edge computing. Now, when we are talking about distributing our storage, that's just one aspect of it, right? You have to distribute it to these edge devices. You may have to distribute it to edge data centers, You need to be able to sync or move data between these You need to be able to sync or move data between these distributed cloud environments, right? So data synchronization is a key component of the tier of of edge computing architectures. And then finally, there's data management. That's all about enforcement of policies, when it comes to data privacy, you know what, data needs to be resident at the edge, what data needs to be filtered, what needs to be aggregated? what data needs to be filtered, what needs to be aggregated? So you need a solution that can provide those hooks that allows you to enforce those policies. So, a database like Couchbase has a critical role to play solution that can be deployed in the cloud, or it can be deployed at the edge. And again, or it can be deployed at the edge. And again, the edge could be a data center or it could be a device. So what about device? We have an embedded database solution for mobile desktop and embedded platforms. And then of course, data movement, comprehensive data synchronization technology. comprehensive data synchronization technology. >> Let's go through specifically some of the database capabilities that are required for businesses in any industry to be successful in edge computing. >> Sure, absolutely. Right. to do sort of reiterate or reinforce the three concepts, right? Data storage, data movement, data management, right? And Couchbase technology because that the stack consists of couchbase server, our flagship fully distributed, no sequel data platform. It can be containerized. It can be deployed in any public or private cloud. It could be deployed at the edge cloud. And then you've a Couchbase lite. Again, no sequel embedded database full featured, right? Anything that you can do with a standalone database, you can do it with the embedded database. Now you can embed that within your mobile applications, within your other embedded applications or desktop applications. And that's great, right? That's the data storage part of it. And, and that's one part of it, but what about the data movement? And that's where you got a data synchronization technology where we facilitate a high throughput, high performance, highly scalable data synchronization, between the edge and the cloud. And of course, as I mentioned, data management is a critical aspect of all this, right? And so the synchronization technology has got components that allow you to set filters, access control policies. And there's a lot of hooks when it comes to data governance. So for instance, if an edge goes out of commission, or if there's a security breach, for instance, you want to isolate the edge, you can do that. The data that was previously synchronized to that edge, you want to be able to poach that data. So we have options the automatically poach the data, if the device is no longer in the hands of the right recipient for instance. those are the critical aspects. Of course, the overarching theme is security, right? And, that goes hand-in-hand with encryption of the data at rest, encryption of the data in motion, then authentication, authorization, access control. >> Security is even more important in given the events of the last 18 months where we've seen a massive rise in ransom, where we've seen a huge rise in DDoS attacks. Let's, double-click more on the security aspect of what Couchbase is delivering. >> Sure, absolutely. So when it comes to security of data at rest, right, even when the Couchbase lite, which is our embedded device, your entire database is encrypted AES-256 data encryption, and then data, when it leaves the device through our data synchronization protocol, everything is encrypted. And of course, when it goes to a sync gateway, the sync gateway is sort of, as I mentioned, the middle tier component, that is responsible for data synchronization between the embedded devices and Couchbase server. That entity is responsible for enforcement of access control policies. So you are guaranteed that only users who should have access to those documents or data are granted access to that. And in fact, we are NoSQL Json database. So which means, everything is modeled in the form of documents, Json documents. And so when we're talking about read, write access control, read access is at the granularity of a document, and write access can be enforced at the granularity of a property within the document. So you may have access to an entire document, but you may only be allowed to update a certain property within the document. So, as I mentioned, when it comes to distributed computing architectures like edge computing, security is even more paramount, right? You have devices going offline, coming back online and, you might have a breach point at one edge environment, whether it is a data center or an edge device, you need to be able to ensure that you have isolated all the other edge components from that breach. And as I mentioned, when it comes to data governance and so on, data retention, for instance, even if it is not a security breach, let's say you do have, for some reason, the owner of a device should no longer have access to that content. You know, their role has changed, they have transitioned to a different company for instance. Then you will have a way of automatically purging all that data that was previously synchronized to the user's device. >> Got it. Okay. Let's continue talking about the events of the last year and a half. Because we saw this massive scatter, 18 months ago of an explosion at the edge when a lot of people went from the office to this work from home, work from any anywhere environment in which we're still in. So how has the pandemic and the events that related to it changed mobile apps and edge computing and what are some of the new requirements that customers have? >> Sure. Well, as you rightly said, right? In fact, if anything, the relevance of mobile devices and applications has just grown in significance through the pandemic. And it's kind of interesting, there are some surveys that have suggested that through the pandemic people have been using their mobile devices as their primary communication device for accessing the internet. And it's kind of interesting because you think, well, everyone is cooped up in their homes. They probably will have access to other forms of data consumption, but no, it's mobile devices. That's what they have primarily been using. So with that, there is also a new range of use cases and applications, which are driven in large part by the events of the pandemic. But I think that's just made things much more efficient. Customer satisfaction, user experience is paramount, is number one. And I think a lot of that is here to stay even following or post pandemic because it's just made things a lot more efficient. And we've seen that through different industries, right? Healthcare, there was always telemedicine medicine, but now for non-essential care, it's always telemedicine, Of course, specific to the pandemic. there was the, tracking, the contact tracing application, right? That's enabled through technologies like Bluetooth and GPS, so they track the whereabouts of infected persons. But then even if you arrive at your doctor's office, right, you wait in your car and you get notified when the doctor's ready to check you in. And then retail sector, E-commerce right? Of course everything was going online, but everything is overwhelming People are shopping online through their mobile devices, than the traditional web based applications. And you order on your phone, you pick up at the store, right? So curbside pickup, you pull into the store, the store clerk is notified of your arrival. They come out to the curb with your order. And here's the interesting bit, you know, it's kind of intuitive that it's going to be e-commerce applications. They got a huge boost through the pandemic. But interestingly, even the experience when it comes to retail in store, that's undergone a transformation because it was all about how do we make the process very efficient. So customers are in and out of the store really quick, right? there was the reason for that. But now we can translate to making the whole shopping experience much more easy. So you walk into the store, you meet a sales associate who can bring up information about the catalog inventory right there on the iPad. And so if you have any questions, whether it's something is available in the store or an access for you're looking for, they can give answers to you immediately. Right? And of course there are companies like Walmart, they have been rolling out applications. Mobile scan and go sort of applications, which is all about, you know, you scan items as you walk through the aisle, do a self checkout, totally contact-less experience. And, the list goes on, right? We talked about healthcare retail, same thing in, in a restaurant, right? A curbside that delivery and pickup, you can now track your delivery order because now it's just a huge surge in order deliveries. And then the same pickup concept, curbside pickup concept, you arrive in your car, the kitchen is notified of your arrival and they come out with your order, very streamlined drive-through. You've got people now coming to your car, taking the order, right there from the car on their tablets, that synced in real time to the backend kitchen, your order. And you get notified when your order is ready. So I think all this is about making things a lot more efficient. It's about customer experience, and edge computing has a big role to play in that. And so I think, if anything is just propelled the growth of mobile applications and use cases. >> Yeah. That that propulsion is something that we've been hearing a lot about the acceleration in the last year and a half. You did a great job there of painting a picture of some of the positives that come out of this accelerating the efficiencies that we all as consumers and in our business life expect to have. And this explosion at the edge that's really become even more of a lifeline for millions and millions and billions of people globally. I got to ask you that from a connectivity perspective, that's another area where we had this expectation as again, consumers or in our business lives we have connectivity. Where does all that talk about 5G; What does 5G fit into edge computing? >> Sure. That's a good question. Because 5G and edge computing sort of go hand in hand so much so that they are being used synonymously in some cases and that's inaccurate. Okay? So because every time people talk about edge computing, there are folks talking about 5G in the same breath, right? But really 5G, as we all know, is a cellular next generation cellular technology, promises, ultra low latency, very high bandwidth. Now we talked about this huge surge, right in mobile applications and new sort of use cases where a lot of the data is generated at the edge. IOT applications are just data intensive applications, right gaming apps and so on. And all of these applications, they demand ultra low latency, right? And they're generating a lot of this data and all that data needs to be processed in real time. So if you have to send all of that data back to the cloud, and then you get the responses, that's a really bad experience. So that's what 5G is here to solve, right? I mean, it's like low latency, high bandwidth, high concurrency. Now that's all great. But then the coverage of 5G, it terminates at the edge of the mobile operator network. So you have all these massive influx of devices generating all that data. And all that stuff is transmitted under a very low latency conditions over a 5G network. But then if all that data from the mobile operator network has to be back hauled to the internet, to the backend servers, then you kind of defeat the whole purpose of ultra low latency applications. So that's where edge computing comes into play because edge computing is really an architecture, right? It's a distributed architecture. So now what mobile operators are doing is deploying what they refer to as NDCs, but it's effectively micro data centers at the edge of the mobile operator network. So you have all this data coming in over the 5G network. Great. They get analyzed, they get processed locally at the edge of the mobile operator network and you get real-time responses. And of course, as needed that data in aggregated or filter form goes back to the cloud. And so that's where the two relate. So in my view, I think edge computing architectures are important to deliver on the promise of 5G, but 5G has propelled the relevance or importance of edge computing as a solution, as a deployment architecture. So very interrelated. >> Very interrelated, very symbiotic. And of course the need for real time data real-time analytics in every industry became very prominent in the last year and a half. We had this expectation that we're going to be able to understand things in real time. And that's often a huge differentiator for businesses. We're out of time, but I want to ask you one more question Priya, and that is where can customers go to get started with Couchbase? >> Oh, absolutely. So Couchbase servers and gateway, you can deploy that, it's available as software. You can download it from our website. Couchbased lite is available for all your mobile applications. So it is available as a download, but you also have the classic package management systems through which you can download Couchbase Lite. And then of course, as I said, you can deploy this standalone, but you can also deploy it in the cloud. So we have marketplace offerings for both Couchbase server and sync gateway. So if you want to deploy it on AWS, as your Google, you can do that as well. >> Excellent. Priya, thank you so much for joining me on the program, talking about Couchbase the evolution, the changes, the opportunities with edge computing and mobile and how Couchbase is involved. I appreciate your time. >> Thank you very much. And thanks for having me. >> For Priya Rajgopal, I'm Lisa Martin. You're watching the Cubes coverage of Couchbase connect, online 2021.

(pulsating music) >> Live from Fisherman's Wharf in San Francisco, it's theCUBE! Covering IBM Chief Data Officer Strategy Summit, Spring 2017. Brought to you by IBM. >> Hey, welcome back everybody. Jeff Frick here with theCUBE. We're at Fisherman's Wharf in San Francisco at the IBM Chief Data Officer Strategy Summit, Spring 2017. It's a mouthful, it's a great event, and it's one of many CDO summits that IBM's putting in around the country, and soon around the world. So check it out. We're happy to be here and really talk to some of the thought leaders about getting into the nitty gritty detail of strategy and execution. So we're excited to be joined by our next guest, Rebecca Shockley. She's an Analytics Global Research Leader for the IBM Institute for Business Value. Welcome, Rebecca. I didn't know about the IBM Institute for Business Value. >> Thank you. >> Absolutely. And Priya V. She said Priya V's good, so you can see the whole name on the bottom, but Priya V. is the CTO of Cognitive/IOT/Watson Health at IBM. Welcome, Priya. >> Thank you. >> So first off, just impressions of the conference? It's been going on all day today. You've got 170 or some-odd CDO's here sharing best practices, listening to the sessions. Any surprising takeaways coming out of any of the sessions you've been at so far? >> On a daily basis I live and breathe data. That's what I help our customers to get better at it, and today is the day where we get to talk about how can we adopt something which is emerging in that space? We talk about data governance, what we need to look at in that space, and cognitive as being the fabric that we are integrating into this data governance actually. It's a great day, and I'm happy to talk to over, like you said, 170 CDO's representing different verticals. >> Excellent. And Rebecca, you do a lot of core research that feeds a lot of the statistics that we've seen on the keynote slides, this and that. And one of the interesting things we talked about off air, was really you guys are coming up with a playbook which is really to help CDO's basically execute and be successful CDO's. Can you tell us about the playbook? >> Well, the playbook was born out of a Gartner statistic that came out I guess two or three years ago that said by 2016 you'll have 90% of organizations will have a CDO and 50% of them will fail. And we didn't think that was very optimistic. >> Jeff: 90% will have them and 50% will fail? >> Yes, and so I can tell you that based on our survey of 6,000 global executives last fall, the number is at 41% in 2016. And I'm hoping that the playbook kept them from being a failure. So what we did with the playbook is basically laid out the six key questions that an organization needs to think about as they're either putting in a CDO office or revamping their CDO offices. Because Gartner wasn't completely unfounded in thinking a lot of CDO offices weren't doing well when they made that prediction. Because it is very difficult to put in place, mostly because of culture change, right? It's a very different kind of way to think. So, but we're certainly not seeing the turnover we were in the early years of CDO's or hopefully the failure rate that Gartner predicted. >> So what are the top two or three of those six that they need to be thinking about? >> So they need to think about their objectives. And one of the things that we found was that when we look at CDO's, there's three different categories that you can really put them in. A data integrator, so is the CDO primarily focused on getting the data together, getting the quality of the data, really bringing the organization up to speed. The next thing that most organizations look at is being a business optimizer. So can they use that data to optimize their internal processes or their external relationships? And then the third category is market innovator. Can they use that data to really innovate, bring in new business models, new data monetization strategies, things like that. The biggest problem we found is that CDO's that we surveyed, and we surveyed 800 CDO's, we're seeing that they're being assessed on all three of those things, and it's hard to do all three at once, largely because if you're still having to focus on getting your data in a place where you can start doing real science against it you're probably not going to be full-time market innovator either. You can't be full-time in two different places. That's not to say as a data integrator you can't bring in data scientists, do some skunk works on some of the early work, find... and we've seen organizations really, like Bank Itau down in Brazil, really in that early stages still come up with some very innovative things to do, but that's more of a one-off, right. If you're being judged on all three of those, that I think is where the failure rate comes in. >> But it sounds like those are kind of sequential, but you can't operate them sequentially cause in theory you never finish the first phase, right? >> You never finish, you're always keeping up with the data. But for some organizations, they really need to, they're still operating with very dirty, very siloed data that you really can't bring together for analytics. Now once you're able to look at that data, you can be doing the other two, optimizing and innovating, at the same time. But your primary focus has to be on getting the data straight. Once you've got a functioning data ecosystem, then the level of attention that you have to put there is going to go down, and you can start working on, focusing on innovation and optimization more as your full-time role. But no, data integrator never goes away completely. >> And cleanser. Then, that's a great strategy. Then, as you said, then the rubber's got to hit the road. And Priya, that's where you play in, the execution point. Like you say, you like to get your hands dirty with the CDO's. So what are you seeing from your point of view? In terms of actually executing, finding early wins, easy paths to success, you know, how to get those early wins basically, right? To validate what you're doing. That's right. Like you said, it's become a universal fact that data governance and things, everything around consolidating data and the value of insights we get off it, that's been established fact. Now CDO's and the rest of the organization, the CIO's and the CTO's, have this mandate to start executing on them. And how do we go about it? That's part of my job at IBM as well. As a CTO, I work with our customers to identify where are the dominant business value? Where are those things which is completely data-driven? Maybe it is cognitive forecasting, or your business requirement could be how can I maximize 40% of my service channel? Which in the end of the day could be a cognitive-enabled data-driven virtual assistant, which is automating and bringing a TCO of huge incredible value. Those are some of the key execution elements we are trying to bring. But like we said, yes, we have to bring in the data, we have to hire the right talent, and we have to have a strategy. All those great things happen. But I always start with a problem, a problem which actually anchors everything together. A problem is a business problem which demonstrates key business values, so we actually know what we are trying to solve, and work backwards in terms of what is the data element to it, what are the technologies and toolkits that we can put on top of it, and who are the right people that we can involve in parallel with the strategy that we have already established. So that's the way we've been going about. We have seen phenomenal successes, huge results, which has been transformative in nature and not just these 170 CDO's. I mean, we want to make sure every one of our customers is able to take advantage of that. >> But it's not just the CDO, it's the entire business. So the IBM Institute on Business Value looks at an enormous amount of research, or does an enormous amount of research and looks at a lot of different issues. So for example, your CDO report is phenomenal, I think you do one for the CMO, a number of different chief officers. How are other functions or other roles within business starting to acculturate to this notion of data as a driver of new behaviors? And then we can talk about, what are some of those new behaviors? The degree to which the leadership is ready to drive that? >> I think the executive suite is really starting to embrace data much more than it has in the past. Primarily because of the digitization of everything, right. Before, the amount of data that you had was somewhat limited. Often it was internal data, and the quality was suspect. As we started digitizing all the business processes and being able to bring in an enormous amount of external data, I think organizationally executives are getting much more comfortable with the ability to use that data to further their goals within the organization. >> So in general, the chief groups are starting to look at data as a way of doing things differently. >> Absolutely. >> And how is that translating into then doing things differently? >> Yeah, so I was just at the session where we talked about how organizations and business units are even coming together because of data governance and the data itself. Because they are having federated units where a certain part of business is enabled and having new insights because we are actually doing these things. And new businesses like monetizing data is something which is happening now. Data as a service. Actually having data as a platform where people can build new applications. I mean the whole new segment of people as data engineers, full stack developers, and data scientists actually. I mean, they are incubated and they end up building lots of new applications which has never been part of a typical business unit. So these are the cultural and the business changes we are starting to see in many organizations actually. Some of them are leading the way because they just did it without knowing actually that's the way they should be doing it. But that's how it influences many organizations. >> I think you were looking for kind of an example as well, so in the keynote this morning one of the gentlemen was talking about working with their CFO, their risk and compliance office, and were able to take the ability to identify a threat within their ecosystem from two days down to three milliseconds. So that's what can happen once you really start being able to utilize the data that's available to an organization much more effectively, is that kind of quantum leap change in being able to understand what's happening in the marketplace, bing able to understand what's happening with consumers or customers or clients, whichever flavor you have, and we see that throughout the organization. So it's not just the CFO, but the CMO, and being able to do much more targeted, much more focused on the consumer side or the client customer side, that's better for me, right. And the marketing teams are seeing 30, 40% increase in their ability to execute campaigns because they're more data-driven now. >> So has the bit flipped where the business units are now coming to the CDO's office and pounding on the door, saying "I need my team"? As opposed to trying to coerce that you no longer use intuition? >> So it depends upon where you are, where the company is. Because what we call that is the snowball effect. It's one of the reasons you have to have the governance in place and get things going kind of in parallel. Because what we see is that most organizations go in skeptically. They're used to running on their gut instinct. That's how they got their jobs mostly, right? They had good instincts, they made good decisions, they got promoted. And so making that transition to being a data-driven organization can be very difficult. What we find though, is that once one section, one segment, one flavor, one good campaign happens, as soon as those results start to mount up in the organization, you start to see a snowball effect. And what I was hearing particularly last year when I was talking to CDO's was that it had taken them so long to get started, but now they had so much demand coming from the business that they want to look at this, and they want to look at that, and they want to look at the other thing, because once you have results, everybody else in the organization wants those same kind of results. >> Just to add to that, data is not anymore viewed as a commodity. If you have seen valuable organizations who know what their asset is, it's not just a commodity. So the parity of... >> Peter: Or even a liability is what it used to be, right? >> Exactly. >> Peter: It's expensive to hold it and store it, and keep track of it. >> Exactly. So the parity of this is very different right now. So people are talking about, how can I take advantage of the intelligence? So business units, they don't come and pound the door rather they are trying to see what data that I can have, or what intelligence that I can have to make my business different shade, or I can value add something more. That's a type of... So I feel based on the experiences that we work with our customers, it's bringing organizations together. And for certain times, yes sometimes the smartness and the best practices come in place that how we can avoid some of the common mistakes that we do, in terms of replicating 800 times or not knowing who else is using. So some of the tools and techniques help us to master those things. It is bringing organizations and leveraging the intelligence that what you find might be useful to her, and what she finds might be useful. Or what we all don't know, that we go figure it out where we can get it. >> So what's the next step in the journey to increase the democratization of the utilization of that data? Because obviously Chief Data Officers, there aren't that many of them, their teams are relatively small. >> Well, 41% of businesses, so there's a large number of them out there. >> Yeah, but these are huge companies with a whole bunch of business units that have tremendous opportunity to optimize around things that they haven't done yet. So how do we continue to kind of move this democratization of both the access and the tools and the utilization of the insights that they're all sitting on? >> I have some bolder expectations on this, because data and the way in which data becomes an asset, not anymore a liability, actually folds up many of the layers of applications that we have. I used to come from an enterprise background in the past. We had layers of application programming which just used data as one single layer. In terms of opportunities for this, there is a lot more deserving silos and deserving layers of IT in a typical organization. When we build data-driven applications, this is all going to change. It's fascinating. This role is in the front and center of everything actually, around data-driven. And you also heard enough about cognitive computing these days, because it is the key ingredient for cognitive computing. We talked about full ease of cognitive computing. It has to start first learning, and data is the first step in terms of learning. And then it goes into process re-engineering, and then you reinvent things and you disrupt things and you bring new experiences or humanize your solution. So it's on a great trajectory. It's going tochange the way we do things. It's going to give new and unexpected things both from a consumer point and from an enterprise point as well. It'll bring effects like consumerization of enterprises and what-not. So I have bolder and broader expectations out of this fascinating data world. >> I think one of the things that made people hesitant before was an unfamiliarity with thinking about using data, say a CSR on the front line using data instead of the scripts he or she had been given, or their own experience. And I think what we're seeing now is A, everybody's personal life is much more digital than it was before, therefore everybody's somewhat more comfortable with interacting. And B, once you start to see those results and they realize that they can move from having to crunch numbers and do all the background work once we can automate that through robotic process automation or cognitive process automation, and let them focus on the more interesting, higher value parts of their job, we've seen that greatly impact the culture change. The culture change question comes whether people are thinking they're going to lose their job because of the data, or whether it's going to let them do more interesting things with their jobs. And I think hopefully we're getting past that "it's me or it" stage, into the, how can I use data to augment the work that I'm doing, and get more personal satisfaction, if not business satisfaction, out of the work that I'm doing. Hopefully getting rid of some of the mundane. >> I think there's also going to be a lot of software that's created that's going to be created in different ways and have different impacts. The reality is, we're creating data incredibly fast. We know that is has enormous value. People are not going to change that rapidly. New types of algorithms are coming on, but many of the algorithms are algorithms we've had for years, so in many respects it's how we render all of that in some of the new software that's not driven by process but driven by data. >> And the beauty of it is this software will be invisible. It will be self-healing, regeneratable software. >> Invisible to some, but very very highly visible to others. I think that's one of the big challenges that IT organizations face, and businesses face. Is how do they think through that new software? So you talked about today, or historically, you talked about your application stack, where you have stacks which would have some little view of the data, and in many respects we need to free that data up, remove it out of the application so we can do new things with it. So how is that process going to either be facilitated, or impeded by the fact that in so many organizations, data is regarded as a commodity, something that's disposable. Do we need to become more explicit in articulating or talking about what it means to think of data as an asset, as something that's valuable? What do you think? >> Yeah, so in the typical application world, when we start, if you really look at it, data comes at the very end of it. Because people start designing what is going to be their mockups, where are they going to integrate with what sources, am I talking to the bank as an API, et cetera. So the data representation comes at the very end. In the current generation of applications, the cognitive applications that we are building, first we start with the data. We understand what are we working on, and we start applying, taking advantage of machines and all these algorithms which existed like you said, many many decades ago. And we take advantage of machines to automate them to get the intelligence, and then we write applications. So you see the order has changed actually. It's a complete reversal. Yes we had typical three-tier, four-tier architecture. But the order of how we perceive and understand the problem is different. But we are very confident. We are trying to maximize 40% of your sales. We are trying to create digital connected dashboards for your CFO where the entire board can make decisions on the fly. So we know the business outcome, but we are starting with the data. So the fundamental change in how software is built, and all these modules of software which you are talking about, why I mentioned invisible, is some are generatable. The AI and cognitive is advanced in such a way that some are generatable. If it understands the data underlying, it can generate what it should do with the data. That's what we are teaching. That's what ontology and all this is about. So that's why I said it's limitless, it's pretty bold, and it's going to change the way we have done things in the past. And like she said, it's only going to complement humans, because we are always better decision-makers, but we need so much of cognitive capability to aid and supplement our decision-making. So that's going to be the way that we run our businesses. >> All right. Priya's painting a pretty picture. I like it. You know, some people see only the dark side. That's clearly the bright side. That's a terrific story, so thank you. So Priya and Rebecca, thanks for taking a few minutes. Hope you enjoy the rest of the show, surrounded by all this big brain power. And I appreciate you stopping by. >> Thanks so much. >> Thank you. >> All right. Jeff Frick and Peter Burris. You're watching theCUBE from the IBM Chief Data Officers Summit, Spring 2017. We'll be right back after this short break. Thanks for watching. (drums pound) (hands clap rhythmically) >> [Computerized Voice] You really crushed it. (quiet synthesizer music) >> My name is Dave Vellante, and I'm a long-time industry analyst. I was at IDC for a number of years and ran the company's largest and most profitable business. I focused on a lot of areas, infrastructure, software, organizations, the CIO community. Cut my teeth there.

>>Welcome everyone to our continuous coverage on the cube of AWS reinvent 2021. I'm your host, Dave Nicholson. And I am absolutely delighted to be joined by two folks from Unisys. I have a company that has been in the business of helping people with everything related to it for a very, very long time. We heard a talk about data monetization at modernization with ANU Priya, rom Raj vice president of cloud solution management at Unisys, along with ANU palms, the high VP and CTO of cloud solution engineering at UNISIS. And, uh, just so that we keep everything clear, I'm just going to call you on new and ANU Palm, and we'll all know who we're talking to. Sure. The funny thing is I'm David Nicholson or Dave Nicholson. Dave Vellante is one of the founders of Silicon angle, the cube. So usually it's two Dave's battling in >>So I get to be David and he's Dave typically. So we're completely, we're completely used to this, right? So, so tell me about what Eunice is doing UNISIS is doing in the arena of app modernization and data modernization and migration into cloud. You Unisys has a long and storied history of managing it in people's environments, you know, in the sort of on-premise world, as well as, as well as cloud now. But, uh, I knew tell us, tell us a little about what you'd assist is doing in this space. And then we'll, we'll double click and dive in. >>Um, so you, you're probably very, very familiar with the six RS of modernization, right? All the way from migration modernization, all the way from replatform rehost to, to the other side of the spectrum, refactor and rearchitect, right? So what is DASA is that it takes clients on that journey, right? So we see clients in different stages of that journey. There are clients that come to us, uh, recently brought on board a pipeline they're very early in their journey. They just talking about their first set of migrations. There are clients that have taken the leap and done 75% of their workload is on cloud, even for Unisys 95% of more than 95% of our workload actually runs on cloud public cloud. So different stages of the journey, but no matter where they are in the journey, really moving the needle on modernization. Right. And what did he mean by modernization? It's it's taking advantage of the innovation in cloud, whether it's seven containers are AI and bringing that to the client so that they can drive those business outcomes. That's what we are passionate about doing. And we can talk to you about a couple of clients where we've done this on a, but I like to unopened to add on. >>Sure. Yeah. And, and just, and before you dive in on a Palm, I want to hear specifically about the inhibitors that you're seeing, the things that causing friction, right. Movement to cloud. >>Yeah. So cloud of the transformative technology is as disruptive and it brings about lots of benefits that are very well understood, but not realized, um, lower total cost of ownership, higher security, innovation, and agility. But the challenges that you see for customers really benefit from moving and migrating to the cloud are related to security and compliance. That comes up to be the top pain point, followed by cost of ownership that are optimizations that you need to do before you can benefit from really leveraging the benefits from the cloud and then innovation and agility, how to drive that. And there are certain things around app and data about innovation, data analytics, AIML that really helps realize those values, but it needs a concerted effort and a drive and a push to transform your infrastructure from where you are today to really get to derive the true benefits from the cloud. >>And we do a cloud barometer study of about thousands of organizations from a Unisys perspective, Dave, and as a Oklahoma saying, um, more than 60% of our clients say security is the biggest inhibitor they want help with security. You >>No, you're saying the inhibitor to going to cloud is security >>To accelerating the cloud journey. They always are perceptive. >>Is that, is that hesitancy, uh, just perception or is it reality? >>That's a great question, >>Dave, and you don't have to be gentle with me. Like you might with a client, you know, you can, you can reach over and smack me and say get over it. You're going to be fine, Dave, >>Actually, I'm a new from leaned into it already. In many cases, when you, when you actually get to your cloud configuration, right. You probably be more secure in the cloud, but it's getting clients confident with that setup. That's where the rubber meets the road. Right. And that's where we come in to say, um, do you understand the shared responsibility model with cloud? What is the cloud provider do? What does being here at AWS reinvent? What has AWS bring to the table for security? This is what the client is responsible for. For example, application security is completely their client's responsibility, right? In most cases. So, um, just working with the clients so that they understand the shared responsibility model and then making sure we protect all the different layers of the stack, but security, right? Even, even as apps are developed, you need to have DevSecOps pipeline, right? So I didn't say dev ops, I said, dev sec ops, because we want to make security a part of developing your applications and deploying them in cloud as well. So that's what we bring to the table and making sure clients feel confident in, in accelerating their cloud journey. So >>You can deal with customers like me, who, who truly believed that my money is safer in a coffee, can buried in my backyard than it is in a bank, right. With all those banking people wandering around. Um, so when you start looking at an environment and you, and you look at the totality of an it infrastructure landscape, how do you go about determining what is the low hanging fruit? What makes sense to move first from is that, is that always an ROI discussion that comes into play and are your customers, I like to give like five questions at the same time to confuse you and are your customers expecting to immediately save money? And how big is the ROI conversation in this? >>Uh, great question. So a couple of things need to be considered first, just to understand where does the customer in the digital transformation journey are there green fee where they only have on premise data center and they're trying to get to the cloud, or they already have dipped their toes and move to the cloud. And in the cloud, how far in advance are they in their transformation journey, have them not have the done apps and data modernization? Do they have, uh, uh, management operations capability for day one and day two cloud ops and fin ops and security ops, and other leveraging the power of the cloud, the copious amounts of data that cloud brings to the table. Uh, the, the important thing to understand is that 80% of the tools that work in the on-prem do not work in the cloud. So you have to understand the very nature of the cloud and to deal with it differently. >>The same old tools and creeks will not work in the cloud. And I call it the three V's in the cloud, velocity volume, and variety of data is different in the cloud. So when you're talking about security, you need to look at the cloud infrastructure, posture management. You need to look at the cloud workload, pasture management. You need to look at the data that's available and analyze and harness the data using AIML and data analytics. So you need a new set of clicks as it were to really harness the power of the cloud to derive the benefits from increased security, lower cost of ownership and innovation and agility. >>And it makes sense. Yeah. >>I mean, I think you touched on touched on it, but fin ops, right. And you asked the question David on, is that the biggest driver in terms of savings to get to the, to the cloud. And I think it's definitely one of the bigger factors, um, because, and be believe to, to realize that we offer a fin op service. And if you know, Upserve is not just for the cloud, but choosing models at different, right. It's not like your data center planning. We talked about the tools being different. It's more than the tools, right? So you could do reserved instances or you could do spot instances, completely different ballgame with AWS, right. Or you could do AWS savings plans. Are you maximizing all of that? And even beyond that, are you thinking beyond that into like AWS container suppose, um, EKS, are you talking about seven less and that could completely change your bill and your total kind of cost of ownership. You talk Dave about past databases, right? So platform as a service, and that could completely change your total cost of ownership there as well. So are you really maximizing that? And do you have a service around that? Do you have a trusted partner who can help you with fin ops is I think an important consideration there? >>Well, I don't know. Pretty, I know you're dying to talk about a customer example, make it real for us. Give us an example of, uh, of this process inaction where UNISIS has helped a customer on the journey. >>Absolutely. Dave. So, um, uh, one example that comes to mind is a large public university and they've got about a half a million students and they've got 20 plus campuses around the U S in California, Sarah, I might've given myself away there. And, and, uh, in, into what they've done is, um, initially they are big into AWS and they are into their cloud, uh, higher into the IBM cloud journey, uh, big time. And they are a hybrid deployment at this point. And initially, uh, they, uh, when they subscribed to our fin ops service, uh, we, we brought in all the different, uh, thinking around working with different organizations, they need to like business planning, right? You need to know which is your most significant apps and what do you want to invest in them in terms of modernization and in tuning your AWS spend. And so we did that. And so we got them about a 33% cost saving and what they did was then they took, looked at all of their AWS accounts across the campuses and said, we want fin ops across all of them. Let's consolidate all of them. So that's, that's the power of a synopsis is about 33% saving right there. Well, that is >>Particularly exciting for me because I assume that they're going to be lowering my kid's tuition next year. So I'll be, I'll be looking forward to that. And now I know Palm, we know why she was kicked out of the, uh, you know, the, the intelligence agency can't keep a secret. Let's, let's, let's talk about an amusement park, uh, famous for its rodent, but I'm not going to say the name. So, so out upon talk about, uh, the technology space that we're in the midst of here at AWS reinvent, right? Um, each time we have a keynote, we're hit with an, almost a mind boggling number of announcements, right? Customers can't keep, keep this stuff straight. They're 575 different kinds of instances. It used to be, we have S3 and we have VC too. Right. Would you like, would you like one, or would you like both, right? How do you help customers make sense of this? >>Yeah, no, that's, that's a great question because, um, the cloud is, uh, I, as I said, cloud has three V's velocity, variety and volume of data and, and the new kinds of services that are available. Day-by-day, it's growing the keys to really figure out, again, map the business objectives that you as a customer or a company are trying to achieve, understand where you are in your digital transformation journey. And then based on the two, uh, and assess where you at and, and companies like Unisys can work with the customer to assess their, what I call the digital transformation posture, which will then give, uh, give us clear indications or recommendations on what are the next stages in the transformation of journey. So whether it's whether you want to improve your security posture, whether you want to improve your cost of ownership, posture, whether you want to go to go to the cloud and leverage DevSecOps to benefit from the innovation and agility, we can help you. >>Unisys has DevSecOps as a service, uh, containers as a service where we can help our customers and partners migrate to the cloud, modernize the apps. And again, based on research, that's out there, you can speed up app deployment and development by 60% by leveraging the power of the cloud. So the benefits out there for customers to get access to, it's a question of finding the right combination of people, process and technology to get you there. And Unisys being a very trusted advisor is certainly able to help you accelerate that journey and get you to meet your business outcomes. So me, >>Um, let me ask the two of you, what might be an uncomfortable question, and that is obviously Unisys is in the business of managing things that aren't in cloud. Also, you have very, very large existing customers that are spending money with you, right? And if they'll just stay still and not do anything and not change, you'll keep making money into the future. Aren't some of these things that you're doing as a trusted advisor, almost counterintuitive from a, from a finance perspective at Unisys, at least in the short term, how do you, how do you balance that? >>It's a, it's a great question, Dave, and for us, we are customer obsessed. So that's, I know one of the AWS principles and we, we live by that as well. Right? So customer comes first and doing the right thing by them, whether it is the total cost of ownership when it's getting the security posture, right. That comes first for us. And if, if moving them to a public cloud will help them achieve that. We will do that. Right. So even if it means that our bill is going to be lower, right. So we'll give you a great example there. Um, Eunice's, as you know, Dave has been in the mainstream business and we've got customers that are still on clear path, right? So even with those customers, we help them with both transitions. We can run clear path to the, on public cloud and we also help with modernization, right? So we always do the right thing by the customer. It's really the customer's tries in terms of what does the business warrant, how much business disruption are they willing to take as we do this modernization journey. And that's what determines us. And that's what makes us trusted advisers. Um, you're not looking out for the bottom line there in terms of how much our bill would be. Yup. >>Well, that's a, that's actually a great place to wrap up. Uh, I think it's hilarious that you mentioned mainframe since you were five years old, you gave me, you gave me a blank stare. When I mentioned stuff, Unisys was doing 20 years ago on a free auto Palm from Unisys. Thank you so much. It's a great point to close on. You're a trusted advisor when you're doing things that are truly in the customer's best interest and not just in your own company's best interests. I'm Dave Nicholson for the cube. We'd like to thank you for joining our continuous coverage at AWS reinvent 2021 stay tuned because we are your leader in hybrid tech event coverage.

>>Live from Denver, Colorado. It's the cube covering com vault go 2019 brought to you by Combolt. >>Hey, welcome back to the Q but Lisa Martin with men and men and we are coming to you alive from Conn logo 19 please to welcome to the cube, a gent from Microsoft Azure. We've got Ben Nichol, principal program manager. Ben, welcome. Thank you. Thanks for having me on. Thanks for coming on. So Microsoft combo, what's going on with the partnership? >>They wouldn't have have great storage pond is in data management space. We've been working with Convolt for 20 years now in Microsoft and and they've been working with us on Azure for about as long as I can remember not being on that the Azure business RET seven years now. So just a long time in cloud terms like doggies and it sort of, they'd been doing a huge amount of their around getting customer data into the cloud, reducing costs, getting more resiliency and then also letting them do more with the data. So they were a pretty good partner to have and they make it much easy for their customers to to go and leverage cloud. So Ben, you know, in my career I've had lots of interactions with the Microsoft storage team. Things have changed a little bit when you're now talking about Azure compared to, you know, more. >>It was the interaction with the operating system or the business suite had. So maybe bring us up to date as those people that might not have followed. You know, we're kind of the storage positioning inside of Microsoft is now that when we talk about Azure and your title. Yeah, we, we sort of look and just just briefly, we worked very heavily with our on premises brethren. They actually inside the O S team is inside of the Azure engineering old male, which is kind of funny, but we do a lot of things there. If he started looking at, firstly on that hybrid side, we have things like Azure files. It's a highly resilient as a service SMB NFS file share up to a hundred terabytes but that interacts directly with windows server to give you Azure file sync. So there is sort of synergies there as well. When I'm doing personally my team, we work on scale storage. >>The big thing we have in there is Al is out blood storage technology, which really is the underpinning technology, full Priya tool storage and Azure which is including our SAS offerings which are hosted on Azure too. So disc is on blood storage, our files on blood storage, you look at Xbox live, all these kinds of stuff is a customer to us. So we build that out and we, we are doing work there and that's really, really interesting and how we do it and that's not looking at going we're going to buy some compute, we're going to buy some storage, we're going to build it out, we're going to run windows or hyper V or maybe VMware with windows running on the VMware, whatever else. This is more a story about wigging to provide you storage as a service. You didn't get a minimum of like 11 nines at your ability and and be able to have that scale to petabytes of capacity in one logical namespace and give you multiple gigabytes, double digit gigabytes of throughput to that storage. >>And now we're even moving about to model multiple protocols. So rest API century today we've got Azure stack storage, you pay API, she can go and use, but we give me that consistency of the actual back end storage and the objects and the data available via more than just one protocol. You can go and access that via HDFS API. As we talk about data lakes all the time. For us, our blood storage is a data Lake. We turn on hierarchal namespace and you can go and access that via our other protocols like as I mentioned HDFS as well. So that is a big story about what we want to do. We want to make that data available at crazy scale, have no limits in the end to the capacity or throughput or performance and over any protocol. That's kind of our line in the Hill about what we want to get to. >>And we've been talking to vault team about some of the solutions that they are putting in the cloud. The new offering metallic that came out. They said if my customer has Azure storage or storage from that other cloud provider, you could just go ahead and use that. Maybe how familiar and how much, I know you've been having a run metallic. We were working, we were pretty tightly with the product team over Convolt around this and my team as well around how do we design and how do we make it work the best and we're going to continue working to optimize as they get beyond initial launch to go, wow, we've got data sets we can analyze, we know how to, we wanted out of tune it. Now really we love the solution particularly more because the default, if you don't select the storage type where you want to go, you will run on Azure. >>So really sort of be kudos to the relationship there. They chose us as a first place we'll go to, but they've also done the choice for customers. Say some customers may want to take it to another cloud. That's fine. It's reasonable. I mean, we totally understand it's going to be a multi-cloud world and that's a reality for any large company. Our goal is to make sure we're growing faster than the competitors, not to knock out the competitors all together because that just won't happen. So they've got that ability to go and yet, Hey, we'll use Azure as default because they feel that way, offering the best support and the best solution there. But then if they have that customer, same customer wants to turn around and use a competitor, Val's fine as well. And I see people talking about that today where they may want to mitigate risks and say, I'm going to do, I'm doing all of office three, six, five on a taken office, three, six, five backup. It's cool. Use metallic, it'll take it maybe to a different region in Asia and they're backing up and they still going, well I'm still all in on Microsoft. They may want to take it to another cloud or even take it back to on premises. So that does happen too because just in case of that moment we can get that data back in a different location. Something happens. >>So metallic talking about that is this new venture is right. It's a Combolt venture and saw that the other day and thought that's interesting. So we dug into it a little bit yesterday and it's like a startup operating within a 20 year old company, which is very interesting. Not just from an incumbent customer perspective, but an incumbent partner perspective. How have you seen over the last few years and particularly bad in the last nine months with big leadership and GTM changes for combo? How has the partnership with Microsoft evolved as a result of those changes? >>Um, it's always been interesting. I guess when you start looking at adventure and everything, since things change a little bit, priorities may change just to be fair, but we've had that tight relationship for a long time. At a relationship level and an exec leadership level, nothing's really changed. But in the way they're building this platform, we sit down out of my team, out of the Azure engineering group and we'll sit down and do things like ideations, like here's where we see gaps in the markets, here's what we believe could happen. And look back in July, we had inspire, which is our partner conference in Las Vegas. When we sat down with their OT, our OT in a room, we'll talking about these kinds of things and this is I think about two months after they may have started the initial development metallic from what I understand, but we will talking about exactly what they're doing with metallic offered as a service in Azure is, Hey, how bout we do this? So we think it's really cool. It opens up a new market to Convolt I think too. I mean they're so strong in the enterprise, but they don't do much in smaller businesses because with a full feature product, it also has inherent complexibility complexity around it. So by doing metallic, is it click, click, next done thing. They're really opening, I think, new markets to them and also to us as a partner. >>I was going to ask, you know, kind of click on that because they developed this very quickly. This is something that I think what student were here yesterday, metallic was kind of conceived design built in about six months. So in terms of like acceleration, that's kind of a new area for Combalt. >>Yeah, and I think, I think they're really embracing the fact about um, let's release our code in production for products, which are sort of getting, getting to the, Hey that product is at the viable stage now, not minimum viable, viable, let's release in production, let's find out how customers are using Atlin, let's keep optimizing and doing that constant iteration, taking that dev ops approach to let's get it out there, let's get it launched. And then let's do these small batches of changes based on customer need, based on tele telemetry. We can actually get in. We can't get the telemetry without having customers. So that's how it's going to keep working. So I think this initial product we see today, it's just going to keep evolving and improving as they get more data, as they get more information, more feedback. Which is exactly what we want to see. >>Well, what will come to the cloud air or something you've been living in for a number of years. Ben, I'd love to hear you've been meeting with customers. They've been asking you questions, gives us some of the, you know, some of the things that, what's top of mind for some of the customers? What kinds of things did they come into Microsoft, Dawn, and how's that all fit together? >>There's many different conferences of interrelate, many different conversations and they'll, we will go from talking about, you know, Python machine learning or AI PowerPoint. >>Yeah. >>It's a things like, you know, when are we going to do incremental snapshots from a manage disks? Get into the weeds on very infrastructure century staff. We're seeing range of conversations there. The big thing I think I see, keep seeing people call out and make assumptions of is that they're not going to be relevant because cloud, I don't know cloud yet. I don't know this whole coup cube thing. Containers. I don't, I don't really understand that as well as I think I need to. And an AI, Oh my gosh, what do I even do there? Because everyone's throwing the words and terms around. But to be honest, I think what's still really evident is cloud is still is tiny fraction of enterprise workloads. Let's be honest, it's growing at a huge rate because it is that small fraction. So again, there's plenty of time for people to learn, but they shouldn't go and try and slip. >>It's not like you're going to learn everything in a technology stack, from networking to development to database management to, to running a data set of power and cooling. You learn the things that are applicable to what you're trying to do. And the same thing goes to cloud. Any of these technologies, go and look at what you need to build for your business. Take it to that step and then go and find out the details and levels you want to know. And as someone who's been on Azure for like a cinema seven years, which is crazy long. That was a, that was literally like being in a startup instead of Microsoft when I joined and I wasn't sure if I wanted to join a licensing company. It's been very evident to me. I will not say I'm an Azure expert and I've been seven years in the platform. >>There are too many things throughout my for me to be an expert in everything on and I think people sort of just have to realize that anyone saying that it's bravado, nothing else. The goal is Microsoft as a platform provider. Hopefully you've got the software and the solution to make a lot of this easier for the customer, so hopefully they shouldn't need to become a Kubernetes expert because it's baked into your platform. They shouldn't have to worry about some of these offerings because it's SAS. Most customers are there some things you need to learn between going from, you know, exchange to go into oath bricks, these five. Absolutely. There are some nuances and things like that, but once you get over that initial hurdle, it should be a little easier. I think it's right and I think going back to that, sort of going back to bare principles going, what is the highest level of distraction that's viable for your business or that application or this workload has to always be done with everything. >>If it's like, well, class, not even viable, run it on premises. Don't, don't need to apologize for not running in cloud. If I as is what's happening for you because of security, because of application architecture, run it that way. Don't feel the need and the pressure to have to push it that way. I think too many people get caught up in the shiny stuff up here, which is what you know 1% of people are doing versus the other 99% which is still happening in a lot of the areas we work and have challenges in today. >>That's a great point that you bring up because there is all the buzz words, right? AI, machine learning cloud. You've got to be cloud ready. You've gotta be data-driven to customer, to your point going, I just need to make sure that what we have set up for our business is going to allow our business one to remain relevant, but to also be able to harness the power of the data that they have to extract new opportunities, new insights, and not get caught up with, shoot, should we be using automation? Should we be using AI? Everybody's talking about it. I liked that you brought up and I find it very respectfully, he said, Hey, I'm not an Azure expert. You'd been there seven, seven dog years like you said. And I think that's what customers probably gained confidence in is hearing the folks like you that they look to for that guidance and that leadership saying, no, I don't know everything. To know that giving them the confidence that they're true, they're trusting you with that data and also helping trusting you to help them make the right decisions for their business. >>Yeah. And that that's, we've got to do that. I mean, I, as a tech guy, it's like I've, I've loved seeing the changes. When I joined Microsoft, I, I wasn't lying. I was almost there go inf I really want to join this company. I was going to go join a startup instead. And I got asked to one stage in an interview going, why do you want to join Microsoft? We see you've never applied to that. I never wanted to, a friend told me to come in and it's just been amazing to see those changes and I'm pretty proud on that. Um, so when we talk about, you know, those, the things we're doing, I mean I think there is no shame going, I'm just going to lift and shift machines because cloud is about flexibility. If you're doing it just on cost, probably doing it for the wrong reason, it's about that flexibility to go and do something. >>Then change within months of slowly make steps to make things better and better as you find a need as you find the ability, whatever it may be. And some of the big things that we focus on right now with customers is we've got a product called Azure advisor. It'll go until people want one. You know, you don't build things in a resilient manner. Hey, do you know this is not ha because of this and you can do this. It's like great. Also will tell you about security vulnerabilities that maybe she had a gateway here for security. Maybe you should do this or this is not patched. But the big thing is that it also goes and tells you, Hey, you're overspending. You don't need this much. It provisions, you provision like a Ferrari, you need a, you just need a Prius, go and run a Prius because it's going to do what you need and need to pay a lot less. >>And that's part of that trust. Getting that understanding. And it's counterintuitive that we're now like it's coming out of my team a lot too, which is great. But seeing these guys were dropping contracts and licenses and basically, you know, once every three years I may call the customer, Hey, how bout a renewal now go from that to now being focused on the customer's actual success and focused on their growth in Azure as a platform of our vast services growth like utilization not in sales has been a huge change. It scared some people away but it's brought a lot more people in and and that sort of counterintuitive spin less money thing actually leads in the longterm to people using more. >>Absolutely. That's definitely not the shrink wrap software company of Microsoft that I remember from the 90s yeah, very might be similar to you know, just as volt to 2019 is not the same combo, but many of us know from with 15 >>years and a good mutual friend of ours, sort of Simon and myself before I took this job, he and I sat down, we're having a beer and discussing the merits, all the not evacuate and things like that. Same with. They are changing such, such a great deal with, you know, what they're putting in the cloud, what they're doing with the data, where they're trying to achieve with things like Hedvig for data management across on premises and cloud with microservices applications and stuff going, Hey, this won't work like this anymore. When you now are doing an on premises and we containers, it's pretty good to see. I'm interested to see how they take that even further to their current audience, which is product predominantly, you know, the it pro, the data center admin, storage manager. >>It's funny when you talked about, um, just the choice that customers have and those saying I, we shouldn't be following the trends because they're the trends. We actually interviewed a couple of hours ago, one of Combolt's customers that is all on prime healthcare company and said, he's like, I want to make a secret that says no cloud and proud and it just, what that was, we don't normally hear from them. We always talk about cloud, but for a company to sit down and look at what's best for our business, whether it's, you know, FedRAMP certification challenges or HIPAA or GDPR or other compelling requirements to keep it on prem, it was just refreshing to hear this customer say, >>yeah, I mean it's, it's appropriate for the do what's right for you. I, yeah, it's no shame in any of them. It's, I mean, you don't, you definitely don't get fans by, by shaming people and not doing something right. And I mean, I, I'm personally very happy with the feet, you know, see sort of hype around things like blockchain died down a little bit. So it's a slow database unless you're using for the specific case of that shared ledger, you know, things like that where people don't have to know blockchain. Now I have to know IOT. It's like, yeah. And that hype gets people there, but it also causes a lot of anxiety and it's good to see someone actually not be ashamed of and like, and they grade the ones when they do take a step and use cloud citizen may be in the business already. They're probably going to do it appropriately because have a reason, not just because we think this would be cool. >>Well not and how much inherent and complexity does that bring in if somebody is really feeling pressured to follow those trends and maybe that's when you end up with this hodgepodge of technologies that don't work well together, you're spending way more in as as business it folks are consumers, you know, consumers in their personal lives, they expect things to be accessible, visible, but also cost efficient because they have so much choice. >>Yeah, the choice choice is hard. It's just a, just the conversation is having recently, for example, just we'll take the storage cause of where we are, right? It's like I'm running something on Azure. I'm a, I'm using Souza. I want an office Mount point, which is available to me in Fs. Great. Perfect. what do I use? It's like, well you use any one of these seven options, like what's the right choice? And that's the thing about being a platform company. We give you a lot of choices but it's still up to you or up to harness. It can really help the customers as well to make the most appropriate choice. And I pushed back really hard on terms like best practices and things. I hate it because again, it's making the assumption this is the best thing to do. It's not. It's always about, you know, what are the patterns that have worked for other people, what are the anti-patterns and the appropriate path for me to take. >>And that's actually how we're building our docs now too. So we keep, we keep focusing at our Azure technology and we're bringing out some of the biggest things we've done is how we manage our documentation. It's all open sourced. It's all in markdown on get hub. So you can go and read a document from someone like myself is doing product management going, this is how to use this product and you're actually this bits wrong. This bit needs to be like this, and you can go in yourself, even now, make a change and we can go, Oh yeah, and take that committed in and do all this kind of stuff in that way. So we're constantly taking those documents in that way, in getting real time feedback from customers who are using it, not just ourself and an echo chamber. >>So you get this great insight and visibility that you never had before. Well, Ben, thank you, Georgie stew and me on the Q this afternoon. Excited to hear what's coming up next for Azure. May appreciate your time. Thank you for streaming event. I, Lisa Martin, you're watching the cue from convo. Go 19.